Drug Therapy

Study 1 Overview (Bayliss et al)

Objective: To examine the effect of a deprescribing educational intervention on medication use in older adults with cognitive impairment.

Design: This was a pragmatic, cluster randomized trial conducted in 8 primary care clinics that are part of a nonprofit health care system.

Setting and participants: The primary care clinic populations ranged from 170 to 1125 patients per clinic. The primary care clinics were randomly assigned to intervention or control using a uniform distribution in blocks by clinic size. Eligibility criteria for participants at those practices included age 65 years or older; health plan enrollment at least 1 year prior to intervention; diagnosis of Alzheimer disease and related dementia (ADRD) or mild cognitive impairment (MCI) by International Statistical Classification of Diseases and Related Health Problems, Tenth Revision code or from problem list; 1 or more chronic conditions from those in the Chronic Conditions Warehouse; and 5 or more long-term medications. Those who scheduled a visit at their primary care clinic in advance were eligible for the intervention. Primary care clinicians in intervention clinics were eligible to receive the clinician portion of the intervention. A total of 1433 participants were enrolled in the intervention group, and 1579 participants were enrolled in the control group.

Intervention: The intervention included 2 components: a patient and family component with materials mailed in advance of their primary care visits and a clinician component comprising monthly educational materials on deprescribing and notification in the electronic health record about visits with patient participants. The patient and family component consisted of a brochure titled “Managing Medication” and a questionnaire on attitudes toward deprescribing intended to educate patients and family about deprescribing. Clinicians at intervention clinics received an educational presentation at a monthly clinician meeting as well as tip sheets and a poster on deprescribing topics, and they also were notified of upcoming appointments with patients who received the patient component of the intervention. For the control group, patients and family did not receive any materials, and clinicians did not receive intervention materials or notification of participants enrolled in the trial. Usual care in both intervention and control groups included medication reconciliation and electronic health record alerts for potentially high-risk medications.

Main outcome measures: The primary outcomes of the study were the number of long-term medications per individual and the proportion of patients prescribed 1 or more potentially inappropriate medications. Outcome measurements were extracted from the electronic clinical data, and outcomes were assessed at 6 months, which involved comparing counts of medications at baseline with medications at 6 months. Long-term medications were defined as medications that are prescribed for 28 days or more based on pharmacy dispensing data. Potentially inappropriate medications (PIMs) were defined using the Beers list of medications to avoid in those with cognitive impairment and opioid medications. Analyses were conducted as intention to treat.

Main results: In the intervention group and control group, 56.2% and 54.4% of participants were women, and the mean age was 80.1 years (SD, 7.2) and 79.9 years (SD, 7.5), respectively. At baseline, the mean number of long-term medications was 7.0 (SD, 2.1) in the intervention group and 7.0 (SD, 2.2) in the control group. The proportion of patients taking any PIMs was 30.5% in the intervention group and 29.6% in the control group. At 6 months, the mean number of long-term medications was 6.4 in the intervention group and 6.5 in the control group, with an adjusted difference of –0.1 (95% CI, –0.2 to 0.04; P = .14); the proportion of patients with any PIMs was 17.8% in the intervention group and 20.9% in the control group, with an adjusted difference of –3.2% (95% CI, –6.2 to 0.4; P = .08). Preplanned analyses to examine subgroup differences for those with a higher number of medications (7+ vs 5 or 6 medications) did not find different effects of the intervention.

Conclusion: This educational intervention on deprescribing did not result in reductions in the number of medications or the use of PIMs in patients with cognitive impairment.

Study 2 Overview (Gedde et al)

Objective: To examine the effect of a deprescribing intervention (COSMOS) on medication use for nursing home residents.

Design: This was a randomized clinical trial.

Setting and participants: This trial was conducted in 67 units in 33 nursing homes in Norway. Participants were nursing home residents recruited from August 2014 to March 2015. Inclusion criteria included adults aged 65 years and older with at least 2 years of residency in nursing homes. Exclusion criteria included diagnosis of schizophrenia and a life expectancy of 6 months or less. Participants were followed for 4 months; participants were considered lost to follow-up if they died or moved from the nursing home unit. The analyses were per protocol and did not include those lost to follow-up or those who did not undergo a medication review in the intervention group. A total of 217 and 211 residents were included in the intervention and control groups, respectively.

Intervention: The intervention contained 5 components: communication and advance care planning, systematic pain management, medication reviews with collegial mentoring, organization of activities adjusted to needs and preferences, and safety. For medication review, the nursing home physician reviewed medications together with a nurse and study physicians who provided mentoring. The medication review involved a structured process that used assessment tools for behavioral and psychological symptoms of dementia (BPSD), activities of daily living (ADL), pain, cognitive status, well-being and quality of life, and clinical metrics of blood pressure, pulse, and body mass index. The study utilized the START/STOPP criteria¹ for medication use in addition to a list of medications with anticholinergic properties for the medication review. In addition, drug interactions were documented through a drug interaction database; the team also incorporated patient wishes and concerns in the medication reviews. The nursing home physician made final decisions on medications. For the control group, nursing home residents received usual care without this intervention.

Main outcome measures: The primary outcome of the study was the mean change in the number of prescribed psychotropic medications, both regularly scheduled and total medications (which also included on-demand drugs) received at 4 months when compared to baseline. Psychotropic medications included antipsychotics, anxiolytics, hypnotics or sedatives, antidepressants, and antidementia drugs. Secondary outcomes included mean changes in BPSD using the Neuropsychiatric Inventory-Nursing home version (NPI-NH) and the Cornell Scale for Depression for Dementia (CSDD) and ADL using the Physical Self Maintenance Scale (PSMS).

Main results: In both the intervention and control groups, 76% of participants were women, and mean age was 86.3 years (SD, 7.95) in the intervention group and 86.6 years (SD, 7.21) in the control group. At baseline, the mean number of total medications was 10.9 (SD, 4.6) in the intervention group and 10.9 (SD, 4.7) in the control group, and the mean number of psychotropic medications was 2.2 (SD, 1.6) and 2.2 (SD, 1.7) in the intervention and control groups, respectively. At 4 months, the mean change from baseline of total psychotropic medications was –0.34 in the intervention group and 0.01 in the control group (P < .001), and the mean change of regularly scheduled psychotropic medications was –0.21 in the intervention group and 0.02 in the control group (P < .001). Measures of BPSD and depression did not differ between intervention and control groups, and ADL showed a small improvement in the intervention group.

Conclusion: This intervention reduced the use of psychotropic medications in nursing home residents without worsening BPSD or depression and may have yielded improvements in ADL.

Commentary

Polypharmacy is common among older adults, as many of them have multiple chronic conditions and often take multiple medications for managing them. Polypharmacy increases the risk of drug interactions and adverse effects from medications; older adults who are frail and/or who have cognitive impairment are especially at risk. Reducing medication use, especially medications likely to cause adverse effects such as those with anticholinergic properties, has the potential to yield beneficial effects while reducing the burden of taking medications. A large randomized trial found that a pharmacist-led education intervention can be effective in reducing PIM use in community-dwelling older adults,² and that targeting patient motivation and capacity to deprescribe could be effective.³ This study by Bayliss and colleagues (Study 1), however, fell short of the effects seen in the earlier D-PRESCRIBE trial. One of the reasons for these findings may be that the clinician portion of the intervention was less intensive than that used in the earlier trial; specifically, in the present study, clinicians were not provided with or expected to utilize tools for structured medication review or deprescribing. Although the intervention primes the patient and family for discussions around deprescribing through the use of a brochure and questionnaire, the clinician portion of the intervention was less structured. Another example of an effective intervention that provided a more structured deprescribing intervention beyond education of clinicians utilized electronic decision-support to assist with deprescribing.⁴

The findings from the Gedde et al study (Study 2) are comparable to those of prior studies in the nursing home population,⁵ where participants are likely to take a large number of medications, including psychotropic medications, and are more likely to be frail. However, Gedde and colleagues employed a bundled intervention⁶ that included other components besides medication review, and thus it is unclear whether the effect on ADL can be attributed to the deprescribing of medications alone. Gedde et al’s finding that deprescribing can reduce the use of psychotropic medications while not leading to differences in behavioral and psychologic symptoms or depression is an important contribution to our knowledge about polypharmacy and deprescribing in older patients. Thus, nursing home residents, their families, and clinicians could expect that the deprescribing of psychotropic medications does not lead to worsening symptoms. Of note, the clinician portion of the intervention in the Gedde et al study was quite structured, and this structure may have contributed to the observed effects.

Applications for Clinical Practice and System Implementation

Both studies add to the literature on deprescribing and may offer options for researchers and clinicians who are considering potential components of an effective deprescribing intervention. Patient activation for deprescribing via the methods used in these 2 studies may help to prime patients for conversations about deprescribing; however, as shown by the Bayliss et al study, a more structured approach to clinical encounters may be needed when deprescribing, such as the use of tools in the electronic health record, in order to reduce the use of medication deemed unnecessary or potentially harmful. Further studies should examine the effect of deprescribing on medication use, but perhaps even more importantly, how deprescribing impacts patient outcomes both in terms of risks and benefits.

Practice Points

• A more structured approach to clinical encounters (eg, the use of tools in the electronic health record) may be needed when deprescribing unnecessary or potentially harmful medications in older patients in community settings.
• In the nursing home setting, structured deprescribing intervention can reduce the use of psychotropic medications while not leading to differences in behavioral and psychologic symptoms or depression.

–William W. Hung, MD, MPH

Study 1 Overview (Bayliss et al)

Objective: To examine the effect of a deprescribing educational intervention on medication use in older adults with cognitive impairment.

Design: This was a pragmatic, cluster randomized trial conducted in 8 primary care clinics that are part of a nonprofit health care system.

Setting and participants: The primary care clinic populations ranged from 170 to 1125 patients per clinic. The primary care clinics were randomly assigned to intervention or control using a uniform distribution in blocks by clinic size. Eligibility criteria for participants at those practices included age 65 years or older; health plan enrollment at least 1 year prior to intervention; diagnosis of Alzheimer disease and related dementia (ADRD) or mild cognitive impairment (MCI) by International Statistical Classification of Diseases and Related Health Problems, Tenth Revision code or from problem list; 1 or more chronic conditions from those in the Chronic Conditions Warehouse; and 5 or more long-term medications. Those who scheduled a visit at their primary care clinic in advance were eligible for the intervention. Primary care clinicians in intervention clinics were eligible to receive the clinician portion of the intervention. A total of 1433 participants were enrolled in the intervention group, and 1579 participants were enrolled in the control group.

Intervention: The intervention included 2 components: a patient and family component with materials mailed in advance of their primary care visits and a clinician component comprising monthly educational materials on deprescribing and notification in the electronic health record about visits with patient participants. The patient and family component consisted of a brochure titled “Managing Medication” and a questionnaire on attitudes toward deprescribing intended to educate patients and family about deprescribing. Clinicians at intervention clinics received an educational presentation at a monthly clinician meeting as well as tip sheets and a poster on deprescribing topics, and they also were notified of upcoming appointments with patients who received the patient component of the intervention. For the control group, patients and family did not receive any materials, and clinicians did not receive intervention materials or notification of participants enrolled in the trial. Usual care in both intervention and control groups included medication reconciliation and electronic health record alerts for potentially high-risk medications.

Main outcome measures: The primary outcomes of the study were the number of long-term medications per individual and the proportion of patients prescribed 1 or more potentially inappropriate medications. Outcome measurements were extracted from the electronic clinical data, and outcomes were assessed at 6 months, which involved comparing counts of medications at baseline with medications at 6 months. Long-term medications were defined as medications that are prescribed for 28 days or more based on pharmacy dispensing data. Potentially inappropriate medications (PIMs) were defined using the Beers list of medications to avoid in those with cognitive impairment and opioid medications. Analyses were conducted as intention to treat.

Main results: In the intervention group and control group, 56.2% and 54.4% of participants were women, and the mean age was 80.1 years (SD, 7.2) and 79.9 years (SD, 7.5), respectively. At baseline, the mean number of long-term medications was 7.0 (SD, 2.1) in the intervention group and 7.0 (SD, 2.2) in the control group. The proportion of patients taking any PIMs was 30.5% in the intervention group and 29.6% in the control group. At 6 months, the mean number of long-term medications was 6.4 in the intervention group and 6.5 in the control group, with an adjusted difference of –0.1 (95% CI, –0.2 to 0.04; P = .14); the proportion of patients with any PIMs was 17.8% in the intervention group and 20.9% in the control group, with an adjusted difference of –3.2% (95% CI, –6.2 to 0.4; P = .08). Preplanned analyses to examine subgroup differences for those with a higher number of medications (7+ vs 5 or 6 medications) did not find different effects of the intervention.

Conclusion: This educational intervention on deprescribing did not result in reductions in the number of medications or the use of PIMs in patients with cognitive impairment.

Study 2 Overview (Gedde et al)

Objective: To examine the effect of a deprescribing intervention (COSMOS) on medication use for nursing home residents.

Design: This was a randomized clinical trial.

Setting and participants: This trial was conducted in 67 units in 33 nursing homes in Norway. Participants were nursing home residents recruited from August 2014 to March 2015. Inclusion criteria included adults aged 65 years and older with at least 2 years of residency in nursing homes. Exclusion criteria included diagnosis of schizophrenia and a life expectancy of 6 months or less. Participants were followed for 4 months; participants were considered lost to follow-up if they died or moved from the nursing home unit. The analyses were per protocol and did not include those lost to follow-up or those who did not undergo a medication review in the intervention group. A total of 217 and 211 residents were included in the intervention and control groups, respectively.

Intervention: The intervention contained 5 components: communication and advance care planning, systematic pain management, medication reviews with collegial mentoring, organization of activities adjusted to needs and preferences, and safety. For medication review, the nursing home physician reviewed medications together with a nurse and study physicians who provided mentoring. The medication review involved a structured process that used assessment tools for behavioral and psychological symptoms of dementia (BPSD), activities of daily living (ADL), pain, cognitive status, well-being and quality of life, and clinical metrics of blood pressure, pulse, and body mass index. The study utilized the START/STOPP criteria¹ for medication use in addition to a list of medications with anticholinergic properties for the medication review. In addition, drug interactions were documented through a drug interaction database; the team also incorporated patient wishes and concerns in the medication reviews. The nursing home physician made final decisions on medications. For the control group, nursing home residents received usual care without this intervention.

Main outcome measures: The primary outcome of the study was the mean change in the number of prescribed psychotropic medications, both regularly scheduled and total medications (which also included on-demand drugs) received at 4 months when compared to baseline. Psychotropic medications included antipsychotics, anxiolytics, hypnotics or sedatives, antidepressants, and antidementia drugs. Secondary outcomes included mean changes in BPSD using the Neuropsychiatric Inventory-Nursing home version (NPI-NH) and the Cornell Scale for Depression for Dementia (CSDD) and ADL using the Physical Self Maintenance Scale (PSMS).

Main results: In both the intervention and control groups, 76% of participants were women, and mean age was 86.3 years (SD, 7.95) in the intervention group and 86.6 years (SD, 7.21) in the control group. At baseline, the mean number of total medications was 10.9 (SD, 4.6) in the intervention group and 10.9 (SD, 4.7) in the control group, and the mean number of psychotropic medications was 2.2 (SD, 1.6) and 2.2 (SD, 1.7) in the intervention and control groups, respectively. At 4 months, the mean change from baseline of total psychotropic medications was –0.34 in the intervention group and 0.01 in the control group (P < .001), and the mean change of regularly scheduled psychotropic medications was –0.21 in the intervention group and 0.02 in the control group (P < .001). Measures of BPSD and depression did not differ between intervention and control groups, and ADL showed a small improvement in the intervention group.

Conclusion: This intervention reduced the use of psychotropic medications in nursing home residents without worsening BPSD or depression and may have yielded improvements in ADL.

Commentary

Polypharmacy is common among older adults, as many of them have multiple chronic conditions and often take multiple medications for managing them. Polypharmacy increases the risk of drug interactions and adverse effects from medications; older adults who are frail and/or who have cognitive impairment are especially at risk. Reducing medication use, especially medications likely to cause adverse effects such as those with anticholinergic properties, has the potential to yield beneficial effects while reducing the burden of taking medications. A large randomized trial found that a pharmacist-led education intervention can be effective in reducing PIM use in community-dwelling older adults,² and that targeting patient motivation and capacity to deprescribe could be effective.³ This study by Bayliss and colleagues (Study 1), however, fell short of the effects seen in the earlier D-PRESCRIBE trial. One of the reasons for these findings may be that the clinician portion of the intervention was less intensive than that used in the earlier trial; specifically, in the present study, clinicians were not provided with or expected to utilize tools for structured medication review or deprescribing. Although the intervention primes the patient and family for discussions around deprescribing through the use of a brochure and questionnaire, the clinician portion of the intervention was less structured. Another example of an effective intervention that provided a more structured deprescribing intervention beyond education of clinicians utilized electronic decision-support to assist with deprescribing.⁴

The findings from the Gedde et al study (Study 2) are comparable to those of prior studies in the nursing home population,⁵ where participants are likely to take a large number of medications, including psychotropic medications, and are more likely to be frail. However, Gedde and colleagues employed a bundled intervention⁶ that included other components besides medication review, and thus it is unclear whether the effect on ADL can be attributed to the deprescribing of medications alone. Gedde et al’s finding that deprescribing can reduce the use of psychotropic medications while not leading to differences in behavioral and psychologic symptoms or depression is an important contribution to our knowledge about polypharmacy and deprescribing in older patients. Thus, nursing home residents, their families, and clinicians could expect that the deprescribing of psychotropic medications does not lead to worsening symptoms. Of note, the clinician portion of the intervention in the Gedde et al study was quite structured, and this structure may have contributed to the observed effects.

Applications for Clinical Practice and System Implementation

Both studies add to the literature on deprescribing and may offer options for researchers and clinicians who are considering potential components of an effective deprescribing intervention. Patient activation for deprescribing via the methods used in these 2 studies may help to prime patients for conversations about deprescribing; however, as shown by the Bayliss et al study, a more structured approach to clinical encounters may be needed when deprescribing, such as the use of tools in the electronic health record, in order to reduce the use of medication deemed unnecessary or potentially harmful. Further studies should examine the effect of deprescribing on medication use, but perhaps even more importantly, how deprescribing impacts patient outcomes both in terms of risks and benefits.

Practice Points

• A more structured approach to clinical encounters (eg, the use of tools in the electronic health record) may be needed when deprescribing unnecessary or potentially harmful medications in older patients in community settings.
• In the nursing home setting, structured deprescribing intervention can reduce the use of psychotropic medications while not leading to differences in behavioral and psychologic symptoms or depression.

–William W. Hung, MD, MPH

Study 1 Overview (Bayliss et al)

Objective: To examine the effect of a deprescribing educational intervention on medication use in older adults with cognitive impairment.

Design: This was a pragmatic, cluster randomized trial conducted in 8 primary care clinics that are part of a nonprofit health care system.

Setting and participants: The primary care clinic populations ranged from 170 to 1125 patients per clinic. The primary care clinics were randomly assigned to intervention or control using a uniform distribution in blocks by clinic size. Eligibility criteria for participants at those practices included age 65 years or older; health plan enrollment at least 1 year prior to intervention; diagnosis of Alzheimer disease and related dementia (ADRD) or mild cognitive impairment (MCI) by International Statistical Classification of Diseases and Related Health Problems, Tenth Revision code or from problem list; 1 or more chronic conditions from those in the Chronic Conditions Warehouse; and 5 or more long-term medications. Those who scheduled a visit at their primary care clinic in advance were eligible for the intervention. Primary care clinicians in intervention clinics were eligible to receive the clinician portion of the intervention. A total of 1433 participants were enrolled in the intervention group, and 1579 participants were enrolled in the control group.

Intervention: The intervention included 2 components: a patient and family component with materials mailed in advance of their primary care visits and a clinician component comprising monthly educational materials on deprescribing and notification in the electronic health record about visits with patient participants. The patient and family component consisted of a brochure titled “Managing Medication” and a questionnaire on attitudes toward deprescribing intended to educate patients and family about deprescribing. Clinicians at intervention clinics received an educational presentation at a monthly clinician meeting as well as tip sheets and a poster on deprescribing topics, and they also were notified of upcoming appointments with patients who received the patient component of the intervention. For the control group, patients and family did not receive any materials, and clinicians did not receive intervention materials or notification of participants enrolled in the trial. Usual care in both intervention and control groups included medication reconciliation and electronic health record alerts for potentially high-risk medications.

Main outcome measures: The primary outcomes of the study were the number of long-term medications per individual and the proportion of patients prescribed 1 or more potentially inappropriate medications. Outcome measurements were extracted from the electronic clinical data, and outcomes were assessed at 6 months, which involved comparing counts of medications at baseline with medications at 6 months. Long-term medications were defined as medications that are prescribed for 28 days or more based on pharmacy dispensing data. Potentially inappropriate medications (PIMs) were defined using the Beers list of medications to avoid in those with cognitive impairment and opioid medications. Analyses were conducted as intention to treat.

Main results: In the intervention group and control group, 56.2% and 54.4% of participants were women, and the mean age was 80.1 years (SD, 7.2) and 79.9 years (SD, 7.5), respectively. At baseline, the mean number of long-term medications was 7.0 (SD, 2.1) in the intervention group and 7.0 (SD, 2.2) in the control group. The proportion of patients taking any PIMs was 30.5% in the intervention group and 29.6% in the control group. At 6 months, the mean number of long-term medications was 6.4 in the intervention group and 6.5 in the control group, with an adjusted difference of –0.1 (95% CI, –0.2 to 0.04; P = .14); the proportion of patients with any PIMs was 17.8% in the intervention group and 20.9% in the control group, with an adjusted difference of –3.2% (95% CI, –6.2 to 0.4; P = .08). Preplanned analyses to examine subgroup differences for those with a higher number of medications (7+ vs 5 or 6 medications) did not find different effects of the intervention.

Conclusion: This educational intervention on deprescribing did not result in reductions in the number of medications or the use of PIMs in patients with cognitive impairment.

Study 2 Overview (Gedde et al)

Objective: To examine the effect of a deprescribing intervention (COSMOS) on medication use for nursing home residents.

Design: This was a randomized clinical trial.

Setting and participants: This trial was conducted in 67 units in 33 nursing homes in Norway. Participants were nursing home residents recruited from August 2014 to March 2015. Inclusion criteria included adults aged 65 years and older with at least 2 years of residency in nursing homes. Exclusion criteria included diagnosis of schizophrenia and a life expectancy of 6 months or less. Participants were followed for 4 months; participants were considered lost to follow-up if they died or moved from the nursing home unit. The analyses were per protocol and did not include those lost to follow-up or those who did not undergo a medication review in the intervention group. A total of 217 and 211 residents were included in the intervention and control groups, respectively.

Intervention: The intervention contained 5 components: communication and advance care planning, systematic pain management, medication reviews with collegial mentoring, organization of activities adjusted to needs and preferences, and safety. For medication review, the nursing home physician reviewed medications together with a nurse and study physicians who provided mentoring. The medication review involved a structured process that used assessment tools for behavioral and psychological symptoms of dementia (BPSD), activities of daily living (ADL), pain, cognitive status, well-being and quality of life, and clinical metrics of blood pressure, pulse, and body mass index. The study utilized the START/STOPP criteria¹ for medication use in addition to a list of medications with anticholinergic properties for the medication review. In addition, drug interactions were documented through a drug interaction database; the team also incorporated patient wishes and concerns in the medication reviews. The nursing home physician made final decisions on medications. For the control group, nursing home residents received usual care without this intervention.

Main outcome measures: The primary outcome of the study was the mean change in the number of prescribed psychotropic medications, both regularly scheduled and total medications (which also included on-demand drugs) received at 4 months when compared to baseline. Psychotropic medications included antipsychotics, anxiolytics, hypnotics or sedatives, antidepressants, and antidementia drugs. Secondary outcomes included mean changes in BPSD using the Neuropsychiatric Inventory-Nursing home version (NPI-NH) and the Cornell Scale for Depression for Dementia (CSDD) and ADL using the Physical Self Maintenance Scale (PSMS).

Main results: In both the intervention and control groups, 76% of participants were women, and mean age was 86.3 years (SD, 7.95) in the intervention group and 86.6 years (SD, 7.21) in the control group. At baseline, the mean number of total medications was 10.9 (SD, 4.6) in the intervention group and 10.9 (SD, 4.7) in the control group, and the mean number of psychotropic medications was 2.2 (SD, 1.6) and 2.2 (SD, 1.7) in the intervention and control groups, respectively. At 4 months, the mean change from baseline of total psychotropic medications was –0.34 in the intervention group and 0.01 in the control group (P < .001), and the mean change of regularly scheduled psychotropic medications was –0.21 in the intervention group and 0.02 in the control group (P < .001). Measures of BPSD and depression did not differ between intervention and control groups, and ADL showed a small improvement in the intervention group.

Conclusion: This intervention reduced the use of psychotropic medications in nursing home residents without worsening BPSD or depression and may have yielded improvements in ADL.

Commentary

Polypharmacy is common among older adults, as many of them have multiple chronic conditions and often take multiple medications for managing them. Polypharmacy increases the risk of drug interactions and adverse effects from medications; older adults who are frail and/or who have cognitive impairment are especially at risk. Reducing medication use, especially medications likely to cause adverse effects such as those with anticholinergic properties, has the potential to yield beneficial effects while reducing the burden of taking medications. A large randomized trial found that a pharmacist-led education intervention can be effective in reducing PIM use in community-dwelling older adults,² and that targeting patient motivation and capacity to deprescribe could be effective.³ This study by Bayliss and colleagues (Study 1), however, fell short of the effects seen in the earlier D-PRESCRIBE trial. One of the reasons for these findings may be that the clinician portion of the intervention was less intensive than that used in the earlier trial; specifically, in the present study, clinicians were not provided with or expected to utilize tools for structured medication review or deprescribing. Although the intervention primes the patient and family for discussions around deprescribing through the use of a brochure and questionnaire, the clinician portion of the intervention was less structured. Another example of an effective intervention that provided a more structured deprescribing intervention beyond education of clinicians utilized electronic decision-support to assist with deprescribing.⁴

The findings from the Gedde et al study (Study 2) are comparable to those of prior studies in the nursing home population,⁵ where participants are likely to take a large number of medications, including psychotropic medications, and are more likely to be frail. However, Gedde and colleagues employed a bundled intervention⁶ that included other components besides medication review, and thus it is unclear whether the effect on ADL can be attributed to the deprescribing of medications alone. Gedde et al’s finding that deprescribing can reduce the use of psychotropic medications while not leading to differences in behavioral and psychologic symptoms or depression is an important contribution to our knowledge about polypharmacy and deprescribing in older patients. Thus, nursing home residents, their families, and clinicians could expect that the deprescribing of psychotropic medications does not lead to worsening symptoms. Of note, the clinician portion of the intervention in the Gedde et al study was quite structured, and this structure may have contributed to the observed effects.

Applications for Clinical Practice and System Implementation

Both studies add to the literature on deprescribing and may offer options for researchers and clinicians who are considering potential components of an effective deprescribing intervention. Patient activation for deprescribing via the methods used in these 2 studies may help to prime patients for conversations about deprescribing; however, as shown by the Bayliss et al study, a more structured approach to clinical encounters may be needed when deprescribing, such as the use of tools in the electronic health record, in order to reduce the use of medication deemed unnecessary or potentially harmful. Further studies should examine the effect of deprescribing on medication use, but perhaps even more importantly, how deprescribing impacts patient outcomes both in terms of risks and benefits.

Practice Points

• A more structured approach to clinical encounters (eg, the use of tools in the electronic health record) may be needed when deprescribing unnecessary or potentially harmful medications in older patients in community settings.
• In the nursing home setting, structured deprescribing intervention can reduce the use of psychotropic medications while not leading to differences in behavioral and psychologic symptoms or depression.

–William W. Hung, MD, MPH

Dr. George Sakoulas is an infectious diseases clinician at Sharp Memorial Hospital in San Diego and professor of pediatrics at the University of California, San Diego School of Medicine. He was the lead investigator in a study published in the May/June 2022 issue of JCOM that found that, when allocated to the appropriate patient type, intravenous immunoglobulin can reduce hospital costs for COVID-19 care. ¹ He joined JCOM’s Editor-in-Chief, Dr. Ebrahim Barkoudah, to discuss the study’s background and highlight its main findings.

The following has been edited for length and clarity.

Dr. Barkoudah Dr. Sakoulas is an investigator and a clinician, bridging both worlds to bring the best evidence to our patients. We’re discussing his new article regarding intravenous immunoglobulin in treating nonventilated COVID-19 patients with moderate-to-severe hypoxia. Dr. Sakoulas, could you please share with our readers the clinical question your study addressed and what your work around COVID-19 management means for clinical practice?

Dr. Sakoulas Thank you. I’m an infectious disease physician. I’ve been treating patients with viral acute respiratory distress syndrome for almost 20 years as an ID doctor. Most of these cases are due to influenza or other viruses. And from time to time, anecdotally and supported by some literature, we’ve been using IVIG, or intravenous immunoglobulin, in some of these cases. And again, I can report anecdotal success with that over the years.

So when COVID emerged in March of 2020, we deployed IVIG in a couple of patients early who were heading downhill. Remember, in March of 2020, we didn’t have the knowledge of steroids helping, patients being ventilated very promptly, and we saw some patients who made a turnaround after treatment with IVIG. We were able to get some support from an industry sponsor and perform and publish a pilot study, enrolling patients early in the pandemic. That study actually showed benefits, which then led the sponsor to fund a phase 3 multicenter clinical trial. Unfortunately, a couple of things happened. First, the trial was designed with the knowledge we had in April of 2020, and again, this is before steroids, before we incorporated proning patients in the ICU, or started ventilating people early. So there were some management changes and evolutions and improvements that happened. And second, the trial was enrolling a very broad repertoire of patients. There were no age limitations, and the trial, ultimately a phase 3 multicenter trial, failed to meet its endpoint.

There were some trends for benefit in younger patients, and as the trial was ongoing, we continued to evolve our knowledge, and we really honed it down to seeing a benefit of using IVIG in patients with COVID with specific criteria in mind. They had to be relatively younger patients, under 65, and not have any major comorbidities. In other words, they weren’t dialysis patients or end-stage disease patients, heart failure patients, cancer or malignancy patients. So, you know, we’re looking at the patients under 65 with obesity, diabetes, and hypertension, who are rapidly declining, going from room air to BiPAP or high-flow oxygen in a short amount of time. And we learned that when using IVIG early, we actually saw patients improve and turn around.

What this article in JCOM highlighted was, number one, incorporating that outcome or that patient type and then looking at the cost of hospitalization of patients who received IVIG versus those that did not. There were 2 groups that were studied. One was the group of patients in that original pilot trial that I discussed who were randomized to receive 1 or the other prospectively; it was an unblinded randomized study. And the second group was a matched case-control study where we had patients treated with IVIG matched by age and comorbidity status and level of hypoxia to patients that did not receive IVIG. We saw a financial benefit in shortening or reducing hospitalizations, really coming down to getting rid of that 20% tail of patients that wound up going to the ICU, getting intubated, and using a high amount of hospital resources that would ramp up the cost of hospitalization. We saw great mitigation of that with IVIG, and even with a small subset of patients, we were able to show a benefit.

Dr. Barkoudah Any thoughts on where we can implement the new findings from your article in our practice at the moment, knowing we now have practice guidelines and protocols to treat COVID-19? There was a tangible benefit in treating the patients the way you approached it in your important work. Could you share with us what would be implementable at the moment?

Dr. Sakoulas I think, fortunately, with the increasing host immunity in the population and decreased virulence of the virus, perhaps we won’t see as many patients of the type that were in these trials going forward, but I suspect we will perhaps in the unvaccinated patients that remain. I believe one-third of the United States is not vaccinated. So there is certainly a vulnerable group of people out there. Potentially, an unvaccinated patient who winds up getting very sick, the patient who is relatively young—what I’m looking at is the 30- to 65-year-old obese, hypertensive, or diabetic patient who comes in and, despite the steroids and the antivirals, rapidly deteriorates into requiring high-flow oxygen. I think implementing IVIG in that patient type would be helpful. I don’t think it’s going to be as helpful in patients who are very elderly, because I think the mechanism of the disease is different in an 80-year-old versus a 50-year-old patient. So again, hopefully, it will not amount to a lot of patients, but I still suspect hospitals are going to see, perhaps in the fall, when they’re expecting a greater number of cases, a trickling of patients that do meet the criteria that I described.

Dr. Barkoudah JCOM’s audience are the QI implementers and hospital leadership. And what caught my eye in your article is your perspective on the pharmacoeconomics of treating COVID-19, and I really appreciate your looking at the cost aspect. Would you talk about the economics of inpatient care, the total care that we provide now that we’re in the age of tocilizumab, and the current state of multiple layers of therapy?

Dr. Sakoulas The reason to look at the economics of it is because IVIG—which is actually not a drug, it’s a blood product—is very expensive. So, we received a considerable amount of administrative pushback implementing this treatment at the beginning outside of the clinical trial setting because it hadn’t been studied on a large scale and because the cost was so high, even though, as a clinician at the bedside, I was seeing a benefit in patients. This study came out of my trying to demonstrate to the folks that are keeping the economics of medicine in mind that, in fact, investing several thousand dollars of treatment in IVIG will save you cost of care, the cost of an ICU bed, the cost of a ventilator, and the cost even of ECMO, which is hugely expensive.

If you look at the numbers in the study, for two-thirds or three-quarters of the patients, your cost of care is actually greater than the controls because you’re giving them IVIG, and it’s increasing the cost of their care, even though three-quarters of the patients are going to do just as well without it. It’s that 20% to 25% of patients that really are going to benefit from it, where you’re reducing your cost of care so much, and you’re getting rid of that very, very expensive 20%, that there’s a cost savings across the board per patient. So, it’s hard to understand when you say you’re losing money on three-quarters of the patients, you’re only saving money on a quarter of the patients, but that cost of saving on that small subset is so substantial it’s really impacting all numbers.

Also, abandoning the outlier principle is sort of an underlying theme in how we think of things. We tend to ignore outliers, not consider them, but I think we really have to pay attention to the more extreme cases because those patients are the ones that drive not just the financial cost of care. Remember, if you’re down to 1 ventilator and you can cut down the use of scarce ICU resources, the cost is sort of even beyond the cost of money. It’s the cost of resources that may become scarce in some settings. So, I think it speaks to that as well.

A lot of the drugs that we use, for example, tocilizumab, were able to be studied in thousands of patients. If you look at the absolute numbers, the benefit of tocilizumab from a magnitude standpoint—low to mid twenties to high twenties—you know, reducing mortality from 29% to 24%. I mean, just take a step back and think about that. Even though it’s statistically significant, try telling a patient, “Well, I’m going to give you this treatment that’s going to reduce mortality from 29% to 24%.” You know, that doesn’t really change anything from a clinical significance standpoint. But they have a P value less than .05, which is our standard, and they were able to do a study with thousands of patients. We didn’t have that luxury with IVIG. No one studied thousands of patients, only retrospectively, and those retrospective studies don’t get the attention because they’re considered biased with all their limitations. But I think one of the difficulties we have here is the balance between statistical and clinical significance. For example, in our pilot study, our ventilation rate was 58% with the non-IVIG patients versus 14% for IVIG patients. So you might say, magnitude-wise, that’s a big number, but the statistical significance of it is borderline because of small numbers.

Anyway, that’s a challenge that we have as clinicians trying to incorporate what’s published—the balancing of statistics, absolute numbers, and practicalities of delivering care. And I think this study highlights some of the nuances that go into that incorporation and those clinical decisions.

Dr. Barkoudah Would you mind sharing with our audience how we can make the connection between the medical outcomes and pharmacoeconomics findings from your article and link it to the bedside and treatment of our patients?

Dr. Sakoulas One of the points this article brings out is the importance of bringing together not just level 1A data, but also small studies with data such as this, where the magnitude of the effect is pretty big but you lose the statistics because of the small numbers. And then also the patients’ aspects of things. I think, as a bedside clinician, you appreciate things, the nuances, much sooner than what percolates out from a level 1A study. Case in point, in the sponsored phase 3 study that we did, and in some other studies that were prospectively done as well, these studies of IVIG simply had an enrollment of patients that was very broad, and not every patient benefits from the same therapy. A great example of this is the sepsis trials with Xigris and those types of agents that failed. You know, there are clinicians to this day who believe that there is a subset of patients that benefit from agents like this. The IVIG story falls a little bit into that category. It comes down to trying to identify the subset of patients that might benefit. And I think we’ve outlined this subset pretty well in our study: the younger, obese diabetic or hypertensive patient who’s rapidly declining.

It really brings together the need to not necessarily toss out these smaller studies, but kind of summarize everything together, and clinicians who are bedside, who are more in tune with the nuances of individual decisions at the individual patient level, might better appreciate these kinds of data. But I think we all have to put it together. IVIG does not make treatment guidelines at national levels and so forth. It’s not even listed in many of them. But there are patients out there who, if you ask them specifically how they felt, including a friend of mine who received the medication, there’s no question from their end, how they felt about this treatment option. Now, some people will get it and will not benefit. We just have to be really tuned into the fact that the same drug does not have the same result for every patient. And just to consider this in the high-risk patients that we talked about in our study.

Dr. Barkoudah While we were prepping for this interview, you made an analogy regarding clinical evidence along the lines of, “Do we need randomized clinical trials to do a parachute-type of experiment,” and we chatted about clinical wisdom. Would you mind sharing with our readers your thoughts on that?

Dr. Sakoulas Sometimes, we try a treatment and it’s very obvious for that particular patient that it helped them. Then you study the treatment in a large trial setting and it doesn’t work. For us bedside clinicians, there are some interventions sometimes that do appear as beneficial as a parachute would be, but yet, there has never been a randomized clinical trial proving that parachutes work. Again, a part of the challenge we have is patients are so different, their immunology is different, the pathogen infecting them is different, the time they present is different. Some present early, some present late. There are just so many moving parts to treating an infection that only a subset of people are going to benefit. And sometimes as clinicians, we’re so nuanced, that we identify a specific subset of patients where we know we can help them. And it’s so obvious for us, like a parachute would be, but to people who are looking at the world from 30,000 feet, they don’t necessarily grasp that because, when you look at all comers, it doesn’t show a benefit.

So the problem is that now those treatments that might help a subset of patients are being denied, and the subset of patients that are going to benefit never get the treatment. Now we have to balance that with a lot of stuff that went on during the pandemic with, you know, ivermectin, hydroxychloroquine, and people pushing those things. Someone asked me once what I thought about hydroxychloroquine, and I said, “Well, somebody in the lab probably showed that it was beneficial, analogous to lighting tissue paper on fire on a plate and taking a cup of water and putting the fire out. Well, now, if you take that cup of water to the Caldor fire that’s burning in California on thousands of acres, you’re not going to be able to put the fire out with that cup of water.” So while it might work in the lab, it’s truly not going to work in a clinical setting. We have to balance individualizing care for patients with some information people are pushing out there that may not be necessarily translatable to the clinical setting.

I think there’s nothing better than being at the bedside, though, and being able to implement something and seeing what works. And really, experience goes a long way in being able to individually treat a patient optimally.

Dr. Barkoudah Thank you for everything you do at the bedside and your work on improving the treatment we have and how we can leverage knowledge to treat our patients. Thank you very much for your time and your scholarly contribution. We appreciate it and I hope the work will continue. We will keep working on treating COVID-19 patients with the best knowledge we have.

Q&A participants: George Sakoulas, MD, Sharp Rees-Stealy Medical Group, La Jolla, CA, and University of California San Diego School of Medicine, San Diego, CA; and Ebrahim Barkoudah, MD, MPH, Department of Medicine, Brigham and Women’s Hospital, Boston, MA.

Disclosures: None reported.

Dr. George Sakoulas is an infectious diseases clinician at Sharp Memorial Hospital in San Diego and professor of pediatrics at the University of California, San Diego School of Medicine. He was the lead investigator in a study published in the May/June 2022 issue of JCOM that found that, when allocated to the appropriate patient type, intravenous immunoglobulin can reduce hospital costs for COVID-19 care. ¹ He joined JCOM’s Editor-in-Chief, Dr. Ebrahim Barkoudah, to discuss the study’s background and highlight its main findings.

The following has been edited for length and clarity.

Dr. Barkoudah Dr. Sakoulas is an investigator and a clinician, bridging both worlds to bring the best evidence to our patients. We’re discussing his new article regarding intravenous immunoglobulin in treating nonventilated COVID-19 patients with moderate-to-severe hypoxia. Dr. Sakoulas, could you please share with our readers the clinical question your study addressed and what your work around COVID-19 management means for clinical practice?

Dr. Sakoulas Thank you. I’m an infectious disease physician. I’ve been treating patients with viral acute respiratory distress syndrome for almost 20 years as an ID doctor. Most of these cases are due to influenza or other viruses. And from time to time, anecdotally and supported by some literature, we’ve been using IVIG, or intravenous immunoglobulin, in some of these cases. And again, I can report anecdotal success with that over the years.

So when COVID emerged in March of 2020, we deployed IVIG in a couple of patients early who were heading downhill. Remember, in March of 2020, we didn’t have the knowledge of steroids helping, patients being ventilated very promptly, and we saw some patients who made a turnaround after treatment with IVIG. We were able to get some support from an industry sponsor and perform and publish a pilot study, enrolling patients early in the pandemic. That study actually showed benefits, which then led the sponsor to fund a phase 3 multicenter clinical trial. Unfortunately, a couple of things happened. First, the trial was designed with the knowledge we had in April of 2020, and again, this is before steroids, before we incorporated proning patients in the ICU, or started ventilating people early. So there were some management changes and evolutions and improvements that happened. And second, the trial was enrolling a very broad repertoire of patients. There were no age limitations, and the trial, ultimately a phase 3 multicenter trial, failed to meet its endpoint.

There were some trends for benefit in younger patients, and as the trial was ongoing, we continued to evolve our knowledge, and we really honed it down to seeing a benefit of using IVIG in patients with COVID with specific criteria in mind. They had to be relatively younger patients, under 65, and not have any major comorbidities. In other words, they weren’t dialysis patients or end-stage disease patients, heart failure patients, cancer or malignancy patients. So, you know, we’re looking at the patients under 65 with obesity, diabetes, and hypertension, who are rapidly declining, going from room air to BiPAP or high-flow oxygen in a short amount of time. And we learned that when using IVIG early, we actually saw patients improve and turn around.

What this article in JCOM highlighted was, number one, incorporating that outcome or that patient type and then looking at the cost of hospitalization of patients who received IVIG versus those that did not. There were 2 groups that were studied. One was the group of patients in that original pilot trial that I discussed who were randomized to receive 1 or the other prospectively; it was an unblinded randomized study. And the second group was a matched case-control study where we had patients treated with IVIG matched by age and comorbidity status and level of hypoxia to patients that did not receive IVIG. We saw a financial benefit in shortening or reducing hospitalizations, really coming down to getting rid of that 20% tail of patients that wound up going to the ICU, getting intubated, and using a high amount of hospital resources that would ramp up the cost of hospitalization. We saw great mitigation of that with IVIG, and even with a small subset of patients, we were able to show a benefit.

Dr. Barkoudah Any thoughts on where we can implement the new findings from your article in our practice at the moment, knowing we now have practice guidelines and protocols to treat COVID-19? There was a tangible benefit in treating the patients the way you approached it in your important work. Could you share with us what would be implementable at the moment?

Dr. Sakoulas I think, fortunately, with the increasing host immunity in the population and decreased virulence of the virus, perhaps we won’t see as many patients of the type that were in these trials going forward, but I suspect we will perhaps in the unvaccinated patients that remain. I believe one-third of the United States is not vaccinated. So there is certainly a vulnerable group of people out there. Potentially, an unvaccinated patient who winds up getting very sick, the patient who is relatively young—what I’m looking at is the 30- to 65-year-old obese, hypertensive, or diabetic patient who comes in and, despite the steroids and the antivirals, rapidly deteriorates into requiring high-flow oxygen. I think implementing IVIG in that patient type would be helpful. I don’t think it’s going to be as helpful in patients who are very elderly, because I think the mechanism of the disease is different in an 80-year-old versus a 50-year-old patient. So again, hopefully, it will not amount to a lot of patients, but I still suspect hospitals are going to see, perhaps in the fall, when they’re expecting a greater number of cases, a trickling of patients that do meet the criteria that I described.

Dr. Barkoudah JCOM’s audience are the QI implementers and hospital leadership. And what caught my eye in your article is your perspective on the pharmacoeconomics of treating COVID-19, and I really appreciate your looking at the cost aspect. Would you talk about the economics of inpatient care, the total care that we provide now that we’re in the age of tocilizumab, and the current state of multiple layers of therapy?

Dr. Sakoulas The reason to look at the economics of it is because IVIG—which is actually not a drug, it’s a blood product—is very expensive. So, we received a considerable amount of administrative pushback implementing this treatment at the beginning outside of the clinical trial setting because it hadn’t been studied on a large scale and because the cost was so high, even though, as a clinician at the bedside, I was seeing a benefit in patients. This study came out of my trying to demonstrate to the folks that are keeping the economics of medicine in mind that, in fact, investing several thousand dollars of treatment in IVIG will save you cost of care, the cost of an ICU bed, the cost of a ventilator, and the cost even of ECMO, which is hugely expensive.

If you look at the numbers in the study, for two-thirds or three-quarters of the patients, your cost of care is actually greater than the controls because you’re giving them IVIG, and it’s increasing the cost of their care, even though three-quarters of the patients are going to do just as well without it. It’s that 20% to 25% of patients that really are going to benefit from it, where you’re reducing your cost of care so much, and you’re getting rid of that very, very expensive 20%, that there’s a cost savings across the board per patient. So, it’s hard to understand when you say you’re losing money on three-quarters of the patients, you’re only saving money on a quarter of the patients, but that cost of saving on that small subset is so substantial it’s really impacting all numbers.

Also, abandoning the outlier principle is sort of an underlying theme in how we think of things. We tend to ignore outliers, not consider them, but I think we really have to pay attention to the more extreme cases because those patients are the ones that drive not just the financial cost of care. Remember, if you’re down to 1 ventilator and you can cut down the use of scarce ICU resources, the cost is sort of even beyond the cost of money. It’s the cost of resources that may become scarce in some settings. So, I think it speaks to that as well.

A lot of the drugs that we use, for example, tocilizumab, were able to be studied in thousands of patients. If you look at the absolute numbers, the benefit of tocilizumab from a magnitude standpoint—low to mid twenties to high twenties—you know, reducing mortality from 29% to 24%. I mean, just take a step back and think about that. Even though it’s statistically significant, try telling a patient, “Well, I’m going to give you this treatment that’s going to reduce mortality from 29% to 24%.” You know, that doesn’t really change anything from a clinical significance standpoint. But they have a P value less than .05, which is our standard, and they were able to do a study with thousands of patients. We didn’t have that luxury with IVIG. No one studied thousands of patients, only retrospectively, and those retrospective studies don’t get the attention because they’re considered biased with all their limitations. But I think one of the difficulties we have here is the balance between statistical and clinical significance. For example, in our pilot study, our ventilation rate was 58% with the non-IVIG patients versus 14% for IVIG patients. So you might say, magnitude-wise, that’s a big number, but the statistical significance of it is borderline because of small numbers.

Anyway, that’s a challenge that we have as clinicians trying to incorporate what’s published—the balancing of statistics, absolute numbers, and practicalities of delivering care. And I think this study highlights some of the nuances that go into that incorporation and those clinical decisions.

Dr. Barkoudah Would you mind sharing with our audience how we can make the connection between the medical outcomes and pharmacoeconomics findings from your article and link it to the bedside and treatment of our patients?

Dr. Sakoulas One of the points this article brings out is the importance of bringing together not just level 1A data, but also small studies with data such as this, where the magnitude of the effect is pretty big but you lose the statistics because of the small numbers. And then also the patients’ aspects of things. I think, as a bedside clinician, you appreciate things, the nuances, much sooner than what percolates out from a level 1A study. Case in point, in the sponsored phase 3 study that we did, and in some other studies that were prospectively done as well, these studies of IVIG simply had an enrollment of patients that was very broad, and not every patient benefits from the same therapy. A great example of this is the sepsis trials with Xigris and those types of agents that failed. You know, there are clinicians to this day who believe that there is a subset of patients that benefit from agents like this. The IVIG story falls a little bit into that category. It comes down to trying to identify the subset of patients that might benefit. And I think we’ve outlined this subset pretty well in our study: the younger, obese diabetic or hypertensive patient who’s rapidly declining.

It really brings together the need to not necessarily toss out these smaller studies, but kind of summarize everything together, and clinicians who are bedside, who are more in tune with the nuances of individual decisions at the individual patient level, might better appreciate these kinds of data. But I think we all have to put it together. IVIG does not make treatment guidelines at national levels and so forth. It’s not even listed in many of them. But there are patients out there who, if you ask them specifically how they felt, including a friend of mine who received the medication, there’s no question from their end, how they felt about this treatment option. Now, some people will get it and will not benefit. We just have to be really tuned into the fact that the same drug does not have the same result for every patient. And just to consider this in the high-risk patients that we talked about in our study.

Dr. Barkoudah While we were prepping for this interview, you made an analogy regarding clinical evidence along the lines of, “Do we need randomized clinical trials to do a parachute-type of experiment,” and we chatted about clinical wisdom. Would you mind sharing with our readers your thoughts on that?

Dr. Sakoulas Sometimes, we try a treatment and it’s very obvious for that particular patient that it helped them. Then you study the treatment in a large trial setting and it doesn’t work. For us bedside clinicians, there are some interventions sometimes that do appear as beneficial as a parachute would be, but yet, there has never been a randomized clinical trial proving that parachutes work. Again, a part of the challenge we have is patients are so different, their immunology is different, the pathogen infecting them is different, the time they present is different. Some present early, some present late. There are just so many moving parts to treating an infection that only a subset of people are going to benefit. And sometimes as clinicians, we’re so nuanced, that we identify a specific subset of patients where we know we can help them. And it’s so obvious for us, like a parachute would be, but to people who are looking at the world from 30,000 feet, they don’t necessarily grasp that because, when you look at all comers, it doesn’t show a benefit.

So the problem is that now those treatments that might help a subset of patients are being denied, and the subset of patients that are going to benefit never get the treatment. Now we have to balance that with a lot of stuff that went on during the pandemic with, you know, ivermectin, hydroxychloroquine, and people pushing those things. Someone asked me once what I thought about hydroxychloroquine, and I said, “Well, somebody in the lab probably showed that it was beneficial, analogous to lighting tissue paper on fire on a plate and taking a cup of water and putting the fire out. Well, now, if you take that cup of water to the Caldor fire that’s burning in California on thousands of acres, you’re not going to be able to put the fire out with that cup of water.” So while it might work in the lab, it’s truly not going to work in a clinical setting. We have to balance individualizing care for patients with some information people are pushing out there that may not be necessarily translatable to the clinical setting.

I think there’s nothing better than being at the bedside, though, and being able to implement something and seeing what works. And really, experience goes a long way in being able to individually treat a patient optimally.

Dr. Barkoudah Thank you for everything you do at the bedside and your work on improving the treatment we have and how we can leverage knowledge to treat our patients. Thank you very much for your time and your scholarly contribution. We appreciate it and I hope the work will continue. We will keep working on treating COVID-19 patients with the best knowledge we have.

Q&A participants: George Sakoulas, MD, Sharp Rees-Stealy Medical Group, La Jolla, CA, and University of California San Diego School of Medicine, San Diego, CA; and Ebrahim Barkoudah, MD, MPH, Department of Medicine, Brigham and Women’s Hospital, Boston, MA.

Disclosures: None reported.

Dr. George Sakoulas is an infectious diseases clinician at Sharp Memorial Hospital in San Diego and professor of pediatrics at the University of California, San Diego School of Medicine. He was the lead investigator in a study published in the May/June 2022 issue of JCOM that found that, when allocated to the appropriate patient type, intravenous immunoglobulin can reduce hospital costs for COVID-19 care. ¹ He joined JCOM’s Editor-in-Chief, Dr. Ebrahim Barkoudah, to discuss the study’s background and highlight its main findings.

The following has been edited for length and clarity.

Dr. Barkoudah Dr. Sakoulas is an investigator and a clinician, bridging both worlds to bring the best evidence to our patients. We’re discussing his new article regarding intravenous immunoglobulin in treating nonventilated COVID-19 patients with moderate-to-severe hypoxia. Dr. Sakoulas, could you please share with our readers the clinical question your study addressed and what your work around COVID-19 management means for clinical practice?

Dr. Sakoulas Thank you. I’m an infectious disease physician. I’ve been treating patients with viral acute respiratory distress syndrome for almost 20 years as an ID doctor. Most of these cases are due to influenza or other viruses. And from time to time, anecdotally and supported by some literature, we’ve been using IVIG, or intravenous immunoglobulin, in some of these cases. And again, I can report anecdotal success with that over the years.

So when COVID emerged in March of 2020, we deployed IVIG in a couple of patients early who were heading downhill. Remember, in March of 2020, we didn’t have the knowledge of steroids helping, patients being ventilated very promptly, and we saw some patients who made a turnaround after treatment with IVIG. We were able to get some support from an industry sponsor and perform and publish a pilot study, enrolling patients early in the pandemic. That study actually showed benefits, which then led the sponsor to fund a phase 3 multicenter clinical trial. Unfortunately, a couple of things happened. First, the trial was designed with the knowledge we had in April of 2020, and again, this is before steroids, before we incorporated proning patients in the ICU, or started ventilating people early. So there were some management changes and evolutions and improvements that happened. And second, the trial was enrolling a very broad repertoire of patients. There were no age limitations, and the trial, ultimately a phase 3 multicenter trial, failed to meet its endpoint.

There were some trends for benefit in younger patients, and as the trial was ongoing, we continued to evolve our knowledge, and we really honed it down to seeing a benefit of using IVIG in patients with COVID with specific criteria in mind. They had to be relatively younger patients, under 65, and not have any major comorbidities. In other words, they weren’t dialysis patients or end-stage disease patients, heart failure patients, cancer or malignancy patients. So, you know, we’re looking at the patients under 65 with obesity, diabetes, and hypertension, who are rapidly declining, going from room air to BiPAP or high-flow oxygen in a short amount of time. And we learned that when using IVIG early, we actually saw patients improve and turn around.

What this article in JCOM highlighted was, number one, incorporating that outcome or that patient type and then looking at the cost of hospitalization of patients who received IVIG versus those that did not. There were 2 groups that were studied. One was the group of patients in that original pilot trial that I discussed who were randomized to receive 1 or the other prospectively; it was an unblinded randomized study. And the second group was a matched case-control study where we had patients treated with IVIG matched by age and comorbidity status and level of hypoxia to patients that did not receive IVIG. We saw a financial benefit in shortening or reducing hospitalizations, really coming down to getting rid of that 20% tail of patients that wound up going to the ICU, getting intubated, and using a high amount of hospital resources that would ramp up the cost of hospitalization. We saw great mitigation of that with IVIG, and even with a small subset of patients, we were able to show a benefit.

Dr. Barkoudah Any thoughts on where we can implement the new findings from your article in our practice at the moment, knowing we now have practice guidelines and protocols to treat COVID-19? There was a tangible benefit in treating the patients the way you approached it in your important work. Could you share with us what would be implementable at the moment?

Dr. Sakoulas I think, fortunately, with the increasing host immunity in the population and decreased virulence of the virus, perhaps we won’t see as many patients of the type that were in these trials going forward, but I suspect we will perhaps in the unvaccinated patients that remain. I believe one-third of the United States is not vaccinated. So there is certainly a vulnerable group of people out there. Potentially, an unvaccinated patient who winds up getting very sick, the patient who is relatively young—what I’m looking at is the 30- to 65-year-old obese, hypertensive, or diabetic patient who comes in and, despite the steroids and the antivirals, rapidly deteriorates into requiring high-flow oxygen. I think implementing IVIG in that patient type would be helpful. I don’t think it’s going to be as helpful in patients who are very elderly, because I think the mechanism of the disease is different in an 80-year-old versus a 50-year-old patient. So again, hopefully, it will not amount to a lot of patients, but I still suspect hospitals are going to see, perhaps in the fall, when they’re expecting a greater number of cases, a trickling of patients that do meet the criteria that I described.

Dr. Barkoudah JCOM’s audience are the QI implementers and hospital leadership. And what caught my eye in your article is your perspective on the pharmacoeconomics of treating COVID-19, and I really appreciate your looking at the cost aspect. Would you talk about the economics of inpatient care, the total care that we provide now that we’re in the age of tocilizumab, and the current state of multiple layers of therapy?

Dr. Sakoulas The reason to look at the economics of it is because IVIG—which is actually not a drug, it’s a blood product—is very expensive. So, we received a considerable amount of administrative pushback implementing this treatment at the beginning outside of the clinical trial setting because it hadn’t been studied on a large scale and because the cost was so high, even though, as a clinician at the bedside, I was seeing a benefit in patients. This study came out of my trying to demonstrate to the folks that are keeping the economics of medicine in mind that, in fact, investing several thousand dollars of treatment in IVIG will save you cost of care, the cost of an ICU bed, the cost of a ventilator, and the cost even of ECMO, which is hugely expensive.

If you look at the numbers in the study, for two-thirds or three-quarters of the patients, your cost of care is actually greater than the controls because you’re giving them IVIG, and it’s increasing the cost of their care, even though three-quarters of the patients are going to do just as well without it. It’s that 20% to 25% of patients that really are going to benefit from it, where you’re reducing your cost of care so much, and you’re getting rid of that very, very expensive 20%, that there’s a cost savings across the board per patient. So, it’s hard to understand when you say you’re losing money on three-quarters of the patients, you’re only saving money on a quarter of the patients, but that cost of saving on that small subset is so substantial it’s really impacting all numbers.

Also, abandoning the outlier principle is sort of an underlying theme in how we think of things. We tend to ignore outliers, not consider them, but I think we really have to pay attention to the more extreme cases because those patients are the ones that drive not just the financial cost of care. Remember, if you’re down to 1 ventilator and you can cut down the use of scarce ICU resources, the cost is sort of even beyond the cost of money. It’s the cost of resources that may become scarce in some settings. So, I think it speaks to that as well.

A lot of the drugs that we use, for example, tocilizumab, were able to be studied in thousands of patients. If you look at the absolute numbers, the benefit of tocilizumab from a magnitude standpoint—low to mid twenties to high twenties—you know, reducing mortality from 29% to 24%. I mean, just take a step back and think about that. Even though it’s statistically significant, try telling a patient, “Well, I’m going to give you this treatment that’s going to reduce mortality from 29% to 24%.” You know, that doesn’t really change anything from a clinical significance standpoint. But they have a P value less than .05, which is our standard, and they were able to do a study with thousands of patients. We didn’t have that luxury with IVIG. No one studied thousands of patients, only retrospectively, and those retrospective studies don’t get the attention because they’re considered biased with all their limitations. But I think one of the difficulties we have here is the balance between statistical and clinical significance. For example, in our pilot study, our ventilation rate was 58% with the non-IVIG patients versus 14% for IVIG patients. So you might say, magnitude-wise, that’s a big number, but the statistical significance of it is borderline because of small numbers.

Anyway, that’s a challenge that we have as clinicians trying to incorporate what’s published—the balancing of statistics, absolute numbers, and practicalities of delivering care. And I think this study highlights some of the nuances that go into that incorporation and those clinical decisions.

Dr. Barkoudah Would you mind sharing with our audience how we can make the connection between the medical outcomes and pharmacoeconomics findings from your article and link it to the bedside and treatment of our patients?

Dr. Sakoulas One of the points this article brings out is the importance of bringing together not just level 1A data, but also small studies with data such as this, where the magnitude of the effect is pretty big but you lose the statistics because of the small numbers. And then also the patients’ aspects of things. I think, as a bedside clinician, you appreciate things, the nuances, much sooner than what percolates out from a level 1A study. Case in point, in the sponsored phase 3 study that we did, and in some other studies that were prospectively done as well, these studies of IVIG simply had an enrollment of patients that was very broad, and not every patient benefits from the same therapy. A great example of this is the sepsis trials with Xigris and those types of agents that failed. You know, there are clinicians to this day who believe that there is a subset of patients that benefit from agents like this. The IVIG story falls a little bit into that category. It comes down to trying to identify the subset of patients that might benefit. And I think we’ve outlined this subset pretty well in our study: the younger, obese diabetic or hypertensive patient who’s rapidly declining.

It really brings together the need to not necessarily toss out these smaller studies, but kind of summarize everything together, and clinicians who are bedside, who are more in tune with the nuances of individual decisions at the individual patient level, might better appreciate these kinds of data. But I think we all have to put it together. IVIG does not make treatment guidelines at national levels and so forth. It’s not even listed in many of them. But there are patients out there who, if you ask them specifically how they felt, including a friend of mine who received the medication, there’s no question from their end, how they felt about this treatment option. Now, some people will get it and will not benefit. We just have to be really tuned into the fact that the same drug does not have the same result for every patient. And just to consider this in the high-risk patients that we talked about in our study.

Dr. Barkoudah While we were prepping for this interview, you made an analogy regarding clinical evidence along the lines of, “Do we need randomized clinical trials to do a parachute-type of experiment,” and we chatted about clinical wisdom. Would you mind sharing with our readers your thoughts on that?

Dr. Sakoulas Sometimes, we try a treatment and it’s very obvious for that particular patient that it helped them. Then you study the treatment in a large trial setting and it doesn’t work. For us bedside clinicians, there are some interventions sometimes that do appear as beneficial as a parachute would be, but yet, there has never been a randomized clinical trial proving that parachutes work. Again, a part of the challenge we have is patients are so different, their immunology is different, the pathogen infecting them is different, the time they present is different. Some present early, some present late. There are just so many moving parts to treating an infection that only a subset of people are going to benefit. And sometimes as clinicians, we’re so nuanced, that we identify a specific subset of patients where we know we can help them. And it’s so obvious for us, like a parachute would be, but to people who are looking at the world from 30,000 feet, they don’t necessarily grasp that because, when you look at all comers, it doesn’t show a benefit.

So the problem is that now those treatments that might help a subset of patients are being denied, and the subset of patients that are going to benefit never get the treatment. Now we have to balance that with a lot of stuff that went on during the pandemic with, you know, ivermectin, hydroxychloroquine, and people pushing those things. Someone asked me once what I thought about hydroxychloroquine, and I said, “Well, somebody in the lab probably showed that it was beneficial, analogous to lighting tissue paper on fire on a plate and taking a cup of water and putting the fire out. Well, now, if you take that cup of water to the Caldor fire that’s burning in California on thousands of acres, you’re not going to be able to put the fire out with that cup of water.” So while it might work in the lab, it’s truly not going to work in a clinical setting. We have to balance individualizing care for patients with some information people are pushing out there that may not be necessarily translatable to the clinical setting.

I think there’s nothing better than being at the bedside, though, and being able to implement something and seeing what works. And really, experience goes a long way in being able to individually treat a patient optimally.

Dr. Barkoudah Thank you for everything you do at the bedside and your work on improving the treatment we have and how we can leverage knowledge to treat our patients. Thank you very much for your time and your scholarly contribution. We appreciate it and I hope the work will continue. We will keep working on treating COVID-19 patients with the best knowledge we have.

Q&A participants: George Sakoulas, MD, Sharp Rees-Stealy Medical Group, La Jolla, CA, and University of California San Diego School of Medicine, San Diego, CA; and Ebrahim Barkoudah, MD, MPH, Department of Medicine, Brigham and Women’s Hospital, Boston, MA.

Disclosures: None reported.

From Memora Health (Dr. Flyckt and Dr. Colbert), San Francisco, CA; and Harvard Medical School (Dr. Colbert), Boston, MA.

A close relative was recently diagnosed with follicular lymphoma. He was cared for at a high-ranked cancer center by physicians with demonstrated expertise, and even had the support of a care navigator. Still, he was often left feeling overwhelmed and confused, holding an inch-thick stack of papers, instructions, and pamphlets. As he left his treatment planning visit, reeling from the emotional burden of his diagnosis and all the unfamiliar terminology, he didn’t know what to do or what to expect. Later, when he experienced early signs of tumor lysis syndrome, he struggled to reach his care team for triage and guidance. When he went to the emergency room, his oncologist was never informed.

This scenario is unfortunately common, and versions of this scenario play out thousands of times each day across the US health system. Within the clinic and hospital setting, patients receive excellent care from their providers, but a disconnect emerges once the patient leaves these medical settings: patients at home struggle to find guidance and support, while care teams lack the tools to engage patients between visits or monitor their health across care settings, providers, or episodes of care.

Leveraging Technology to Move From Episodes of Care to Complex Care Journeys

The use of automated messaging, artificial intelligence and natural language processing–driven chat experiences, and text-based support is becoming more common. However, health care lags behind other industries in the adoption of these technologies.^1,2 The slow pace can be warranted, given that health care is more complicated and higher risk than inquiring about a lost package, ordering groceries, or applying for a mortgage. At the same time, many of the consumer engagement tools used to guide an applicant through the multiple steps and complexities of their home loan process or to prompt viewers to select new shows to binge have applications in health care.

Over the past few years, technologies have emerged that guide patients through complex care journeys and allow care teams to monitor and engage patients between visits. These solutions come in different formats, but generally patients can receive messages on their phones that contain disease-specific educational content, prompts to fill prescriptions and take medications, and reminders and guidance on how to prepare for appointments and procedures. These programs also collect relevant data from patients through survey and electronic patient-reported outcomes instruments, as well as connected patient monitoring devices, that help track patient progress and identify issues as they arise. Many programs also incorporate symptom triage pathways and use natural language processing to respond automatically to patient questions and concerns.^3,4

These technology solutions can automate many tasks that in the past required a care team member to spend hours on the phone. Newly freed from such repetitive tasks, care teams can now focus on more in-depth interactions with those patients who are most in need—the types of interactions that are more satisfying and rewarding. Such assistance is particularly needed today with the staffing shortages faced by most health systems.⁵

In addition, technology allows teams to see the panel of patients they are caring for and to quickly identify and take action on any specific needs or issues. Care teams can focus on any patient and see where they are in their journey. When appropriate, some solutions also allow care teams to engage directly with patients through text-messaging, creating a seamless experience and unified communication channel. Ideally, these solutions should be linked or embedded within the electronic health record or other primary system of record, so that teams can easily access these tools through their existing workflows and avoid creating yet another interface to navigate.

The Impact of Low-Tech Solutions to Deliver High-Touch Support

There is evidence showing that digital patient navigation tools impact patient care. In the oncology setting, patients with a digital navigator have achieved over 95% adherence rates with complex oral chemotherapy regimens (Memora Health Unpublished Data. 2022.). In the postpartum setting, a text message–based program improved screening rates for postpartum depression and did so with very high patient satisfaction ratings.⁶ Particularly notable is the fact that this depression screening program achieved these results in a population that was predominantly low income, with more than half belonging to underrepresented minority populations.⁶

We believe these digital patient navigation technologies, specifically low-tech solutions that don’t require app downloads, portal log-ins, or high-speed internet, will transform care delivery over the next 5 to 10 years. Successful management of complex conditions like diabetes or cancer requires more than 3 hours of care each day,⁷ yet most patients spend only 1 or 2 hours per month directly interacting with their health care providers. However, most patients carry their phones with them at all times, and artificial intelligence–enabled text support is “always on” to provide support, monitoring, and guidance, wherever a patient happens to be when assistance is needed.

Shifting the Model to Support a Lifetime of Care

While still in the early stages of development, these tools have the potential to radically alter the practice of medicine, shifting the focus from episodic interactions to continuous journey-based care delivery. Outside of an acute event bringing a patient into the clinic or emergency room, many patients go a year or more without seeing their primary care providers.⁸ During that time, an immense amount of information is underreported or completely lost. Capturing this information in real-time and more holistically over a person’s lifetime of care could provide physicians better insight to both better manage and more fully evaluate the success of treatment plans by tracking patient symptoms, pain, and functional status over time. With this more longitudinal view of the patient, we see a pathway towards achieving the Quadruple Aim: patients who are more supported will achieve better outcomes at lower cost, they will have a better experience, and care teams will be empowered to focus their time on more satisfying activities rather than repetitive administrative tasks.

Corresponding author: James A. Colbert, MD, MBA; jamie@memorahealth.com

Disclosures: Dr. Flyckt and Dr. Colbert are employed by Memora Health, an organization that helps health care systems digitize and automate care journeys.

From Memora Health (Dr. Flyckt and Dr. Colbert), San Francisco, CA; and Harvard Medical School (Dr. Colbert), Boston, MA.

A close relative was recently diagnosed with follicular lymphoma. He was cared for at a high-ranked cancer center by physicians with demonstrated expertise, and even had the support of a care navigator. Still, he was often left feeling overwhelmed and confused, holding an inch-thick stack of papers, instructions, and pamphlets. As he left his treatment planning visit, reeling from the emotional burden of his diagnosis and all the unfamiliar terminology, he didn’t know what to do or what to expect. Later, when he experienced early signs of tumor lysis syndrome, he struggled to reach his care team for triage and guidance. When he went to the emergency room, his oncologist was never informed.

This scenario is unfortunately common, and versions of this scenario play out thousands of times each day across the US health system. Within the clinic and hospital setting, patients receive excellent care from their providers, but a disconnect emerges once the patient leaves these medical settings: patients at home struggle to find guidance and support, while care teams lack the tools to engage patients between visits or monitor their health across care settings, providers, or episodes of care.

Leveraging Technology to Move From Episodes of Care to Complex Care Journeys

The use of automated messaging, artificial intelligence and natural language processing–driven chat experiences, and text-based support is becoming more common. However, health care lags behind other industries in the adoption of these technologies.^1,2 The slow pace can be warranted, given that health care is more complicated and higher risk than inquiring about a lost package, ordering groceries, or applying for a mortgage. At the same time, many of the consumer engagement tools used to guide an applicant through the multiple steps and complexities of their home loan process or to prompt viewers to select new shows to binge have applications in health care.

Over the past few years, technologies have emerged that guide patients through complex care journeys and allow care teams to monitor and engage patients between visits. These solutions come in different formats, but generally patients can receive messages on their phones that contain disease-specific educational content, prompts to fill prescriptions and take medications, and reminders and guidance on how to prepare for appointments and procedures. These programs also collect relevant data from patients through survey and electronic patient-reported outcomes instruments, as well as connected patient monitoring devices, that help track patient progress and identify issues as they arise. Many programs also incorporate symptom triage pathways and use natural language processing to respond automatically to patient questions and concerns.^3,4

These technology solutions can automate many tasks that in the past required a care team member to spend hours on the phone. Newly freed from such repetitive tasks, care teams can now focus on more in-depth interactions with those patients who are most in need—the types of interactions that are more satisfying and rewarding. Such assistance is particularly needed today with the staffing shortages faced by most health systems.⁵

In addition, technology allows teams to see the panel of patients they are caring for and to quickly identify and take action on any specific needs or issues. Care teams can focus on any patient and see where they are in their journey. When appropriate, some solutions also allow care teams to engage directly with patients through text-messaging, creating a seamless experience and unified communication channel. Ideally, these solutions should be linked or embedded within the electronic health record or other primary system of record, so that teams can easily access these tools through their existing workflows and avoid creating yet another interface to navigate.

The Impact of Low-Tech Solutions to Deliver High-Touch Support

There is evidence showing that digital patient navigation tools impact patient care. In the oncology setting, patients with a digital navigator have achieved over 95% adherence rates with complex oral chemotherapy regimens (Memora Health Unpublished Data. 2022.). In the postpartum setting, a text message–based program improved screening rates for postpartum depression and did so with very high patient satisfaction ratings.⁶ Particularly notable is the fact that this depression screening program achieved these results in a population that was predominantly low income, with more than half belonging to underrepresented minority populations.⁶

We believe these digital patient navigation technologies, specifically low-tech solutions that don’t require app downloads, portal log-ins, or high-speed internet, will transform care delivery over the next 5 to 10 years. Successful management of complex conditions like diabetes or cancer requires more than 3 hours of care each day,⁷ yet most patients spend only 1 or 2 hours per month directly interacting with their health care providers. However, most patients carry their phones with them at all times, and artificial intelligence–enabled text support is “always on” to provide support, monitoring, and guidance, wherever a patient happens to be when assistance is needed.

Shifting the Model to Support a Lifetime of Care

While still in the early stages of development, these tools have the potential to radically alter the practice of medicine, shifting the focus from episodic interactions to continuous journey-based care delivery. Outside of an acute event bringing a patient into the clinic or emergency room, many patients go a year or more without seeing their primary care providers.⁸ During that time, an immense amount of information is underreported or completely lost. Capturing this information in real-time and more holistically over a person’s lifetime of care could provide physicians better insight to both better manage and more fully evaluate the success of treatment plans by tracking patient symptoms, pain, and functional status over time. With this more longitudinal view of the patient, we see a pathway towards achieving the Quadruple Aim: patients who are more supported will achieve better outcomes at lower cost, they will have a better experience, and care teams will be empowered to focus their time on more satisfying activities rather than repetitive administrative tasks.

Corresponding author: James A. Colbert, MD, MBA; jamie@memorahealth.com

Disclosures: Dr. Flyckt and Dr. Colbert are employed by Memora Health, an organization that helps health care systems digitize and automate care journeys.

From Memora Health (Dr. Flyckt and Dr. Colbert), San Francisco, CA; and Harvard Medical School (Dr. Colbert), Boston, MA.

A close relative was recently diagnosed with follicular lymphoma. He was cared for at a high-ranked cancer center by physicians with demonstrated expertise, and even had the support of a care navigator. Still, he was often left feeling overwhelmed and confused, holding an inch-thick stack of papers, instructions, and pamphlets. As he left his treatment planning visit, reeling from the emotional burden of his diagnosis and all the unfamiliar terminology, he didn’t know what to do or what to expect. Later, when he experienced early signs of tumor lysis syndrome, he struggled to reach his care team for triage and guidance. When he went to the emergency room, his oncologist was never informed.

This scenario is unfortunately common, and versions of this scenario play out thousands of times each day across the US health system. Within the clinic and hospital setting, patients receive excellent care from their providers, but a disconnect emerges once the patient leaves these medical settings: patients at home struggle to find guidance and support, while care teams lack the tools to engage patients between visits or monitor their health across care settings, providers, or episodes of care.

Leveraging Technology to Move From Episodes of Care to Complex Care Journeys

The use of automated messaging, artificial intelligence and natural language processing–driven chat experiences, and text-based support is becoming more common. However, health care lags behind other industries in the adoption of these technologies.^1,2 The slow pace can be warranted, given that health care is more complicated and higher risk than inquiring about a lost package, ordering groceries, or applying for a mortgage. At the same time, many of the consumer engagement tools used to guide an applicant through the multiple steps and complexities of their home loan process or to prompt viewers to select new shows to binge have applications in health care.

Over the past few years, technologies have emerged that guide patients through complex care journeys and allow care teams to monitor and engage patients between visits. These solutions come in different formats, but generally patients can receive messages on their phones that contain disease-specific educational content, prompts to fill prescriptions and take medications, and reminders and guidance on how to prepare for appointments and procedures. These programs also collect relevant data from patients through survey and electronic patient-reported outcomes instruments, as well as connected patient monitoring devices, that help track patient progress and identify issues as they arise. Many programs also incorporate symptom triage pathways and use natural language processing to respond automatically to patient questions and concerns.^3,4

These technology solutions can automate many tasks that in the past required a care team member to spend hours on the phone. Newly freed from such repetitive tasks, care teams can now focus on more in-depth interactions with those patients who are most in need—the types of interactions that are more satisfying and rewarding. Such assistance is particularly needed today with the staffing shortages faced by most health systems.⁵

In addition, technology allows teams to see the panel of patients they are caring for and to quickly identify and take action on any specific needs or issues. Care teams can focus on any patient and see where they are in their journey. When appropriate, some solutions also allow care teams to engage directly with patients through text-messaging, creating a seamless experience and unified communication channel. Ideally, these solutions should be linked or embedded within the electronic health record or other primary system of record, so that teams can easily access these tools through their existing workflows and avoid creating yet another interface to navigate.

The Impact of Low-Tech Solutions to Deliver High-Touch Support

There is evidence showing that digital patient navigation tools impact patient care. In the oncology setting, patients with a digital navigator have achieved over 95% adherence rates with complex oral chemotherapy regimens (Memora Health Unpublished Data. 2022.). In the postpartum setting, a text message–based program improved screening rates for postpartum depression and did so with very high patient satisfaction ratings.⁶ Particularly notable is the fact that this depression screening program achieved these results in a population that was predominantly low income, with more than half belonging to underrepresented minority populations.⁶

We believe these digital patient navigation technologies, specifically low-tech solutions that don’t require app downloads, portal log-ins, or high-speed internet, will transform care delivery over the next 5 to 10 years. Successful management of complex conditions like diabetes or cancer requires more than 3 hours of care each day,⁷ yet most patients spend only 1 or 2 hours per month directly interacting with their health care providers. However, most patients carry their phones with them at all times, and artificial intelligence–enabled text support is “always on” to provide support, monitoring, and guidance, wherever a patient happens to be when assistance is needed.

Shifting the Model to Support a Lifetime of Care

While still in the early stages of development, these tools have the potential to radically alter the practice of medicine, shifting the focus from episodic interactions to continuous journey-based care delivery. Outside of an acute event bringing a patient into the clinic or emergency room, many patients go a year or more without seeing their primary care providers.⁸ During that time, an immense amount of information is underreported or completely lost. Capturing this information in real-time and more holistically over a person’s lifetime of care could provide physicians better insight to both better manage and more fully evaluate the success of treatment plans by tracking patient symptoms, pain, and functional status over time. With this more longitudinal view of the patient, we see a pathway towards achieving the Quadruple Aim: patients who are more supported will achieve better outcomes at lower cost, they will have a better experience, and care teams will be empowered to focus their time on more satisfying activities rather than repetitive administrative tasks.

Corresponding author: James A. Colbert, MD, MBA; jamie@memorahealth.com

Disclosures: Dr. Flyckt and Dr. Colbert are employed by Memora Health, an organization that helps health care systems digitize and automate care journeys.

Study 1 Overview (Cortés et al)

Objective: To compare the efficacy and safety of trastuzumab deruxtecan with those of trastuzumab emtansine in patients with HER2-positive metastatic breast cancer previously treated with trastuzumab and taxane.

Design: Phase 3, multicenter, open-label randomized trial conducted at 169 centers and 15 countries.

Setting and participants: Eligible patients had to have unresectable or metastatic HER2-positive breast cancer that had progressed during or after treatment with trastuzumab and a taxane or had disease that progressed within 6 months after neoadjuvant or adjuvant treatment involving trastuzumab or taxane. Patients with stable or previously treated brain metastases were eligible. Patients were not eligible for the study if they had symptomatic brain metastases, prior exposure to trastuzumab emtansine, or a history of interstitial lung disease.

Intervention: Patients were randomized in a 1-to-1 fashion to receive either trastuzumab deruxtecan 5.4 mg/kg every 3 weeks or trastuzumab emtansine 3.6 mg/kg every 3 weeks. Patients were stratified according to hormone-receptor status, prior treatment with epratuzumab, and the presence or absence of visceral disease.

Main outcome measures: The primary endpoint of the study was progression-free survival as determined by an independent central review. Secondary endpoints included overall survival, overall response, and safety.

Main results: A total of 524 patients were enrolled in the study, with 261 patients randomized to trastuzumab deruxtecan and 263 patients randomized to trastuzumab emtansine. The demographic and baseline characteristics were similar between the 2 cohorts, and 60% of patients in both groups received prior epratuzumab therapy. Stable brain metastases were present in around 20% of patients in each group, and 70% of patients in each group had visceral disease. The median duration of follow-up was 16.2 months with trastuzumab deruxtecan and 15.3 months with trastuzumab emtansine.

The median progression-free survival was not reached in the trastuzumab deruxtecan group and was 6.8 months in the trastuzumab emtansine group (95% CI, 5.6-8.2). At 12 months the percentage of patients alive without disease progression was significantly larger in the trastuzumab deruxtecan group compared with the trastuzumab emtansine group. The hazard ratio for disease progression or death from any cause was 0.28 (95% CI, 0.22-0.37; P < .001). Subgroup analyses showed a benefit in progression-free survival with trastuzumab deruxtecan across all subgroups.

At the time of this analysis, the percentage of patients who were alive at 12 months was 94% with trastuzumab deruxtecan and 85.9% with trastuzumab emtansine. The response rates were significantly higher with trastuzumab deruxtecan compared with trastuzumab emtansine (79.7% vs 34.2%). A complete response was seen in 16% of patients in the trastuzumab deruxtecan arm, compared with 8.7% of patients in the trastuzumab emtansine group. The disease control rate (complete response, partial response, or stable disease) was higher in the trastuzumab deruxtecan group compared with the trastuzumab emtansine group (96.6% vs 76.8%).

Serious adverse events were reported in 19% of patients in the trastuzumab deruxtecan group and 18% of patients in the trastuzumab emtansine group. Discontinuation due to adverse events was higher in the trastuzumab deruxtecan group, with 13.6% of patients discontinuing trastuzumab deruxtecan. Grade 3 or higher adverse events were seen in 52% of patients treated with trastuzumab deruxtecan and 48% of patients treated with trastuzumab emtansine. The most commonly reported adverse event with trastuzumab deruxtecan was nausea/vomiting and fatigue. These adverse events were seen more in the trastuzumab deruxtecan group compared with the trastuzumab emtansine group. No drug-related grade 5 adverse events were reported.

In the trastuzumab deruxtecan group, 10.5% of patients receiving trastuzumab deruxtecan developed interstitial lung disease or pneumonitis. Seven patients had grade 1 events, 18 patients had grade 2 events, and 2 patients had grade 3 events. No grade 4 or 5 events were noted in either treatment group. The median time to onset of interstitial lung disease or pneumonitis in those receiving trastuzumab deruxtecan was 168 days (range, 33-507). Discontinuation of therapy due to interstitial lung disease or pneumonitis occurred in 8% of patients receiving trastuzumab deruxtecan and 1% of patients receiving trastuzumab emtansine.

Conclusion: Trastuzumab deruxtecan significantly decreases the risk of disease progression or death compared to trastuzumab emtansine in patients with HER2-positive metastatic breast cancer who have progressed on prior trastuzumab and taxane-based therapy.

Study 2 Overview (Modi et al)

Objective: To assess the efficacy of trastuzumab deruxtecan in patients with unresectable or metastatic breast cancer with low levels of HER2 expression.

Design: This was a randomized, 2-group, open-label, phase 3 trial.

Setting and participants: The trial was designed with a planned enrollment of 480 patients with hormone receptor–positive disease and 60 patients with hormone receptor–negative disease. Patients were randomized in a 2:1 ratio. Randomization was stratified according to HER2 status (immunohistochemical [IHC] 1+ vs IHC 2+/in situ hybridization [ISH] negative), number of prior lines of therapy, and hormone-receptor status. IHC scores for HER2 expression were determined through central testing. Specimens that had HER2 IHC scores of 2+ were reflexed to ISH. Specimens were considered HER2-low-expressing if they had an IHC score of 1+ or if they had an IHC score of 2+ and were ISH negative.

Eligible patients had to have received chemotherapy for metastatic disease or had disease recurrence during or within 6 months after completing adjuvant chemotherapy. Patients with hormone receptor–positive disease must have had at least 1 line of endocrine therapy. Patients were eligible if they had stable brain metastases. Patients with interstitial lung disease were excluded.

Intervention: Patients were randomized to receive trastuzumab deruxtecan 5.4 mg/kg every 3 weeks or physician’s choice of chemotherapy (capecitabine, eribulin, gemcitabine, paclitaxel, or nab-paclitaxel).

Main outcome measures: The primary endpoint was progression-free survival in patients with hormone receptor–positive disease. Secondary endpoints were progression-free survival among all patients, overall survival in hormone receptor–positive patients, and overall survival in all patients. Additional secondary endpoints included objective response rates, duration of response, and efficacy in hormone receptor–negative patients.

Main results: A total of 373 patients were assigned to the trastuzumab deruxtecan group and 184 patients were assigned to the physician’s choice chemotherapy group; 88% of patients in each cohort were hormone receptor–positive. In the physician’s choice chemotherapy group, 51% received eribulin, 20% received capecitabine, 10% received nab-paclitaxel, 10% received gemcitabine, and 8% received paclitaxel. The demographic and baseline characteristics were similar between both cohorts. The median duration of follow-up was 18.4 months.

The median progression-free survival in the hormone receptor–positive cohort was 10.1 months in the trastuzumab deruxtecan group and 5.4 months in the physician’s choice chemotherapy group (HR, 0.51; 95% CI, 0.4-0.64). Subgroup analyses revealed a benefit across all subgroups. The median progression-free survival among patients with a HER2 IHC score of 1+ and those with a HER2 IHC score of 2+/negative ISH were identical. In patients who received a prior CDK 4/6 inhibitor, the median progression-free survival was also 10 months in the trastuzumab deruxtecan group. In those who were CDK 4/6- naïve, the progression-free survival was 11.7 months. The progression-free survival in all patients was 9.9 months in the trastuzumab deruxtecan group and 5.1 months in the physician’s choice chemotherapy group (HR, 0.46; 95% CI, 0.24-0.89).

The median overall survival in the hormone receptor–positive cohort was 23.9 months in the trastuzumab deruxtecan group compared with 17.5 months in the physician’s choice chemotherapy group (HR, 0.64; 95% CI, 0.48-0.86; P = .003). The median overall survival in the entire population was 23.4 months in the trastuzumab deruxtecan group vs 16.8 months in the physician’s choice chemotherapy group. In the hormone receptor–negative cohort, the median overall survival was 18.2 months in the trastuzumab deruxtecan group and 8.3 months in the physician’s choice chemotherapy group. Complete responses were seen in 3.6% in the trastuzumab deruxtecan group and 0.6% and the physician’s choice chemotherapy group. The median duration of response was 10.7 months in the trastuzumab deruxtecan group and 6.8 months in the physician’s choice chemotherapy group.

Incidence of serious adverse events was 27% in the trastuzumab deruxtecan group and 25% in the physician’s choice chemotherapy group. Grade 3 or higher events occurred in 52% of the trastuzumab deruxtecan group and 67% of the physician’s choice chemotherapy group. Discontinuation due to adverse events occurred in 16% in the trastuzumab deruxtecan group and 18% in the physician’s choice chemotherapy group; 14 patients in the trastuzumab deruxtecan group and 5 patients in the physician’s choice chemotherapy group had an adverse event that was associated with death. Death due to pneumonitis in the trastuzumab deruxtecan group occurred in 2 patients. Drug-related interstitial lung disease or pneumonitis occurred in 45 patients who received trastuzumab deruxtecan. The majority of these events were grade 1 and grade 2. However, 3 patients had grade 5 interstitial lung disease or pneumonitis.

Conclusion: Treatment with trastuzumab deruxtecan led to a significant improvement in progression-free survival compared to physician’s choice chemotherapy in patients with HER2-low metastatic breast cancer.

Commentary

Trastuzumab deruxtecan is an antibody drug conjugate that consists of a humanized anti-HER2 monoclonal antibody linked to a topoisomerase 1 inhibitor. This antibody drug conjugate is unique compared with prior antibody drug conjugates such as trastuzumab emtansine in that it has a high drug-to-antibody ratio (~8). Furthermore, there appears to be a unique bystander effect resulting in off-target cytotoxicity to neighboring tumor cells, enhancing the efficacy of this novel therapy. Prior studies of trastuzumab deruxtecan have shown durable activity in heavily pretreated patients with metastatic HER2-positive breast cancer.¹

HER2-positive breast cancer represents approximately 20% of breast cancer cases in women.² Historically, HER2 positivity has been defined by strong HER2 expression with IHC staining (ie, score 3+) or HER2 amplification through ISH. Conversely, HER2-negative disease has historically been defined as those with IHC scores of 0 or 1+. This group represents approximately 60% of HER2-negative metastatic breast cancer patients.³ These patients have limited targeted treatment options after progressing on primary therapy. Prior data has shown that patients with low HER2 expression represent a heterogeneous population and thus, the historic categorization of HER2 status as positive or negative may in fact not adequately characterize the proportion of patients who may derive clinical benefit from HER2-directed therapies. Nevertheless, there have been no data to date that have shown improved outcomes in low HER2 expressers with anti-HER2 therapies.

The current studies add to the rapidly growing body of literature outlining the efficacy of the novel antibody drug conjugate trastuzumab deruxtecan. The implications of the data presented in these 2 studies are immediately practice changing.

In the DESTINY-Breast03 trial, Cortéz and colleagues show that trastuzumab deruxtecan therapy significantly prolongs progression-free survival compared with trastuzumab emtansine in patients with HER2-positive metastatic breast cancer who have progressed on first-line trastuzumab and taxane-based therapy. With a hazard ratio of 0.28 for disease progression or death, the efficacy of trastuzumab deruxtecan highlighted in this trial clearly makes this the standard of care in the second-line setting for patients with metastatic HER2-positive breast cancer. The overall survival in this trial was immature at the time of this analysis, and thus continued follow-up to validate the results noted here are warranted.

The DESTINY-Breast04 trial by Modi et al expands the cohort of patients who benefit from trastuzumab deruxtecan profoundly. This study defines a population of patients with HER2-low metastatic breast cancer who will now be eligible for HER2-directed therapies. These data show that therapy with trastuzumab deruxtecan leads to a significant and clinically meaningful improvement in both progression-free survival and overall survival compared with chemotherapy in patients with metastatic breast cancer with low expression of HER2. This benefit was seen in both the estrogen receptor–positive cohort as well as the entire population, including pre-treated triple-negative disease. Furthermore, this study does not define a threshold of HER2 expression by IHC that predicts benefit with trastuzumab deruxtecan. Patients with an IHC score of 1+ as well as those with a score of 2+/ISH negative both benefit to a similar extent from trastuzumab deruxtecan. Interestingly, in the DAISY trial, antitumor activity was noted with trastuzumab deruxtecan even in those without any detectable HER2 expression on IHC.⁴ Given the inconsistency and potential false negatives of IHC along with heterogeneous HER2 expression, further work is needed to better identify patients with low levels of HER2 expression who may benefit from this novel antibody drug conjugate. Thus, a reliable test to quantitatively assess the level of HER2 expression is needed in order to determine more accurately which patients will benefit from trastuzumab deruxtecan.

Last, trastuzumab deruxtecan has been associated with interstitial lung disease and pneumonitis. Interstitial lung disease and pneumonitis occurred in approximately 10% of patients who received trastuzumab deruxtecan in the DESTINY-Breast03 trial and about 12% of patients in the DESTINY-Breast04 trial. Most of these events were grade 1 and grade 2. Nevertheless, clinicians must be aware of this risk and monitor patients frequently for the development of pneumonitis or interstitial lung disease.

Application for Clinical Practice and System Implementation

The results of the current studies show a longer progression-free survival with trastuzumab deruxtecan in both HER2-low expressing metastatic breast cancer and HER2-positive metastatic breast cancer following taxane and trastuzumab-based therapy. These results are clearly practice changing and represent a new standard of care in these patient populations. It is incumbent upon treating oncologists to work with our pathology colleagues to assess HER2 IHC thoroughly in order to identify all potential patients who may benefit from trastuzumab deruxtecan in the metastatic setting. The continued advancement of anti-HER2 therapy will undoubtedly have a significant impact on patient outcomes going forward.

Practice Points

• With a hazard ratio of 0.28 for disease progression or death, the efficacy of trastuzumab deruxtecan highlighted in the DESTINY-Breast03 trial clearly makes this the standard of care in the second-line setting for patients with metastatic HER2-positive breast cancer.
• In the DESTINY-Breast04 trial, a significant and clinically meaningful improvement in both progression-free survival and overall survival compared with chemotherapy was seen in patients with metastatic breast cancer with low expression of HER2, including both the estrogen receptor–positive cohort as well as the entire population, including those with pre-treated triple-negative disease.

­—Daniel Isaac, DO, MS

Study 1 Overview (Cortés et al)

Objective: To compare the efficacy and safety of trastuzumab deruxtecan with those of trastuzumab emtansine in patients with HER2-positive metastatic breast cancer previously treated with trastuzumab and taxane.

Design: Phase 3, multicenter, open-label randomized trial conducted at 169 centers and 15 countries.

Setting and participants: Eligible patients had to have unresectable or metastatic HER2-positive breast cancer that had progressed during or after treatment with trastuzumab and a taxane or had disease that progressed within 6 months after neoadjuvant or adjuvant treatment involving trastuzumab or taxane. Patients with stable or previously treated brain metastases were eligible. Patients were not eligible for the study if they had symptomatic brain metastases, prior exposure to trastuzumab emtansine, or a history of interstitial lung disease.

Intervention: Patients were randomized in a 1-to-1 fashion to receive either trastuzumab deruxtecan 5.4 mg/kg every 3 weeks or trastuzumab emtansine 3.6 mg/kg every 3 weeks. Patients were stratified according to hormone-receptor status, prior treatment with epratuzumab, and the presence or absence of visceral disease.

Main outcome measures: The primary endpoint of the study was progression-free survival as determined by an independent central review. Secondary endpoints included overall survival, overall response, and safety.

Main results: A total of 524 patients were enrolled in the study, with 261 patients randomized to trastuzumab deruxtecan and 263 patients randomized to trastuzumab emtansine. The demographic and baseline characteristics were similar between the 2 cohorts, and 60% of patients in both groups received prior epratuzumab therapy. Stable brain metastases were present in around 20% of patients in each group, and 70% of patients in each group had visceral disease. The median duration of follow-up was 16.2 months with trastuzumab deruxtecan and 15.3 months with trastuzumab emtansine.

The median progression-free survival was not reached in the trastuzumab deruxtecan group and was 6.8 months in the trastuzumab emtansine group (95% CI, 5.6-8.2). At 12 months the percentage of patients alive without disease progression was significantly larger in the trastuzumab deruxtecan group compared with the trastuzumab emtansine group. The hazard ratio for disease progression or death from any cause was 0.28 (95% CI, 0.22-0.37; P < .001). Subgroup analyses showed a benefit in progression-free survival with trastuzumab deruxtecan across all subgroups.

At the time of this analysis, the percentage of patients who were alive at 12 months was 94% with trastuzumab deruxtecan and 85.9% with trastuzumab emtansine. The response rates were significantly higher with trastuzumab deruxtecan compared with trastuzumab emtansine (79.7% vs 34.2%). A complete response was seen in 16% of patients in the trastuzumab deruxtecan arm, compared with 8.7% of patients in the trastuzumab emtansine group. The disease control rate (complete response, partial response, or stable disease) was higher in the trastuzumab deruxtecan group compared with the trastuzumab emtansine group (96.6% vs 76.8%).

Serious adverse events were reported in 19% of patients in the trastuzumab deruxtecan group and 18% of patients in the trastuzumab emtansine group. Discontinuation due to adverse events was higher in the trastuzumab deruxtecan group, with 13.6% of patients discontinuing trastuzumab deruxtecan. Grade 3 or higher adverse events were seen in 52% of patients treated with trastuzumab deruxtecan and 48% of patients treated with trastuzumab emtansine. The most commonly reported adverse event with trastuzumab deruxtecan was nausea/vomiting and fatigue. These adverse events were seen more in the trastuzumab deruxtecan group compared with the trastuzumab emtansine group. No drug-related grade 5 adverse events were reported.

In the trastuzumab deruxtecan group, 10.5% of patients receiving trastuzumab deruxtecan developed interstitial lung disease or pneumonitis. Seven patients had grade 1 events, 18 patients had grade 2 events, and 2 patients had grade 3 events. No grade 4 or 5 events were noted in either treatment group. The median time to onset of interstitial lung disease or pneumonitis in those receiving trastuzumab deruxtecan was 168 days (range, 33-507). Discontinuation of therapy due to interstitial lung disease or pneumonitis occurred in 8% of patients receiving trastuzumab deruxtecan and 1% of patients receiving trastuzumab emtansine.

Conclusion: Trastuzumab deruxtecan significantly decreases the risk of disease progression or death compared to trastuzumab emtansine in patients with HER2-positive metastatic breast cancer who have progressed on prior trastuzumab and taxane-based therapy.

Study 2 Overview (Modi et al)

Objective: To assess the efficacy of trastuzumab deruxtecan in patients with unresectable or metastatic breast cancer with low levels of HER2 expression.

Design: This was a randomized, 2-group, open-label, phase 3 trial.

Setting and participants: The trial was designed with a planned enrollment of 480 patients with hormone receptor–positive disease and 60 patients with hormone receptor–negative disease. Patients were randomized in a 2:1 ratio. Randomization was stratified according to HER2 status (immunohistochemical [IHC] 1+ vs IHC 2+/in situ hybridization [ISH] negative), number of prior lines of therapy, and hormone-receptor status. IHC scores for HER2 expression were determined through central testing. Specimens that had HER2 IHC scores of 2+ were reflexed to ISH. Specimens were considered HER2-low-expressing if they had an IHC score of 1+ or if they had an IHC score of 2+ and were ISH negative.

Eligible patients had to have received chemotherapy for metastatic disease or had disease recurrence during or within 6 months after completing adjuvant chemotherapy. Patients with hormone receptor–positive disease must have had at least 1 line of endocrine therapy. Patients were eligible if they had stable brain metastases. Patients with interstitial lung disease were excluded.

Intervention: Patients were randomized to receive trastuzumab deruxtecan 5.4 mg/kg every 3 weeks or physician’s choice of chemotherapy (capecitabine, eribulin, gemcitabine, paclitaxel, or nab-paclitaxel).

Main outcome measures: The primary endpoint was progression-free survival in patients with hormone receptor–positive disease. Secondary endpoints were progression-free survival among all patients, overall survival in hormone receptor–positive patients, and overall survival in all patients. Additional secondary endpoints included objective response rates, duration of response, and efficacy in hormone receptor–negative patients.

Main results: A total of 373 patients were assigned to the trastuzumab deruxtecan group and 184 patients were assigned to the physician’s choice chemotherapy group; 88% of patients in each cohort were hormone receptor–positive. In the physician’s choice chemotherapy group, 51% received eribulin, 20% received capecitabine, 10% received nab-paclitaxel, 10% received gemcitabine, and 8% received paclitaxel. The demographic and baseline characteristics were similar between both cohorts. The median duration of follow-up was 18.4 months.

The median progression-free survival in the hormone receptor–positive cohort was 10.1 months in the trastuzumab deruxtecan group and 5.4 months in the physician’s choice chemotherapy group (HR, 0.51; 95% CI, 0.4-0.64). Subgroup analyses revealed a benefit across all subgroups. The median progression-free survival among patients with a HER2 IHC score of 1+ and those with a HER2 IHC score of 2+/negative ISH were identical. In patients who received a prior CDK 4/6 inhibitor, the median progression-free survival was also 10 months in the trastuzumab deruxtecan group. In those who were CDK 4/6- naïve, the progression-free survival was 11.7 months. The progression-free survival in all patients was 9.9 months in the trastuzumab deruxtecan group and 5.1 months in the physician’s choice chemotherapy group (HR, 0.46; 95% CI, 0.24-0.89).

The median overall survival in the hormone receptor–positive cohort was 23.9 months in the trastuzumab deruxtecan group compared with 17.5 months in the physician’s choice chemotherapy group (HR, 0.64; 95% CI, 0.48-0.86; P = .003). The median overall survival in the entire population was 23.4 months in the trastuzumab deruxtecan group vs 16.8 months in the physician’s choice chemotherapy group. In the hormone receptor–negative cohort, the median overall survival was 18.2 months in the trastuzumab deruxtecan group and 8.3 months in the physician’s choice chemotherapy group. Complete responses were seen in 3.6% in the trastuzumab deruxtecan group and 0.6% and the physician’s choice chemotherapy group. The median duration of response was 10.7 months in the trastuzumab deruxtecan group and 6.8 months in the physician’s choice chemotherapy group.

Incidence of serious adverse events was 27% in the trastuzumab deruxtecan group and 25% in the physician’s choice chemotherapy group. Grade 3 or higher events occurred in 52% of the trastuzumab deruxtecan group and 67% of the physician’s choice chemotherapy group. Discontinuation due to adverse events occurred in 16% in the trastuzumab deruxtecan group and 18% in the physician’s choice chemotherapy group; 14 patients in the trastuzumab deruxtecan group and 5 patients in the physician’s choice chemotherapy group had an adverse event that was associated with death. Death due to pneumonitis in the trastuzumab deruxtecan group occurred in 2 patients. Drug-related interstitial lung disease or pneumonitis occurred in 45 patients who received trastuzumab deruxtecan. The majority of these events were grade 1 and grade 2. However, 3 patients had grade 5 interstitial lung disease or pneumonitis.

Conclusion: Treatment with trastuzumab deruxtecan led to a significant improvement in progression-free survival compared to physician’s choice chemotherapy in patients with HER2-low metastatic breast cancer.

Commentary

Trastuzumab deruxtecan is an antibody drug conjugate that consists of a humanized anti-HER2 monoclonal antibody linked to a topoisomerase 1 inhibitor. This antibody drug conjugate is unique compared with prior antibody drug conjugates such as trastuzumab emtansine in that it has a high drug-to-antibody ratio (~8). Furthermore, there appears to be a unique bystander effect resulting in off-target cytotoxicity to neighboring tumor cells, enhancing the efficacy of this novel therapy. Prior studies of trastuzumab deruxtecan have shown durable activity in heavily pretreated patients with metastatic HER2-positive breast cancer.¹

HER2-positive breast cancer represents approximately 20% of breast cancer cases in women.² Historically, HER2 positivity has been defined by strong HER2 expression with IHC staining (ie, score 3+) or HER2 amplification through ISH. Conversely, HER2-negative disease has historically been defined as those with IHC scores of 0 or 1+. This group represents approximately 60% of HER2-negative metastatic breast cancer patients.³ These patients have limited targeted treatment options after progressing on primary therapy. Prior data has shown that patients with low HER2 expression represent a heterogeneous population and thus, the historic categorization of HER2 status as positive or negative may in fact not adequately characterize the proportion of patients who may derive clinical benefit from HER2-directed therapies. Nevertheless, there have been no data to date that have shown improved outcomes in low HER2 expressers with anti-HER2 therapies.

The current studies add to the rapidly growing body of literature outlining the efficacy of the novel antibody drug conjugate trastuzumab deruxtecan. The implications of the data presented in these 2 studies are immediately practice changing.

In the DESTINY-Breast03 trial, Cortéz and colleagues show that trastuzumab deruxtecan therapy significantly prolongs progression-free survival compared with trastuzumab emtansine in patients with HER2-positive metastatic breast cancer who have progressed on first-line trastuzumab and taxane-based therapy. With a hazard ratio of 0.28 for disease progression or death, the efficacy of trastuzumab deruxtecan highlighted in this trial clearly makes this the standard of care in the second-line setting for patients with metastatic HER2-positive breast cancer. The overall survival in this trial was immature at the time of this analysis, and thus continued follow-up to validate the results noted here are warranted.

The DESTINY-Breast04 trial by Modi et al expands the cohort of patients who benefit from trastuzumab deruxtecan profoundly. This study defines a population of patients with HER2-low metastatic breast cancer who will now be eligible for HER2-directed therapies. These data show that therapy with trastuzumab deruxtecan leads to a significant and clinically meaningful improvement in both progression-free survival and overall survival compared with chemotherapy in patients with metastatic breast cancer with low expression of HER2. This benefit was seen in both the estrogen receptor–positive cohort as well as the entire population, including pre-treated triple-negative disease. Furthermore, this study does not define a threshold of HER2 expression by IHC that predicts benefit with trastuzumab deruxtecan. Patients with an IHC score of 1+ as well as those with a score of 2+/ISH negative both benefit to a similar extent from trastuzumab deruxtecan. Interestingly, in the DAISY trial, antitumor activity was noted with trastuzumab deruxtecan even in those without any detectable HER2 expression on IHC.⁴ Given the inconsistency and potential false negatives of IHC along with heterogeneous HER2 expression, further work is needed to better identify patients with low levels of HER2 expression who may benefit from this novel antibody drug conjugate. Thus, a reliable test to quantitatively assess the level of HER2 expression is needed in order to determine more accurately which patients will benefit from trastuzumab deruxtecan.

Last, trastuzumab deruxtecan has been associated with interstitial lung disease and pneumonitis. Interstitial lung disease and pneumonitis occurred in approximately 10% of patients who received trastuzumab deruxtecan in the DESTINY-Breast03 trial and about 12% of patients in the DESTINY-Breast04 trial. Most of these events were grade 1 and grade 2. Nevertheless, clinicians must be aware of this risk and monitor patients frequently for the development of pneumonitis or interstitial lung disease.

Application for Clinical Practice and System Implementation

The results of the current studies show a longer progression-free survival with trastuzumab deruxtecan in both HER2-low expressing metastatic breast cancer and HER2-positive metastatic breast cancer following taxane and trastuzumab-based therapy. These results are clearly practice changing and represent a new standard of care in these patient populations. It is incumbent upon treating oncologists to work with our pathology colleagues to assess HER2 IHC thoroughly in order to identify all potential patients who may benefit from trastuzumab deruxtecan in the metastatic setting. The continued advancement of anti-HER2 therapy will undoubtedly have a significant impact on patient outcomes going forward.

Practice Points

• With a hazard ratio of 0.28 for disease progression or death, the efficacy of trastuzumab deruxtecan highlighted in the DESTINY-Breast03 trial clearly makes this the standard of care in the second-line setting for patients with metastatic HER2-positive breast cancer.
• In the DESTINY-Breast04 trial, a significant and clinically meaningful improvement in both progression-free survival and overall survival compared with chemotherapy was seen in patients with metastatic breast cancer with low expression of HER2, including both the estrogen receptor–positive cohort as well as the entire population, including those with pre-treated triple-negative disease.

­—Daniel Isaac, DO, MS

Study 1 Overview (Cortés et al)

Objective: To compare the efficacy and safety of trastuzumab deruxtecan with those of trastuzumab emtansine in patients with HER2-positive metastatic breast cancer previously treated with trastuzumab and taxane.

Design: Phase 3, multicenter, open-label randomized trial conducted at 169 centers and 15 countries.

Setting and participants: Eligible patients had to have unresectable or metastatic HER2-positive breast cancer that had progressed during or after treatment with trastuzumab and a taxane or had disease that progressed within 6 months after neoadjuvant or adjuvant treatment involving trastuzumab or taxane. Patients with stable or previously treated brain metastases were eligible. Patients were not eligible for the study if they had symptomatic brain metastases, prior exposure to trastuzumab emtansine, or a history of interstitial lung disease.

Intervention: Patients were randomized in a 1-to-1 fashion to receive either trastuzumab deruxtecan 5.4 mg/kg every 3 weeks or trastuzumab emtansine 3.6 mg/kg every 3 weeks. Patients were stratified according to hormone-receptor status, prior treatment with epratuzumab, and the presence or absence of visceral disease.

Main outcome measures: The primary endpoint of the study was progression-free survival as determined by an independent central review. Secondary endpoints included overall survival, overall response, and safety.

Main results: A total of 524 patients were enrolled in the study, with 261 patients randomized to trastuzumab deruxtecan and 263 patients randomized to trastuzumab emtansine. The demographic and baseline characteristics were similar between the 2 cohorts, and 60% of patients in both groups received prior epratuzumab therapy. Stable brain metastases were present in around 20% of patients in each group, and 70% of patients in each group had visceral disease. The median duration of follow-up was 16.2 months with trastuzumab deruxtecan and 15.3 months with trastuzumab emtansine.

The median progression-free survival was not reached in the trastuzumab deruxtecan group and was 6.8 months in the trastuzumab emtansine group (95% CI, 5.6-8.2). At 12 months the percentage of patients alive without disease progression was significantly larger in the trastuzumab deruxtecan group compared with the trastuzumab emtansine group. The hazard ratio for disease progression or death from any cause was 0.28 (95% CI, 0.22-0.37; P < .001). Subgroup analyses showed a benefit in progression-free survival with trastuzumab deruxtecan across all subgroups.

At the time of this analysis, the percentage of patients who were alive at 12 months was 94% with trastuzumab deruxtecan and 85.9% with trastuzumab emtansine. The response rates were significantly higher with trastuzumab deruxtecan compared with trastuzumab emtansine (79.7% vs 34.2%). A complete response was seen in 16% of patients in the trastuzumab deruxtecan arm, compared with 8.7% of patients in the trastuzumab emtansine group. The disease control rate (complete response, partial response, or stable disease) was higher in the trastuzumab deruxtecan group compared with the trastuzumab emtansine group (96.6% vs 76.8%).

Serious adverse events were reported in 19% of patients in the trastuzumab deruxtecan group and 18% of patients in the trastuzumab emtansine group. Discontinuation due to adverse events was higher in the trastuzumab deruxtecan group, with 13.6% of patients discontinuing trastuzumab deruxtecan. Grade 3 or higher adverse events were seen in 52% of patients treated with trastuzumab deruxtecan and 48% of patients treated with trastuzumab emtansine. The most commonly reported adverse event with trastuzumab deruxtecan was nausea/vomiting and fatigue. These adverse events were seen more in the trastuzumab deruxtecan group compared with the trastuzumab emtansine group. No drug-related grade 5 adverse events were reported.

In the trastuzumab deruxtecan group, 10.5% of patients receiving trastuzumab deruxtecan developed interstitial lung disease or pneumonitis. Seven patients had grade 1 events, 18 patients had grade 2 events, and 2 patients had grade 3 events. No grade 4 or 5 events were noted in either treatment group. The median time to onset of interstitial lung disease or pneumonitis in those receiving trastuzumab deruxtecan was 168 days (range, 33-507). Discontinuation of therapy due to interstitial lung disease or pneumonitis occurred in 8% of patients receiving trastuzumab deruxtecan and 1% of patients receiving trastuzumab emtansine.

Conclusion: Trastuzumab deruxtecan significantly decreases the risk of disease progression or death compared to trastuzumab emtansine in patients with HER2-positive metastatic breast cancer who have progressed on prior trastuzumab and taxane-based therapy.

Study 2 Overview (Modi et al)

Objective: To assess the efficacy of trastuzumab deruxtecan in patients with unresectable or metastatic breast cancer with low levels of HER2 expression.

Design: This was a randomized, 2-group, open-label, phase 3 trial.

Setting and participants: The trial was designed with a planned enrollment of 480 patients with hormone receptor–positive disease and 60 patients with hormone receptor–negative disease. Patients were randomized in a 2:1 ratio. Randomization was stratified according to HER2 status (immunohistochemical [IHC] 1+ vs IHC 2+/in situ hybridization [ISH] negative), number of prior lines of therapy, and hormone-receptor status. IHC scores for HER2 expression were determined through central testing. Specimens that had HER2 IHC scores of 2+ were reflexed to ISH. Specimens were considered HER2-low-expressing if they had an IHC score of 1+ or if they had an IHC score of 2+ and were ISH negative.

Eligible patients had to have received chemotherapy for metastatic disease or had disease recurrence during or within 6 months after completing adjuvant chemotherapy. Patients with hormone receptor–positive disease must have had at least 1 line of endocrine therapy. Patients were eligible if they had stable brain metastases. Patients with interstitial lung disease were excluded.

Intervention: Patients were randomized to receive trastuzumab deruxtecan 5.4 mg/kg every 3 weeks or physician’s choice of chemotherapy (capecitabine, eribulin, gemcitabine, paclitaxel, or nab-paclitaxel).

Main outcome measures: The primary endpoint was progression-free survival in patients with hormone receptor–positive disease. Secondary endpoints were progression-free survival among all patients, overall survival in hormone receptor–positive patients, and overall survival in all patients. Additional secondary endpoints included objective response rates, duration of response, and efficacy in hormone receptor–negative patients.

Main results: A total of 373 patients were assigned to the trastuzumab deruxtecan group and 184 patients were assigned to the physician’s choice chemotherapy group; 88% of patients in each cohort were hormone receptor–positive. In the physician’s choice chemotherapy group, 51% received eribulin, 20% received capecitabine, 10% received nab-paclitaxel, 10% received gemcitabine, and 8% received paclitaxel. The demographic and baseline characteristics were similar between both cohorts. The median duration of follow-up was 18.4 months.

The median progression-free survival in the hormone receptor–positive cohort was 10.1 months in the trastuzumab deruxtecan group and 5.4 months in the physician’s choice chemotherapy group (HR, 0.51; 95% CI, 0.4-0.64). Subgroup analyses revealed a benefit across all subgroups. The median progression-free survival among patients with a HER2 IHC score of 1+ and those with a HER2 IHC score of 2+/negative ISH were identical. In patients who received a prior CDK 4/6 inhibitor, the median progression-free survival was also 10 months in the trastuzumab deruxtecan group. In those who were CDK 4/6- naïve, the progression-free survival was 11.7 months. The progression-free survival in all patients was 9.9 months in the trastuzumab deruxtecan group and 5.1 months in the physician’s choice chemotherapy group (HR, 0.46; 95% CI, 0.24-0.89).

The median overall survival in the hormone receptor–positive cohort was 23.9 months in the trastuzumab deruxtecan group compared with 17.5 months in the physician’s choice chemotherapy group (HR, 0.64; 95% CI, 0.48-0.86; P = .003). The median overall survival in the entire population was 23.4 months in the trastuzumab deruxtecan group vs 16.8 months in the physician’s choice chemotherapy group. In the hormone receptor–negative cohort, the median overall survival was 18.2 months in the trastuzumab deruxtecan group and 8.3 months in the physician’s choice chemotherapy group. Complete responses were seen in 3.6% in the trastuzumab deruxtecan group and 0.6% and the physician’s choice chemotherapy group. The median duration of response was 10.7 months in the trastuzumab deruxtecan group and 6.8 months in the physician’s choice chemotherapy group.

Incidence of serious adverse events was 27% in the trastuzumab deruxtecan group and 25% in the physician’s choice chemotherapy group. Grade 3 or higher events occurred in 52% of the trastuzumab deruxtecan group and 67% of the physician’s choice chemotherapy group. Discontinuation due to adverse events occurred in 16% in the trastuzumab deruxtecan group and 18% in the physician’s choice chemotherapy group; 14 patients in the trastuzumab deruxtecan group and 5 patients in the physician’s choice chemotherapy group had an adverse event that was associated with death. Death due to pneumonitis in the trastuzumab deruxtecan group occurred in 2 patients. Drug-related interstitial lung disease or pneumonitis occurred in 45 patients who received trastuzumab deruxtecan. The majority of these events were grade 1 and grade 2. However, 3 patients had grade 5 interstitial lung disease or pneumonitis.

Conclusion: Treatment with trastuzumab deruxtecan led to a significant improvement in progression-free survival compared to physician’s choice chemotherapy in patients with HER2-low metastatic breast cancer.

Commentary

Trastuzumab deruxtecan is an antibody drug conjugate that consists of a humanized anti-HER2 monoclonal antibody linked to a topoisomerase 1 inhibitor. This antibody drug conjugate is unique compared with prior antibody drug conjugates such as trastuzumab emtansine in that it has a high drug-to-antibody ratio (~8). Furthermore, there appears to be a unique bystander effect resulting in off-target cytotoxicity to neighboring tumor cells, enhancing the efficacy of this novel therapy. Prior studies of trastuzumab deruxtecan have shown durable activity in heavily pretreated patients with metastatic HER2-positive breast cancer.¹

HER2-positive breast cancer represents approximately 20% of breast cancer cases in women.² Historically, HER2 positivity has been defined by strong HER2 expression with IHC staining (ie, score 3+) or HER2 amplification through ISH. Conversely, HER2-negative disease has historically been defined as those with IHC scores of 0 or 1+. This group represents approximately 60% of HER2-negative metastatic breast cancer patients.³ These patients have limited targeted treatment options after progressing on primary therapy. Prior data has shown that patients with low HER2 expression represent a heterogeneous population and thus, the historic categorization of HER2 status as positive or negative may in fact not adequately characterize the proportion of patients who may derive clinical benefit from HER2-directed therapies. Nevertheless, there have been no data to date that have shown improved outcomes in low HER2 expressers with anti-HER2 therapies.

The current studies add to the rapidly growing body of literature outlining the efficacy of the novel antibody drug conjugate trastuzumab deruxtecan. The implications of the data presented in these 2 studies are immediately practice changing.

In the DESTINY-Breast03 trial, Cortéz and colleagues show that trastuzumab deruxtecan therapy significantly prolongs progression-free survival compared with trastuzumab emtansine in patients with HER2-positive metastatic breast cancer who have progressed on first-line trastuzumab and taxane-based therapy. With a hazard ratio of 0.28 for disease progression or death, the efficacy of trastuzumab deruxtecan highlighted in this trial clearly makes this the standard of care in the second-line setting for patients with metastatic HER2-positive breast cancer. The overall survival in this trial was immature at the time of this analysis, and thus continued follow-up to validate the results noted here are warranted.

The DESTINY-Breast04 trial by Modi et al expands the cohort of patients who benefit from trastuzumab deruxtecan profoundly. This study defines a population of patients with HER2-low metastatic breast cancer who will now be eligible for HER2-directed therapies. These data show that therapy with trastuzumab deruxtecan leads to a significant and clinically meaningful improvement in both progression-free survival and overall survival compared with chemotherapy in patients with metastatic breast cancer with low expression of HER2. This benefit was seen in both the estrogen receptor–positive cohort as well as the entire population, including pre-treated triple-negative disease. Furthermore, this study does not define a threshold of HER2 expression by IHC that predicts benefit with trastuzumab deruxtecan. Patients with an IHC score of 1+ as well as those with a score of 2+/ISH negative both benefit to a similar extent from trastuzumab deruxtecan. Interestingly, in the DAISY trial, antitumor activity was noted with trastuzumab deruxtecan even in those without any detectable HER2 expression on IHC.⁴ Given the inconsistency and potential false negatives of IHC along with heterogeneous HER2 expression, further work is needed to better identify patients with low levels of HER2 expression who may benefit from this novel antibody drug conjugate. Thus, a reliable test to quantitatively assess the level of HER2 expression is needed in order to determine more accurately which patients will benefit from trastuzumab deruxtecan.

Last, trastuzumab deruxtecan has been associated with interstitial lung disease and pneumonitis. Interstitial lung disease and pneumonitis occurred in approximately 10% of patients who received trastuzumab deruxtecan in the DESTINY-Breast03 trial and about 12% of patients in the DESTINY-Breast04 trial. Most of these events were grade 1 and grade 2. Nevertheless, clinicians must be aware of this risk and monitor patients frequently for the development of pneumonitis or interstitial lung disease.

Application for Clinical Practice and System Implementation

The results of the current studies show a longer progression-free survival with trastuzumab deruxtecan in both HER2-low expressing metastatic breast cancer and HER2-positive metastatic breast cancer following taxane and trastuzumab-based therapy. These results are clearly practice changing and represent a new standard of care in these patient populations. It is incumbent upon treating oncologists to work with our pathology colleagues to assess HER2 IHC thoroughly in order to identify all potential patients who may benefit from trastuzumab deruxtecan in the metastatic setting. The continued advancement of anti-HER2 therapy will undoubtedly have a significant impact on patient outcomes going forward.

Practice Points

• With a hazard ratio of 0.28 for disease progression or death, the efficacy of trastuzumab deruxtecan highlighted in the DESTINY-Breast03 trial clearly makes this the standard of care in the second-line setting for patients with metastatic HER2-positive breast cancer.
• In the DESTINY-Breast04 trial, a significant and clinically meaningful improvement in both progression-free survival and overall survival compared with chemotherapy was seen in patients with metastatic breast cancer with low expression of HER2, including both the estrogen receptor–positive cohort as well as the entire population, including those with pre-treated triple-negative disease.

­—Daniel Isaac, DO, MS

From Sharp Memorial Hospital, San Diego, CA (Drs. Poremba, Dehner, Perreiter, Semma, and Mills), Sharp Rees-Stealy Medical Group, San Diego, CA (Dr. Sakoulas), and Collaborative to Halt Antibiotic-Resistant Microbes (CHARM), Department of Pediatrics, University of California San Diego School of Medicine, La Jolla, CA (Dr. Sakoulas).

Abstract

Objective: To compare the costs of hospitalization of patients with moderate-to-severe COVID-19 who received intravenous immunoglobulin (IVIG) with those of patients of similar comorbidity and illness severity who did not.

Design: Analysis 1 was a case-control study of 10 nonventilated, moderately to severely hypoxic patients with COVID-19 who received IVIG (Privigen [CSL Behring]) matched 1:2 with 20 control patients of similar age, body mass index, degree of hypoxemia, and comorbidities. Analysis 2 consisted of patients enrolled in a previously published, randomized, open-label prospective study of 14 patients with COVID-19 receiving standard of care vs 13 patients who received standard of care plus IVIG (Octagam 10% [Octapharma]).

Setting and participants: Patients with COVID-19 with moderate-to-severe hypoxemia hospitalized at a single site located in San Diego, California.

Measurements: Direct cost of hospitalization.

Results: In the first (case-control) population, mean total direct costs, including IVIG, for the treatment group were $21,982 per IVIG-treated case vs $42,431 per case for matched non-IVIG-receiving controls, representing a net cost reduction of $20,449 (48%) per case. For the second (randomized) group, mean total direct costs, including IVIG, for the treatment group were $28,268 per case vs $62,707 per case for untreated controls, representing a net cost reduction of $34,439 (55%) per case. Of the patients who did not receive IVIG, 24% had hospital costs exceeding $80,000; none of the IVIG-treated patients had costs exceeding this amount (P = .016, Fisher exact test).

Conclusion: If allocated early to the appropriate patient type (moderate-to-severe illness without end-organ comorbidities and age <70 years), IVIG can significantly reduce hospital costs in COVID-19 care. More important, in our study it reduced the demand for scarce critical care resources during the COVID-19 pandemic.

Keywords: IVIG, SARS-CoV-2, cost saving, direct hospital costs.

Intravenous immunoglobulin (IVIG) has been available in most hospitals for 4 decades, with broad therapeutic applications in the treatment of Kawasaki disease and a variety of inflammatory, infectious, autoimmune, and viral diseases, via multifactorial mechanisms of immune modulation.¹ Reports of COVID-19−associated multisystem inflammatory syndrome in adults and children have supported the use of IVIG in treatment.^2,3 Previous studies of IVIG treatment for COVID-19 have produced mixed results. Although retrospective studies have largely been positive,^4-8 prospective clinical trials have been mixed, with some favorable results^9-11 and another, more recent study showing no benefit.¹² However, there is still considerable debate regarding whether some subgroups of patients with COVID-19 may benefit from IVIG; the studies that support this argument, however, have been diluted by broad clinical trials that lack granularity among the heterogeneity of patient characteristics and the timing of IVIG administration.^13,14 One study suggests that patients with COVID-19 who may be particularly poised to benefit from IVIG are those who are younger, have fewer comorbidities, and are treated early.⁸

At our institution, we selectively utilized IVIG to treat patients within 48 hours of rapidly increasing oxygen requirements due to COVID-19, targeting those younger than 70 years, with no previous irreversible end-organ damage, no significant comorbidities (renal failure, heart failure, dementia, active cancer malignancies), and no active treatment for cancer. We analyzed the costs of care of these IVIG (Privigen) recipients and compared them to costs for patients with COVID-19 matched by comorbidities, age, and illness severity who did not receive IVIG. To look for consistency, we examined the cost of care of COVID-19 patients who received IVIG (Octagam) as compared to controls from a previously published pilot trial.¹⁰

Methods

Setting and Treatment

All patients in this study were hospitalized at a single site located in San Diego, California. Treatment patients in both cohorts received IVIG 0.5 g/kg adjusted for body weight daily for 3 consecutive days.

Patient Cohort #1: Retrospective Case-Control Trial

Intravenous immunoglobulin (Privigen 10%, CSL Behring) was utilized off-label to treat moderately to severely ill non-intensive care unit (ICU) patients with COVID-19 requiring ≥3 L of oxygen by nasal cannula who were not mechanically ventilated but were considered at high risk for respiratory failure. Preset exclusion criteria for off-label use of IVIG in the treatment of COVID-19 were age >70 years, active malignancy, organ transplant recipient, renal failure, heart failure, or dementia. Controls were obtained from a list of all admitted patients with COVID-19, matched to cases 2:1 on the basis of age (±10 years), body mass index (±1), gender, comorbidities present at admission (eg, hypertension, diabetes mellitus, lung disease, or history of tobacco use), and maximum oxygen requirements within the first 48 hours of admission. In situations where more than 2 potential matched controls were identified for a patient, the 2 controls closest in age to the treatment patient were selected. One IVIG patient was excluded because only 1 matched-age control could be found. Pregnant patients who otherwise fulfilled the criteria for IVIG administration were also excluded from this analysis.

Patient Cohort #2: Prospective, Randomized, Open-Label Trial

Use of IVIG (Octagam 10%, Octapharma) in COVID-19 was studied in a previously published, prospective, open-label randomized trial.¹⁰ This pilot trial included 16 IVIG-treated patients and 17 control patients, of which 13 and 14 patients, respectively, had hospital cost data available for analysis.¹⁰ Most notably, COVID-19 patients in this study were required to have ≥4 L of oxygen via nasal cannula to maintain arterial oxygen saturationof ≤96%.

Outcomes

Cost data were independently obtained from our finance team, which provided us with the total direct cost and the total pharmaceutical cost associated with each admission. We also compared total length of stay (LOS) and ICU LOS between treatment arms, as these were presumed to be the major drivers of cost difference.

Statistics

Nonparametric comparisons of medians were performed with the Mann-Whitney U test. Comparison of means was done by Student t test. Categorical data were analyzed by Fisher exact test.

This analysis was initiated as an internal quality assessment. It received approval from the Sharp Healthcare Institutional Review Board (research@sharp.com), and was granted a waiver of subject authorization and consent given the retrospective nature of the study.

Results

Case-Control Analysis

A total of 10 hypoxic patients with COVID-19 received Privigen IVIG outside of clinical trial settings. None of the patients was vaccinated against SARS-CoV-2, as hospitalization occurred prior to vaccine availability. In addition, the original SARS-CoV-2 strain was circulating while these patients were hospitalized, preceding subsequent emerging variants. Oxygen requirements within the first 48 hours ranged from 3 L via nasal cannula to requiring bi-level positive pressure airway therapy with 100% oxygen; median age was 56 years and median Charlson comorbidity index was 1. These 10 patients were each matched to 2 control patients hospitalized during a comparable time period and who, based on oxygen requirements, did not receive IVIG. The 20 control patients had a median age of 58.5 years and a Charlson comorbidity index of 1 (Table 1). Rates of comorbidities, such as hypertension, diabetes mellitus, and obesity, were identical in the 2 groups. None of the patients in either group died during the index hospitalization. Fewer control patients received glucocorticoids, which was reflective of lower illness severity/degree of hypoxia in some controls.

Health care utilization in terms of costs and hospital LOS between the 2 groups are shown in Table 2. The mean total direct hospital cost per case, including IVIG and other drug costs, for the 10 IVIG-treated COVID-19 patients was $21,982 vs $42,431 for the matched controls, a reduction of $20,449 (48%) per case (P = .6187) with IVIG. This difference was heavily driven by 4 control patients (20%) with hospital costs >$80,000, marked by need for ICU transfer, mechanical ventilation during admission, and longer hospital stays. This reduction in progression to mechanical ventilation was consistent with our previously published, open-label, randomized prospective IVIG study, the financial assessment of which is reviewed below. While total direct costs were lower in the treatment arm, the mean drug cost for the treatment arm was $3122 greater than the mean drug cost in the control arm (P = .001622), consistent with the high cost of IVIG therapy (Table 2).

LOS information was obtained, as this was thought to be a primary driver of direct costs. The average LOS in the IVIG arm was 8.4 days, and the average LOS in the control arm was 13.6 days (P = NS). The average ICU LOS in the IVIG arm was 0 days, while the average ICU LOS in the control arm was 5.3 days (P = .04). As with the differences in cost, the differences in LOS were primarily driven by the 4 outlier cases in our control arm, who each had a LOS >25 days, as well as an ICU LOS >20 days.

Randomized, Open-Label, Patient Cohort Analysis

Patient characteristics, LOS, and rates of mechanical ventilation for the IVIG and control patients were previously published and showed a reduction in mechanical ventilation and hospital LOS with IVIG treatment.¹⁰ In this group of patients, 1 patient treated with IVIG (6%) and 3 patients not treated with IVIG (18%) died. To determine the consistency of these results from the case-control patients with a set of patients obtained from clinical trial randomization, we examined the health care costs of patients from the prior study.¹⁰ As with the case-control group, patients in this portion of the analysis were hospitalized before vaccines were available and prior to any identified variants.

Comparing the hospital cost of the IVIG-treated patients to the control patients from this trial revealed results similar to the matched case-control analysis discussed earlier. Average total direct cost per case, including IVIG, for the IVIG treatment group was $28,268, vs $62,707 per case for non-IVIG controls. This represented a net cost reduction of $34,439 (55%) per case, very similar to that of the prior cohort.

IVIG Reduces Costly Outlier Cases

The case-control and randomized trial groups, yielding a combined 23 IVIG and 34 control patients, showed a median cost per case of $22,578 (range $10,115-$70,929) and $22,645 (range $4723-$279,797) for the IVIG and control groups, respectively. Cases with a cost >$80,000 were 0/23 (0%) vs 8/34 (24%) in the IVIG and control groups, respectively (P = .016, Fisher exact test).

Improving care while simultaneously keeping care costs below reimbursement payment levels received from third-party payers is paramount to the financial survival of health care systems. IVIG appears to do this by reducing the number of patients with COVID-19 who progress to ICU care. We compared the costs of care of our combined case-control and randomized trial cohorts to published data on average reimbursements hospitals receive for COVID-19 care from Medicaid, Medicare, and private insurance (Figure).¹⁵ IVIG demonstrated a reduction in cases where costs exceed reimbursement. Indeed, a comparison of net revenue per case of the case-control group showed significantly higher revenue for the IVIG group compared to controls ($52,704 vs $34,712, P = .0338, Table 2).

Discussion

As reflected in at least 1 other study,¹⁶ our hospital had been successfully utilizing IVIG in the treatment of viral acute respiratory distress syndrome (ARDS) prior to COVID-19. Therefore, we moved quickly to perform a randomized, open-label pilot study of IVIG (Octagam 10%) in COVID-19, and noted significant clinical benefit that might translate into hospital cost savings.¹⁰ Over the course of the pandemic, evidence has accumulated that IVIG may play an important role in COVID-19 therapeutics, as summarized in a recent review.¹⁷ However, despite promising but inconsistent results, the relatively high acquisition costs of IVIG raised questions as to its pharmacoeconomic value, particularly with such a high volume of COVID-19 patients with hypoxia, in light of limited clinical data.

COVID-19 therapeutics data can be categorized into either high-quality trials showing marginal benefit for some agents or low-quality trials showing greater benefit for other agents, with IVIG studies falling into the latter category.¹⁸ This phenomenon may speak to the pathophysiological heterogeneity of the COVID-19 patient population. High-quality trials enrolling broad patient types lack the granularity to capture and single out relevant patient subsets who would derive maximal therapeutic benefit, with those subsets diluted by other patient types for which no benefit is seen. Meanwhile, the more granular low-quality trials are criticized as underpowered and lacking in translatability to practice.

Positive results from our pilot trial allowed the use of IVIG (Privigen) off-label in hospitalized COVID-19 patients restricted to specific criteria. Patients had to be moderately to severely ill, requiring >3 L of oxygen via nasal cannula; show high risk of clinical deterioration based on respiratory rate and decline in respiratory status; and have underlying comorbidities (such as hypertension, obesity, or diabetes mellitus). However, older patients (>age 70 years) and those with underlying comorbidities marked by organ failure (such as heart failure, renal failure, dementia, or receipt of organ transplant) and active malignancy were excluded, as their clinical outcome in COVID-19 may be considered less modifiable by therapeutics, while simultaneously carrying potentially a higher risk of adverse events from IVIG (volume overload, renal failure). These exclusions are reflected in the overall low Charlson comorbidity index (mean of 1) of the patients in the case-control study arm. As anticipated, we found a net cost reduction: $20,449 (48%) per case among the 10 IVIG-treated patients compared to the 20 matched controls.

We then went back to the patients from the randomized prospective trial and compared costs for the 13 of 16 IVIG patients and 14 of 17 of the control patients for whom data were available. Among untreated controls, we found a net cost reduction of $34,439 (55%) per case. The higher costs seen in the randomized patient cohort compared to the latter case-control group may be due to a combination of the fact that the treated patients had slightly higher comorbidity indices than the case-control group (median Charlson comorbidity index of 2 in both groups) and the fact that they were treated earlier in the pandemic (May/June 2020), as opposed to the case-control group patients, who were treated in November/December 2020.

It was notable that the cost savings across both groups were derived largely from the reduction in the approximately 20% to 25% of control patients who went on to critical illness, including mechanical ventilation, extracorporeal membrane oxygenation (ECMO), and prolonged ICU stays. Indeed, 8 of 34 of the control patients—but none of the 23 IVIG-treated patients—generated hospital costs in excess of $80,000, a difference that was statistically significant even for such a small sample size. Therefore, reducing these very costly outlier events translated into net savings across the board.

In addition to lowering costs, reducing progression to critical illness is extremely important during heavy waves of COVID-19, when the sheer volume of patients results in severe strain due to the relative scarcity of ICU beds, mechanical ventilators, and ECMO. Therefore, reducing the need for these resources would have a vital role that cannot be measured economically.

The major limitations of this study include the small sample size and the potential lack of generalizability of these results to all hospital centers and treating providers. Our group has considerable experience in IVIG utilization in COVID-19 and, as a result, has identified a “sweet spot,” where benefits were seen clinically and economically. However, it remains to be determined whether IVIG will benefit patients with greater illness severity, such as those in the ICU, on mechanical ventilation, or ECMO. Furthermore, while a significant morbidity and mortality burden of COVID-19 rests in extremely elderly patients and those with end-organ comorbidities such as renal failure and heart failure, it is uncertain whether their COVID-19 adverse outcomes can be improved with IVIG or other therapies. We believe such patients may limit the pharmacoeconomic value of IVIG due to their generally poorer prognosis, regardless of intervention. On the other hand, COVID-19 patients who are not that severely ill, with minimal to no hypoxia, generally will do well regardless of therapy. Therefore, IVIG intervention may be an unnecessary treatment expense. Evidence for this was suggested in our pilot trial¹⁰ and supported in a recent meta-analysis of IVIG therapy in COVID-19.¹⁹

Several other therapeutic options with high acquisition costs have seen an increase in use during the COVID-19 pandemic despite relatively lukewarm data. Remdesivir, the first drug found to have a beneficial effect on hospitalized patients with COVID-19, is priced at $3120 for a complete 5-day treatment course in the United States. This was in line with initial pricing models from the Institute for Clinical and Economic Review (ICER) in May 2020, assuming a mortality benefit with remdesivir use. After the SOLIDARITY trial was published, which showed no mortality benefit associated with remdesivir, ICER updated their pricing models in June 2020 and released a statement that the price of remdesivir was too high to align with demonstrated benefits.^20,21 More recent data demonstrate that remdesivir may be beneficial, but only if administered to patients with fewer than 6 days of symptoms.²² However, only a minority of patients present to the hospital early enough in their illness for remdesivir to be beneficial.²²

Tocilizumab, an interleukin-6 inhibitor, saw an increase in use during the pandemic. An 800-mg treatment course for COVID-19 costs $3584. The efficacy of this treatment option came into question after the COVACTA trial failed to show a difference in clinical status or mortality in COVID-19 patients who received tocilizumab vs placebo.^23,24 A more recent study pointed to a survival benefit of tocilizumab in COVID-19, driven by a very large sample size (>4000), yielding statistically significant, but perhaps clinically less significant, effects on survival.²⁵ This latter study points to the extremely large sample sizes required to capture statistically significant benefits of expensive interventions in COVID-19, which our data demonstrate may benefit only a fraction of patients (20%-25% of patients in the case of IVIG). A more granular clinical assessment of these other interventions is needed to be able to capture the patient subtypes where tocilizumab, remdesivir, and other therapies will be cost effective in the treatment of COVID-19 or other virally mediated cases of ARDS.

Conclusion

While IVIG has a high acquisition cost, the drug’s use in hypoxic COVID-19 patients resulted in reduced costs per COVID-19 case of approximately 50% and use of less critical care resources. The difference was consistent between 2 cohorts (randomized trial vs off-label use in prespecified COVID-19 patient types), IVIG products used (Octagam 10% and Privigen), and time period in the pandemic (waves 1 and 2 in May/June 2020 vs wave 3 in November/December 2020), thereby adjusting for potential differences in circulating viral strains. Furthermore, patients from both groups predated SARS-CoV-2 vaccine availability and major circulating viral variants (eg, delta, omicron), thereby eliminating confounding on outcomes posed by these factors. Control patients’ higher costs of care were driven largely by the approximately 25% of patients who required costly hospital critical care resources, a group mitigated by IVIG. When allocated to the appropriate patient type (patients with moderate-to-severe but not critical illness, <age 70 without preexisting comorbidities of end-organ failure or active cancer), IVIG can reduce hospital costs for COVID-19 care. Identification of specific patient populations where IVIG has the most anticipated benefits in viral illness is needed.

Corresponding author: George Sakoulas, MD, Sharp Rees-Stealy Medical Group, 2020 Genesee Avenue, 2nd Floor, San Diego, CA 92123; gsakoulas@health.ucsd.edu

Disclosures: Dr Sakoulas has worked as a consultant for Abbvie, Paratek, and Octapharma, has served as a speaker for Abbvie and Paratek, and has received research funding from Octapharma. The other authors did not report any disclosures.

From Sharp Memorial Hospital, San Diego, CA (Drs. Poremba, Dehner, Perreiter, Semma, and Mills), Sharp Rees-Stealy Medical Group, San Diego, CA (Dr. Sakoulas), and Collaborative to Halt Antibiotic-Resistant Microbes (CHARM), Department of Pediatrics, University of California San Diego School of Medicine, La Jolla, CA (Dr. Sakoulas).

Abstract

Objective: To compare the costs of hospitalization of patients with moderate-to-severe COVID-19 who received intravenous immunoglobulin (IVIG) with those of patients of similar comorbidity and illness severity who did not.

Design: Analysis 1 was a case-control study of 10 nonventilated, moderately to severely hypoxic patients with COVID-19 who received IVIG (Privigen [CSL Behring]) matched 1:2 with 20 control patients of similar age, body mass index, degree of hypoxemia, and comorbidities. Analysis 2 consisted of patients enrolled in a previously published, randomized, open-label prospective study of 14 patients with COVID-19 receiving standard of care vs 13 patients who received standard of care plus IVIG (Octagam 10% [Octapharma]).

Setting and participants: Patients with COVID-19 with moderate-to-severe hypoxemia hospitalized at a single site located in San Diego, California.

Measurements: Direct cost of hospitalization.

Results: In the first (case-control) population, mean total direct costs, including IVIG, for the treatment group were $21,982 per IVIG-treated case vs $42,431 per case for matched non-IVIG-receiving controls, representing a net cost reduction of $20,449 (48%) per case. For the second (randomized) group, mean total direct costs, including IVIG, for the treatment group were $28,268 per case vs $62,707 per case for untreated controls, representing a net cost reduction of $34,439 (55%) per case. Of the patients who did not receive IVIG, 24% had hospital costs exceeding $80,000; none of the IVIG-treated patients had costs exceeding this amount (P = .016, Fisher exact test).

Conclusion: If allocated early to the appropriate patient type (moderate-to-severe illness without end-organ comorbidities and age <70 years), IVIG can significantly reduce hospital costs in COVID-19 care. More important, in our study it reduced the demand for scarce critical care resources during the COVID-19 pandemic.

Keywords: IVIG, SARS-CoV-2, cost saving, direct hospital costs.

Intravenous immunoglobulin (IVIG) has been available in most hospitals for 4 decades, with broad therapeutic applications in the treatment of Kawasaki disease and a variety of inflammatory, infectious, autoimmune, and viral diseases, via multifactorial mechanisms of immune modulation.¹ Reports of COVID-19−associated multisystem inflammatory syndrome in adults and children have supported the use of IVIG in treatment.^2,3 Previous studies of IVIG treatment for COVID-19 have produced mixed results. Although retrospective studies have largely been positive,^4-8 prospective clinical trials have been mixed, with some favorable results^9-11 and another, more recent study showing no benefit.¹² However, there is still considerable debate regarding whether some subgroups of patients with COVID-19 may benefit from IVIG; the studies that support this argument, however, have been diluted by broad clinical trials that lack granularity among the heterogeneity of patient characteristics and the timing of IVIG administration.^13,14 One study suggests that patients with COVID-19 who may be particularly poised to benefit from IVIG are those who are younger, have fewer comorbidities, and are treated early.⁸

At our institution, we selectively utilized IVIG to treat patients within 48 hours of rapidly increasing oxygen requirements due to COVID-19, targeting those younger than 70 years, with no previous irreversible end-organ damage, no significant comorbidities (renal failure, heart failure, dementia, active cancer malignancies), and no active treatment for cancer. We analyzed the costs of care of these IVIG (Privigen) recipients and compared them to costs for patients with COVID-19 matched by comorbidities, age, and illness severity who did not receive IVIG. To look for consistency, we examined the cost of care of COVID-19 patients who received IVIG (Octagam) as compared to controls from a previously published pilot trial.¹⁰

Methods

Setting and Treatment

All patients in this study were hospitalized at a single site located in San Diego, California. Treatment patients in both cohorts received IVIG 0.5 g/kg adjusted for body weight daily for 3 consecutive days.

Patient Cohort #1: Retrospective Case-Control Trial

Intravenous immunoglobulin (Privigen 10%, CSL Behring) was utilized off-label to treat moderately to severely ill non-intensive care unit (ICU) patients with COVID-19 requiring ≥3 L of oxygen by nasal cannula who were not mechanically ventilated but were considered at high risk for respiratory failure. Preset exclusion criteria for off-label use of IVIG in the treatment of COVID-19 were age >70 years, active malignancy, organ transplant recipient, renal failure, heart failure, or dementia. Controls were obtained from a list of all admitted patients with COVID-19, matched to cases 2:1 on the basis of age (±10 years), body mass index (±1), gender, comorbidities present at admission (eg, hypertension, diabetes mellitus, lung disease, or history of tobacco use), and maximum oxygen requirements within the first 48 hours of admission. In situations where more than 2 potential matched controls were identified for a patient, the 2 controls closest in age to the treatment patient were selected. One IVIG patient was excluded because only 1 matched-age control could be found. Pregnant patients who otherwise fulfilled the criteria for IVIG administration were also excluded from this analysis.

Patient Cohort #2: Prospective, Randomized, Open-Label Trial

Use of IVIG (Octagam 10%, Octapharma) in COVID-19 was studied in a previously published, prospective, open-label randomized trial.¹⁰ This pilot trial included 16 IVIG-treated patients and 17 control patients, of which 13 and 14 patients, respectively, had hospital cost data available for analysis.¹⁰ Most notably, COVID-19 patients in this study were required to have ≥4 L of oxygen via nasal cannula to maintain arterial oxygen saturationof ≤96%.

Outcomes

Cost data were independently obtained from our finance team, which provided us with the total direct cost and the total pharmaceutical cost associated with each admission. We also compared total length of stay (LOS) and ICU LOS between treatment arms, as these were presumed to be the major drivers of cost difference.

Statistics

Nonparametric comparisons of medians were performed with the Mann-Whitney U test. Comparison of means was done by Student t test. Categorical data were analyzed by Fisher exact test.

This analysis was initiated as an internal quality assessment. It received approval from the Sharp Healthcare Institutional Review Board (research@sharp.com), and was granted a waiver of subject authorization and consent given the retrospective nature of the study.

Results

Case-Control Analysis

A total of 10 hypoxic patients with COVID-19 received Privigen IVIG outside of clinical trial settings. None of the patients was vaccinated against SARS-CoV-2, as hospitalization occurred prior to vaccine availability. In addition, the original SARS-CoV-2 strain was circulating while these patients were hospitalized, preceding subsequent emerging variants. Oxygen requirements within the first 48 hours ranged from 3 L via nasal cannula to requiring bi-level positive pressure airway therapy with 100% oxygen; median age was 56 years and median Charlson comorbidity index was 1. These 10 patients were each matched to 2 control patients hospitalized during a comparable time period and who, based on oxygen requirements, did not receive IVIG. The 20 control patients had a median age of 58.5 years and a Charlson comorbidity index of 1 (Table 1). Rates of comorbidities, such as hypertension, diabetes mellitus, and obesity, were identical in the 2 groups. None of the patients in either group died during the index hospitalization. Fewer control patients received glucocorticoids, which was reflective of lower illness severity/degree of hypoxia in some controls.

Health care utilization in terms of costs and hospital LOS between the 2 groups are shown in Table 2. The mean total direct hospital cost per case, including IVIG and other drug costs, for the 10 IVIG-treated COVID-19 patients was $21,982 vs $42,431 for the matched controls, a reduction of $20,449 (48%) per case (P = .6187) with IVIG. This difference was heavily driven by 4 control patients (20%) with hospital costs >$80,000, marked by need for ICU transfer, mechanical ventilation during admission, and longer hospital stays. This reduction in progression to mechanical ventilation was consistent with our previously published, open-label, randomized prospective IVIG study, the financial assessment of which is reviewed below. While total direct costs were lower in the treatment arm, the mean drug cost for the treatment arm was $3122 greater than the mean drug cost in the control arm (P = .001622), consistent with the high cost of IVIG therapy (Table 2).

LOS information was obtained, as this was thought to be a primary driver of direct costs. The average LOS in the IVIG arm was 8.4 days, and the average LOS in the control arm was 13.6 days (P = NS). The average ICU LOS in the IVIG arm was 0 days, while the average ICU LOS in the control arm was 5.3 days (P = .04). As with the differences in cost, the differences in LOS were primarily driven by the 4 outlier cases in our control arm, who each had a LOS >25 days, as well as an ICU LOS >20 days.

Randomized, Open-Label, Patient Cohort Analysis

Patient characteristics, LOS, and rates of mechanical ventilation for the IVIG and control patients were previously published and showed a reduction in mechanical ventilation and hospital LOS with IVIG treatment.¹⁰ In this group of patients, 1 patient treated with IVIG (6%) and 3 patients not treated with IVIG (18%) died. To determine the consistency of these results from the case-control patients with a set of patients obtained from clinical trial randomization, we examined the health care costs of patients from the prior study.¹⁰ As with the case-control group, patients in this portion of the analysis were hospitalized before vaccines were available and prior to any identified variants.

Comparing the hospital cost of the IVIG-treated patients to the control patients from this trial revealed results similar to the matched case-control analysis discussed earlier. Average total direct cost per case, including IVIG, for the IVIG treatment group was $28,268, vs $62,707 per case for non-IVIG controls. This represented a net cost reduction of $34,439 (55%) per case, very similar to that of the prior cohort.

IVIG Reduces Costly Outlier Cases

The case-control and randomized trial groups, yielding a combined 23 IVIG and 34 control patients, showed a median cost per case of $22,578 (range $10,115-$70,929) and $22,645 (range $4723-$279,797) for the IVIG and control groups, respectively. Cases with a cost >$80,000 were 0/23 (0%) vs 8/34 (24%) in the IVIG and control groups, respectively (P = .016, Fisher exact test).

Improving care while simultaneously keeping care costs below reimbursement payment levels received from third-party payers is paramount to the financial survival of health care systems. IVIG appears to do this by reducing the number of patients with COVID-19 who progress to ICU care. We compared the costs of care of our combined case-control and randomized trial cohorts to published data on average reimbursements hospitals receive for COVID-19 care from Medicaid, Medicare, and private insurance (Figure).¹⁵ IVIG demonstrated a reduction in cases where costs exceed reimbursement. Indeed, a comparison of net revenue per case of the case-control group showed significantly higher revenue for the IVIG group compared to controls ($52,704 vs $34,712, P = .0338, Table 2).

Discussion

As reflected in at least 1 other study,¹⁶ our hospital had been successfully utilizing IVIG in the treatment of viral acute respiratory distress syndrome (ARDS) prior to COVID-19. Therefore, we moved quickly to perform a randomized, open-label pilot study of IVIG (Octagam 10%) in COVID-19, and noted significant clinical benefit that might translate into hospital cost savings.¹⁰ Over the course of the pandemic, evidence has accumulated that IVIG may play an important role in COVID-19 therapeutics, as summarized in a recent review.¹⁷ However, despite promising but inconsistent results, the relatively high acquisition costs of IVIG raised questions as to its pharmacoeconomic value, particularly with such a high volume of COVID-19 patients with hypoxia, in light of limited clinical data.

COVID-19 therapeutics data can be categorized into either high-quality trials showing marginal benefit for some agents or low-quality trials showing greater benefit for other agents, with IVIG studies falling into the latter category.¹⁸ This phenomenon may speak to the pathophysiological heterogeneity of the COVID-19 patient population. High-quality trials enrolling broad patient types lack the granularity to capture and single out relevant patient subsets who would derive maximal therapeutic benefit, with those subsets diluted by other patient types for which no benefit is seen. Meanwhile, the more granular low-quality trials are criticized as underpowered and lacking in translatability to practice.

Positive results from our pilot trial allowed the use of IVIG (Privigen) off-label in hospitalized COVID-19 patients restricted to specific criteria. Patients had to be moderately to severely ill, requiring >3 L of oxygen via nasal cannula; show high risk of clinical deterioration based on respiratory rate and decline in respiratory status; and have underlying comorbidities (such as hypertension, obesity, or diabetes mellitus). However, older patients (>age 70 years) and those with underlying comorbidities marked by organ failure (such as heart failure, renal failure, dementia, or receipt of organ transplant) and active malignancy were excluded, as their clinical outcome in COVID-19 may be considered less modifiable by therapeutics, while simultaneously carrying potentially a higher risk of adverse events from IVIG (volume overload, renal failure). These exclusions are reflected in the overall low Charlson comorbidity index (mean of 1) of the patients in the case-control study arm. As anticipated, we found a net cost reduction: $20,449 (48%) per case among the 10 IVIG-treated patients compared to the 20 matched controls.

We then went back to the patients from the randomized prospective trial and compared costs for the 13 of 16 IVIG patients and 14 of 17 of the control patients for whom data were available. Among untreated controls, we found a net cost reduction of $34,439 (55%) per case. The higher costs seen in the randomized patient cohort compared to the latter case-control group may be due to a combination of the fact that the treated patients had slightly higher comorbidity indices than the case-control group (median Charlson comorbidity index of 2 in both groups) and the fact that they were treated earlier in the pandemic (May/June 2020), as opposed to the case-control group patients, who were treated in November/December 2020.

It was notable that the cost savings across both groups were derived largely from the reduction in the approximately 20% to 25% of control patients who went on to critical illness, including mechanical ventilation, extracorporeal membrane oxygenation (ECMO), and prolonged ICU stays. Indeed, 8 of 34 of the control patients—but none of the 23 IVIG-treated patients—generated hospital costs in excess of $80,000, a difference that was statistically significant even for such a small sample size. Therefore, reducing these very costly outlier events translated into net savings across the board.

In addition to lowering costs, reducing progression to critical illness is extremely important during heavy waves of COVID-19, when the sheer volume of patients results in severe strain due to the relative scarcity of ICU beds, mechanical ventilators, and ECMO. Therefore, reducing the need for these resources would have a vital role that cannot be measured economically.

The major limitations of this study include the small sample size and the potential lack of generalizability of these results to all hospital centers and treating providers. Our group has considerable experience in IVIG utilization in COVID-19 and, as a result, has identified a “sweet spot,” where benefits were seen clinically and economically. However, it remains to be determined whether IVIG will benefit patients with greater illness severity, such as those in the ICU, on mechanical ventilation, or ECMO. Furthermore, while a significant morbidity and mortality burden of COVID-19 rests in extremely elderly patients and those with end-organ comorbidities such as renal failure and heart failure, it is uncertain whether their COVID-19 adverse outcomes can be improved with IVIG or other therapies. We believe such patients may limit the pharmacoeconomic value of IVIG due to their generally poorer prognosis, regardless of intervention. On the other hand, COVID-19 patients who are not that severely ill, with minimal to no hypoxia, generally will do well regardless of therapy. Therefore, IVIG intervention may be an unnecessary treatment expense. Evidence for this was suggested in our pilot trial¹⁰ and supported in a recent meta-analysis of IVIG therapy in COVID-19.¹⁹

Several other therapeutic options with high acquisition costs have seen an increase in use during the COVID-19 pandemic despite relatively lukewarm data. Remdesivir, the first drug found to have a beneficial effect on hospitalized patients with COVID-19, is priced at $3120 for a complete 5-day treatment course in the United States. This was in line with initial pricing models from the Institute for Clinical and Economic Review (ICER) in May 2020, assuming a mortality benefit with remdesivir use. After the SOLIDARITY trial was published, which showed no mortality benefit associated with remdesivir, ICER updated their pricing models in June 2020 and released a statement that the price of remdesivir was too high to align with demonstrated benefits.^20,21 More recent data demonstrate that remdesivir may be beneficial, but only if administered to patients with fewer than 6 days of symptoms.²² However, only a minority of patients present to the hospital early enough in their illness for remdesivir to be beneficial.²²

Tocilizumab, an interleukin-6 inhibitor, saw an increase in use during the pandemic. An 800-mg treatment course for COVID-19 costs $3584. The efficacy of this treatment option came into question after the COVACTA trial failed to show a difference in clinical status or mortality in COVID-19 patients who received tocilizumab vs placebo.^23,24 A more recent study pointed to a survival benefit of tocilizumab in COVID-19, driven by a very large sample size (>4000), yielding statistically significant, but perhaps clinically less significant, effects on survival.²⁵ This latter study points to the extremely large sample sizes required to capture statistically significant benefits of expensive interventions in COVID-19, which our data demonstrate may benefit only a fraction of patients (20%-25% of patients in the case of IVIG). A more granular clinical assessment of these other interventions is needed to be able to capture the patient subtypes where tocilizumab, remdesivir, and other therapies will be cost effective in the treatment of COVID-19 or other virally mediated cases of ARDS.

Conclusion

While IVIG has a high acquisition cost, the drug’s use in hypoxic COVID-19 patients resulted in reduced costs per COVID-19 case of approximately 50% and use of less critical care resources. The difference was consistent between 2 cohorts (randomized trial vs off-label use in prespecified COVID-19 patient types), IVIG products used (Octagam 10% and Privigen), and time period in the pandemic (waves 1 and 2 in May/June 2020 vs wave 3 in November/December 2020), thereby adjusting for potential differences in circulating viral strains. Furthermore, patients from both groups predated SARS-CoV-2 vaccine availability and major circulating viral variants (eg, delta, omicron), thereby eliminating confounding on outcomes posed by these factors. Control patients’ higher costs of care were driven largely by the approximately 25% of patients who required costly hospital critical care resources, a group mitigated by IVIG. When allocated to the appropriate patient type (patients with moderate-to-severe but not critical illness, <age 70 without preexisting comorbidities of end-organ failure or active cancer), IVIG can reduce hospital costs for COVID-19 care. Identification of specific patient populations where IVIG has the most anticipated benefits in viral illness is needed.

Corresponding author: George Sakoulas, MD, Sharp Rees-Stealy Medical Group, 2020 Genesee Avenue, 2nd Floor, San Diego, CA 92123; gsakoulas@health.ucsd.edu

Disclosures: Dr Sakoulas has worked as a consultant for Abbvie, Paratek, and Octapharma, has served as a speaker for Abbvie and Paratek, and has received research funding from Octapharma. The other authors did not report any disclosures.

From Sharp Memorial Hospital, San Diego, CA (Drs. Poremba, Dehner, Perreiter, Semma, and Mills), Sharp Rees-Stealy Medical Group, San Diego, CA (Dr. Sakoulas), and Collaborative to Halt Antibiotic-Resistant Microbes (CHARM), Department of Pediatrics, University of California San Diego School of Medicine, La Jolla, CA (Dr. Sakoulas).

Abstract

Objective: To compare the costs of hospitalization of patients with moderate-to-severe COVID-19 who received intravenous immunoglobulin (IVIG) with those of patients of similar comorbidity and illness severity who did not.

Design: Analysis 1 was a case-control study of 10 nonventilated, moderately to severely hypoxic patients with COVID-19 who received IVIG (Privigen [CSL Behring]) matched 1:2 with 20 control patients of similar age, body mass index, degree of hypoxemia, and comorbidities. Analysis 2 consisted of patients enrolled in a previously published, randomized, open-label prospective study of 14 patients with COVID-19 receiving standard of care vs 13 patients who received standard of care plus IVIG (Octagam 10% [Octapharma]).

Setting and participants: Patients with COVID-19 with moderate-to-severe hypoxemia hospitalized at a single site located in San Diego, California.

Measurements: Direct cost of hospitalization.

Results: In the first (case-control) population, mean total direct costs, including IVIG, for the treatment group were $21,982 per IVIG-treated case vs $42,431 per case for matched non-IVIG-receiving controls, representing a net cost reduction of $20,449 (48%) per case. For the second (randomized) group, mean total direct costs, including IVIG, for the treatment group were $28,268 per case vs $62,707 per case for untreated controls, representing a net cost reduction of $34,439 (55%) per case. Of the patients who did not receive IVIG, 24% had hospital costs exceeding $80,000; none of the IVIG-treated patients had costs exceeding this amount (P = .016, Fisher exact test).

Conclusion: If allocated early to the appropriate patient type (moderate-to-severe illness without end-organ comorbidities and age <70 years), IVIG can significantly reduce hospital costs in COVID-19 care. More important, in our study it reduced the demand for scarce critical care resources during the COVID-19 pandemic.

Keywords: IVIG, SARS-CoV-2, cost saving, direct hospital costs.

Intravenous immunoglobulin (IVIG) has been available in most hospitals for 4 decades, with broad therapeutic applications in the treatment of Kawasaki disease and a variety of inflammatory, infectious, autoimmune, and viral diseases, via multifactorial mechanisms of immune modulation.¹ Reports of COVID-19−associated multisystem inflammatory syndrome in adults and children have supported the use of IVIG in treatment.^2,3 Previous studies of IVIG treatment for COVID-19 have produced mixed results. Although retrospective studies have largely been positive,^4-8 prospective clinical trials have been mixed, with some favorable results^9-11 and another, more recent study showing no benefit.¹² However, there is still considerable debate regarding whether some subgroups of patients with COVID-19 may benefit from IVIG; the studies that support this argument, however, have been diluted by broad clinical trials that lack granularity among the heterogeneity of patient characteristics and the timing of IVIG administration.^13,14 One study suggests that patients with COVID-19 who may be particularly poised to benefit from IVIG are those who are younger, have fewer comorbidities, and are treated early.⁸

At our institution, we selectively utilized IVIG to treat patients within 48 hours of rapidly increasing oxygen requirements due to COVID-19, targeting those younger than 70 years, with no previous irreversible end-organ damage, no significant comorbidities (renal failure, heart failure, dementia, active cancer malignancies), and no active treatment for cancer. We analyzed the costs of care of these IVIG (Privigen) recipients and compared them to costs for patients with COVID-19 matched by comorbidities, age, and illness severity who did not receive IVIG. To look for consistency, we examined the cost of care of COVID-19 patients who received IVIG (Octagam) as compared to controls from a previously published pilot trial.¹⁰

Methods

Setting and Treatment

All patients in this study were hospitalized at a single site located in San Diego, California. Treatment patients in both cohorts received IVIG 0.5 g/kg adjusted for body weight daily for 3 consecutive days.

Patient Cohort #1: Retrospective Case-Control Trial

Intravenous immunoglobulin (Privigen 10%, CSL Behring) was utilized off-label to treat moderately to severely ill non-intensive care unit (ICU) patients with COVID-19 requiring ≥3 L of oxygen by nasal cannula who were not mechanically ventilated but were considered at high risk for respiratory failure. Preset exclusion criteria for off-label use of IVIG in the treatment of COVID-19 were age >70 years, active malignancy, organ transplant recipient, renal failure, heart failure, or dementia. Controls were obtained from a list of all admitted patients with COVID-19, matched to cases 2:1 on the basis of age (±10 years), body mass index (±1), gender, comorbidities present at admission (eg, hypertension, diabetes mellitus, lung disease, or history of tobacco use), and maximum oxygen requirements within the first 48 hours of admission. In situations where more than 2 potential matched controls were identified for a patient, the 2 controls closest in age to the treatment patient were selected. One IVIG patient was excluded because only 1 matched-age control could be found. Pregnant patients who otherwise fulfilled the criteria for IVIG administration were also excluded from this analysis.

Patient Cohort #2: Prospective, Randomized, Open-Label Trial

Use of IVIG (Octagam 10%, Octapharma) in COVID-19 was studied in a previously published, prospective, open-label randomized trial.¹⁰ This pilot trial included 16 IVIG-treated patients and 17 control patients, of which 13 and 14 patients, respectively, had hospital cost data available for analysis.¹⁰ Most notably, COVID-19 patients in this study were required to have ≥4 L of oxygen via nasal cannula to maintain arterial oxygen saturationof ≤96%.

Outcomes

Cost data were independently obtained from our finance team, which provided us with the total direct cost and the total pharmaceutical cost associated with each admission. We also compared total length of stay (LOS) and ICU LOS between treatment arms, as these were presumed to be the major drivers of cost difference.

Statistics

Nonparametric comparisons of medians were performed with the Mann-Whitney U test. Comparison of means was done by Student t test. Categorical data were analyzed by Fisher exact test.

This analysis was initiated as an internal quality assessment. It received approval from the Sharp Healthcare Institutional Review Board (research@sharp.com), and was granted a waiver of subject authorization and consent given the retrospective nature of the study.

Results

Case-Control Analysis

A total of 10 hypoxic patients with COVID-19 received Privigen IVIG outside of clinical trial settings. None of the patients was vaccinated against SARS-CoV-2, as hospitalization occurred prior to vaccine availability. In addition, the original SARS-CoV-2 strain was circulating while these patients were hospitalized, preceding subsequent emerging variants. Oxygen requirements within the first 48 hours ranged from 3 L via nasal cannula to requiring bi-level positive pressure airway therapy with 100% oxygen; median age was 56 years and median Charlson comorbidity index was 1. These 10 patients were each matched to 2 control patients hospitalized during a comparable time period and who, based on oxygen requirements, did not receive IVIG. The 20 control patients had a median age of 58.5 years and a Charlson comorbidity index of 1 (Table 1). Rates of comorbidities, such as hypertension, diabetes mellitus, and obesity, were identical in the 2 groups. None of the patients in either group died during the index hospitalization. Fewer control patients received glucocorticoids, which was reflective of lower illness severity/degree of hypoxia in some controls.

Health care utilization in terms of costs and hospital LOS between the 2 groups are shown in Table 2. The mean total direct hospital cost per case, including IVIG and other drug costs, for the 10 IVIG-treated COVID-19 patients was $21,982 vs $42,431 for the matched controls, a reduction of $20,449 (48%) per case (P = .6187) with IVIG. This difference was heavily driven by 4 control patients (20%) with hospital costs >$80,000, marked by need for ICU transfer, mechanical ventilation during admission, and longer hospital stays. This reduction in progression to mechanical ventilation was consistent with our previously published, open-label, randomized prospective IVIG study, the financial assessment of which is reviewed below. While total direct costs were lower in the treatment arm, the mean drug cost for the treatment arm was $3122 greater than the mean drug cost in the control arm (P = .001622), consistent with the high cost of IVIG therapy (Table 2).

LOS information was obtained, as this was thought to be a primary driver of direct costs. The average LOS in the IVIG arm was 8.4 days, and the average LOS in the control arm was 13.6 days (P = NS). The average ICU LOS in the IVIG arm was 0 days, while the average ICU LOS in the control arm was 5.3 days (P = .04). As with the differences in cost, the differences in LOS were primarily driven by the 4 outlier cases in our control arm, who each had a LOS >25 days, as well as an ICU LOS >20 days.

Randomized, Open-Label, Patient Cohort Analysis

Patient characteristics, LOS, and rates of mechanical ventilation for the IVIG and control patients were previously published and showed a reduction in mechanical ventilation and hospital LOS with IVIG treatment.¹⁰ In this group of patients, 1 patient treated with IVIG (6%) and 3 patients not treated with IVIG (18%) died. To determine the consistency of these results from the case-control patients with a set of patients obtained from clinical trial randomization, we examined the health care costs of patients from the prior study.¹⁰ As with the case-control group, patients in this portion of the analysis were hospitalized before vaccines were available and prior to any identified variants.

Comparing the hospital cost of the IVIG-treated patients to the control patients from this trial revealed results similar to the matched case-control analysis discussed earlier. Average total direct cost per case, including IVIG, for the IVIG treatment group was $28,268, vs $62,707 per case for non-IVIG controls. This represented a net cost reduction of $34,439 (55%) per case, very similar to that of the prior cohort.

IVIG Reduces Costly Outlier Cases

The case-control and randomized trial groups, yielding a combined 23 IVIG and 34 control patients, showed a median cost per case of $22,578 (range $10,115-$70,929) and $22,645 (range $4723-$279,797) for the IVIG and control groups, respectively. Cases with a cost >$80,000 were 0/23 (0%) vs 8/34 (24%) in the IVIG and control groups, respectively (P = .016, Fisher exact test).

Improving care while simultaneously keeping care costs below reimbursement payment levels received from third-party payers is paramount to the financial survival of health care systems. IVIG appears to do this by reducing the number of patients with COVID-19 who progress to ICU care. We compared the costs of care of our combined case-control and randomized trial cohorts to published data on average reimbursements hospitals receive for COVID-19 care from Medicaid, Medicare, and private insurance (Figure).¹⁵ IVIG demonstrated a reduction in cases where costs exceed reimbursement. Indeed, a comparison of net revenue per case of the case-control group showed significantly higher revenue for the IVIG group compared to controls ($52,704 vs $34,712, P = .0338, Table 2).

Discussion

As reflected in at least 1 other study,¹⁶ our hospital had been successfully utilizing IVIG in the treatment of viral acute respiratory distress syndrome (ARDS) prior to COVID-19. Therefore, we moved quickly to perform a randomized, open-label pilot study of IVIG (Octagam 10%) in COVID-19, and noted significant clinical benefit that might translate into hospital cost savings.¹⁰ Over the course of the pandemic, evidence has accumulated that IVIG may play an important role in COVID-19 therapeutics, as summarized in a recent review.¹⁷ However, despite promising but inconsistent results, the relatively high acquisition costs of IVIG raised questions as to its pharmacoeconomic value, particularly with such a high volume of COVID-19 patients with hypoxia, in light of limited clinical data.

COVID-19 therapeutics data can be categorized into either high-quality trials showing marginal benefit for some agents or low-quality trials showing greater benefit for other agents, with IVIG studies falling into the latter category.¹⁸ This phenomenon may speak to the pathophysiological heterogeneity of the COVID-19 patient population. High-quality trials enrolling broad patient types lack the granularity to capture and single out relevant patient subsets who would derive maximal therapeutic benefit, with those subsets diluted by other patient types for which no benefit is seen. Meanwhile, the more granular low-quality trials are criticized as underpowered and lacking in translatability to practice.

Positive results from our pilot trial allowed the use of IVIG (Privigen) off-label in hospitalized COVID-19 patients restricted to specific criteria. Patients had to be moderately to severely ill, requiring >3 L of oxygen via nasal cannula; show high risk of clinical deterioration based on respiratory rate and decline in respiratory status; and have underlying comorbidities (such as hypertension, obesity, or diabetes mellitus). However, older patients (>age 70 years) and those with underlying comorbidities marked by organ failure (such as heart failure, renal failure, dementia, or receipt of organ transplant) and active malignancy were excluded, as their clinical outcome in COVID-19 may be considered less modifiable by therapeutics, while simultaneously carrying potentially a higher risk of adverse events from IVIG (volume overload, renal failure). These exclusions are reflected in the overall low Charlson comorbidity index (mean of 1) of the patients in the case-control study arm. As anticipated, we found a net cost reduction: $20,449 (48%) per case among the 10 IVIG-treated patients compared to the 20 matched controls.

We then went back to the patients from the randomized prospective trial and compared costs for the 13 of 16 IVIG patients and 14 of 17 of the control patients for whom data were available. Among untreated controls, we found a net cost reduction of $34,439 (55%) per case. The higher costs seen in the randomized patient cohort compared to the latter case-control group may be due to a combination of the fact that the treated patients had slightly higher comorbidity indices than the case-control group (median Charlson comorbidity index of 2 in both groups) and the fact that they were treated earlier in the pandemic (May/June 2020), as opposed to the case-control group patients, who were treated in November/December 2020.

It was notable that the cost savings across both groups were derived largely from the reduction in the approximately 20% to 25% of control patients who went on to critical illness, including mechanical ventilation, extracorporeal membrane oxygenation (ECMO), and prolonged ICU stays. Indeed, 8 of 34 of the control patients—but none of the 23 IVIG-treated patients—generated hospital costs in excess of $80,000, a difference that was statistically significant even for such a small sample size. Therefore, reducing these very costly outlier events translated into net savings across the board.

In addition to lowering costs, reducing progression to critical illness is extremely important during heavy waves of COVID-19, when the sheer volume of patients results in severe strain due to the relative scarcity of ICU beds, mechanical ventilators, and ECMO. Therefore, reducing the need for these resources would have a vital role that cannot be measured economically.

The major limitations of this study include the small sample size and the potential lack of generalizability of these results to all hospital centers and treating providers. Our group has considerable experience in IVIG utilization in COVID-19 and, as a result, has identified a “sweet spot,” where benefits were seen clinically and economically. However, it remains to be determined whether IVIG will benefit patients with greater illness severity, such as those in the ICU, on mechanical ventilation, or ECMO. Furthermore, while a significant morbidity and mortality burden of COVID-19 rests in extremely elderly patients and those with end-organ comorbidities such as renal failure and heart failure, it is uncertain whether their COVID-19 adverse outcomes can be improved with IVIG or other therapies. We believe such patients may limit the pharmacoeconomic value of IVIG due to their generally poorer prognosis, regardless of intervention. On the other hand, COVID-19 patients who are not that severely ill, with minimal to no hypoxia, generally will do well regardless of therapy. Therefore, IVIG intervention may be an unnecessary treatment expense. Evidence for this was suggested in our pilot trial¹⁰ and supported in a recent meta-analysis of IVIG therapy in COVID-19.¹⁹

Several other therapeutic options with high acquisition costs have seen an increase in use during the COVID-19 pandemic despite relatively lukewarm data. Remdesivir, the first drug found to have a beneficial effect on hospitalized patients with COVID-19, is priced at $3120 for a complete 5-day treatment course in the United States. This was in line with initial pricing models from the Institute for Clinical and Economic Review (ICER) in May 2020, assuming a mortality benefit with remdesivir use. After the SOLIDARITY trial was published, which showed no mortality benefit associated with remdesivir, ICER updated their pricing models in June 2020 and released a statement that the price of remdesivir was too high to align with demonstrated benefits.^20,21 More recent data demonstrate that remdesivir may be beneficial, but only if administered to patients with fewer than 6 days of symptoms.²² However, only a minority of patients present to the hospital early enough in their illness for remdesivir to be beneficial.²²

Tocilizumab, an interleukin-6 inhibitor, saw an increase in use during the pandemic. An 800-mg treatment course for COVID-19 costs $3584. The efficacy of this treatment option came into question after the COVACTA trial failed to show a difference in clinical status or mortality in COVID-19 patients who received tocilizumab vs placebo.^23,24 A more recent study pointed to a survival benefit of tocilizumab in COVID-19, driven by a very large sample size (>4000), yielding statistically significant, but perhaps clinically less significant, effects on survival.²⁵ This latter study points to the extremely large sample sizes required to capture statistically significant benefits of expensive interventions in COVID-19, which our data demonstrate may benefit only a fraction of patients (20%-25% of patients in the case of IVIG). A more granular clinical assessment of these other interventions is needed to be able to capture the patient subtypes where tocilizumab, remdesivir, and other therapies will be cost effective in the treatment of COVID-19 or other virally mediated cases of ARDS.

Conclusion

While IVIG has a high acquisition cost, the drug’s use in hypoxic COVID-19 patients resulted in reduced costs per COVID-19 case of approximately 50% and use of less critical care resources. The difference was consistent between 2 cohorts (randomized trial vs off-label use in prespecified COVID-19 patient types), IVIG products used (Octagam 10% and Privigen), and time period in the pandemic (waves 1 and 2 in May/June 2020 vs wave 3 in November/December 2020), thereby adjusting for potential differences in circulating viral strains. Furthermore, patients from both groups predated SARS-CoV-2 vaccine availability and major circulating viral variants (eg, delta, omicron), thereby eliminating confounding on outcomes posed by these factors. Control patients’ higher costs of care were driven largely by the approximately 25% of patients who required costly hospital critical care resources, a group mitigated by IVIG. When allocated to the appropriate patient type (patients with moderate-to-severe but not critical illness, <age 70 without preexisting comorbidities of end-organ failure or active cancer), IVIG can reduce hospital costs for COVID-19 care. Identification of specific patient populations where IVIG has the most anticipated benefits in viral illness is needed.

Corresponding author: George Sakoulas, MD, Sharp Rees-Stealy Medical Group, 2020 Genesee Avenue, 2nd Floor, San Diego, CA 92123; gsakoulas@health.ucsd.edu

Disclosures: Dr Sakoulas has worked as a consultant for Abbvie, Paratek, and Octapharma, has served as a speaker for Abbvie and Paratek, and has received research funding from Octapharma. The other authors did not report any disclosures.

Study Overview

Objective: To evaluate whether the addition of the potent androgen-receptor inhibitor (ARA) darolutamide to the standard doublet androgen-deprivation therapy (ADT) and docetaxel in metastatic, hormone-sensitive prostate cancer (mHSPC) would increase survival.

Design: A randomized, double-blind, placebo-controlled, multicenter, phase 3 study. The results reported in this publication are from the prespecified interim analysis.

Intervention: Patients with mHSPC were randomly assigned to receive either darolutamide 600 mg twice daily or placebo. All patients received standard ADT with 6 cycles of docetaxel 75 mg/m² on day 1 every 21 days along with prednisone given within 6 weeks after randomization. Patients receiving luteinizing hormone–releasing hormone (LHRH) agonists as ADT were bridged with at least 4 weeks of first-generation antiandrogen therapy, which was discontinued before randomization. Treatments were continued until symptomatic disease progression, a change in neoplastic therapy, unacceptable toxicity, patient or physician decision, death, or nonadherence.

Setting and participants: Eligible patients included those newly diagnosed with mHSPC with metastases detected on contrast-enhanced computed tomography (CT) or magnetic resonance imaging (MRI) and bone scan. Patients were excluded if they had regional lymph node–only involvement or if they had received more than 12 weeks of ADT before randomization. Between November 2016 and June 2018, 1306 patients (651 in the darolutamide group and 655 in the placebo group) were randomized in a 1:1 manner to receive darolutamide 600 mg twice daily or placebo in addition to ADT and docetaxel. Randomization was stratified based on the TNM staging system (M1a—nonregional lymph node–only metastasis, M1b—bone metastasis with or without lymph node, or M1c—bone metastases) as well as baseline alkaline phosphatase levels.

Main outcome measures: The primary end point for the study was overall survival. Other meaningful secondary end points included time to castration resistance, time to pain progression, time to first symptomatic skeletal event, symptomatic skeletal event-free survival, time to subsequent systemic antineoplastic therapy, time to worsening of disease-related physical symptoms, initiation of opioid therapy for ≥7 days, and safety.

Results: The baseline and demographic characteristics were well balanced between the 2 groups. Median age was 67 years. Nearly 80% of patients had bone metastasis, and approximately 17% had visceral metastasis. At the data cutoff date for the primary analysis, the median duration of therapy was 41 months for darolutamide compared with 16.7 months in the placebo group; 45.9% in the darolutamide group and 19.1% in the placebo group were receiving the allotted trial therapy at the time of the analysis. Six cycles of docetaxel were completed in approximately 85% of patients in both arms. Median overall survival follow-up was 43.7 months (darolutamide) and 42.4 months (placebo). A significant improvement in overall survival was observed in the darolutamide group. The risk of death was 32.5% lower in the darolutamide cohort than in the placebo cohort (hazard ratio [HR], 0.68; 95% CI, 0.57-0.80; P < .001). The overall survival at 4 years was 62.7% (95% CI, 58.7-66.7) in the darolutamide arm and 50.4% (95% CI, 46.3-54.6) in the placebo arm. The overall survival results remained favorable across most subgroups.

Darolutamide was associated with improvement in all key secondary endpoints. Time to castration-resistance was significantly longer in the darolutamide group (HR, 0.36; 95% CI, 0.30-0.42; P < .001). Time to pain progression was also significantly longer in the darolutamide group (HR, 0.79; 95% CI, 0.66-0.95; P = .01). Time to first symptomatic skeletal events (HR, 0.71; 95% CI, 0.54-0.94; P = .02) and time to initiation of subsequent systemic therapy (HR, 0.39; 95% CI, 0.33-0.46; P < .001) were also found to be longer in the darolutamide group.

Safety: The risk of grade 3 or higher adverse events was similar across the 2 groups. Most common adverse events were known toxic effects of docetaxel therapy and were highest during the initial period when both groups received this therapy. These side effects progressively decreased after the initial period. The most common grade 3 or 4 adverse event was neutropenia, and its frequency was similar between the darolutamide and placebo groups (33.7% and 34.2%, respectively). The most frequently reported adverse events were alopecia, neutropenia, fatigue, and anemia and were similar between the groups. Adverse events of special significance, including fatigue, falls, fractures, and cardiovascular events, were also similar between the 2 groups. Adverse events causing deaths in each arm were low and similar (4.1% in the darolutamide group and 4.0% in the placebo group). The rates of discontinuation of darolutamide or placebo were similar (13.5% and 10.6%, respectively).

Conclusion: Among patients with mHSPC, overall survival was significantly longer among patients who received darolutamide plus ADT and docetaxel than among those who received ADT and docetaxel alone. This was observed despite a high percentage of patients in the placebo group receiving subsequent systemic therapy at the time of progression. The survival benefit of darolutamide was maintained across most subgroups. An improvement was also observed in the darolutamide arm in terms of key secondary end points. The adverse events were similar across the groups and were consistent with known safety profiles of ADT and docetaxel, and no new safety signals were identified in this trial.

Commentary

The results of the current study add to the body of literature supporting multi-agent systemic therapy in newly diagnosed mHSPC. Prior phase 3 trials of combination therapy using androgen-receptor pathway inhibitors, ADT, and docetaxel have shown conflicting results. The results from the previously reported PEACE-1 study showed improved overall survival among patients who received abiraterone with ADT and docetaxel as compared with those who received ADT and docetaxel alone.¹ However, as noted by the authors, the subgroup of patients in the ENZAMET trial who received docetaxel, enzalutamide, and ADT did not appear to have a survival advantage compared with those who received ADT and docetaxel alone.² The results from the current ARASENS trial provide compelling evidence in a population of prospectively randomized patients that combination therapy with darolutamide, docetaxel, and ADT improves overall survival in men with mHSPC. The survival advantage was maintained across subgroups analyzed in this study. Improvements were observed in regards to several key secondary end points with use of darolutamide. This benefit was maintained despite many patients receiving subsequent therapy at the time of progression. Importantly, there did not appear to be a significant increase in toxicity with triplet therapy. However, it is important to note that this cohort of patients appeared largely asymptomatic at the time of enrollment, with 70% of patients having an Eastern Cooperative Oncology Group performance status of 0.

Additionally, the average age in this study was 67 years, with only about 15% of the population being older than 75 years. In the reported subgroup analysis, those older than 75 years appeared to derive a similar benefit in overall survival, however. Whether triplet therapy should be universally adopted in all patients remains unclear. For example, there is a subset of patients with mHSPC with favorable- risk disease (ie, those with recurrent metastatic disease, node-only disease). In this population, the risk-benefit analysis is less clear, and whether these patients should receive this combination is not certain. Nevertheless, the results of this well-designed study are compelling and certainly represent a potential new standard treatment option for men with mHSPC. One of the strengths of this study was its large sample size that allowed for vigorous statistical analysis to evaluate the efficacy of darolutamide in combination with ADT and docetaxel.

Application for Clinical Practice

The ARASENS study provides convincing evidence that in men with mHSPC, the addition of darolutamide to docetaxel and ADT improves overall survival. This combination appeared to be well tolerated, with no evidence of increased toxicity noted. Certainly, this combination represents a potential new standard treatment option in this population; however, further understanding of which subgroups of men benefit from enhanced therapy is needed to aid in proper patient selection. 

—Santosh Kagathur, MD, and Daniel Isaac, DO, MS
Michigan State University, East Lansing, MI

Study Overview

Objective: To evaluate whether the addition of the potent androgen-receptor inhibitor (ARA) darolutamide to the standard doublet androgen-deprivation therapy (ADT) and docetaxel in metastatic, hormone-sensitive prostate cancer (mHSPC) would increase survival.

Design: A randomized, double-blind, placebo-controlled, multicenter, phase 3 study. The results reported in this publication are from the prespecified interim analysis.

Intervention: Patients with mHSPC were randomly assigned to receive either darolutamide 600 mg twice daily or placebo. All patients received standard ADT with 6 cycles of docetaxel 75 mg/m² on day 1 every 21 days along with prednisone given within 6 weeks after randomization. Patients receiving luteinizing hormone–releasing hormone (LHRH) agonists as ADT were bridged with at least 4 weeks of first-generation antiandrogen therapy, which was discontinued before randomization. Treatments were continued until symptomatic disease progression, a change in neoplastic therapy, unacceptable toxicity, patient or physician decision, death, or nonadherence.

Setting and participants: Eligible patients included those newly diagnosed with mHSPC with metastases detected on contrast-enhanced computed tomography (CT) or magnetic resonance imaging (MRI) and bone scan. Patients were excluded if they had regional lymph node–only involvement or if they had received more than 12 weeks of ADT before randomization. Between November 2016 and June 2018, 1306 patients (651 in the darolutamide group and 655 in the placebo group) were randomized in a 1:1 manner to receive darolutamide 600 mg twice daily or placebo in addition to ADT and docetaxel. Randomization was stratified based on the TNM staging system (M1a—nonregional lymph node–only metastasis, M1b—bone metastasis with or without lymph node, or M1c—bone metastases) as well as baseline alkaline phosphatase levels.

Main outcome measures: The primary end point for the study was overall survival. Other meaningful secondary end points included time to castration resistance, time to pain progression, time to first symptomatic skeletal event, symptomatic skeletal event-free survival, time to subsequent systemic antineoplastic therapy, time to worsening of disease-related physical symptoms, initiation of opioid therapy for ≥7 days, and safety.

Results: The baseline and demographic characteristics were well balanced between the 2 groups. Median age was 67 years. Nearly 80% of patients had bone metastasis, and approximately 17% had visceral metastasis. At the data cutoff date for the primary analysis, the median duration of therapy was 41 months for darolutamide compared with 16.7 months in the placebo group; 45.9% in the darolutamide group and 19.1% in the placebo group were receiving the allotted trial therapy at the time of the analysis. Six cycles of docetaxel were completed in approximately 85% of patients in both arms. Median overall survival follow-up was 43.7 months (darolutamide) and 42.4 months (placebo). A significant improvement in overall survival was observed in the darolutamide group. The risk of death was 32.5% lower in the darolutamide cohort than in the placebo cohort (hazard ratio [HR], 0.68; 95% CI, 0.57-0.80; P < .001). The overall survival at 4 years was 62.7% (95% CI, 58.7-66.7) in the darolutamide arm and 50.4% (95% CI, 46.3-54.6) in the placebo arm. The overall survival results remained favorable across most subgroups.

Darolutamide was associated with improvement in all key secondary endpoints. Time to castration-resistance was significantly longer in the darolutamide group (HR, 0.36; 95% CI, 0.30-0.42; P < .001). Time to pain progression was also significantly longer in the darolutamide group (HR, 0.79; 95% CI, 0.66-0.95; P = .01). Time to first symptomatic skeletal events (HR, 0.71; 95% CI, 0.54-0.94; P = .02) and time to initiation of subsequent systemic therapy (HR, 0.39; 95% CI, 0.33-0.46; P < .001) were also found to be longer in the darolutamide group.

Safety: The risk of grade 3 or higher adverse events was similar across the 2 groups. Most common adverse events were known toxic effects of docetaxel therapy and were highest during the initial period when both groups received this therapy. These side effects progressively decreased after the initial period. The most common grade 3 or 4 adverse event was neutropenia, and its frequency was similar between the darolutamide and placebo groups (33.7% and 34.2%, respectively). The most frequently reported adverse events were alopecia, neutropenia, fatigue, and anemia and were similar between the groups. Adverse events of special significance, including fatigue, falls, fractures, and cardiovascular events, were also similar between the 2 groups. Adverse events causing deaths in each arm were low and similar (4.1% in the darolutamide group and 4.0% in the placebo group). The rates of discontinuation of darolutamide or placebo were similar (13.5% and 10.6%, respectively).

Conclusion: Among patients with mHSPC, overall survival was significantly longer among patients who received darolutamide plus ADT and docetaxel than among those who received ADT and docetaxel alone. This was observed despite a high percentage of patients in the placebo group receiving subsequent systemic therapy at the time of progression. The survival benefit of darolutamide was maintained across most subgroups. An improvement was also observed in the darolutamide arm in terms of key secondary end points. The adverse events were similar across the groups and were consistent with known safety profiles of ADT and docetaxel, and no new safety signals were identified in this trial.

Commentary

The results of the current study add to the body of literature supporting multi-agent systemic therapy in newly diagnosed mHSPC. Prior phase 3 trials of combination therapy using androgen-receptor pathway inhibitors, ADT, and docetaxel have shown conflicting results. The results from the previously reported PEACE-1 study showed improved overall survival among patients who received abiraterone with ADT and docetaxel as compared with those who received ADT and docetaxel alone.¹ However, as noted by the authors, the subgroup of patients in the ENZAMET trial who received docetaxel, enzalutamide, and ADT did not appear to have a survival advantage compared with those who received ADT and docetaxel alone.² The results from the current ARASENS trial provide compelling evidence in a population of prospectively randomized patients that combination therapy with darolutamide, docetaxel, and ADT improves overall survival in men with mHSPC. The survival advantage was maintained across subgroups analyzed in this study. Improvements were observed in regards to several key secondary end points with use of darolutamide. This benefit was maintained despite many patients receiving subsequent therapy at the time of progression. Importantly, there did not appear to be a significant increase in toxicity with triplet therapy. However, it is important to note that this cohort of patients appeared largely asymptomatic at the time of enrollment, with 70% of patients having an Eastern Cooperative Oncology Group performance status of 0.

Additionally, the average age in this study was 67 years, with only about 15% of the population being older than 75 years. In the reported subgroup analysis, those older than 75 years appeared to derive a similar benefit in overall survival, however. Whether triplet therapy should be universally adopted in all patients remains unclear. For example, there is a subset of patients with mHSPC with favorable- risk disease (ie, those with recurrent metastatic disease, node-only disease). In this population, the risk-benefit analysis is less clear, and whether these patients should receive this combination is not certain. Nevertheless, the results of this well-designed study are compelling and certainly represent a potential new standard treatment option for men with mHSPC. One of the strengths of this study was its large sample size that allowed for vigorous statistical analysis to evaluate the efficacy of darolutamide in combination with ADT and docetaxel.

Application for Clinical Practice

The ARASENS study provides convincing evidence that in men with mHSPC, the addition of darolutamide to docetaxel and ADT improves overall survival. This combination appeared to be well tolerated, with no evidence of increased toxicity noted. Certainly, this combination represents a potential new standard treatment option in this population; however, further understanding of which subgroups of men benefit from enhanced therapy is needed to aid in proper patient selection. 

—Santosh Kagathur, MD, and Daniel Isaac, DO, MS
Michigan State University, East Lansing, MI

Study Overview

Objective: To evaluate whether the addition of the potent androgen-receptor inhibitor (ARA) darolutamide to the standard doublet androgen-deprivation therapy (ADT) and docetaxel in metastatic, hormone-sensitive prostate cancer (mHSPC) would increase survival.

Design: A randomized, double-blind, placebo-controlled, multicenter, phase 3 study. The results reported in this publication are from the prespecified interim analysis.

Intervention: Patients with mHSPC were randomly assigned to receive either darolutamide 600 mg twice daily or placebo. All patients received standard ADT with 6 cycles of docetaxel 75 mg/m² on day 1 every 21 days along with prednisone given within 6 weeks after randomization. Patients receiving luteinizing hormone–releasing hormone (LHRH) agonists as ADT were bridged with at least 4 weeks of first-generation antiandrogen therapy, which was discontinued before randomization. Treatments were continued until symptomatic disease progression, a change in neoplastic therapy, unacceptable toxicity, patient or physician decision, death, or nonadherence.

Setting and participants: Eligible patients included those newly diagnosed with mHSPC with metastases detected on contrast-enhanced computed tomography (CT) or magnetic resonance imaging (MRI) and bone scan. Patients were excluded if they had regional lymph node–only involvement or if they had received more than 12 weeks of ADT before randomization. Between November 2016 and June 2018, 1306 patients (651 in the darolutamide group and 655 in the placebo group) were randomized in a 1:1 manner to receive darolutamide 600 mg twice daily or placebo in addition to ADT and docetaxel. Randomization was stratified based on the TNM staging system (M1a—nonregional lymph node–only metastasis, M1b—bone metastasis with or without lymph node, or M1c—bone metastases) as well as baseline alkaline phosphatase levels.

Main outcome measures: The primary end point for the study was overall survival. Other meaningful secondary end points included time to castration resistance, time to pain progression, time to first symptomatic skeletal event, symptomatic skeletal event-free survival, time to subsequent systemic antineoplastic therapy, time to worsening of disease-related physical symptoms, initiation of opioid therapy for ≥7 days, and safety.

Results: The baseline and demographic characteristics were well balanced between the 2 groups. Median age was 67 years. Nearly 80% of patients had bone metastasis, and approximately 17% had visceral metastasis. At the data cutoff date for the primary analysis, the median duration of therapy was 41 months for darolutamide compared with 16.7 months in the placebo group; 45.9% in the darolutamide group and 19.1% in the placebo group were receiving the allotted trial therapy at the time of the analysis. Six cycles of docetaxel were completed in approximately 85% of patients in both arms. Median overall survival follow-up was 43.7 months (darolutamide) and 42.4 months (placebo). A significant improvement in overall survival was observed in the darolutamide group. The risk of death was 32.5% lower in the darolutamide cohort than in the placebo cohort (hazard ratio [HR], 0.68; 95% CI, 0.57-0.80; P < .001). The overall survival at 4 years was 62.7% (95% CI, 58.7-66.7) in the darolutamide arm and 50.4% (95% CI, 46.3-54.6) in the placebo arm. The overall survival results remained favorable across most subgroups.

Darolutamide was associated with improvement in all key secondary endpoints. Time to castration-resistance was significantly longer in the darolutamide group (HR, 0.36; 95% CI, 0.30-0.42; P < .001). Time to pain progression was also significantly longer in the darolutamide group (HR, 0.79; 95% CI, 0.66-0.95; P = .01). Time to first symptomatic skeletal events (HR, 0.71; 95% CI, 0.54-0.94; P = .02) and time to initiation of subsequent systemic therapy (HR, 0.39; 95% CI, 0.33-0.46; P < .001) were also found to be longer in the darolutamide group.

Safety: The risk of grade 3 or higher adverse events was similar across the 2 groups. Most common adverse events were known toxic effects of docetaxel therapy and were highest during the initial period when both groups received this therapy. These side effects progressively decreased after the initial period. The most common grade 3 or 4 adverse event was neutropenia, and its frequency was similar between the darolutamide and placebo groups (33.7% and 34.2%, respectively). The most frequently reported adverse events were alopecia, neutropenia, fatigue, and anemia and were similar between the groups. Adverse events of special significance, including fatigue, falls, fractures, and cardiovascular events, were also similar between the 2 groups. Adverse events causing deaths in each arm were low and similar (4.1% in the darolutamide group and 4.0% in the placebo group). The rates of discontinuation of darolutamide or placebo were similar (13.5% and 10.6%, respectively).

Conclusion: Among patients with mHSPC, overall survival was significantly longer among patients who received darolutamide plus ADT and docetaxel than among those who received ADT and docetaxel alone. This was observed despite a high percentage of patients in the placebo group receiving subsequent systemic therapy at the time of progression. The survival benefit of darolutamide was maintained across most subgroups. An improvement was also observed in the darolutamide arm in terms of key secondary end points. The adverse events were similar across the groups and were consistent with known safety profiles of ADT and docetaxel, and no new safety signals were identified in this trial.

Commentary

The results of the current study add to the body of literature supporting multi-agent systemic therapy in newly diagnosed mHSPC. Prior phase 3 trials of combination therapy using androgen-receptor pathway inhibitors, ADT, and docetaxel have shown conflicting results. The results from the previously reported PEACE-1 study showed improved overall survival among patients who received abiraterone with ADT and docetaxel as compared with those who received ADT and docetaxel alone.¹ However, as noted by the authors, the subgroup of patients in the ENZAMET trial who received docetaxel, enzalutamide, and ADT did not appear to have a survival advantage compared with those who received ADT and docetaxel alone.² The results from the current ARASENS trial provide compelling evidence in a population of prospectively randomized patients that combination therapy with darolutamide, docetaxel, and ADT improves overall survival in men with mHSPC. The survival advantage was maintained across subgroups analyzed in this study. Improvements were observed in regards to several key secondary end points with use of darolutamide. This benefit was maintained despite many patients receiving subsequent therapy at the time of progression. Importantly, there did not appear to be a significant increase in toxicity with triplet therapy. However, it is important to note that this cohort of patients appeared largely asymptomatic at the time of enrollment, with 70% of patients having an Eastern Cooperative Oncology Group performance status of 0.

Additionally, the average age in this study was 67 years, with only about 15% of the population being older than 75 years. In the reported subgroup analysis, those older than 75 years appeared to derive a similar benefit in overall survival, however. Whether triplet therapy should be universally adopted in all patients remains unclear. For example, there is a subset of patients with mHSPC with favorable- risk disease (ie, those with recurrent metastatic disease, node-only disease). In this population, the risk-benefit analysis is less clear, and whether these patients should receive this combination is not certain. Nevertheless, the results of this well-designed study are compelling and certainly represent a potential new standard treatment option for men with mHSPC. One of the strengths of this study was its large sample size that allowed for vigorous statistical analysis to evaluate the efficacy of darolutamide in combination with ADT and docetaxel.

Application for Clinical Practice

The ARASENS study provides convincing evidence that in men with mHSPC, the addition of darolutamide to docetaxel and ADT improves overall survival. This combination appeared to be well tolerated, with no evidence of increased toxicity noted. Certainly, this combination represents a potential new standard treatment option in this population; however, further understanding of which subgroups of men benefit from enhanced therapy is needed to aid in proper patient selection. 

—Santosh Kagathur, MD, and Daniel Isaac, DO, MS
Michigan State University, East Lansing, MI

From Neurology/Chronic Pain Management Services, Department of Veterans Affairs (VA) Maryland Health Care System, Baltimore VA Medical Center, Baltimore, MD (Dr. Uche), and School of Nursing, Washburn University, Topeka, KS (Drs. Jamison and Waugh).

Abstract

Objective: The purpose of this quality improvement project was to abstract and analyze previously collected data from veterans with high-impact chronic pain who attended the Empower Veterans Program (EVP) offered by a Veterans Administration facility in the northeastern United States.

Methods: This quality improvement project used data collected from veterans with chronic pain who completed the veterans health care facility’s EVP between August 2017 and August 2019. Pre- and post-intervention data on pain intensity, pain interference, quality of life, and pain catastrophizing were compared using paired t-tests.

Results: Although data were abstracted from 115 patients, the final sample included 67 patients who completed both pre-and postintervention questionnaires. Baseline measures of completers and noncompleters were similar. Comparison of pre and post mean scores on completers showed statistically significant findings (P = .004) based on the Bonferroni correction. The medium and large effect sizes (Cohen’s d) indicated clinically significant improvements for veterans who completed the program. Veterans reported high levels of satisfaction with the program.

Conclusion: Veterans with chronic high-impact noncancer pain who completed the EVP had reduced pain intensity, pain interference, pain catastrophizing as well as improved quality of life and satisfaction with their health.

Keywords: musculoskeletal pain, Veterans Affairs, complementary and integrative health, acceptance and commitment therapy, mind-body therapies, whole health, multidisciplinary pain management.

More than 100 million American adults suffer from chronic pain; costs associated with managing chronic pain are approximately $635 billion each year.¹ Chronic pain is prevalent among military veterans, affecting one-third of the 9 million veterans who receive care from Veterans Health Administration (VHA) facilities.² The biopsychosocial impact of chronic pain on the general population, and specifically on veterans, has been compounded by the opioid crisis. The effects of chronic pain and the opioid crisis have fueled interest in the use of complementary and integrative health (CIH) modalities in the management of chronic noncancer pain. Providers are increasingly developing treatment programs that incorporate CIH in their management of chronic noncancer pain.

One such program is the Empower Veterans Program (EVP). Originally developed at the Atlanta Veterans Affairs Health Care System, the EVP is a CIH modality based on the biopsychosocial model of pain developed by psychiatrist George Engel in 1977.³ The biopsychosocial model of pain recognizes that pain is a complex, multidimensional, biopsychosocial experience. Under this model, the mind and body work in unison as interconnected entities. Because the model acknowledges biological, psychological, and social components of pain and illness,⁴ treatment focuses on all aspects of a person’s health, life, and relationships.

The EVP fits into the VHA Pain Management Stepped Care Model and is an adjunctive complement for that model.^5-7 The EVP complements care at the first step, where patient/family provide self-care and where care is provided by patient-aligned primary care teams, at the second step, which includes secondary consultation with multidisciplinary pain medicine specialty teams and other specialists, and at the third step, with the addition of tertiary interdisciplinary pain centers.

The VA Maryland Health Care System (VAMHCS) implemented the EVP as part of a quality improvement project for the management of chronic pain. The objectives of the program were to reduce pain intensity, pain catastrophizing, and pain interference, as well as improve functionality and quality of life among veterans with chronic high-impact noncancer pain. More than 2 years after the program was implemented, collected data had not been analyzed. The purpose of this quality improvement project was to abstract and analyze the previously collected data from veterans with high-impact chronic pain who attended an EVP offered by the VAMHCS. The results of the data analysis were used to inform decisions regarding the future of the program.

Methods

This quality improvement project used the Plan-Do-Study-Act (PDSA) process.⁸ The first 2 phases of the PDSA cycle (Plan and Do) were completed by a team of VA employees from the VAMHCS, who donated their time to establish and implement the program at the project site. This team consisted of psychologists, a physical therapist, a social worker, and a chaplain, and included support from medical administrative staff. This team planned and implemented the EVP at the VA facility based on the model developed at the Atlanta VA Health Care System. During the “Do” phase, the team collected data on pain intensity, pain interference, quality of life, and pain negative cognition (catastrophizing) before the intervention and post intervention. They also collected data on program outcome (patient treatment satisfaction) post intervention. Because these employees did not have time to retrieve and analyze the data, they welcomed the opportunity to have the data analyzed by the investigators during the Study phase of the PDSA cycle. Based on the results of the analysis, recommendations for program changes were made during the Act phase of the cycle.

Intervention

The EVP was developed as a 10-week (30 hours) interdisciplinary CIH approach that coached veterans with chronic pain to live fuller lives based on their individual values and what matters to them. EVP is the “What Else” management modality for the 5% of veterans with high-impact chronic pain.⁹ The EVP provided functional restoration through its components of whole health, mindfulness training, coaching calls, acceptance and commitment therapy, and mindful movement. It used the Wheel of Health with the 4 key components of me, self-care, professional care, and community.^10,11

Veterans who had a diagnosis of chronic nonmalignant pain for 3 months or more and who agreed to participate in the EVP at this facility attended 3-hour classes every Tuesday with a cohort of 8 to 12 peers and engaged in one-on-one coaching with interdisciplinary team members. During the class sessions, veterans were coached to understand and accept their pain and commit to maintaining function despite their pain. Mindful movement by the physical therapist emphasized the pivotal place of exercise in pain management. The therapist used the mantra “Motion is Lotion.”⁹ The guiding principle of the EVP was that small incremental changes can have a big impact on the individual’s whole life. Emphasis was placed on increasing self-efficacy and mindful awareness for veterans with high-impact pain by giving them “Skills before Pills.”⁹

Outcome Measures

Outcome measures included the Numerical Pain Rating Scale (NPRS), the Multidimensional Pain Inventory (MPI), the World Health Organization Quality of Life assessment (WHOQOL-BREF), the Pain Catastrophizing Scale (PCS), and the Pain Treatment Satisfaction Scale (PTSS). Cronbach alpha coefficients were calculated to assess internal consistency reliability of these measures in the sample of veterans who completed the EVP.

NPRS. The NPRS is ubiquitous as a screening tool in many health care environments and its use is mandated by the VA health care system.¹² The choice of the NPRS as the tool for pain screening in the VA health care system was based on a large body of research that supports the reliability and validity of the NPRS as a single index of pain intensity or severity. Studies suggest that the NPRS is valid for use in the assessment of acute, cancer, or chronic nonmalignant pain and in varied clinical settings.¹³ The NPRS has 4 items, each on a scale of 0 to 10. For the purpose of this project, only 3 items were used. The 3 items assessed the worst pain, usual pain, and the current pain (right now). The higher the score, the higher the pain intensity. Cronbach alpha coefficients on the NPRS obtained from the current sample of veterans were 0.85 on both pre- and postintervention assessments.

MPI. The MPI is an easily accessible, reliable, and valid self-report questionnaire that measures the impact of pain on an individual’s life, quality of social support, and general activity.¹⁴ This instrument is a short version of the West Haven-Yale MPI.¹⁵ The MPI contains 9 items rated on a scale from 0 to 6. The higher the score, the greater pain interference a person is experiencing. The MPI produces reliable, valid information for diagnostic purposes and for therapy outcome studies.¹⁶ The MPI had a Cronbach alpha of 0.90 on pre-intervention and 0.92 on postintervention assessments in the current sample.

WHOQOL-BREF. The WHOQOL-BREF is a measure of quality of life and is an abbreviated version of the WHOQOL-100. Quality of life is defined by the World Health Organization¹⁷ “as an individuals’ perception of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards and concerns.” The WHOQOL-BREF contains 26 items. The first 2 items were examined separately; the first item asks individuals to rate their overall quality of life and the second asks individuals how satisfied they are with their health. The remaining 24 items were used to calculate the following 4 domain scores: physical health, psychological health, social relationship, and environment.¹⁸ Each item is measured on a scale of 1 to 5. Higher scores denote higher or better quality of life. Domain scores have demonstrated good reliability and validity.^19-21 Cronbach alpha coefficients for the domain subscales ranged from 0.63 to 0.84 in the current sample, with the lowest alphas for the 3-item Social Relationships Domain.

PCS. The PCS is a widely used measure of catastrophic thinking related to pain. Catastrophizing has been conceived by Sullivan and colleagues as “an exaggerated negative mental set brought to bear during actual or anticipated painful experience.”²² The PCS provides a total score and scores for the following subscales: rumination, magnification, and helplessness.²³ It has been used in a variety of chronic pain populations and has demonstrated good reliability and validity in clinical as well as nonclinical samples.^24-26 The PCS has 13 items rated on a scale of 0 to 4. Higher scores mean greater negative pain cognition (catastrophizing). In the current sample, the PCS total scale had a Cronbach alpha coefficient of 0.95 and 0.94 on the 2 assessments. The coefficients for the subscales ranged from 0.81 to 0.90.

PTSS. The PTSS is a 5-item tool that measures patient satisfaction with pain treatment. It includes items that address overall satisfaction, staff warmth, staff skill level, ease of scheduling appointments, and recommendation of the program to other veterans. It was derived from the post-treatment version of The Pain Outcome Questionnaire-VA and has demonstrated reliability and validity.²⁷ The questions are scaled from 0 to 10. High scores on the PTSS denote high patient satisfaction with the EVP. The Cronbach alpha coefficient on the PTSS obtained from the current sample was 0.80.

Data Gathering and Analysis

Prior to starting the Study phase, Washburn University’s Institutional Review Board (IRB) and the VA IRB approved the project. The VA IRB, through its affiliate, gave a Not Human Research Determination and granted a waiver of informed consent and the Health Insurance Portability and Accountability Act authorization. The VA facility’s Research and Development department also approved the quality improvement project.

Once these approvals were obtained, the Study phase began with the abstraction of retrospective data obtained from veterans who participated in the VA health care facility’s EVP between August 2017 and August 2019. Most of the measurement tools changed in August 2019, and for this reason data abstraction was limited to the time period August 2017 to August 2019. The first author (JUU) abstracted data for both program completers and noncompleters. The second (MJ) and third (SW) authors analyzed the data in SPSS 24 and calculated effect sizes.

Veterans who completed the program were compared to veterans who did not complete the program on age, gender, and baseline measures. The investigators used independent samples t-tests to compare completers and noncompleters on age, pain intensity, pain interference, quality of life, and pain catastrophizing. They used the chi-square test of independence to analyze the association between gender and program completion.

Data were included in the pre- and postintervention analysis if the veteran completed the NPRS, MPI, WHOQOL-BREF, and PCS pre and post intervention. This became an important eligibility requirement as some of the tools/measures were changed towards the end of the review period in 2019. Pre- and postintervention data on pain intensity, pain interference, quality of life, pain catastrophizing, and patient satisfaction were compared using paired samples t-test at .004 level of significance based on the Bonferroni correction.²⁸ Data on patient satisfaction with pain treatment were collected at program completion (week 8 or 10) and were analyzed using descriptive statistics.

Effect sizes (Cohen’s d) were calculated to determine the substantive significance or magnitude of the mean differences in scores. Effect sizes (expressed as absolute values of Cohen’s d) were calculated as the mean difference divided by the standard deviation. Values of 0.2 were considered a small effect size, 0.5 a medium effect size, and 0.8 a large effect size.²⁹

Data were abstracted for 115 veterans who started the EVP. Of these, 48 left the program, leaving 67 veterans (58%) who completed the program. Completers and noncompleters were similar in age, gender, and baseline measures (Table 1). Fifty-three (79%) completers and 35 (73%) noncompleters were male. A chi-square test of independence showed no significant association between gender and program completion (χ^2₁ [N = 115] = .595, P = .440).

Comparison of pre-and postintervention mean scale scores resulted in statistically significant differences for all comparisons (Table 2). These comparisons yielded improvements in the desired direction. For example, the scores on the NPRS, the MPI, and the PCS (along with its subscales) decreased, revealing reductions in pain severity, the impact of pain on the veterans’ lives, and pain catastrophizing. The 2 individual item scores on the WHOQOL-BREF increased, indicating improvements in perceived quality of life and satisfaction with health. The domain scores on the WHOQOL-BREF increased, revealing improvements in pain-related quality of life. The moderate to large effect sizes indicated clinically significant improvements for veterans with chronic high-impact pain who completed the EVP.

Analysis of data obtained using the PTSS yielded high mean scores for items that focused on patient satisfaction with treatment (Table 3). Scaled statistics yielded a mean (SD) of 46.95 (4.40). These results denoted overall patient satisfaction with the EVP.

Discussion

The purpose of this quality improvement project was to abstract and analyze previously collected data from veterans with high-impact chronic pain who attended the EVP. Comparison of pre-intervention and postintervention data obtained from 67 veterans who completed the program revealed improvements in pain intensity, pain interference, negative cognition (catastrophizing), and quality of life. The differences were statistically significant and clinically meaningful, with medium and large effect sizes. In addition, veterans reported high satisfaction with the EVP.

The EVP includes CIH approaches that have demonstrated effectiveness among veterans and other populations with chronic pain. A wealth of studies, for example, support the effectiveness of CIH approaches among veterans.^30-34 Other studies focus on specific CIH approaches that are components of the EVP. Evidence supports, for example, the efficacy of mindfulness-based stress reduction,^35-39 acceptance and commitment therapy,^40-43 brief peer support intervention,⁴⁴ and interdisciplinary biopsychosocial rehabilitation.^45,46

While empirical evidence supports components of the EVP, only one study focused on the outcomes of the Atlanta VA EVP among veterans with chronic pain. Results of a qualitative study conducted by Penney and Haro⁴⁷ described the experience of veterans with the EVP. Those veterans reported adopting new self-care or lifestyle practices for pain management and health, accepting pain, being better able to adjust and set boundaries, feeling more in control, participating in life, and changing their medication use.

The mean baseline scores from the current sample were similar to samples of patients with chronic pain in other studies (NPRS,⁴⁸ MPI,⁴⁸ and PCS^48-51). After converting scores on the WHOQOL-BREF from those that ranged from 4 to 20 to those that ranged from 0 to 100,¹⁸ the scores from the current sample were similar to those of other studies of patients with chronic pain.^48,52,53Several strengths of the project should be noted. Data were collected using well established measurement tools that had previously demonstrated reliability and validity. All the tools used in data collection demonstrated good internal consistency reliabilities in the current sample of veterans. Weaknesses of the project include the use of a convenience sample of veterans and small sample size. Data were not available on the number of veterans who were offered participation or on how many veterans declined enrollment. The sample of veterans who chose to participate in the EVP may or may not have been representative of the population of veterans with high-impact chronic pain. As a pre- and postintervention design with no comparison group, the results are subject to multiple threats to internal validity, including the Hawthorne effect, maturation in the form of healing, and attrition. Reasons for leaving the program had not been recorded, so the investigators had no way of knowing factors that may have contributed to attrition. Also, data on when veterans left the program were unavailable. Research is needed with a control group to reduce the effect of confounding variables on the outcome measures. This project used data collected at a single VA facility, which limits its generalizability.

While completers and noncompleters of the EVP were similar on age, gender, and baseline measures, there may have been unidentified characteristics that influenced program completion. The investigators noticed the presence of more missing data among noncompleters compared to completers on the pre-intervention PCS; thus, noncompleters may have scored lower than completers on this instrument simply because there were more individual items that were unanswered/missing among this group of noncompleters.

Data were analyzed using a limited number of outcome measures that had previously been collected. Other outcome measures might include whether EVP participants reduced their use of medications, clinical resources, and personnel. Future projects, for example, could determine whether the EVP is effective in reducing opioid analgesic medication use and decreasing primary care and emergency department visits. Cost-benefit analyses could be completed to determine whether EVP is associated with financial savings.

Because no follow-up assessments were made to determine whether improvements were maintained over time, the project focus was limited to an evaluation of the short-term changes in the outcome measures. Future projects could include a follow-up assessment of the veterans 1- or 2-years post completion of the EVP.

Data for the project were collected prior to the COVID-19 pandemic, when the EVP was implemented through face-to-face meetings with participants and their peers. It is not clear how changes to the delivery of the program (such as offering it through telehealth) might impact veterans’ satisfaction with the program, willingness to complete it, and other variables of interest.

The results of this project were made available to stakeholders with recommendations for program expansion both at the current location and at other VA facilities, including the recommendation to hire additional personnel that would implement the program. As the VA network of facilities expand the EVP program and adapt it for telehealth delivery, the investigators recommended a similar analysis of data be performed following telehealth delivery. If delivery through telehealth is shown to improve outcome measures, the EVP could provide pain management treatment options for patients challenged by transportation barriers, including rural veterans.

Conclusion

This quality improvement project provided evidence of improvement in measures of pain severity, pain interference, negative cognition (catastrophizing), quality of life, and patient treatment satisfaction among veterans with chronic high-impact pain. Findings have been well received by the northeastern VA as well as the Veterans Integrated Systems Network 5. The results of the analyses were used to inform decisions regarding the future of the program.

Disclaimer: This material is the result of work supported with resources and the use of facilities at the VA Maryland Health Care System, Baltimore, Maryland. The views expressed are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs or the United States Government.

Acknowledgments: The authors thank Dr. Arianna Perra, the recent past coordinator of the Empower Veterans Program (EVP), who provided initial insights and support that motivated the decision to evaluate the program. We also thank the veterans and VA EVP clinicians who contributed data for the evaluation, and Dr. Michael Saenger (Director, TelePain-EVP: EVP) and Dr. Robert Lavin for their ongoing support, care, and concern for veteran patients. We also thank Dr. Beverly Bradley and the neurology service administrative team for their guidance in the process of obtaining necessary VA approvals for this project.

Corresponding author: Jessica U. Uche, DNP, CRNP-Family; jessica.uche@va.gov

doi:10.12788/jcom.0089

From Neurology/Chronic Pain Management Services, Department of Veterans Affairs (VA) Maryland Health Care System, Baltimore VA Medical Center, Baltimore, MD (Dr. Uche), and School of Nursing, Washburn University, Topeka, KS (Drs. Jamison and Waugh).

Abstract

Objective: The purpose of this quality improvement project was to abstract and analyze previously collected data from veterans with high-impact chronic pain who attended the Empower Veterans Program (EVP) offered by a Veterans Administration facility in the northeastern United States.

Methods: This quality improvement project used data collected from veterans with chronic pain who completed the veterans health care facility’s EVP between August 2017 and August 2019. Pre- and post-intervention data on pain intensity, pain interference, quality of life, and pain catastrophizing were compared using paired t-tests.

Results: Although data were abstracted from 115 patients, the final sample included 67 patients who completed both pre-and postintervention questionnaires. Baseline measures of completers and noncompleters were similar. Comparison of pre and post mean scores on completers showed statistically significant findings (P = .004) based on the Bonferroni correction. The medium and large effect sizes (Cohen’s d) indicated clinically significant improvements for veterans who completed the program. Veterans reported high levels of satisfaction with the program.

Conclusion: Veterans with chronic high-impact noncancer pain who completed the EVP had reduced pain intensity, pain interference, pain catastrophizing as well as improved quality of life and satisfaction with their health.

Keywords: musculoskeletal pain, Veterans Affairs, complementary and integrative health, acceptance and commitment therapy, mind-body therapies, whole health, multidisciplinary pain management.

More than 100 million American adults suffer from chronic pain; costs associated with managing chronic pain are approximately $635 billion each year.¹ Chronic pain is prevalent among military veterans, affecting one-third of the 9 million veterans who receive care from Veterans Health Administration (VHA) facilities.² The biopsychosocial impact of chronic pain on the general population, and specifically on veterans, has been compounded by the opioid crisis. The effects of chronic pain and the opioid crisis have fueled interest in the use of complementary and integrative health (CIH) modalities in the management of chronic noncancer pain. Providers are increasingly developing treatment programs that incorporate CIH in their management of chronic noncancer pain.

One such program is the Empower Veterans Program (EVP). Originally developed at the Atlanta Veterans Affairs Health Care System, the EVP is a CIH modality based on the biopsychosocial model of pain developed by psychiatrist George Engel in 1977.³ The biopsychosocial model of pain recognizes that pain is a complex, multidimensional, biopsychosocial experience. Under this model, the mind and body work in unison as interconnected entities. Because the model acknowledges biological, psychological, and social components of pain and illness,⁴ treatment focuses on all aspects of a person’s health, life, and relationships.

The EVP fits into the VHA Pain Management Stepped Care Model and is an adjunctive complement for that model.^5-7 The EVP complements care at the first step, where patient/family provide self-care and where care is provided by patient-aligned primary care teams, at the second step, which includes secondary consultation with multidisciplinary pain medicine specialty teams and other specialists, and at the third step, with the addition of tertiary interdisciplinary pain centers.

The VA Maryland Health Care System (VAMHCS) implemented the EVP as part of a quality improvement project for the management of chronic pain. The objectives of the program were to reduce pain intensity, pain catastrophizing, and pain interference, as well as improve functionality and quality of life among veterans with chronic high-impact noncancer pain. More than 2 years after the program was implemented, collected data had not been analyzed. The purpose of this quality improvement project was to abstract and analyze the previously collected data from veterans with high-impact chronic pain who attended an EVP offered by the VAMHCS. The results of the data analysis were used to inform decisions regarding the future of the program.

Methods

This quality improvement project used the Plan-Do-Study-Act (PDSA) process.⁸ The first 2 phases of the PDSA cycle (Plan and Do) were completed by a team of VA employees from the VAMHCS, who donated their time to establish and implement the program at the project site. This team consisted of psychologists, a physical therapist, a social worker, and a chaplain, and included support from medical administrative staff. This team planned and implemented the EVP at the VA facility based on the model developed at the Atlanta VA Health Care System. During the “Do” phase, the team collected data on pain intensity, pain interference, quality of life, and pain negative cognition (catastrophizing) before the intervention and post intervention. They also collected data on program outcome (patient treatment satisfaction) post intervention. Because these employees did not have time to retrieve and analyze the data, they welcomed the opportunity to have the data analyzed by the investigators during the Study phase of the PDSA cycle. Based on the results of the analysis, recommendations for program changes were made during the Act phase of the cycle.

Intervention

The EVP was developed as a 10-week (30 hours) interdisciplinary CIH approach that coached veterans with chronic pain to live fuller lives based on their individual values and what matters to them. EVP is the “What Else” management modality for the 5% of veterans with high-impact chronic pain.⁹ The EVP provided functional restoration through its components of whole health, mindfulness training, coaching calls, acceptance and commitment therapy, and mindful movement. It used the Wheel of Health with the 4 key components of me, self-care, professional care, and community.^10,11

Veterans who had a diagnosis of chronic nonmalignant pain for 3 months or more and who agreed to participate in the EVP at this facility attended 3-hour classes every Tuesday with a cohort of 8 to 12 peers and engaged in one-on-one coaching with interdisciplinary team members. During the class sessions, veterans were coached to understand and accept their pain and commit to maintaining function despite their pain. Mindful movement by the physical therapist emphasized the pivotal place of exercise in pain management. The therapist used the mantra “Motion is Lotion.”⁹ The guiding principle of the EVP was that small incremental changes can have a big impact on the individual’s whole life. Emphasis was placed on increasing self-efficacy and mindful awareness for veterans with high-impact pain by giving them “Skills before Pills.”⁹

Outcome Measures

Outcome measures included the Numerical Pain Rating Scale (NPRS), the Multidimensional Pain Inventory (MPI), the World Health Organization Quality of Life assessment (WHOQOL-BREF), the Pain Catastrophizing Scale (PCS), and the Pain Treatment Satisfaction Scale (PTSS). Cronbach alpha coefficients were calculated to assess internal consistency reliability of these measures in the sample of veterans who completed the EVP.

NPRS. The NPRS is ubiquitous as a screening tool in many health care environments and its use is mandated by the VA health care system.¹² The choice of the NPRS as the tool for pain screening in the VA health care system was based on a large body of research that supports the reliability and validity of the NPRS as a single index of pain intensity or severity. Studies suggest that the NPRS is valid for use in the assessment of acute, cancer, or chronic nonmalignant pain and in varied clinical settings.¹³ The NPRS has 4 items, each on a scale of 0 to 10. For the purpose of this project, only 3 items were used. The 3 items assessed the worst pain, usual pain, and the current pain (right now). The higher the score, the higher the pain intensity. Cronbach alpha coefficients on the NPRS obtained from the current sample of veterans were 0.85 on both pre- and postintervention assessments.

MPI. The MPI is an easily accessible, reliable, and valid self-report questionnaire that measures the impact of pain on an individual’s life, quality of social support, and general activity.¹⁴ This instrument is a short version of the West Haven-Yale MPI.¹⁵ The MPI contains 9 items rated on a scale from 0 to 6. The higher the score, the greater pain interference a person is experiencing. The MPI produces reliable, valid information for diagnostic purposes and for therapy outcome studies.¹⁶ The MPI had a Cronbach alpha of 0.90 on pre-intervention and 0.92 on postintervention assessments in the current sample.

WHOQOL-BREF. The WHOQOL-BREF is a measure of quality of life and is an abbreviated version of the WHOQOL-100. Quality of life is defined by the World Health Organization¹⁷ “as an individuals’ perception of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards and concerns.” The WHOQOL-BREF contains 26 items. The first 2 items were examined separately; the first item asks individuals to rate their overall quality of life and the second asks individuals how satisfied they are with their health. The remaining 24 items were used to calculate the following 4 domain scores: physical health, psychological health, social relationship, and environment.¹⁸ Each item is measured on a scale of 1 to 5. Higher scores denote higher or better quality of life. Domain scores have demonstrated good reliability and validity.^19-21 Cronbach alpha coefficients for the domain subscales ranged from 0.63 to 0.84 in the current sample, with the lowest alphas for the 3-item Social Relationships Domain.

PCS. The PCS is a widely used measure of catastrophic thinking related to pain. Catastrophizing has been conceived by Sullivan and colleagues as “an exaggerated negative mental set brought to bear during actual or anticipated painful experience.”²² The PCS provides a total score and scores for the following subscales: rumination, magnification, and helplessness.²³ It has been used in a variety of chronic pain populations and has demonstrated good reliability and validity in clinical as well as nonclinical samples.^24-26 The PCS has 13 items rated on a scale of 0 to 4. Higher scores mean greater negative pain cognition (catastrophizing). In the current sample, the PCS total scale had a Cronbach alpha coefficient of 0.95 and 0.94 on the 2 assessments. The coefficients for the subscales ranged from 0.81 to 0.90.

PTSS. The PTSS is a 5-item tool that measures patient satisfaction with pain treatment. It includes items that address overall satisfaction, staff warmth, staff skill level, ease of scheduling appointments, and recommendation of the program to other veterans. It was derived from the post-treatment version of The Pain Outcome Questionnaire-VA and has demonstrated reliability and validity.²⁷ The questions are scaled from 0 to 10. High scores on the PTSS denote high patient satisfaction with the EVP. The Cronbach alpha coefficient on the PTSS obtained from the current sample was 0.80.

Data Gathering and Analysis

Prior to starting the Study phase, Washburn University’s Institutional Review Board (IRB) and the VA IRB approved the project. The VA IRB, through its affiliate, gave a Not Human Research Determination and granted a waiver of informed consent and the Health Insurance Portability and Accountability Act authorization. The VA facility’s Research and Development department also approved the quality improvement project.

Once these approvals were obtained, the Study phase began with the abstraction of retrospective data obtained from veterans who participated in the VA health care facility’s EVP between August 2017 and August 2019. Most of the measurement tools changed in August 2019, and for this reason data abstraction was limited to the time period August 2017 to August 2019. The first author (JUU) abstracted data for both program completers and noncompleters. The second (MJ) and third (SW) authors analyzed the data in SPSS 24 and calculated effect sizes.

Veterans who completed the program were compared to veterans who did not complete the program on age, gender, and baseline measures. The investigators used independent samples t-tests to compare completers and noncompleters on age, pain intensity, pain interference, quality of life, and pain catastrophizing. They used the chi-square test of independence to analyze the association between gender and program completion.

Data were included in the pre- and postintervention analysis if the veteran completed the NPRS, MPI, WHOQOL-BREF, and PCS pre and post intervention. This became an important eligibility requirement as some of the tools/measures were changed towards the end of the review period in 2019. Pre- and postintervention data on pain intensity, pain interference, quality of life, pain catastrophizing, and patient satisfaction were compared using paired samples t-test at .004 level of significance based on the Bonferroni correction.²⁸ Data on patient satisfaction with pain treatment were collected at program completion (week 8 or 10) and were analyzed using descriptive statistics.

Effect sizes (Cohen’s d) were calculated to determine the substantive significance or magnitude of the mean differences in scores. Effect sizes (expressed as absolute values of Cohen’s d) were calculated as the mean difference divided by the standard deviation. Values of 0.2 were considered a small effect size, 0.5 a medium effect size, and 0.8 a large effect size.²⁹

Data were abstracted for 115 veterans who started the EVP. Of these, 48 left the program, leaving 67 veterans (58%) who completed the program. Completers and noncompleters were similar in age, gender, and baseline measures (Table 1). Fifty-three (79%) completers and 35 (73%) noncompleters were male. A chi-square test of independence showed no significant association between gender and program completion (χ^2₁ [N = 115] = .595, P = .440).

Comparison of pre-and postintervention mean scale scores resulted in statistically significant differences for all comparisons (Table 2). These comparisons yielded improvements in the desired direction. For example, the scores on the NPRS, the MPI, and the PCS (along with its subscales) decreased, revealing reductions in pain severity, the impact of pain on the veterans’ lives, and pain catastrophizing. The 2 individual item scores on the WHOQOL-BREF increased, indicating improvements in perceived quality of life and satisfaction with health. The domain scores on the WHOQOL-BREF increased, revealing improvements in pain-related quality of life. The moderate to large effect sizes indicated clinically significant improvements for veterans with chronic high-impact pain who completed the EVP.

Analysis of data obtained using the PTSS yielded high mean scores for items that focused on patient satisfaction with treatment (Table 3). Scaled statistics yielded a mean (SD) of 46.95 (4.40). These results denoted overall patient satisfaction with the EVP.

Discussion

The purpose of this quality improvement project was to abstract and analyze previously collected data from veterans with high-impact chronic pain who attended the EVP. Comparison of pre-intervention and postintervention data obtained from 67 veterans who completed the program revealed improvements in pain intensity, pain interference, negative cognition (catastrophizing), and quality of life. The differences were statistically significant and clinically meaningful, with medium and large effect sizes. In addition, veterans reported high satisfaction with the EVP.

The EVP includes CIH approaches that have demonstrated effectiveness among veterans and other populations with chronic pain. A wealth of studies, for example, support the effectiveness of CIH approaches among veterans.^30-34 Other studies focus on specific CIH approaches that are components of the EVP. Evidence supports, for example, the efficacy of mindfulness-based stress reduction,^35-39 acceptance and commitment therapy,^40-43 brief peer support intervention,⁴⁴ and interdisciplinary biopsychosocial rehabilitation.^45,46

While empirical evidence supports components of the EVP, only one study focused on the outcomes of the Atlanta VA EVP among veterans with chronic pain. Results of a qualitative study conducted by Penney and Haro⁴⁷ described the experience of veterans with the EVP. Those veterans reported adopting new self-care or lifestyle practices for pain management and health, accepting pain, being better able to adjust and set boundaries, feeling more in control, participating in life, and changing their medication use.

The mean baseline scores from the current sample were similar to samples of patients with chronic pain in other studies (NPRS,⁴⁸ MPI,⁴⁸ and PCS^48-51). After converting scores on the WHOQOL-BREF from those that ranged from 4 to 20 to those that ranged from 0 to 100,¹⁸ the scores from the current sample were similar to those of other studies of patients with chronic pain.^48,52,53Several strengths of the project should be noted. Data were collected using well established measurement tools that had previously demonstrated reliability and validity. All the tools used in data collection demonstrated good internal consistency reliabilities in the current sample of veterans. Weaknesses of the project include the use of a convenience sample of veterans and small sample size. Data were not available on the number of veterans who were offered participation or on how many veterans declined enrollment. The sample of veterans who chose to participate in the EVP may or may not have been representative of the population of veterans with high-impact chronic pain. As a pre- and postintervention design with no comparison group, the results are subject to multiple threats to internal validity, including the Hawthorne effect, maturation in the form of healing, and attrition. Reasons for leaving the program had not been recorded, so the investigators had no way of knowing factors that may have contributed to attrition. Also, data on when veterans left the program were unavailable. Research is needed with a control group to reduce the effect of confounding variables on the outcome measures. This project used data collected at a single VA facility, which limits its generalizability.

While completers and noncompleters of the EVP were similar on age, gender, and baseline measures, there may have been unidentified characteristics that influenced program completion. The investigators noticed the presence of more missing data among noncompleters compared to completers on the pre-intervention PCS; thus, noncompleters may have scored lower than completers on this instrument simply because there were more individual items that were unanswered/missing among this group of noncompleters.

Data were analyzed using a limited number of outcome measures that had previously been collected. Other outcome measures might include whether EVP participants reduced their use of medications, clinical resources, and personnel. Future projects, for example, could determine whether the EVP is effective in reducing opioid analgesic medication use and decreasing primary care and emergency department visits. Cost-benefit analyses could be completed to determine whether EVP is associated with financial savings.

Because no follow-up assessments were made to determine whether improvements were maintained over time, the project focus was limited to an evaluation of the short-term changes in the outcome measures. Future projects could include a follow-up assessment of the veterans 1- or 2-years post completion of the EVP.

Data for the project were collected prior to the COVID-19 pandemic, when the EVP was implemented through face-to-face meetings with participants and their peers. It is not clear how changes to the delivery of the program (such as offering it through telehealth) might impact veterans’ satisfaction with the program, willingness to complete it, and other variables of interest.

The results of this project were made available to stakeholders with recommendations for program expansion both at the current location and at other VA facilities, including the recommendation to hire additional personnel that would implement the program. As the VA network of facilities expand the EVP program and adapt it for telehealth delivery, the investigators recommended a similar analysis of data be performed following telehealth delivery. If delivery through telehealth is shown to improve outcome measures, the EVP could provide pain management treatment options for patients challenged by transportation barriers, including rural veterans.

Conclusion

This quality improvement project provided evidence of improvement in measures of pain severity, pain interference, negative cognition (catastrophizing), quality of life, and patient treatment satisfaction among veterans with chronic high-impact pain. Findings have been well received by the northeastern VA as well as the Veterans Integrated Systems Network 5. The results of the analyses were used to inform decisions regarding the future of the program.

Disclaimer: This material is the result of work supported with resources and the use of facilities at the VA Maryland Health Care System, Baltimore, Maryland. The views expressed are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs or the United States Government.

Acknowledgments: The authors thank Dr. Arianna Perra, the recent past coordinator of the Empower Veterans Program (EVP), who provided initial insights and support that motivated the decision to evaluate the program. We also thank the veterans and VA EVP clinicians who contributed data for the evaluation, and Dr. Michael Saenger (Director, TelePain-EVP: EVP) and Dr. Robert Lavin for their ongoing support, care, and concern for veteran patients. We also thank Dr. Beverly Bradley and the neurology service administrative team for their guidance in the process of obtaining necessary VA approvals for this project.

Corresponding author: Jessica U. Uche, DNP, CRNP-Family; jessica.uche@va.gov

doi:10.12788/jcom.0089

From Neurology/Chronic Pain Management Services, Department of Veterans Affairs (VA) Maryland Health Care System, Baltimore VA Medical Center, Baltimore, MD (Dr. Uche), and School of Nursing, Washburn University, Topeka, KS (Drs. Jamison and Waugh).

Abstract

Objective: The purpose of this quality improvement project was to abstract and analyze previously collected data from veterans with high-impact chronic pain who attended the Empower Veterans Program (EVP) offered by a Veterans Administration facility in the northeastern United States.

Methods: This quality improvement project used data collected from veterans with chronic pain who completed the veterans health care facility’s EVP between August 2017 and August 2019. Pre- and post-intervention data on pain intensity, pain interference, quality of life, and pain catastrophizing were compared using paired t-tests.

Results: Although data were abstracted from 115 patients, the final sample included 67 patients who completed both pre-and postintervention questionnaires. Baseline measures of completers and noncompleters were similar. Comparison of pre and post mean scores on completers showed statistically significant findings (P = .004) based on the Bonferroni correction. The medium and large effect sizes (Cohen’s d) indicated clinically significant improvements for veterans who completed the program. Veterans reported high levels of satisfaction with the program.

Conclusion: Veterans with chronic high-impact noncancer pain who completed the EVP had reduced pain intensity, pain interference, pain catastrophizing as well as improved quality of life and satisfaction with their health.

Keywords: musculoskeletal pain, Veterans Affairs, complementary and integrative health, acceptance and commitment therapy, mind-body therapies, whole health, multidisciplinary pain management.

More than 100 million American adults suffer from chronic pain; costs associated with managing chronic pain are approximately $635 billion each year.¹ Chronic pain is prevalent among military veterans, affecting one-third of the 9 million veterans who receive care from Veterans Health Administration (VHA) facilities.² The biopsychosocial impact of chronic pain on the general population, and specifically on veterans, has been compounded by the opioid crisis. The effects of chronic pain and the opioid crisis have fueled interest in the use of complementary and integrative health (CIH) modalities in the management of chronic noncancer pain. Providers are increasingly developing treatment programs that incorporate CIH in their management of chronic noncancer pain.

One such program is the Empower Veterans Program (EVP). Originally developed at the Atlanta Veterans Affairs Health Care System, the EVP is a CIH modality based on the biopsychosocial model of pain developed by psychiatrist George Engel in 1977.³ The biopsychosocial model of pain recognizes that pain is a complex, multidimensional, biopsychosocial experience. Under this model, the mind and body work in unison as interconnected entities. Because the model acknowledges biological, psychological, and social components of pain and illness,⁴ treatment focuses on all aspects of a person’s health, life, and relationships.

The EVP fits into the VHA Pain Management Stepped Care Model and is an adjunctive complement for that model.^5-7 The EVP complements care at the first step, where patient/family provide self-care and where care is provided by patient-aligned primary care teams, at the second step, which includes secondary consultation with multidisciplinary pain medicine specialty teams and other specialists, and at the third step, with the addition of tertiary interdisciplinary pain centers.

The VA Maryland Health Care System (VAMHCS) implemented the EVP as part of a quality improvement project for the management of chronic pain. The objectives of the program were to reduce pain intensity, pain catastrophizing, and pain interference, as well as improve functionality and quality of life among veterans with chronic high-impact noncancer pain. More than 2 years after the program was implemented, collected data had not been analyzed. The purpose of this quality improvement project was to abstract and analyze the previously collected data from veterans with high-impact chronic pain who attended an EVP offered by the VAMHCS. The results of the data analysis were used to inform decisions regarding the future of the program.

Methods

This quality improvement project used the Plan-Do-Study-Act (PDSA) process.⁸ The first 2 phases of the PDSA cycle (Plan and Do) were completed by a team of VA employees from the VAMHCS, who donated their time to establish and implement the program at the project site. This team consisted of psychologists, a physical therapist, a social worker, and a chaplain, and included support from medical administrative staff. This team planned and implemented the EVP at the VA facility based on the model developed at the Atlanta VA Health Care System. During the “Do” phase, the team collected data on pain intensity, pain interference, quality of life, and pain negative cognition (catastrophizing) before the intervention and post intervention. They also collected data on program outcome (patient treatment satisfaction) post intervention. Because these employees did not have time to retrieve and analyze the data, they welcomed the opportunity to have the data analyzed by the investigators during the Study phase of the PDSA cycle. Based on the results of the analysis, recommendations for program changes were made during the Act phase of the cycle.

Intervention

The EVP was developed as a 10-week (30 hours) interdisciplinary CIH approach that coached veterans with chronic pain to live fuller lives based on their individual values and what matters to them. EVP is the “What Else” management modality for the 5% of veterans with high-impact chronic pain.⁹ The EVP provided functional restoration through its components of whole health, mindfulness training, coaching calls, acceptance and commitment therapy, and mindful movement. It used the Wheel of Health with the 4 key components of me, self-care, professional care, and community.^10,11

Veterans who had a diagnosis of chronic nonmalignant pain for 3 months or more and who agreed to participate in the EVP at this facility attended 3-hour classes every Tuesday with a cohort of 8 to 12 peers and engaged in one-on-one coaching with interdisciplinary team members. During the class sessions, veterans were coached to understand and accept their pain and commit to maintaining function despite their pain. Mindful movement by the physical therapist emphasized the pivotal place of exercise in pain management. The therapist used the mantra “Motion is Lotion.”⁹ The guiding principle of the EVP was that small incremental changes can have a big impact on the individual’s whole life. Emphasis was placed on increasing self-efficacy and mindful awareness for veterans with high-impact pain by giving them “Skills before Pills.”⁹

Outcome Measures

Outcome measures included the Numerical Pain Rating Scale (NPRS), the Multidimensional Pain Inventory (MPI), the World Health Organization Quality of Life assessment (WHOQOL-BREF), the Pain Catastrophizing Scale (PCS), and the Pain Treatment Satisfaction Scale (PTSS). Cronbach alpha coefficients were calculated to assess internal consistency reliability of these measures in the sample of veterans who completed the EVP.

NPRS. The NPRS is ubiquitous as a screening tool in many health care environments and its use is mandated by the VA health care system.¹² The choice of the NPRS as the tool for pain screening in the VA health care system was based on a large body of research that supports the reliability and validity of the NPRS as a single index of pain intensity or severity. Studies suggest that the NPRS is valid for use in the assessment of acute, cancer, or chronic nonmalignant pain and in varied clinical settings.¹³ The NPRS has 4 items, each on a scale of 0 to 10. For the purpose of this project, only 3 items were used. The 3 items assessed the worst pain, usual pain, and the current pain (right now). The higher the score, the higher the pain intensity. Cronbach alpha coefficients on the NPRS obtained from the current sample of veterans were 0.85 on both pre- and postintervention assessments.

MPI. The MPI is an easily accessible, reliable, and valid self-report questionnaire that measures the impact of pain on an individual’s life, quality of social support, and general activity.¹⁴ This instrument is a short version of the West Haven-Yale MPI.¹⁵ The MPI contains 9 items rated on a scale from 0 to 6. The higher the score, the greater pain interference a person is experiencing. The MPI produces reliable, valid information for diagnostic purposes and for therapy outcome studies.¹⁶ The MPI had a Cronbach alpha of 0.90 on pre-intervention and 0.92 on postintervention assessments in the current sample.

WHOQOL-BREF. The WHOQOL-BREF is a measure of quality of life and is an abbreviated version of the WHOQOL-100. Quality of life is defined by the World Health Organization¹⁷ “as an individuals’ perception of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards and concerns.” The WHOQOL-BREF contains 26 items. The first 2 items were examined separately; the first item asks individuals to rate their overall quality of life and the second asks individuals how satisfied they are with their health. The remaining 24 items were used to calculate the following 4 domain scores: physical health, psychological health, social relationship, and environment.¹⁸ Each item is measured on a scale of 1 to 5. Higher scores denote higher or better quality of life. Domain scores have demonstrated good reliability and validity.^19-21 Cronbach alpha coefficients for the domain subscales ranged from 0.63 to 0.84 in the current sample, with the lowest alphas for the 3-item Social Relationships Domain.

PCS. The PCS is a widely used measure of catastrophic thinking related to pain. Catastrophizing has been conceived by Sullivan and colleagues as “an exaggerated negative mental set brought to bear during actual or anticipated painful experience.”²² The PCS provides a total score and scores for the following subscales: rumination, magnification, and helplessness.²³ It has been used in a variety of chronic pain populations and has demonstrated good reliability and validity in clinical as well as nonclinical samples.^24-26 The PCS has 13 items rated on a scale of 0 to 4. Higher scores mean greater negative pain cognition (catastrophizing). In the current sample, the PCS total scale had a Cronbach alpha coefficient of 0.95 and 0.94 on the 2 assessments. The coefficients for the subscales ranged from 0.81 to 0.90.

PTSS. The PTSS is a 5-item tool that measures patient satisfaction with pain treatment. It includes items that address overall satisfaction, staff warmth, staff skill level, ease of scheduling appointments, and recommendation of the program to other veterans. It was derived from the post-treatment version of The Pain Outcome Questionnaire-VA and has demonstrated reliability and validity.²⁷ The questions are scaled from 0 to 10. High scores on the PTSS denote high patient satisfaction with the EVP. The Cronbach alpha coefficient on the PTSS obtained from the current sample was 0.80.

Data Gathering and Analysis

Prior to starting the Study phase, Washburn University’s Institutional Review Board (IRB) and the VA IRB approved the project. The VA IRB, through its affiliate, gave a Not Human Research Determination and granted a waiver of informed consent and the Health Insurance Portability and Accountability Act authorization. The VA facility’s Research and Development department also approved the quality improvement project.

Once these approvals were obtained, the Study phase began with the abstraction of retrospective data obtained from veterans who participated in the VA health care facility’s EVP between August 2017 and August 2019. Most of the measurement tools changed in August 2019, and for this reason data abstraction was limited to the time period August 2017 to August 2019. The first author (JUU) abstracted data for both program completers and noncompleters. The second (MJ) and third (SW) authors analyzed the data in SPSS 24 and calculated effect sizes.

Veterans who completed the program were compared to veterans who did not complete the program on age, gender, and baseline measures. The investigators used independent samples t-tests to compare completers and noncompleters on age, pain intensity, pain interference, quality of life, and pain catastrophizing. They used the chi-square test of independence to analyze the association between gender and program completion.

Data were included in the pre- and postintervention analysis if the veteran completed the NPRS, MPI, WHOQOL-BREF, and PCS pre and post intervention. This became an important eligibility requirement as some of the tools/measures were changed towards the end of the review period in 2019. Pre- and postintervention data on pain intensity, pain interference, quality of life, pain catastrophizing, and patient satisfaction were compared using paired samples t-test at .004 level of significance based on the Bonferroni correction.²⁸ Data on patient satisfaction with pain treatment were collected at program completion (week 8 or 10) and were analyzed using descriptive statistics.

Effect sizes (Cohen’s d) were calculated to determine the substantive significance or magnitude of the mean differences in scores. Effect sizes (expressed as absolute values of Cohen’s d) were calculated as the mean difference divided by the standard deviation. Values of 0.2 were considered a small effect size, 0.5 a medium effect size, and 0.8 a large effect size.²⁹

Data were abstracted for 115 veterans who started the EVP. Of these, 48 left the program, leaving 67 veterans (58%) who completed the program. Completers and noncompleters were similar in age, gender, and baseline measures (Table 1). Fifty-three (79%) completers and 35 (73%) noncompleters were male. A chi-square test of independence showed no significant association between gender and program completion (χ^2₁ [N = 115] = .595, P = .440).

Comparison of pre-and postintervention mean scale scores resulted in statistically significant differences for all comparisons (Table 2). These comparisons yielded improvements in the desired direction. For example, the scores on the NPRS, the MPI, and the PCS (along with its subscales) decreased, revealing reductions in pain severity, the impact of pain on the veterans’ lives, and pain catastrophizing. The 2 individual item scores on the WHOQOL-BREF increased, indicating improvements in perceived quality of life and satisfaction with health. The domain scores on the WHOQOL-BREF increased, revealing improvements in pain-related quality of life. The moderate to large effect sizes indicated clinically significant improvements for veterans with chronic high-impact pain who completed the EVP.

Analysis of data obtained using the PTSS yielded high mean scores for items that focused on patient satisfaction with treatment (Table 3). Scaled statistics yielded a mean (SD) of 46.95 (4.40). These results denoted overall patient satisfaction with the EVP.

Discussion

The purpose of this quality improvement project was to abstract and analyze previously collected data from veterans with high-impact chronic pain who attended the EVP. Comparison of pre-intervention and postintervention data obtained from 67 veterans who completed the program revealed improvements in pain intensity, pain interference, negative cognition (catastrophizing), and quality of life. The differences were statistically significant and clinically meaningful, with medium and large effect sizes. In addition, veterans reported high satisfaction with the EVP.

The EVP includes CIH approaches that have demonstrated effectiveness among veterans and other populations with chronic pain. A wealth of studies, for example, support the effectiveness of CIH approaches among veterans.^30-34 Other studies focus on specific CIH approaches that are components of the EVP. Evidence supports, for example, the efficacy of mindfulness-based stress reduction,^35-39 acceptance and commitment therapy,^40-43 brief peer support intervention,⁴⁴ and interdisciplinary biopsychosocial rehabilitation.^45,46

While empirical evidence supports components of the EVP, only one study focused on the outcomes of the Atlanta VA EVP among veterans with chronic pain. Results of a qualitative study conducted by Penney and Haro⁴⁷ described the experience of veterans with the EVP. Those veterans reported adopting new self-care or lifestyle practices for pain management and health, accepting pain, being better able to adjust and set boundaries, feeling more in control, participating in life, and changing their medication use.

The mean baseline scores from the current sample were similar to samples of patients with chronic pain in other studies (NPRS,⁴⁸ MPI,⁴⁸ and PCS^48-51). After converting scores on the WHOQOL-BREF from those that ranged from 4 to 20 to those that ranged from 0 to 100,¹⁸ the scores from the current sample were similar to those of other studies of patients with chronic pain.^48,52,53Several strengths of the project should be noted. Data were collected using well established measurement tools that had previously demonstrated reliability and validity. All the tools used in data collection demonstrated good internal consistency reliabilities in the current sample of veterans. Weaknesses of the project include the use of a convenience sample of veterans and small sample size. Data were not available on the number of veterans who were offered participation or on how many veterans declined enrollment. The sample of veterans who chose to participate in the EVP may or may not have been representative of the population of veterans with high-impact chronic pain. As a pre- and postintervention design with no comparison group, the results are subject to multiple threats to internal validity, including the Hawthorne effect, maturation in the form of healing, and attrition. Reasons for leaving the program had not been recorded, so the investigators had no way of knowing factors that may have contributed to attrition. Also, data on when veterans left the program were unavailable. Research is needed with a control group to reduce the effect of confounding variables on the outcome measures. This project used data collected at a single VA facility, which limits its generalizability.

While completers and noncompleters of the EVP were similar on age, gender, and baseline measures, there may have been unidentified characteristics that influenced program completion. The investigators noticed the presence of more missing data among noncompleters compared to completers on the pre-intervention PCS; thus, noncompleters may have scored lower than completers on this instrument simply because there were more individual items that were unanswered/missing among this group of noncompleters.

Data were analyzed using a limited number of outcome measures that had previously been collected. Other outcome measures might include whether EVP participants reduced their use of medications, clinical resources, and personnel. Future projects, for example, could determine whether the EVP is effective in reducing opioid analgesic medication use and decreasing primary care and emergency department visits. Cost-benefit analyses could be completed to determine whether EVP is associated with financial savings.

Because no follow-up assessments were made to determine whether improvements were maintained over time, the project focus was limited to an evaluation of the short-term changes in the outcome measures. Future projects could include a follow-up assessment of the veterans 1- or 2-years post completion of the EVP.

Data for the project were collected prior to the COVID-19 pandemic, when the EVP was implemented through face-to-face meetings with participants and their peers. It is not clear how changes to the delivery of the program (such as offering it through telehealth) might impact veterans’ satisfaction with the program, willingness to complete it, and other variables of interest.

The results of this project were made available to stakeholders with recommendations for program expansion both at the current location and at other VA facilities, including the recommendation to hire additional personnel that would implement the program. As the VA network of facilities expand the EVP program and adapt it for telehealth delivery, the investigators recommended a similar analysis of data be performed following telehealth delivery. If delivery through telehealth is shown to improve outcome measures, the EVP could provide pain management treatment options for patients challenged by transportation barriers, including rural veterans.

Conclusion

This quality improvement project provided evidence of improvement in measures of pain severity, pain interference, negative cognition (catastrophizing), quality of life, and patient treatment satisfaction among veterans with chronic high-impact pain. Findings have been well received by the northeastern VA as well as the Veterans Integrated Systems Network 5. The results of the analyses were used to inform decisions regarding the future of the program.

Disclaimer: This material is the result of work supported with resources and the use of facilities at the VA Maryland Health Care System, Baltimore, Maryland. The views expressed are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs or the United States Government.

Acknowledgments: The authors thank Dr. Arianna Perra, the recent past coordinator of the Empower Veterans Program (EVP), who provided initial insights and support that motivated the decision to evaluate the program. We also thank the veterans and VA EVP clinicians who contributed data for the evaluation, and Dr. Michael Saenger (Director, TelePain-EVP: EVP) and Dr. Robert Lavin for their ongoing support, care, and concern for veteran patients. We also thank Dr. Beverly Bradley and the neurology service administrative team for their guidance in the process of obtaining necessary VA approvals for this project.

Corresponding author: Jessica U. Uche, DNP, CRNP-Family; jessica.uche@va.gov

doi:10.12788/jcom.0089

From the Veterans Affairs Ann Arbor Healthcare System Medicine Service (Dr. Cusick), University of Michigan College of Pharmacy, Clinical Pharmacy Service, Michigan Medicine (Dr. Hanigan), Department of Internal Medicine Clinical Experience and Quality, Michigan Medicine (Linda Bashaw), Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI (Dr. Heidemann), and the Operational Excellence Department, Sparrow Health System, Lansing, MI (Matthew Johnson).

Abstract

Background: Diagnosis of heparin-induced thrombocytopenia (HIT) requires completion of an enzyme-linked immunosorbent assay (ELISA)–based heparin-platelet factor 4 (PF4) antibody test. If this test is negative, HIT is excluded. If positive, a serotonin-release assay (SRA) test is indicated. The SRA is expensive and sometimes inappropriately ordered despite negative PF4 results, leading to unnecessary treatment with argatroban while awaiting SRA results.

Objectives: The primary objectives of this project were to reduce unnecessary SRA testing and argatroban utilization in patients with suspected HIT.

Methods: The authors implemented an intervention at a tertiary care academic hospital in November 2017 targeting patients hospitalized with suspected HIT. The intervention was controlled at the level of the laboratory and prevented ordering of SRA tests in the absence of a positive PF4 test. The number of SRA tests performed and argatroban bags administered were identified retrospectively via chart review before the intervention (January 2016 to November 2017) and post intervention (December 2017 to March 2020). Associated costs were calculated based on institutional SRA testing cost as well as the average wholesale price of argatroban.

Results: SRA testing decreased from an average of 3.7 SRA results per 1000 admissions before the intervention to an average of 0.6 results per 1000 admissions post intervention. The number of 50-mL argatroban bags used per 1000 admissions decreased from 18.8 prior to the intervention to 14.3 post intervention. Total estimated cost savings per 1000 admissions was $2361.20.

Conclusion: An evidence-based testing strategy for HIT can be effectively implemented at the level of the laboratory. This approach led to reductions in SRA testing and argatroban utilization with resultant cost savings.

Keywords: HIT, argatroban, anticoagulation, serotonin-release assay.

Thrombocytopenia is a common finding in hospitalized patients.^1,2 Heparin-induced thrombocytopenia (HIT) is one of the many potential causes of thrombocytopenia in hospitalized patients and occurs when antibodies to the heparin-platelet factor 4 (PF4) complex develop after heparin exposure. This triggers a cascade of events, leading to platelet activation, platelet consumption, and thrombosis. While HIT is relatively rare, occurring in 0.3% to 0.5% of critically ill patients, many patients will be tested to rule out this potentially life-threatening cause of thrombocytopenia.³

The diagnosis of HIT utilizes a combination of both clinical suspicion and laboratory testing.⁴ The 4T score (Table) was developed to evaluate the clinical probability of HIT and involves assessing the degree and timing of thrombocytopenia, the presence or absence of thrombosis, and other potential causes of the thrombocytopenia.⁵ The 4T score is designed to be utilized to identify patients who require laboratory testing for HIT; however, it has low inter-rater agreement in patients undergoing evaluation for HIT,⁶ and, in our experience, completion of this scoring is time-consuming.

The enzyme-linked immunosorbent assay (ELISA) is a commonly used laboratory test to diagnose HIT that detects antibodies to the heparin-PF4 complex utilizing optical density (OD) units. When using an OD cutoff of 0.400, ELISA PF4 (PF4) tests have a sensitivity of 99.6%, but poor specificity at 69.3%.⁷ When the PF4 antibody test is positive with an OD ≥0.400, then a functional test is used to determine whether the antibodies detected will activate platelets. The serotonin-release assay (SRA) is a functional test that measures 14C-labeled serotonin release from donor platelets when mixed with patient serum or plasma containing HIT antibodies. In the correct clinical context, a positive ELISA PF4 antibody test along with a positive SRA is diagnostic of HIT.⁸

The process of diagnosing HIT in a timely and cost-effective manner is dependent on the clinician’s experience in diagnosing HIT as well as access to the laboratory testing necessary to confirm the diagnosis. PF4 antibody tests are time-consuming and not always available daily and/or are not available onsite. The SRA requires access to donor platelets and specialized radioactivity counting equipment, making it available only at particular centers.

The treatment of HIT is more straightforward and involves stopping all heparin products and starting a nonheparin anticoagulant. The direct thrombin inhibitor argatroban is one of the standard nonheparin anticoagulants used in patients with suspected HIT.⁴ While it is expensive, its short half-life and lack of renal clearance make it ideal for treatment of hospitalized patients with suspected HIT, many of whom need frequent procedures and/or have renal disease.

At our academic tertiary care center, we performed a retrospective analysis that showed inappropriate ordering of diagnostic HIT testing as well as unnecessary use of argatroban even when there was low suspicion for HIT based on laboratory findings. The aim of our project was to reduce unnecessary HIT testing and argatroban utilization without overburdening providers or interfering with established workflows.

Methods

Setting

The University of Michigan (UM) hospital is a 1000-bed tertiary care center in Ann Arbor, Michigan. The UM guidelines reflect evidence-based guidelines for the diagnosis and treatment of HIT.⁴ In 2016 the UM guidelines for laboratory testing included sending the PF4 antibody test first when there was clinical suspicion of HIT. The SRA was to be sent separately only when the PF4 returned positive (OD ≥ 0.400). Standard guidelines at UM also included switching patients with suspected HIT from heparin to a nonheparin anticoagulant and stopping all heparin products while awaiting the SRA results. The direct thrombin inhibitor argatroban is utilized at UM and monitored with anti-IIa levels. University of Michigan Hospital utilizes the Immucor PF4 IgG ELISA for detecting heparin-associated antibodies.⁹ In 2016, this PF4 test was performed in the UM onsite laboratory Monday through Friday. At UM the SRA is performed off site, with a turnaround time of 3 to 5 business days.

Baseline Data

We retrospectively reviewed PF4 and SRA testing as well as argatroban usage from December 2016 to May 2017. Despite the institutional guidelines, providers were sending PF4 and SRA simultaneously as soon as HIT was suspected; 62% of PF4 tests were ordered simultaneously with the SRA, but only 8% of these PF4 tests were positive with an OD ≥0.400. Of those patients with negative PF4 testing, argatroban was continued until the SRA returned negative, leading to many days of unnecessary argatroban usage. An informal survey of the anticoagulation pharmacists revealed that many recommended discontinuing argatroban when the PF4 test was negative, but providers routinely did not feel comfortable with this approach. This suggested many providers misunderstood the performance characteristics of the PF4 test.

Intervention

Our team consisted of hematology and internal medicine faculty, pharmacists, coagulation laboratory personnel, and quality improvement specialists. We designed and implemented an intervention in November 2017 focused on controlling the ordering of the SRA test. We chose to focus on this step due to the excellent sensitivity of the PF4 test with a cutoff of OD <0.400 and the significant expense of the SRA test. Under direction of the Coagulation Laboratory Director, a standard operating procedure was developed where the coagulation laboratory personnel did not send out the SRA until a positive PF4 test (OD ≥ 0.400) was reported. If the PF4 was negative, the SRA was canceled and the ordering provider received notification of the cancelled test via the electronic medical record, accompanied by education about HIT testing (Figure 1). In addition, the lab increased the availability of PF4 testing from 5 days to 7 days a week so there were no delays in tests ordered on Fridays or weekends.

Outcomes

Our primary goals were to decrease both SRA testing and argatroban use. Secondarily, we examined the cost-effectiveness of this intervention. We hypothesized that controlling the SRA testing at the laboratory level would decrease both SRA testing and argatroban use.

Data Collection

Pre- and postintervention data were collected retrospectively. Pre-intervention data were from January 2016 through November 2017, and postintervention data were from December 2017 through March 2020. The number of SRA tests performed were identified retrospectively via review of electronic ordering records. All patients who had a hospital admission after January 1, 2016, were included. These patients were filtered to include only those who had a result for an SRA test. In order to calculate cost-savings, we identified both the number of SRA tests ordered retrospectively as well as patients who had both an SRA resulted and had been administered argatroban. Cost-savings were calculated based on our institutional cost of $357 per SRA test.

At our institution, argatroban is supplied in 50-mL bags; therefore, we utilized the number of bags to identify argatroban usage. Savings were calculated using the average wholesale price (AWP) of $292.50 per 50-mL bag. The amounts billed or collected for the SRA testing or argatroban treatment were not collected. Costs were estimated using only direct costs to the institution. Safety data were not collected. As the intent of our project was a quality improvement activity, this project did not require institutional review board regulation per our institutional guidance.

Results

During the pre-intervention period, the average number of admissions (adults and children) at UM was 5863 per month. Post intervention there was an average of 5842 admissions per month. A total of 1192 PF4 tests were ordered before the intervention and 1148 were ordered post intervention. Prior to the intervention, 481 SRA tests were completed, while post intervention 105 were completed. Serotonin-release testing decreased from an average of 3.7 SRA results per 1000 admissions during the pre-intervention period to an average of 0.6 per 1000 admissions post intervention (Figure 2). Cost-savings were $1045 per 1000 admissions.

During the pre-intervention period, 2539 bags of argatroban were used, while 2337 bags were used post intervention. The number of 50-mL argatroban bags used per 1000 admissions decreased from 18.8 before the intervention to 14.3 post intervention. Cost-savings were $1316.20 per 1000 admissions. Figure 3 illustrates the monthly argatroban utilization per 1000 admissions during each quarter from January 2016 through March 2020.

Discussion

We designed and implemented an evidence-based strategy for HIT at our academic institution which led to a decrease in unnecessary SRA testing and argatroban utilization, with associated cost savings. By focusing on a single point of intervention at the laboratory level where SRA tests were held and canceled if the PF4 test was negative, we helped offload the decision-making from the provider while simultaneously providing just-in-time education to the provider. This intervention was designed with input from multiple stakeholders, including physicians, quality improvement specialists, pharmacists, and coagulation laboratory personnel.

Serotonin-release testing dramatically decreased post intervention even though a similar number of PF4 tests were performed before and after the intervention. This suggests that the decrease in SRA testing was a direct consequence of our intervention. Post intervention the number of completed SRA tests was 9% of the number of PF4 tests sent. This is consistent with our baseline pre-intervention data showing that only 8% of all PF4 tests sent were positive.

While the absolute number of argatroban bags utilized did not dramatically decrease after the intervention, the quarterly rate did, particularly after 2018. Given that argatroban data were only drawn from patients with a concurrent SRA test, this decrease is clearly from decreased usage in patients with suspected HIT. We suspect the decrease occurred because argatroban was not being continued while awaiting an SRA test in patients with a negative PF4 test. Decreasing the utilization of argatroban not only saved money but also reduced days of exposure to argatroban. While we do not have data regarding adverse events related to argatroban prior to the intervention, it is logical to conclude that reducing unnecessary exposure to argatroban reduces the risk of adverse events related to bleeding. Future studies would ideally address specific safety outcome metrics such as adverse events, bleeding risk, or missed diagnoses of HIT.

Our institutional guidelines for the diagnosis of HIT are evidence-based and helpful but are rarely followed by busy inpatient providers. Controlling the utilization of the SRA at the laboratory level had several advantages. First, removing SRA decision-making from providers who are not experts in the diagnosis of HIT guaranteed adherence to evidence-based guidelines. Second, pharmacists could safely recommend discontinuing argatroban when the PF4 test was negative as there was no SRA pending. Third, with cancellation at the laboratory level there was no need to further burden providers with yet another alert in the electronic health record. Fourth, just-in-time education was provided to the providers with justification for why the SRA test was canceled. Last, ruling out HIT within 24 hours with the PF4 test alone allowed providers to evaluate patients for other causes of thrombocytopenia much earlier than the 3 to 5 business days before the SRA results returned.

A limitation of this study is that it was conducted at a single center. Our approach is also limited by the lack of universal applicability. At our institution we are fortunate to have PF4 testing available in our coagulation laboratory 7 days a week. In addition, the coagulation laboratory controls sending the SRA to the reference laboratory. The specific intervention of controlling the SRA testing is therefore applicable only to institutions similar to ours; however, the concept of removing control of specialized testing from the provider is not unique. Inpatient thrombophilia testing has been a successful target of this approach.^11-13 While electronic alerts and education of individual providers can also be effective initially, the effectiveness of these interventions has been repeatedly shown to wane over time.^14-16

Conclusion

At our institution we were able to implement practical, evidence-based testing for HIT by implementing control over SRA testing at the level of the laboratory. This approach led to decreased argatroban utilization and cost savings.

Corresponding author: Alice Cusick, MD; LTC Charles S Kettles VA Medical Center, 2215 Fuller Road, Ann Arbor, MI 48105; mccoyag@med.umich.edu

Disclosures: None reported.

doi: 10.12788/jcom.0087

From the Veterans Affairs Ann Arbor Healthcare System Medicine Service (Dr. Cusick), University of Michigan College of Pharmacy, Clinical Pharmacy Service, Michigan Medicine (Dr. Hanigan), Department of Internal Medicine Clinical Experience and Quality, Michigan Medicine (Linda Bashaw), Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI (Dr. Heidemann), and the Operational Excellence Department, Sparrow Health System, Lansing, MI (Matthew Johnson).

Abstract

Background: Diagnosis of heparin-induced thrombocytopenia (HIT) requires completion of an enzyme-linked immunosorbent assay (ELISA)–based heparin-platelet factor 4 (PF4) antibody test. If this test is negative, HIT is excluded. If positive, a serotonin-release assay (SRA) test is indicated. The SRA is expensive and sometimes inappropriately ordered despite negative PF4 results, leading to unnecessary treatment with argatroban while awaiting SRA results.

Objectives: The primary objectives of this project were to reduce unnecessary SRA testing and argatroban utilization in patients with suspected HIT.

Methods: The authors implemented an intervention at a tertiary care academic hospital in November 2017 targeting patients hospitalized with suspected HIT. The intervention was controlled at the level of the laboratory and prevented ordering of SRA tests in the absence of a positive PF4 test. The number of SRA tests performed and argatroban bags administered were identified retrospectively via chart review before the intervention (January 2016 to November 2017) and post intervention (December 2017 to March 2020). Associated costs were calculated based on institutional SRA testing cost as well as the average wholesale price of argatroban.

Results: SRA testing decreased from an average of 3.7 SRA results per 1000 admissions before the intervention to an average of 0.6 results per 1000 admissions post intervention. The number of 50-mL argatroban bags used per 1000 admissions decreased from 18.8 prior to the intervention to 14.3 post intervention. Total estimated cost savings per 1000 admissions was $2361.20.

Conclusion: An evidence-based testing strategy for HIT can be effectively implemented at the level of the laboratory. This approach led to reductions in SRA testing and argatroban utilization with resultant cost savings.

Keywords: HIT, argatroban, anticoagulation, serotonin-release assay.

Thrombocytopenia is a common finding in hospitalized patients.^1,2 Heparin-induced thrombocytopenia (HIT) is one of the many potential causes of thrombocytopenia in hospitalized patients and occurs when antibodies to the heparin-platelet factor 4 (PF4) complex develop after heparin exposure. This triggers a cascade of events, leading to platelet activation, platelet consumption, and thrombosis. While HIT is relatively rare, occurring in 0.3% to 0.5% of critically ill patients, many patients will be tested to rule out this potentially life-threatening cause of thrombocytopenia.³

The diagnosis of HIT utilizes a combination of both clinical suspicion and laboratory testing.⁴ The 4T score (Table) was developed to evaluate the clinical probability of HIT and involves assessing the degree and timing of thrombocytopenia, the presence or absence of thrombosis, and other potential causes of the thrombocytopenia.⁵ The 4T score is designed to be utilized to identify patients who require laboratory testing for HIT; however, it has low inter-rater agreement in patients undergoing evaluation for HIT,⁶ and, in our experience, completion of this scoring is time-consuming.

The enzyme-linked immunosorbent assay (ELISA) is a commonly used laboratory test to diagnose HIT that detects antibodies to the heparin-PF4 complex utilizing optical density (OD) units. When using an OD cutoff of 0.400, ELISA PF4 (PF4) tests have a sensitivity of 99.6%, but poor specificity at 69.3%.⁷ When the PF4 antibody test is positive with an OD ≥0.400, then a functional test is used to determine whether the antibodies detected will activate platelets. The serotonin-release assay (SRA) is a functional test that measures 14C-labeled serotonin release from donor platelets when mixed with patient serum or plasma containing HIT antibodies. In the correct clinical context, a positive ELISA PF4 antibody test along with a positive SRA is diagnostic of HIT.⁸

The process of diagnosing HIT in a timely and cost-effective manner is dependent on the clinician’s experience in diagnosing HIT as well as access to the laboratory testing necessary to confirm the diagnosis. PF4 antibody tests are time-consuming and not always available daily and/or are not available onsite. The SRA requires access to donor platelets and specialized radioactivity counting equipment, making it available only at particular centers.

The treatment of HIT is more straightforward and involves stopping all heparin products and starting a nonheparin anticoagulant. The direct thrombin inhibitor argatroban is one of the standard nonheparin anticoagulants used in patients with suspected HIT.⁴ While it is expensive, its short half-life and lack of renal clearance make it ideal for treatment of hospitalized patients with suspected HIT, many of whom need frequent procedures and/or have renal disease.

At our academic tertiary care center, we performed a retrospective analysis that showed inappropriate ordering of diagnostic HIT testing as well as unnecessary use of argatroban even when there was low suspicion for HIT based on laboratory findings. The aim of our project was to reduce unnecessary HIT testing and argatroban utilization without overburdening providers or interfering with established workflows.

Methods

Setting

The University of Michigan (UM) hospital is a 1000-bed tertiary care center in Ann Arbor, Michigan. The UM guidelines reflect evidence-based guidelines for the diagnosis and treatment of HIT.⁴ In 2016 the UM guidelines for laboratory testing included sending the PF4 antibody test first when there was clinical suspicion of HIT. The SRA was to be sent separately only when the PF4 returned positive (OD ≥ 0.400). Standard guidelines at UM also included switching patients with suspected HIT from heparin to a nonheparin anticoagulant and stopping all heparin products while awaiting the SRA results. The direct thrombin inhibitor argatroban is utilized at UM and monitored with anti-IIa levels. University of Michigan Hospital utilizes the Immucor PF4 IgG ELISA for detecting heparin-associated antibodies.⁹ In 2016, this PF4 test was performed in the UM onsite laboratory Monday through Friday. At UM the SRA is performed off site, with a turnaround time of 3 to 5 business days.

Baseline Data

We retrospectively reviewed PF4 and SRA testing as well as argatroban usage from December 2016 to May 2017. Despite the institutional guidelines, providers were sending PF4 and SRA simultaneously as soon as HIT was suspected; 62% of PF4 tests were ordered simultaneously with the SRA, but only 8% of these PF4 tests were positive with an OD ≥0.400. Of those patients with negative PF4 testing, argatroban was continued until the SRA returned negative, leading to many days of unnecessary argatroban usage. An informal survey of the anticoagulation pharmacists revealed that many recommended discontinuing argatroban when the PF4 test was negative, but providers routinely did not feel comfortable with this approach. This suggested many providers misunderstood the performance characteristics of the PF4 test.

Intervention

Our team consisted of hematology and internal medicine faculty, pharmacists, coagulation laboratory personnel, and quality improvement specialists. We designed and implemented an intervention in November 2017 focused on controlling the ordering of the SRA test. We chose to focus on this step due to the excellent sensitivity of the PF4 test with a cutoff of OD <0.400 and the significant expense of the SRA test. Under direction of the Coagulation Laboratory Director, a standard operating procedure was developed where the coagulation laboratory personnel did not send out the SRA until a positive PF4 test (OD ≥ 0.400) was reported. If the PF4 was negative, the SRA was canceled and the ordering provider received notification of the cancelled test via the electronic medical record, accompanied by education about HIT testing (Figure 1). In addition, the lab increased the availability of PF4 testing from 5 days to 7 days a week so there were no delays in tests ordered on Fridays or weekends.

Outcomes

Our primary goals were to decrease both SRA testing and argatroban use. Secondarily, we examined the cost-effectiveness of this intervention. We hypothesized that controlling the SRA testing at the laboratory level would decrease both SRA testing and argatroban use.

Data Collection

Pre- and postintervention data were collected retrospectively. Pre-intervention data were from January 2016 through November 2017, and postintervention data were from December 2017 through March 2020. The number of SRA tests performed were identified retrospectively via review of electronic ordering records. All patients who had a hospital admission after January 1, 2016, were included. These patients were filtered to include only those who had a result for an SRA test. In order to calculate cost-savings, we identified both the number of SRA tests ordered retrospectively as well as patients who had both an SRA resulted and had been administered argatroban. Cost-savings were calculated based on our institutional cost of $357 per SRA test.

At our institution, argatroban is supplied in 50-mL bags; therefore, we utilized the number of bags to identify argatroban usage. Savings were calculated using the average wholesale price (AWP) of $292.50 per 50-mL bag. The amounts billed or collected for the SRA testing or argatroban treatment were not collected. Costs were estimated using only direct costs to the institution. Safety data were not collected. As the intent of our project was a quality improvement activity, this project did not require institutional review board regulation per our institutional guidance.

Results

During the pre-intervention period, the average number of admissions (adults and children) at UM was 5863 per month. Post intervention there was an average of 5842 admissions per month. A total of 1192 PF4 tests were ordered before the intervention and 1148 were ordered post intervention. Prior to the intervention, 481 SRA tests were completed, while post intervention 105 were completed. Serotonin-release testing decreased from an average of 3.7 SRA results per 1000 admissions during the pre-intervention period to an average of 0.6 per 1000 admissions post intervention (Figure 2). Cost-savings were $1045 per 1000 admissions.

During the pre-intervention period, 2539 bags of argatroban were used, while 2337 bags were used post intervention. The number of 50-mL argatroban bags used per 1000 admissions decreased from 18.8 before the intervention to 14.3 post intervention. Cost-savings were $1316.20 per 1000 admissions. Figure 3 illustrates the monthly argatroban utilization per 1000 admissions during each quarter from January 2016 through March 2020.

Discussion

We designed and implemented an evidence-based strategy for HIT at our academic institution which led to a decrease in unnecessary SRA testing and argatroban utilization, with associated cost savings. By focusing on a single point of intervention at the laboratory level where SRA tests were held and canceled if the PF4 test was negative, we helped offload the decision-making from the provider while simultaneously providing just-in-time education to the provider. This intervention was designed with input from multiple stakeholders, including physicians, quality improvement specialists, pharmacists, and coagulation laboratory personnel.

Serotonin-release testing dramatically decreased post intervention even though a similar number of PF4 tests were performed before and after the intervention. This suggests that the decrease in SRA testing was a direct consequence of our intervention. Post intervention the number of completed SRA tests was 9% of the number of PF4 tests sent. This is consistent with our baseline pre-intervention data showing that only 8% of all PF4 tests sent were positive.

While the absolute number of argatroban bags utilized did not dramatically decrease after the intervention, the quarterly rate did, particularly after 2018. Given that argatroban data were only drawn from patients with a concurrent SRA test, this decrease is clearly from decreased usage in patients with suspected HIT. We suspect the decrease occurred because argatroban was not being continued while awaiting an SRA test in patients with a negative PF4 test. Decreasing the utilization of argatroban not only saved money but also reduced days of exposure to argatroban. While we do not have data regarding adverse events related to argatroban prior to the intervention, it is logical to conclude that reducing unnecessary exposure to argatroban reduces the risk of adverse events related to bleeding. Future studies would ideally address specific safety outcome metrics such as adverse events, bleeding risk, or missed diagnoses of HIT.

Our institutional guidelines for the diagnosis of HIT are evidence-based and helpful but are rarely followed by busy inpatient providers. Controlling the utilization of the SRA at the laboratory level had several advantages. First, removing SRA decision-making from providers who are not experts in the diagnosis of HIT guaranteed adherence to evidence-based guidelines. Second, pharmacists could safely recommend discontinuing argatroban when the PF4 test was negative as there was no SRA pending. Third, with cancellation at the laboratory level there was no need to further burden providers with yet another alert in the electronic health record. Fourth, just-in-time education was provided to the providers with justification for why the SRA test was canceled. Last, ruling out HIT within 24 hours with the PF4 test alone allowed providers to evaluate patients for other causes of thrombocytopenia much earlier than the 3 to 5 business days before the SRA results returned.

A limitation of this study is that it was conducted at a single center. Our approach is also limited by the lack of universal applicability. At our institution we are fortunate to have PF4 testing available in our coagulation laboratory 7 days a week. In addition, the coagulation laboratory controls sending the SRA to the reference laboratory. The specific intervention of controlling the SRA testing is therefore applicable only to institutions similar to ours; however, the concept of removing control of specialized testing from the provider is not unique. Inpatient thrombophilia testing has been a successful target of this approach.^11-13 While electronic alerts and education of individual providers can also be effective initially, the effectiveness of these interventions has been repeatedly shown to wane over time.^14-16

Conclusion

At our institution we were able to implement practical, evidence-based testing for HIT by implementing control over SRA testing at the level of the laboratory. This approach led to decreased argatroban utilization and cost savings.

Corresponding author: Alice Cusick, MD; LTC Charles S Kettles VA Medical Center, 2215 Fuller Road, Ann Arbor, MI 48105; mccoyag@med.umich.edu

Disclosures: None reported.

doi: 10.12788/jcom.0087

From the Veterans Affairs Ann Arbor Healthcare System Medicine Service (Dr. Cusick), University of Michigan College of Pharmacy, Clinical Pharmacy Service, Michigan Medicine (Dr. Hanigan), Department of Internal Medicine Clinical Experience and Quality, Michigan Medicine (Linda Bashaw), Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI (Dr. Heidemann), and the Operational Excellence Department, Sparrow Health System, Lansing, MI (Matthew Johnson).

Abstract

Background: Diagnosis of heparin-induced thrombocytopenia (HIT) requires completion of an enzyme-linked immunosorbent assay (ELISA)–based heparin-platelet factor 4 (PF4) antibody test. If this test is negative, HIT is excluded. If positive, a serotonin-release assay (SRA) test is indicated. The SRA is expensive and sometimes inappropriately ordered despite negative PF4 results, leading to unnecessary treatment with argatroban while awaiting SRA results.

Objectives: The primary objectives of this project were to reduce unnecessary SRA testing and argatroban utilization in patients with suspected HIT.

Methods: The authors implemented an intervention at a tertiary care academic hospital in November 2017 targeting patients hospitalized with suspected HIT. The intervention was controlled at the level of the laboratory and prevented ordering of SRA tests in the absence of a positive PF4 test. The number of SRA tests performed and argatroban bags administered were identified retrospectively via chart review before the intervention (January 2016 to November 2017) and post intervention (December 2017 to March 2020). Associated costs were calculated based on institutional SRA testing cost as well as the average wholesale price of argatroban.

Results: SRA testing decreased from an average of 3.7 SRA results per 1000 admissions before the intervention to an average of 0.6 results per 1000 admissions post intervention. The number of 50-mL argatroban bags used per 1000 admissions decreased from 18.8 prior to the intervention to 14.3 post intervention. Total estimated cost savings per 1000 admissions was $2361.20.

Conclusion: An evidence-based testing strategy for HIT can be effectively implemented at the level of the laboratory. This approach led to reductions in SRA testing and argatroban utilization with resultant cost savings.

Keywords: HIT, argatroban, anticoagulation, serotonin-release assay.

Thrombocytopenia is a common finding in hospitalized patients.^1,2 Heparin-induced thrombocytopenia (HIT) is one of the many potential causes of thrombocytopenia in hospitalized patients and occurs when antibodies to the heparin-platelet factor 4 (PF4) complex develop after heparin exposure. This triggers a cascade of events, leading to platelet activation, platelet consumption, and thrombosis. While HIT is relatively rare, occurring in 0.3% to 0.5% of critically ill patients, many patients will be tested to rule out this potentially life-threatening cause of thrombocytopenia.³

The diagnosis of HIT utilizes a combination of both clinical suspicion and laboratory testing.⁴ The 4T score (Table) was developed to evaluate the clinical probability of HIT and involves assessing the degree and timing of thrombocytopenia, the presence or absence of thrombosis, and other potential causes of the thrombocytopenia.⁵ The 4T score is designed to be utilized to identify patients who require laboratory testing for HIT; however, it has low inter-rater agreement in patients undergoing evaluation for HIT,⁶ and, in our experience, completion of this scoring is time-consuming.

The enzyme-linked immunosorbent assay (ELISA) is a commonly used laboratory test to diagnose HIT that detects antibodies to the heparin-PF4 complex utilizing optical density (OD) units. When using an OD cutoff of 0.400, ELISA PF4 (PF4) tests have a sensitivity of 99.6%, but poor specificity at 69.3%.⁷ When the PF4 antibody test is positive with an OD ≥0.400, then a functional test is used to determine whether the antibodies detected will activate platelets. The serotonin-release assay (SRA) is a functional test that measures 14C-labeled serotonin release from donor platelets when mixed with patient serum or plasma containing HIT antibodies. In the correct clinical context, a positive ELISA PF4 antibody test along with a positive SRA is diagnostic of HIT.⁸

The process of diagnosing HIT in a timely and cost-effective manner is dependent on the clinician’s experience in diagnosing HIT as well as access to the laboratory testing necessary to confirm the diagnosis. PF4 antibody tests are time-consuming and not always available daily and/or are not available onsite. The SRA requires access to donor platelets and specialized radioactivity counting equipment, making it available only at particular centers.

The treatment of HIT is more straightforward and involves stopping all heparin products and starting a nonheparin anticoagulant. The direct thrombin inhibitor argatroban is one of the standard nonheparin anticoagulants used in patients with suspected HIT.⁴ While it is expensive, its short half-life and lack of renal clearance make it ideal for treatment of hospitalized patients with suspected HIT, many of whom need frequent procedures and/or have renal disease.

At our academic tertiary care center, we performed a retrospective analysis that showed inappropriate ordering of diagnostic HIT testing as well as unnecessary use of argatroban even when there was low suspicion for HIT based on laboratory findings. The aim of our project was to reduce unnecessary HIT testing and argatroban utilization without overburdening providers or interfering with established workflows.

Methods

Setting

The University of Michigan (UM) hospital is a 1000-bed tertiary care center in Ann Arbor, Michigan. The UM guidelines reflect evidence-based guidelines for the diagnosis and treatment of HIT.⁴ In 2016 the UM guidelines for laboratory testing included sending the PF4 antibody test first when there was clinical suspicion of HIT. The SRA was to be sent separately only when the PF4 returned positive (OD ≥ 0.400). Standard guidelines at UM also included switching patients with suspected HIT from heparin to a nonheparin anticoagulant and stopping all heparin products while awaiting the SRA results. The direct thrombin inhibitor argatroban is utilized at UM and monitored with anti-IIa levels. University of Michigan Hospital utilizes the Immucor PF4 IgG ELISA for detecting heparin-associated antibodies.⁹ In 2016, this PF4 test was performed in the UM onsite laboratory Monday through Friday. At UM the SRA is performed off site, with a turnaround time of 3 to 5 business days.

Baseline Data

We retrospectively reviewed PF4 and SRA testing as well as argatroban usage from December 2016 to May 2017. Despite the institutional guidelines, providers were sending PF4 and SRA simultaneously as soon as HIT was suspected; 62% of PF4 tests were ordered simultaneously with the SRA, but only 8% of these PF4 tests were positive with an OD ≥0.400. Of those patients with negative PF4 testing, argatroban was continued until the SRA returned negative, leading to many days of unnecessary argatroban usage. An informal survey of the anticoagulation pharmacists revealed that many recommended discontinuing argatroban when the PF4 test was negative, but providers routinely did not feel comfortable with this approach. This suggested many providers misunderstood the performance characteristics of the PF4 test.

Intervention

Our team consisted of hematology and internal medicine faculty, pharmacists, coagulation laboratory personnel, and quality improvement specialists. We designed and implemented an intervention in November 2017 focused on controlling the ordering of the SRA test. We chose to focus on this step due to the excellent sensitivity of the PF4 test with a cutoff of OD <0.400 and the significant expense of the SRA test. Under direction of the Coagulation Laboratory Director, a standard operating procedure was developed where the coagulation laboratory personnel did not send out the SRA until a positive PF4 test (OD ≥ 0.400) was reported. If the PF4 was negative, the SRA was canceled and the ordering provider received notification of the cancelled test via the electronic medical record, accompanied by education about HIT testing (Figure 1). In addition, the lab increased the availability of PF4 testing from 5 days to 7 days a week so there were no delays in tests ordered on Fridays or weekends.

Outcomes

Our primary goals were to decrease both SRA testing and argatroban use. Secondarily, we examined the cost-effectiveness of this intervention. We hypothesized that controlling the SRA testing at the laboratory level would decrease both SRA testing and argatroban use.

Data Collection

Pre- and postintervention data were collected retrospectively. Pre-intervention data were from January 2016 through November 2017, and postintervention data were from December 2017 through March 2020. The number of SRA tests performed were identified retrospectively via review of electronic ordering records. All patients who had a hospital admission after January 1, 2016, were included. These patients were filtered to include only those who had a result for an SRA test. In order to calculate cost-savings, we identified both the number of SRA tests ordered retrospectively as well as patients who had both an SRA resulted and had been administered argatroban. Cost-savings were calculated based on our institutional cost of $357 per SRA test.

At our institution, argatroban is supplied in 50-mL bags; therefore, we utilized the number of bags to identify argatroban usage. Savings were calculated using the average wholesale price (AWP) of $292.50 per 50-mL bag. The amounts billed or collected for the SRA testing or argatroban treatment were not collected. Costs were estimated using only direct costs to the institution. Safety data were not collected. As the intent of our project was a quality improvement activity, this project did not require institutional review board regulation per our institutional guidance.

Results

During the pre-intervention period, the average number of admissions (adults and children) at UM was 5863 per month. Post intervention there was an average of 5842 admissions per month. A total of 1192 PF4 tests were ordered before the intervention and 1148 were ordered post intervention. Prior to the intervention, 481 SRA tests were completed, while post intervention 105 were completed. Serotonin-release testing decreased from an average of 3.7 SRA results per 1000 admissions during the pre-intervention period to an average of 0.6 per 1000 admissions post intervention (Figure 2). Cost-savings were $1045 per 1000 admissions.

During the pre-intervention period, 2539 bags of argatroban were used, while 2337 bags were used post intervention. The number of 50-mL argatroban bags used per 1000 admissions decreased from 18.8 before the intervention to 14.3 post intervention. Cost-savings were $1316.20 per 1000 admissions. Figure 3 illustrates the monthly argatroban utilization per 1000 admissions during each quarter from January 2016 through March 2020.

Discussion

We designed and implemented an evidence-based strategy for HIT at our academic institution which led to a decrease in unnecessary SRA testing and argatroban utilization, with associated cost savings. By focusing on a single point of intervention at the laboratory level where SRA tests were held and canceled if the PF4 test was negative, we helped offload the decision-making from the provider while simultaneously providing just-in-time education to the provider. This intervention was designed with input from multiple stakeholders, including physicians, quality improvement specialists, pharmacists, and coagulation laboratory personnel.

Serotonin-release testing dramatically decreased post intervention even though a similar number of PF4 tests were performed before and after the intervention. This suggests that the decrease in SRA testing was a direct consequence of our intervention. Post intervention the number of completed SRA tests was 9% of the number of PF4 tests sent. This is consistent with our baseline pre-intervention data showing that only 8% of all PF4 tests sent were positive.

While the absolute number of argatroban bags utilized did not dramatically decrease after the intervention, the quarterly rate did, particularly after 2018. Given that argatroban data were only drawn from patients with a concurrent SRA test, this decrease is clearly from decreased usage in patients with suspected HIT. We suspect the decrease occurred because argatroban was not being continued while awaiting an SRA test in patients with a negative PF4 test. Decreasing the utilization of argatroban not only saved money but also reduced days of exposure to argatroban. While we do not have data regarding adverse events related to argatroban prior to the intervention, it is logical to conclude that reducing unnecessary exposure to argatroban reduces the risk of adverse events related to bleeding. Future studies would ideally address specific safety outcome metrics such as adverse events, bleeding risk, or missed diagnoses of HIT.

Our institutional guidelines for the diagnosis of HIT are evidence-based and helpful but are rarely followed by busy inpatient providers. Controlling the utilization of the SRA at the laboratory level had several advantages. First, removing SRA decision-making from providers who are not experts in the diagnosis of HIT guaranteed adherence to evidence-based guidelines. Second, pharmacists could safely recommend discontinuing argatroban when the PF4 test was negative as there was no SRA pending. Third, with cancellation at the laboratory level there was no need to further burden providers with yet another alert in the electronic health record. Fourth, just-in-time education was provided to the providers with justification for why the SRA test was canceled. Last, ruling out HIT within 24 hours with the PF4 test alone allowed providers to evaluate patients for other causes of thrombocytopenia much earlier than the 3 to 5 business days before the SRA results returned.

A limitation of this study is that it was conducted at a single center. Our approach is also limited by the lack of universal applicability. At our institution we are fortunate to have PF4 testing available in our coagulation laboratory 7 days a week. In addition, the coagulation laboratory controls sending the SRA to the reference laboratory. The specific intervention of controlling the SRA testing is therefore applicable only to institutions similar to ours; however, the concept of removing control of specialized testing from the provider is not unique. Inpatient thrombophilia testing has been a successful target of this approach.^11-13 While electronic alerts and education of individual providers can also be effective initially, the effectiveness of these interventions has been repeatedly shown to wane over time.^14-16

Conclusion

At our institution we were able to implement practical, evidence-based testing for HIT by implementing control over SRA testing at the level of the laboratory. This approach led to decreased argatroban utilization and cost savings.

Corresponding author: Alice Cusick, MD; LTC Charles S Kettles VA Medical Center, 2215 Fuller Road, Ann Arbor, MI 48105; mccoyag@med.umich.edu

Disclosures: None reported.

doi: 10.12788/jcom.0087

From Hassenfeld Children’s Hospital at NYU Langone Health, New York, NY (Dr Gallagher), T1D Exchange, Boston, MA (Saketh Rompicherla; Drs Ebekozien, Noor, Odugbesan, and Mungmode; Nicole Rioles, Emma Ospelt), University of Mississippi School of Population Health, Jackson, MS (Dr. Ebekozien), Icahn School of Medicine at Mount Sinai, New York, NY (Drs. Wilkes, O’Malley, and Rapaport), Weill Cornell Medicine, New York, NY (Drs. Antal and Feuer), NYU Long Island School of Medicine, Mineola, NY (Dr. Gabriel), NYU Langone Health, New York, NY (Dr. Golden), Barbara Davis Center, Aurora, CO (Dr. Alonso), Texas Children’s Hospital/Baylor College of Medicine, Houston, TX (Dr. Lyons), Stanford University, Stanford, CA (Dr. Prahalad), Children Mercy Kansas City, MO (Dr. Clements), Indiana University School of Medicine, IN (Dr. Neyman), Rady Children’s Hospital, University of California, San Diego, CA (Dr. Demeterco-Berggren).

Background: Patient outcomes of COVID-19 have improved throughout the pandemic. However, because it is not known whether outcomes of COVID-19 in the type 1 diabetes (T1D) population improved over time, we investigated differences in COVID-19 outcomes for patients with T1D in the United States.

Methods: We analyzed data collected via a registry of patients with T1D and COVID-19 from 56 sites between April 2020 and January 2021. We grouped cases into first surge (April 9, 2020, to July 31, 2020, n = 188) and late surge (August 1, 2020, to January 31, 2021, n = 410), and then compared outcomes between both groups using descriptive statistics and logistic regression models.

Results: Adverse outcomes were more frequent during the first surge, including diabetic ketoacidosis (32% vs 15%, P < .001), severe hypoglycemia (4% vs 1%, P = .04), and hospitalization (52% vs 22%, P < .001). Patients in the first surge were older (28 [SD,18.8] years vs 18.0 [SD, 11.1] years, P < .001), had higher median hemoglobin A1c levels (9.3 [interquartile range {IQR}, 4.0] vs 8.4 (IQR, 2.8), P < .001), and were more likely to use public insurance (107 [57%] vs 154 [38%], P < .001). The odds of hospitalization for adults in the first surge were 5 times higher compared to the late surge (odds ratio, 5.01; 95% CI, 2.11-12.63).

Conclusion: Patients with T1D who presented with COVID-19 during the first surge had a higher proportion of adverse outcomes than those who presented in a later surge.

Keywords: TD1, diabetic ketoacidosis, hypoglycemia.

After the World Health Organization declared the disease caused by the novel coronavirus SARS-CoV-2, COVID-19, a pandemic on March 11, 2020, the Centers for Disease Control and Prevention identified patients with diabetes as high risk for severe illness.^1-7 The case-fatality rate for COVID-19 has significantly improved over the past 2 years. Public health measures, less severe COVID-19 variants, increased access to testing, and new treatments for COVID-19 have contributed to improved outcomes.

The T1D Exchange has previously published findings on COVID-19 outcomes for patients with type 1 diabetes (T1D) using data from the T1D COVID-19 Surveillance Registry.^8-12 Given improved outcomes in COVID-19 in the general population, we sought to determine if outcomes for cases of COVID-19 reported to this registry changed over time.

Methods

This study was coordinated by the T1D Exchange and approved as nonhuman subject research by the Western Institutional Review Board. All participating centers also obtained local institutional review board approval. No identifiable patient information was collected as part of this noninterventional, cross-sectional study.

The T1D Exchange Multi-center COVID-19 Surveillance Study collected data from endocrinology clinics that completed a retrospective chart review and submitted information to T1D Exchange via an online questionnaire for all patients with T1D at their sites who tested positive for COVID-19.^13,14 The questionnaire was administered using the Qualtrics survey platform (www.qualtrics.com version XM) and contained 33 pre-coded and free-text response fields to collect patient and clinical attributes.

Each participating center identified 1 team member for reporting to avoid duplicate case submission. Each submitted case was reviewed for potential errors and incomplete information. The coordinating center verified the number of cases per site for data quality assurance.

Quantitative data were represented as mean (standard deviation) or median (interquartile range). Categorical data were described as the number (percentage) of patients. Summary statistics, including frequency and percentage for categorical variables, were calculated for all patient-related and clinical characteristics. The date August 1, 2021, was selected as the end of the first surge based on a review of national COVID-19 surges.

We used the Fisher’s exact test to assess associations between hospitalization and demographics, HbA1c, diabetes duration, symptoms, and adverse outcomes. In addition, multivariate logistic regression was used to calculate odds ratios (OR). Logistic regression models were used to determine the association between time of surge and hospitalization separately for both the pediatric and adult populations. Each model was adjusted for potential sociodemographic confounders, specifically age, sex, race, insurance, and HbA1c.

All tests were 2-sided, with type 1 error set at 5%. Fisher’s exact test and logistic regression were performed using statistical software R, version 3.6.2 (R Foundation for Statistical Computing).

Results

The characteristics of COVID-19 cases in patients with T1D that were reported early in the pandemic, before August 1, 2020 (first surge), compared with those of cases reported on and after August 1, 2020 (later surges) are shown in Table 1.

Patients with T1D who presented with COVID-19 during the first surge as compared to the later surges were older (mean age 28 [SD, 18.0] years vs 18.8 [SD, 11.1] years; P < .001) and had a longer duration of diabetes (P < .001). The first-surge group also had more patients with >20 years’ diabetes duration (20% vs 9%, P < .001). Obesity, hypertension, and chronic kidney disease were also more commonly reported in first-surge cases (all P < .001).

There was a significant difference in race and ethnicity reported in the first surge vs the later surge cases, with fewer patients identifying as non-Hispanic White (39% vs, 63%, P < .001) and more patients identifying as non-Hispanic Black (29% vs 12%, P < .001). The groups also differed significantly in terms of insurance type, with more people on public insurance in the first-surge group (57% vs 38%, P < .001). In addition, median HbA1c was higher (9.3% vs 8.4%, P < .001) and continuous glucose monitor and insulin pump use were less common (P = .02 and <.001, respectively) in the early surge.

All symptoms and adverse outcomes were reported more often in the first surge, including diabetic ketoacidosis (DKA; 32% vs 15%; P < .001) and severe hypoglycemia (4% vs 1%, P = .04). Hospitalization (52% vs 13%, P < .001) and ICU admission (24% vs 9%, P < .001) were reported more often in the first-surge group.

Regression Analyses

Table 2 shows the results of logistic regression analyses for hospitalization in the pediatric (≤19 years of age) and adult (>19 years of age) groups, along with the odds of hospitalization during the first vs late surge among COVID-positive people with T1D. Adult patients who tested positive in the first surge were about 5 times more likely to be hospitalized than adults who tested positive for infection in the late surge after adjusting for age, insurance type, sex, race, and HbA1c levels. Pediatric patients also had an increased odds for hospitalization during the first surge, but this increase was not statistically significant.

Discussion

Our analysis of COVID-19 cases in patients with T1D reported by diabetes providers across the United States found that adverse outcomes were more prevalent early in the pandemic. There may be a number of reasons for this difference in outcomes between patients who presented in the first surge vs a later surge. First, because testing for COVID-19 was extremely limited and reserved for hospitalized patients early in the pandemic, the first-surge patients with confirmed COVID-19 likely represent a skewed population of higher-acuity patients. This may also explain the relative paucity of cases in younger patients reported early in the pandemic. Second, worse outcomes in the early surge may also have been associated with overwhelmed hospitals in New York City at the start of the outbreak. According to Cummings et al, the abrupt surge of critically ill patients hospitalized with severe acute respiratory distress syndrome initially outpaced their capacity to provide prone-positioning ventilation, which has been expanded since then.¹⁵ While there was very little hypertension, cardiovascular disease, or kidney disease reported in the pediatric groups, there was a higher prevalence of obesity in the pediatric group from the mid-Atlantic region. Obesity has been associated with a worse prognosis for COVID-19 illness in children.¹⁶ Finally, there were 5 deaths reported in this study, all of which were reported during the first surge. Older age and increased rates of cardiovascular disease and chronic kidney disease in the first surge cases likely contributed to worse outcomes for adults in mid-Atlantic region relative to the other regions. Minority race and the use of public insurance, risk factors for more severe outcomes in all regions, were also more common in cases reported from the mid-Atlantic region.

This study has several limitations. First, it is a cross-sectional study that relies upon voluntary provider reports. Second, availability of COVID-19 testing was limited in all regions in spring 2020. Third, different regions of the country experienced subsequent surges at different times within the reported timeframes in this analysis. Fourth, this report time period does not include the impact of the newer COVID-19 variants. Finally, trends in COVID-19 outcomes were affected by the evolution of care that developed throughout 2020.

Conclusion

Adult patients with T1D and COVID-19 who reported during the first surge had about 5 times higher hospitalization odds than those who presented in a later surge.

Corresponding author: Osagie Ebekozien, MD, MPH, 11 Avenue de Lafayette, Boston, MA 02111; oebekozien@t1dexchange.org

Disclosures: Dr Ebekozien reports receiving research grants from Medtronic Diabetes, Eli Lilly, and Dexcom, and receiving honoraria from Medtronic Diabetes.

From Hassenfeld Children’s Hospital at NYU Langone Health, New York, NY (Dr Gallagher), T1D Exchange, Boston, MA (Saketh Rompicherla; Drs Ebekozien, Noor, Odugbesan, and Mungmode; Nicole Rioles, Emma Ospelt), University of Mississippi School of Population Health, Jackson, MS (Dr. Ebekozien), Icahn School of Medicine at Mount Sinai, New York, NY (Drs. Wilkes, O’Malley, and Rapaport), Weill Cornell Medicine, New York, NY (Drs. Antal and Feuer), NYU Long Island School of Medicine, Mineola, NY (Dr. Gabriel), NYU Langone Health, New York, NY (Dr. Golden), Barbara Davis Center, Aurora, CO (Dr. Alonso), Texas Children’s Hospital/Baylor College of Medicine, Houston, TX (Dr. Lyons), Stanford University, Stanford, CA (Dr. Prahalad), Children Mercy Kansas City, MO (Dr. Clements), Indiana University School of Medicine, IN (Dr. Neyman), Rady Children’s Hospital, University of California, San Diego, CA (Dr. Demeterco-Berggren).

Background: Patient outcomes of COVID-19 have improved throughout the pandemic. However, because it is not known whether outcomes of COVID-19 in the type 1 diabetes (T1D) population improved over time, we investigated differences in COVID-19 outcomes for patients with T1D in the United States.

Methods: We analyzed data collected via a registry of patients with T1D and COVID-19 from 56 sites between April 2020 and January 2021. We grouped cases into first surge (April 9, 2020, to July 31, 2020, n = 188) and late surge (August 1, 2020, to January 31, 2021, n = 410), and then compared outcomes between both groups using descriptive statistics and logistic regression models.

Results: Adverse outcomes were more frequent during the first surge, including diabetic ketoacidosis (32% vs 15%, P < .001), severe hypoglycemia (4% vs 1%, P = .04), and hospitalization (52% vs 22%, P < .001). Patients in the first surge were older (28 [SD,18.8] years vs 18.0 [SD, 11.1] years, P < .001), had higher median hemoglobin A1c levels (9.3 [interquartile range {IQR}, 4.0] vs 8.4 (IQR, 2.8), P < .001), and were more likely to use public insurance (107 [57%] vs 154 [38%], P < .001). The odds of hospitalization for adults in the first surge were 5 times higher compared to the late surge (odds ratio, 5.01; 95% CI, 2.11-12.63).

Conclusion: Patients with T1D who presented with COVID-19 during the first surge had a higher proportion of adverse outcomes than those who presented in a later surge.

Keywords: TD1, diabetic ketoacidosis, hypoglycemia.

After the World Health Organization declared the disease caused by the novel coronavirus SARS-CoV-2, COVID-19, a pandemic on March 11, 2020, the Centers for Disease Control and Prevention identified patients with diabetes as high risk for severe illness.^1-7 The case-fatality rate for COVID-19 has significantly improved over the past 2 years. Public health measures, less severe COVID-19 variants, increased access to testing, and new treatments for COVID-19 have contributed to improved outcomes.

The T1D Exchange has previously published findings on COVID-19 outcomes for patients with type 1 diabetes (T1D) using data from the T1D COVID-19 Surveillance Registry.^8-12 Given improved outcomes in COVID-19 in the general population, we sought to determine if outcomes for cases of COVID-19 reported to this registry changed over time.

Methods

This study was coordinated by the T1D Exchange and approved as nonhuman subject research by the Western Institutional Review Board. All participating centers also obtained local institutional review board approval. No identifiable patient information was collected as part of this noninterventional, cross-sectional study.

The T1D Exchange Multi-center COVID-19 Surveillance Study collected data from endocrinology clinics that completed a retrospective chart review and submitted information to T1D Exchange via an online questionnaire for all patients with T1D at their sites who tested positive for COVID-19.^13,14 The questionnaire was administered using the Qualtrics survey platform (www.qualtrics.com version XM) and contained 33 pre-coded and free-text response fields to collect patient and clinical attributes.

Each participating center identified 1 team member for reporting to avoid duplicate case submission. Each submitted case was reviewed for potential errors and incomplete information. The coordinating center verified the number of cases per site for data quality assurance.

Quantitative data were represented as mean (standard deviation) or median (interquartile range). Categorical data were described as the number (percentage) of patients. Summary statistics, including frequency and percentage for categorical variables, were calculated for all patient-related and clinical characteristics. The date August 1, 2021, was selected as the end of the first surge based on a review of national COVID-19 surges.

We used the Fisher’s exact test to assess associations between hospitalization and demographics, HbA1c, diabetes duration, symptoms, and adverse outcomes. In addition, multivariate logistic regression was used to calculate odds ratios (OR). Logistic regression models were used to determine the association between time of surge and hospitalization separately for both the pediatric and adult populations. Each model was adjusted for potential sociodemographic confounders, specifically age, sex, race, insurance, and HbA1c.

All tests were 2-sided, with type 1 error set at 5%. Fisher’s exact test and logistic regression were performed using statistical software R, version 3.6.2 (R Foundation for Statistical Computing).

Results

The characteristics of COVID-19 cases in patients with T1D that were reported early in the pandemic, before August 1, 2020 (first surge), compared with those of cases reported on and after August 1, 2020 (later surges) are shown in Table 1.

Patients with T1D who presented with COVID-19 during the first surge as compared to the later surges were older (mean age 28 [SD, 18.0] years vs 18.8 [SD, 11.1] years; P < .001) and had a longer duration of diabetes (P < .001). The first-surge group also had more patients with >20 years’ diabetes duration (20% vs 9%, P < .001). Obesity, hypertension, and chronic kidney disease were also more commonly reported in first-surge cases (all P < .001).

There was a significant difference in race and ethnicity reported in the first surge vs the later surge cases, with fewer patients identifying as non-Hispanic White (39% vs, 63%, P < .001) and more patients identifying as non-Hispanic Black (29% vs 12%, P < .001). The groups also differed significantly in terms of insurance type, with more people on public insurance in the first-surge group (57% vs 38%, P < .001). In addition, median HbA1c was higher (9.3% vs 8.4%, P < .001) and continuous glucose monitor and insulin pump use were less common (P = .02 and <.001, respectively) in the early surge.

All symptoms and adverse outcomes were reported more often in the first surge, including diabetic ketoacidosis (DKA; 32% vs 15%; P < .001) and severe hypoglycemia (4% vs 1%, P = .04). Hospitalization (52% vs 13%, P < .001) and ICU admission (24% vs 9%, P < .001) were reported more often in the first-surge group.

Regression Analyses

Table 2 shows the results of logistic regression analyses for hospitalization in the pediatric (≤19 years of age) and adult (>19 years of age) groups, along with the odds of hospitalization during the first vs late surge among COVID-positive people with T1D. Adult patients who tested positive in the first surge were about 5 times more likely to be hospitalized than adults who tested positive for infection in the late surge after adjusting for age, insurance type, sex, race, and HbA1c levels. Pediatric patients also had an increased odds for hospitalization during the first surge, but this increase was not statistically significant.

Discussion

Our analysis of COVID-19 cases in patients with T1D reported by diabetes providers across the United States found that adverse outcomes were more prevalent early in the pandemic. There may be a number of reasons for this difference in outcomes between patients who presented in the first surge vs a later surge. First, because testing for COVID-19 was extremely limited and reserved for hospitalized patients early in the pandemic, the first-surge patients with confirmed COVID-19 likely represent a skewed population of higher-acuity patients. This may also explain the relative paucity of cases in younger patients reported early in the pandemic. Second, worse outcomes in the early surge may also have been associated with overwhelmed hospitals in New York City at the start of the outbreak. According to Cummings et al, the abrupt surge of critically ill patients hospitalized with severe acute respiratory distress syndrome initially outpaced their capacity to provide prone-positioning ventilation, which has been expanded since then.¹⁵ While there was very little hypertension, cardiovascular disease, or kidney disease reported in the pediatric groups, there was a higher prevalence of obesity in the pediatric group from the mid-Atlantic region. Obesity has been associated with a worse prognosis for COVID-19 illness in children.¹⁶ Finally, there were 5 deaths reported in this study, all of which were reported during the first surge. Older age and increased rates of cardiovascular disease and chronic kidney disease in the first surge cases likely contributed to worse outcomes for adults in mid-Atlantic region relative to the other regions. Minority race and the use of public insurance, risk factors for more severe outcomes in all regions, were also more common in cases reported from the mid-Atlantic region.

This study has several limitations. First, it is a cross-sectional study that relies upon voluntary provider reports. Second, availability of COVID-19 testing was limited in all regions in spring 2020. Third, different regions of the country experienced subsequent surges at different times within the reported timeframes in this analysis. Fourth, this report time period does not include the impact of the newer COVID-19 variants. Finally, trends in COVID-19 outcomes were affected by the evolution of care that developed throughout 2020.

Conclusion

Adult patients with T1D and COVID-19 who reported during the first surge had about 5 times higher hospitalization odds than those who presented in a later surge.

Corresponding author: Osagie Ebekozien, MD, MPH, 11 Avenue de Lafayette, Boston, MA 02111; oebekozien@t1dexchange.org

Disclosures: Dr Ebekozien reports receiving research grants from Medtronic Diabetes, Eli Lilly, and Dexcom, and receiving honoraria from Medtronic Diabetes.

From Hassenfeld Children’s Hospital at NYU Langone Health, New York, NY (Dr Gallagher), T1D Exchange, Boston, MA (Saketh Rompicherla; Drs Ebekozien, Noor, Odugbesan, and Mungmode; Nicole Rioles, Emma Ospelt), University of Mississippi School of Population Health, Jackson, MS (Dr. Ebekozien), Icahn School of Medicine at Mount Sinai, New York, NY (Drs. Wilkes, O’Malley, and Rapaport), Weill Cornell Medicine, New York, NY (Drs. Antal and Feuer), NYU Long Island School of Medicine, Mineola, NY (Dr. Gabriel), NYU Langone Health, New York, NY (Dr. Golden), Barbara Davis Center, Aurora, CO (Dr. Alonso), Texas Children’s Hospital/Baylor College of Medicine, Houston, TX (Dr. Lyons), Stanford University, Stanford, CA (Dr. Prahalad), Children Mercy Kansas City, MO (Dr. Clements), Indiana University School of Medicine, IN (Dr. Neyman), Rady Children’s Hospital, University of California, San Diego, CA (Dr. Demeterco-Berggren).

Background: Patient outcomes of COVID-19 have improved throughout the pandemic. However, because it is not known whether outcomes of COVID-19 in the type 1 diabetes (T1D) population improved over time, we investigated differences in COVID-19 outcomes for patients with T1D in the United States.

Methods: We analyzed data collected via a registry of patients with T1D and COVID-19 from 56 sites between April 2020 and January 2021. We grouped cases into first surge (April 9, 2020, to July 31, 2020, n = 188) and late surge (August 1, 2020, to January 31, 2021, n = 410), and then compared outcomes between both groups using descriptive statistics and logistic regression models.

Results: Adverse outcomes were more frequent during the first surge, including diabetic ketoacidosis (32% vs 15%, P < .001), severe hypoglycemia (4% vs 1%, P = .04), and hospitalization (52% vs 22%, P < .001). Patients in the first surge were older (28 [SD,18.8] years vs 18.0 [SD, 11.1] years, P < .001), had higher median hemoglobin A1c levels (9.3 [interquartile range {IQR}, 4.0] vs 8.4 (IQR, 2.8), P < .001), and were more likely to use public insurance (107 [57%] vs 154 [38%], P < .001). The odds of hospitalization for adults in the first surge were 5 times higher compared to the late surge (odds ratio, 5.01; 95% CI, 2.11-12.63).

Conclusion: Patients with T1D who presented with COVID-19 during the first surge had a higher proportion of adverse outcomes than those who presented in a later surge.

Keywords: TD1, diabetic ketoacidosis, hypoglycemia.

After the World Health Organization declared the disease caused by the novel coronavirus SARS-CoV-2, COVID-19, a pandemic on March 11, 2020, the Centers for Disease Control and Prevention identified patients with diabetes as high risk for severe illness.^1-7 The case-fatality rate for COVID-19 has significantly improved over the past 2 years. Public health measures, less severe COVID-19 variants, increased access to testing, and new treatments for COVID-19 have contributed to improved outcomes.

The T1D Exchange has previously published findings on COVID-19 outcomes for patients with type 1 diabetes (T1D) using data from the T1D COVID-19 Surveillance Registry.^8-12 Given improved outcomes in COVID-19 in the general population, we sought to determine if outcomes for cases of COVID-19 reported to this registry changed over time.

Methods

This study was coordinated by the T1D Exchange and approved as nonhuman subject research by the Western Institutional Review Board. All participating centers also obtained local institutional review board approval. No identifiable patient information was collected as part of this noninterventional, cross-sectional study.

The T1D Exchange Multi-center COVID-19 Surveillance Study collected data from endocrinology clinics that completed a retrospective chart review and submitted information to T1D Exchange via an online questionnaire for all patients with T1D at their sites who tested positive for COVID-19.^13,14 The questionnaire was administered using the Qualtrics survey platform (www.qualtrics.com version XM) and contained 33 pre-coded and free-text response fields to collect patient and clinical attributes.

Each participating center identified 1 team member for reporting to avoid duplicate case submission. Each submitted case was reviewed for potential errors and incomplete information. The coordinating center verified the number of cases per site for data quality assurance.

Quantitative data were represented as mean (standard deviation) or median (interquartile range). Categorical data were described as the number (percentage) of patients. Summary statistics, including frequency and percentage for categorical variables, were calculated for all patient-related and clinical characteristics. The date August 1, 2021, was selected as the end of the first surge based on a review of national COVID-19 surges.

We used the Fisher’s exact test to assess associations between hospitalization and demographics, HbA1c, diabetes duration, symptoms, and adverse outcomes. In addition, multivariate logistic regression was used to calculate odds ratios (OR). Logistic regression models were used to determine the association between time of surge and hospitalization separately for both the pediatric and adult populations. Each model was adjusted for potential sociodemographic confounders, specifically age, sex, race, insurance, and HbA1c.

All tests were 2-sided, with type 1 error set at 5%. Fisher’s exact test and logistic regression were performed using statistical software R, version 3.6.2 (R Foundation for Statistical Computing).

Results

The characteristics of COVID-19 cases in patients with T1D that were reported early in the pandemic, before August 1, 2020 (first surge), compared with those of cases reported on and after August 1, 2020 (later surges) are shown in Table 1.

Patients with T1D who presented with COVID-19 during the first surge as compared to the later surges were older (mean age 28 [SD, 18.0] years vs 18.8 [SD, 11.1] years; P < .001) and had a longer duration of diabetes (P < .001). The first-surge group also had more patients with >20 years’ diabetes duration (20% vs 9%, P < .001). Obesity, hypertension, and chronic kidney disease were also more commonly reported in first-surge cases (all P < .001).

There was a significant difference in race and ethnicity reported in the first surge vs the later surge cases, with fewer patients identifying as non-Hispanic White (39% vs, 63%, P < .001) and more patients identifying as non-Hispanic Black (29% vs 12%, P < .001). The groups also differed significantly in terms of insurance type, with more people on public insurance in the first-surge group (57% vs 38%, P < .001). In addition, median HbA1c was higher (9.3% vs 8.4%, P < .001) and continuous glucose monitor and insulin pump use were less common (P = .02 and <.001, respectively) in the early surge.

All symptoms and adverse outcomes were reported more often in the first surge, including diabetic ketoacidosis (DKA; 32% vs 15%; P < .001) and severe hypoglycemia (4% vs 1%, P = .04). Hospitalization (52% vs 13%, P < .001) and ICU admission (24% vs 9%, P < .001) were reported more often in the first-surge group.

Regression Analyses

Table 2 shows the results of logistic regression analyses for hospitalization in the pediatric (≤19 years of age) and adult (>19 years of age) groups, along with the odds of hospitalization during the first vs late surge among COVID-positive people with T1D. Adult patients who tested positive in the first surge were about 5 times more likely to be hospitalized than adults who tested positive for infection in the late surge after adjusting for age, insurance type, sex, race, and HbA1c levels. Pediatric patients also had an increased odds for hospitalization during the first surge, but this increase was not statistically significant.

Discussion

Our analysis of COVID-19 cases in patients with T1D reported by diabetes providers across the United States found that adverse outcomes were more prevalent early in the pandemic. There may be a number of reasons for this difference in outcomes between patients who presented in the first surge vs a later surge. First, because testing for COVID-19 was extremely limited and reserved for hospitalized patients early in the pandemic, the first-surge patients with confirmed COVID-19 likely represent a skewed population of higher-acuity patients. This may also explain the relative paucity of cases in younger patients reported early in the pandemic. Second, worse outcomes in the early surge may also have been associated with overwhelmed hospitals in New York City at the start of the outbreak. According to Cummings et al, the abrupt surge of critically ill patients hospitalized with severe acute respiratory distress syndrome initially outpaced their capacity to provide prone-positioning ventilation, which has been expanded since then.¹⁵ While there was very little hypertension, cardiovascular disease, or kidney disease reported in the pediatric groups, there was a higher prevalence of obesity in the pediatric group from the mid-Atlantic region. Obesity has been associated with a worse prognosis for COVID-19 illness in children.¹⁶ Finally, there were 5 deaths reported in this study, all of which were reported during the first surge. Older age and increased rates of cardiovascular disease and chronic kidney disease in the first surge cases likely contributed to worse outcomes for adults in mid-Atlantic region relative to the other regions. Minority race and the use of public insurance, risk factors for more severe outcomes in all regions, were also more common in cases reported from the mid-Atlantic region.

This study has several limitations. First, it is a cross-sectional study that relies upon voluntary provider reports. Second, availability of COVID-19 testing was limited in all regions in spring 2020. Third, different regions of the country experienced subsequent surges at different times within the reported timeframes in this analysis. Fourth, this report time period does not include the impact of the newer COVID-19 variants. Finally, trends in COVID-19 outcomes were affected by the evolution of care that developed throughout 2020.

Conclusion

Adult patients with T1D and COVID-19 who reported during the first surge had about 5 times higher hospitalization odds than those who presented in a later surge.

Corresponding author: Osagie Ebekozien, MD, MPH, 11 Avenue de Lafayette, Boston, MA 02111; oebekozien@t1dexchange.org

Disclosures: Dr Ebekozien reports receiving research grants from Medtronic Diabetes, Eli Lilly, and Dexcom, and receiving honoraria from Medtronic Diabetes.

From the Department of Emergency Medicine, Santa Croce e Carle Hospital, Cuneo, Italy (Drs. Abram, Tosello, Emanuele Bernardi, Allione, Cavalot, Dutto, Corsini, Martini, Sciolla, Sara Bernardi, and Lauria). From the School of Emergency Medicine, University of Turin, Turin, Italy (Drs. Paglietta and Giamello).

Objective: This retrospective and prospective cohort study was designed to describe the characteristics, treatments, and outcomes of patients with SARS-CoV-2 infection (COVID-19) admitted to subintensive care units (SICU) and to identify the variables associated with outcomes. SICUs have been extremely stressed during the pandemic, but most data regarding critically ill COVID-19 patients come from intensive care units (ICUs). Studies about COVID-19 patients in SICUs are lacking.

Setting and participants: The study included 88 COVID-19 patients admitted to our SICU in Cuneo, Italy, between March and May 2020.

Measurements: Clinical and ventilatory data were collected, and patients were divided by outcome. Multivariable logistic regression analysis examined the variables associated with negative outcomes (transfer to the ICU, palliation, or death in a SICU).

Results: A total of 60 patients (68%) had a positive outcome, and 28 patients (32%) had a negative outcome; 69 patients (78%) underwent continuous positive airway pressure (CPAP). Pronation (n = 37 [42%]) had been more frequently adopted in patients who had a positive outcome vs a negative outcome (n = 30 [50%] vs n = 7 [25%]; P = .048), and the median (interquartile range) Pao₂/Fio₂ ratio after 6 hours of prone positioning was lower in patients who had a negative outcome vs a positive outcome (144 [140-168] vs 249 [195-268], P = .006). Independent predictors of a negative outcome were diabetes (odds ratio [OR], 8.22; 95% CI, 1.50-44.70; P = .015), higher D-dimer (OR, 1.28; 95% CI, 1.04-1.57; P = .019), higher lactate dehydrogenase level (OR, 1.003; 95% CI, 1.000-1.006; P = .039), and lower lymphocytes count (OR, 0.996; 95% CI, 0.993-0.999; P = .004).

Conclusion: SICUs have a fundamental role in the treatment of critically ill patients with COVID-19, who require long-term CPAP and pronation cycles. Diabetes, lymphopenia, and high D-dimer and LDH levels are associated with negative outcomes.

Keywords: emergency medicine, noninvasive ventilation, prone position, continuous positive airway pressure.

The COVID-19 pandemic has led to large increases in hospital admissions. Subintensive care units (SICUs) are among the wards most under pressure worldwide,¹ dealing with the increased number of critically ill patients who need noninvasive ventilation, as well as serving as the best alternative to overfilled intensive care units (ICUs). In Italy, SICUs are playing a fundamental role in the management of COVID-19 patients, providing early treatment of respiratory failure by continuous noninvasive ventilation in order to reduce the need for intubation.^2-5 Nevertheless, the great majority of available data about critically ill COVID-19 patients comes from ICUs. Full studies about outcomes of patients in SICUs are lacking and need to be conducted.

We sought to evaluate the characteristics and outcomes of patients admitted to our SICU for COVID-19 to describe the treatments they needed and their impact on prognosis, and to identify the variables associated with patient outcomes.

Methods

Study Design

This cohort study used data from patients who were admitted in the very first weeks of the pandemic. Data were collected retrospectively as well as prospectively, since the ethical committee approved our project. The quality and quantity of data in the 2 groups were comparable.

Data were collected from electronic and written medical records gathered during the patient’s entire stay in our SICU. Data were entered in a database with limited and controlled access. This study complied with the Declaration of Helsinki and was approved by the local ethics committees (ID: MEDURG10).

Study Population

We studied 88 consecutive patients admitted to the SICU of the Santa Croce e Carle Teaching Hospital, Cuneo, Italy, for COVID-19, from March 8 to May 1, 2020. The diagnosis was based on acute respiratory failure associated with SARS-CoV-2 RNA detection on nasopharyngeal swab or tracheal aspirate and/or typical COVID-19 features on a pulmonary computed tomography (CT) scan.⁶ Exclusion criteria were age younger than 18 years and patient denial of permission to use their data for research purposes (the great majority of patients could actively give consent; for patients who were too sick to do so, family members were asked whether they were aware of any reason why the patient would deny consent).

The past medical history and recent symptoms description were obtained by manually reviewing medical records. Epidemiological exposure was defined as contact with SARS-CoV-2–positive people or staying in an epidemic outbreak area. Initial vital parameters, venous blood tests, arterial blood gas analysis, chest x-ray, as well as the result of the nasopharyngeal swab were gathered from the emergency department (ED) examination. (Additional swabs could be requested when the first one was negative but clinical suspicion for COVID-19 was high.) Upon admission to the SICU, a standardized panel of blood tests was performed, which was repeated the next day and then every 48 hours. Arterial blood gas analysis was performed when clinically indicated, at least twice a day, or following a scheduled time in patients undergoing pronation. Charlson Comorbidity Index⁷ and MuLBSTA score⁸ were calculated based on the collected data.

Imaging

Chest ultrasonography was performed in the ED at the time of hospitalization and once a day in the SICU. Pulmonary high-resolution computed tomography (HRCT) was performed when clinically indicated or when the results of nasopharyngeal swabs and/or x-ray results were discordant with COVID-19 clinical suspicion. Contrast CT was performed when pulmonary embolism was suspected.

Medical Therapy

Hydroxychloroquine, antiviral agents, tocilizumab, and ruxolitinib were used in the early phase of the pandemic, then were dismissed after evidence of no efficacy.^9-11 Steroids and low-molecular-weight heparin were used afterward. Enoxaparin was used at the standard prophylactic dosage, and 70% of the anticoagulant dosage was also adopted in patients with moderate-to-severe COVID-19 and D-dimer values >3 times the normal value.^12-14 Antibiotics were given when a bacterial superinfection was suspected.

Oxygen and Ventilatory Therapy

Oxygen support or noninvasive ventilation were started based on patients’ respiratory efficacy, estimated by respiratory rate and the ratio of partial pressure of arterial oxygen and fraction of inspired oxygen (P/F ratio).^15,16 Oxygen support was delivered through nasal cannula, Venturi mask, or reservoir mask. Noninvasive ventilation was performed by continuous positive airway pressure (CPAP) when the P/F ratio was <250 or the respiratory rate was >25 breaths per minute, using the helmet interface.^5,17 Prone positioning during CPAP^18-20 was adopted in patients meeting the acute respiratory distress syndrome (ARDS) criteria²¹ and having persistence of respiratory distress and P/F <300 after a 1-hour trial of CPAP.

The prone position was maintained based on patient tolerance. P/F ratio was measured before pronation (T0), after 1 hour of prone position (T1), before resupination (T2), and 6 hours after resupination (T3). With the same timing, the patient was asked to rate their comfort in each position, from 0 (lack of comfort) to 10 (optimal comfort). Delta P/F was defined as the difference between P/F at T3 and basal P/F at T0.

Outcomes

Positive outcomes were defined as patient discharge from the SICU or transfer to a lower-intensity care ward for treatment continuation. Negative outcomes were defined as need for transfer to the ICU, transfer to another ward for palliation, or death in the SICU.

Statistical Analysis

Continuous data are reported as median and interquartile range (IQR); normal distribution of variables was tested using the Shapiro-Wilk test. Categorical variables were reported as absolute number and percentage. The Mann-Whitney test was used to compare continuous variables between groups, and chi-square test with continuity correction was used for categorical variables. The variables that were most significantly associated with a negative outcome on the univariate analysis were included in a stepwise logistic regression analysis, in order to identify independent predictors of patient outcome. Statistical analysis was performed using JASP (JASP Team) software.

Study Population

Of the 88 patients included in the study, 70% were male; the median age was 66 years (IQR, 60-77). In most patients, the diagnosis of COVID-19 was derived from a positive SARS-CoV-2 nasopharyngeal swab. Six patients, however, maintained a negative swab at all determinations but had clinical and imaging features strongly suggesting COVID-19. No patients met the exclusion criteria. Most patients came from the ED (n = 58 [66%]) or general wards (n = 22 [25%]), while few were transferred from the ICU (n = 8 [9%]). The median length of stay in the SICU was 4 days (IQR, 2-7). An epidemiological link to affected persons or a known virus exposure was identifiable in 37 patients (42%).

Clinical, Laboratory, and Imaging Data

The clinical and anthropometric characteristics of patients are shown in Table 1. Hypertension and smoking habits were prevalent in our population, and the median Charlson Comorbidity Index was 3. Most patients experienced fever, dyspnea, and cough during the days before hospitalization.

Laboratory data showed a marked inflammatory milieu in all studied patients, both at baseline and after 24 and 72 hours. Lymphopenia was observed, along with a significant increase of lactate dehydrogenase (LDH), C-reactive protein (CPR), and D-dimer, and a mild increase of procalcitonin. N-terminal pro-brain natriuretic peptide (NT-proBNP) values were also increased, with normal troponin I values (Table 2).

Medical Therapy

Most patients (89%) received hydroxychloroquine, whereas steroids were used in one-third of the population (36%). Immunomodulators (tocilizumab and ruxolitinib) were restricted to 12 patients (14%). Empirical antiviral therapy was introduced in the first 41 patients (47%). Enoxaparin was the default agent for thromboembolism prophylaxis, and 6 patients (7%) received 70% of the anticoagulating dose.

Oxygen and Ventilatory Therapy

Basal median P/F ratio was 253 (IQR, 218-291), and respiratory rate at triage was 20 breaths/min (IQR, 16-28), underlining a moderate-to-severe respiratory insufficiency at presentation. A total of 69 patients (78%) underwent CPAP, with a median positive end-expiratory pressure (PEEP) of 10.0 cm H₂O (IQR, 7.5-10.0) and fraction of inspired oxygen (Fio₂) of 0.40 (IQR, 0.40-0.50). In 37 patients (42%) who received ongoing CPAP, prone positioning was adopted. In this subgroup, respiratory rate was not significantly different from baseline to resupination (24 vs 25 breaths/min). The median P/F improved from 197 (IQR, 154-236) at baseline to 217 (IQR, 180-262) after pronation (the duration of the prone position was variable, depending on patients’ tolerance: 1 to 6 hours or every pronation cycle). The median delta P/F ratio was 39.4 (IQR, –17.0 to 78.0).

Outcomes

A total of 28 patients (32%) had a negative outcome in the SICU: 8 patients (9%) died, having no clinical indication for higher-intensity care; 6 patients (7%) were transferred to general wards for palliation; and 14 patients (16%) needed an upgrade of cure intensity and were transferred to the ICU. Of these 14 patients, 9 died in the ICU. The total in-hospital mortality of COVID-19 patients, including patients transferred from the SICU to general wards in fair condition, was 27% (n = 24). Clinical, laboratory, and therapeutic characteristics between the 2 groups are shown in Table 4.

Patients who had a negative outcome were significantly older and had more comorbidities, as suggested by a significantly higher prevalence of diabetes and higher Charlson Comorbidity scores (reflecting the mortality risk based on age and comorbidities). The median MuLBSTA score, which estimates the 90-day mortality risk from viral pneumonia, was also higher in patients who had a negative outcome (9.33%). Symptom occurrence was not different in patients with a negative outcome (apart from cough, which was less frequent), but these patients underwent hospitalization earlier—since the appearance of their first COVID-19 symptoms—compared to patients who had a positive outcome. No difference was found in antihypertensive therapy with angiotensin-converting enzyme inhibitors or angiotensin-receptor blockers among outcome groups.

More pronounced laboratory abnormalities were found in patients who had a negative outcome, compared to patients who had a positive outcome: lower lymphocytes and higher C-reactive protein (CRP), procalcitonin, D-dimer, LDH, and NT-proBNP. We found no differences in the radiological distribution of pulmonary involvement in patients who had negative or positive outcomes, nor in the adopted medical treatment.

Data showed no difference in CPAP implementation in the 2 groups. However, prone positioning had been more frequently adopted in the group of patients who had a positive outcome, compared with patients who had a negative outcome. No differences of basal P/F were found in patients who had a negative or positive outcome, but the median P/F after 6 hours of prone position was significantly lower in patients who had a negative outcome. The delta P/F ratio did not differ in the 2 groups of patients.

Multivariate Analysis

A logistic regression model was created, including the variables significantly associated with outcomes in the univariate analysis (age, sex, history of diabetes, lymphocytes, CRP, procalcitonin, LDH, NT-proBNP, and D-dimer). In the multivariate analysis, independent predictors of a negative outcome were history of diabetes (odds ratio [OR], 8.22; 95% CI, 1.50-44.70; P =.015), high D-dimer values (OR, 1.28; CI, 1.04-1.57; P = .019), high LDH values (OR, 1.003; CI, 1.000-1.006; P = .039), and low lymphocytes count (OR, 0.996; CI, 0.993-0.999; P = .004).

Discussion

Role of Subintensive Units and Mortality

The novelty of our report is its attempt to investigate the specific group of COVID-19 patients admitted to a SICU. In Italy, SICUs receive acutely ill, spontaneously breathing patients who need (invasive) hemodynamic monitoring, vasoactive medication, renal replacement therapy, chest- tube placement, thrombolysis, and respiratory noninvasive support. The nurse-to-patient ratio is higher than for general wards (usually 1 nurse to every 4 or 5 patients), though lower than for ICUs. In northern Italy, a great number of COVID-19 patients have required this kind of high-intensity care during the pandemic: Noninvasive ventilation support had to be maintained for several days, pronation maneuvers required a high number of people 2 or 3 times a day, and strict monitoring had to be assured. The SICU setting allows patients to buy time as a bridge to progressive reduction of pulmonary involvement, sometimes preventing the need for intubation.

The high prevalence of negative outcomes in the SICU underlines the complexity of COVID-19 patients in this setting. In fact, published data about mortality for patients with severe COVID-19 pneumonia are similar to ours.^22,23

Clinical, Laboratory, and Imaging Data

Our analysis confirmed a high rate of comorbidities in COVID-19 patients²⁴ and their prognostic role with age.^25,26 A marked inflammatory milieu was a negative prognostic indicator, and associated concomitant bacterial superinfection could have led to a worse prognosis (procalcitonin was associated with negative outcomes).²⁷ The cardiovascular system was nevertheless stressed, as suggested by higher values of NT-proBNP in patients with negative outcomes, which could reflect sepsis-related systemic involvement.²⁸

It is known that the pulmonary damage caused by SARS-CoV-2 has a dynamic radiological and clinical course, with early areas of subsegmental consolidation, and bilateral ground-glass opacities predominating later in the course of the disease.²⁹ This could explain why in our population we found no specific radiological pattern leading to a worse outcome.

Medical Therapy

No specific pharmacological therapy was found to be associated with a positive outcome in our study, just like antiviral and immunomodulator therapies failed to demonstrate effectiveness in subsequent pandemic surges. The low statistical power of our study did not allow us to give insight into the effectiveness of steroids and heparin at any dosage.

PEEP Support and Prone Positioning

Continuous positive airway pressure was initiated in the majority of patients and maintained for several days. This was an absolute novelty, because we rarely had to keep patients in helmets for long. This was feasible thanks to the SICU’s high nurse-to-patient ratio and the possibility of providing monitored sedation. Patients who could no longer tolerate CPAP helmets or did not improve with CPAP support were evaluated with anesthetists for programming further management. No initial data on respiratory rate, level of hypoxemia, or oxygen support need (level of PEEP and Fio₂) could discriminate between outcomes.

Prone positioning during CPAP was implemented in 42% of our study population: P/F ratio amelioration after prone positioning was highly variable, ranging from very good P/F ratio improvements to few responses or no response. No significantly greater delta P/F ratio was seen after the first prone positioning cycle in patients who had a positive outcome, probably due to the small size of our population, but we observed a clear positive trend. Interestingly, patients showing a negative outcome had a lower percentage of long-term responses to prone positioning: 6 hours after resupination, they lost the benefit of prone positioning in terms of P/F ratio amelioration. Similarly, a greater number of patients tolerating prone positioning had a positive outcome. These data give insight on the possible benefits of prone positioning in a noninvasively supported cohort of patients, which has been mentioned in previous studies.^30,31

Outcomes and Variables Associated With Negative Outcomes

After correction for age and sex, we found in multiple regression analysis that higher D-dimer and LDH values, lymphopenia, and history of diabetes were independently associated with a worse outcome. Although our results had low statistical significance, we consider the trend of the obtained odds ratios important from a clinical point of view. These results could lead to greater attention being placed on COVID-19 patients who present with these characteristics upon their arrival to the ED because they have increased risk of death or intensive care need. Clinicians should consider SICU admission for these patients in order to guarantee closer monitoring and possibly more aggressive ventilatory treatments, earlier pronation, or earlier transfer to the ICU.

Limitations

The major limitation to our study is undoubtedly its statistical power, due to its relatively low patient population. Particularly, the small number of patients who underwent pronation did not allow speculation about the efficacy of this technique, although preliminary data seem promising. However, ours is among the first studies regarding patients with COVID-19 admitted to a SICU, and these preliminary data truthfully describe the Italian, and perhaps international, experience with the first surge of the pandemic.

Conclusions

Our data highlight the primary role of the SICU in COVID-19 in adequately treating critically ill patients who have high care needs different from intubation, and who require noninvasive ventilation for prolonged times as well as frequent pronation cycles. This setting of care may represent a valid, reliable, and effective option for critically ill respiratory patients. History of diabetes, lymphopenia, and high D-dimer and LDH values are independently associated with negative outcomes, and patients presenting with these characteristics should be strictly monitored.

Acknowledgments: The authors thank the Informatica System S.R.L., as well as Allessando Mendolia for the pro bono creation of the ISCovidCollect data collecting app.

Corresponding author: Sara Abram, MD, via Coppino, 12100 Cuneo, Italy; sara.abram84@gmail.com.

Disclosures: None.

From the Department of Emergency Medicine, Santa Croce e Carle Hospital, Cuneo, Italy (Drs. Abram, Tosello, Emanuele Bernardi, Allione, Cavalot, Dutto, Corsini, Martini, Sciolla, Sara Bernardi, and Lauria). From the School of Emergency Medicine, University of Turin, Turin, Italy (Drs. Paglietta and Giamello).

Objective: This retrospective and prospective cohort study was designed to describe the characteristics, treatments, and outcomes of patients with SARS-CoV-2 infection (COVID-19) admitted to subintensive care units (SICU) and to identify the variables associated with outcomes. SICUs have been extremely stressed during the pandemic, but most data regarding critically ill COVID-19 patients come from intensive care units (ICUs). Studies about COVID-19 patients in SICUs are lacking.

Setting and participants: The study included 88 COVID-19 patients admitted to our SICU in Cuneo, Italy, between March and May 2020.

Measurements: Clinical and ventilatory data were collected, and patients were divided by outcome. Multivariable logistic regression analysis examined the variables associated with negative outcomes (transfer to the ICU, palliation, or death in a SICU).

Results: A total of 60 patients (68%) had a positive outcome, and 28 patients (32%) had a negative outcome; 69 patients (78%) underwent continuous positive airway pressure (CPAP). Pronation (n = 37 [42%]) had been more frequently adopted in patients who had a positive outcome vs a negative outcome (n = 30 [50%] vs n = 7 [25%]; P = .048), and the median (interquartile range) Pao₂/Fio₂ ratio after 6 hours of prone positioning was lower in patients who had a negative outcome vs a positive outcome (144 [140-168] vs 249 [195-268], P = .006). Independent predictors of a negative outcome were diabetes (odds ratio [OR], 8.22; 95% CI, 1.50-44.70; P = .015), higher D-dimer (OR, 1.28; 95% CI, 1.04-1.57; P = .019), higher lactate dehydrogenase level (OR, 1.003; 95% CI, 1.000-1.006; P = .039), and lower lymphocytes count (OR, 0.996; 95% CI, 0.993-0.999; P = .004).

Conclusion: SICUs have a fundamental role in the treatment of critically ill patients with COVID-19, who require long-term CPAP and pronation cycles. Diabetes, lymphopenia, and high D-dimer and LDH levels are associated with negative outcomes.

Keywords: emergency medicine, noninvasive ventilation, prone position, continuous positive airway pressure.

The COVID-19 pandemic has led to large increases in hospital admissions. Subintensive care units (SICUs) are among the wards most under pressure worldwide,¹ dealing with the increased number of critically ill patients who need noninvasive ventilation, as well as serving as the best alternative to overfilled intensive care units (ICUs). In Italy, SICUs are playing a fundamental role in the management of COVID-19 patients, providing early treatment of respiratory failure by continuous noninvasive ventilation in order to reduce the need for intubation.^2-5 Nevertheless, the great majority of available data about critically ill COVID-19 patients comes from ICUs. Full studies about outcomes of patients in SICUs are lacking and need to be conducted.

We sought to evaluate the characteristics and outcomes of patients admitted to our SICU for COVID-19 to describe the treatments they needed and their impact on prognosis, and to identify the variables associated with patient outcomes.

Methods

Study Design

This cohort study used data from patients who were admitted in the very first weeks of the pandemic. Data were collected retrospectively as well as prospectively, since the ethical committee approved our project. The quality and quantity of data in the 2 groups were comparable.

Data were collected from electronic and written medical records gathered during the patient’s entire stay in our SICU. Data were entered in a database with limited and controlled access. This study complied with the Declaration of Helsinki and was approved by the local ethics committees (ID: MEDURG10).

Study Population

We studied 88 consecutive patients admitted to the SICU of the Santa Croce e Carle Teaching Hospital, Cuneo, Italy, for COVID-19, from March 8 to May 1, 2020. The diagnosis was based on acute respiratory failure associated with SARS-CoV-2 RNA detection on nasopharyngeal swab or tracheal aspirate and/or typical COVID-19 features on a pulmonary computed tomography (CT) scan.⁶ Exclusion criteria were age younger than 18 years and patient denial of permission to use their data for research purposes (the great majority of patients could actively give consent; for patients who were too sick to do so, family members were asked whether they were aware of any reason why the patient would deny consent).

The past medical history and recent symptoms description were obtained by manually reviewing medical records. Epidemiological exposure was defined as contact with SARS-CoV-2–positive people or staying in an epidemic outbreak area. Initial vital parameters, venous blood tests, arterial blood gas analysis, chest x-ray, as well as the result of the nasopharyngeal swab were gathered from the emergency department (ED) examination. (Additional swabs could be requested when the first one was negative but clinical suspicion for COVID-19 was high.) Upon admission to the SICU, a standardized panel of blood tests was performed, which was repeated the next day and then every 48 hours. Arterial blood gas analysis was performed when clinically indicated, at least twice a day, or following a scheduled time in patients undergoing pronation. Charlson Comorbidity Index⁷ and MuLBSTA score⁸ were calculated based on the collected data.

Imaging

Chest ultrasonography was performed in the ED at the time of hospitalization and once a day in the SICU. Pulmonary high-resolution computed tomography (HRCT) was performed when clinically indicated or when the results of nasopharyngeal swabs and/or x-ray results were discordant with COVID-19 clinical suspicion. Contrast CT was performed when pulmonary embolism was suspected.

Medical Therapy

Hydroxychloroquine, antiviral agents, tocilizumab, and ruxolitinib were used in the early phase of the pandemic, then were dismissed after evidence of no efficacy.^9-11 Steroids and low-molecular-weight heparin were used afterward. Enoxaparin was used at the standard prophylactic dosage, and 70% of the anticoagulant dosage was also adopted in patients with moderate-to-severe COVID-19 and D-dimer values >3 times the normal value.^12-14 Antibiotics were given when a bacterial superinfection was suspected.

Oxygen and Ventilatory Therapy

Oxygen support or noninvasive ventilation were started based on patients’ respiratory efficacy, estimated by respiratory rate and the ratio of partial pressure of arterial oxygen and fraction of inspired oxygen (P/F ratio).^15,16 Oxygen support was delivered through nasal cannula, Venturi mask, or reservoir mask. Noninvasive ventilation was performed by continuous positive airway pressure (CPAP) when the P/F ratio was <250 or the respiratory rate was >25 breaths per minute, using the helmet interface.^5,17 Prone positioning during CPAP^18-20 was adopted in patients meeting the acute respiratory distress syndrome (ARDS) criteria²¹ and having persistence of respiratory distress and P/F <300 after a 1-hour trial of CPAP.

The prone position was maintained based on patient tolerance. P/F ratio was measured before pronation (T0), after 1 hour of prone position (T1), before resupination (T2), and 6 hours after resupination (T3). With the same timing, the patient was asked to rate their comfort in each position, from 0 (lack of comfort) to 10 (optimal comfort). Delta P/F was defined as the difference between P/F at T3 and basal P/F at T0.

Outcomes

Positive outcomes were defined as patient discharge from the SICU or transfer to a lower-intensity care ward for treatment continuation. Negative outcomes were defined as need for transfer to the ICU, transfer to another ward for palliation, or death in the SICU.

Statistical Analysis

Continuous data are reported as median and interquartile range (IQR); normal distribution of variables was tested using the Shapiro-Wilk test. Categorical variables were reported as absolute number and percentage. The Mann-Whitney test was used to compare continuous variables between groups, and chi-square test with continuity correction was used for categorical variables. The variables that were most significantly associated with a negative outcome on the univariate analysis were included in a stepwise logistic regression analysis, in order to identify independent predictors of patient outcome. Statistical analysis was performed using JASP (JASP Team) software.

Study Population

Of the 88 patients included in the study, 70% were male; the median age was 66 years (IQR, 60-77). In most patients, the diagnosis of COVID-19 was derived from a positive SARS-CoV-2 nasopharyngeal swab. Six patients, however, maintained a negative swab at all determinations but had clinical and imaging features strongly suggesting COVID-19. No patients met the exclusion criteria. Most patients came from the ED (n = 58 [66%]) or general wards (n = 22 [25%]), while few were transferred from the ICU (n = 8 [9%]). The median length of stay in the SICU was 4 days (IQR, 2-7). An epidemiological link to affected persons or a known virus exposure was identifiable in 37 patients (42%).

Clinical, Laboratory, and Imaging Data

The clinical and anthropometric characteristics of patients are shown in Table 1. Hypertension and smoking habits were prevalent in our population, and the median Charlson Comorbidity Index was 3. Most patients experienced fever, dyspnea, and cough during the days before hospitalization.

Laboratory data showed a marked inflammatory milieu in all studied patients, both at baseline and after 24 and 72 hours. Lymphopenia was observed, along with a significant increase of lactate dehydrogenase (LDH), C-reactive protein (CPR), and D-dimer, and a mild increase of procalcitonin. N-terminal pro-brain natriuretic peptide (NT-proBNP) values were also increased, with normal troponin I values (Table 2).

Medical Therapy

Most patients (89%) received hydroxychloroquine, whereas steroids were used in one-third of the population (36%). Immunomodulators (tocilizumab and ruxolitinib) were restricted to 12 patients (14%). Empirical antiviral therapy was introduced in the first 41 patients (47%). Enoxaparin was the default agent for thromboembolism prophylaxis, and 6 patients (7%) received 70% of the anticoagulating dose.

Oxygen and Ventilatory Therapy

Basal median P/F ratio was 253 (IQR, 218-291), and respiratory rate at triage was 20 breaths/min (IQR, 16-28), underlining a moderate-to-severe respiratory insufficiency at presentation. A total of 69 patients (78%) underwent CPAP, with a median positive end-expiratory pressure (PEEP) of 10.0 cm H₂O (IQR, 7.5-10.0) and fraction of inspired oxygen (Fio₂) of 0.40 (IQR, 0.40-0.50). In 37 patients (42%) who received ongoing CPAP, prone positioning was adopted. In this subgroup, respiratory rate was not significantly different from baseline to resupination (24 vs 25 breaths/min). The median P/F improved from 197 (IQR, 154-236) at baseline to 217 (IQR, 180-262) after pronation (the duration of the prone position was variable, depending on patients’ tolerance: 1 to 6 hours or every pronation cycle). The median delta P/F ratio was 39.4 (IQR, –17.0 to 78.0).

Outcomes

A total of 28 patients (32%) had a negative outcome in the SICU: 8 patients (9%) died, having no clinical indication for higher-intensity care; 6 patients (7%) were transferred to general wards for palliation; and 14 patients (16%) needed an upgrade of cure intensity and were transferred to the ICU. Of these 14 patients, 9 died in the ICU. The total in-hospital mortality of COVID-19 patients, including patients transferred from the SICU to general wards in fair condition, was 27% (n = 24). Clinical, laboratory, and therapeutic characteristics between the 2 groups are shown in Table 4.

Patients who had a negative outcome were significantly older and had more comorbidities, as suggested by a significantly higher prevalence of diabetes and higher Charlson Comorbidity scores (reflecting the mortality risk based on age and comorbidities). The median MuLBSTA score, which estimates the 90-day mortality risk from viral pneumonia, was also higher in patients who had a negative outcome (9.33%). Symptom occurrence was not different in patients with a negative outcome (apart from cough, which was less frequent), but these patients underwent hospitalization earlier—since the appearance of their first COVID-19 symptoms—compared to patients who had a positive outcome. No difference was found in antihypertensive therapy with angiotensin-converting enzyme inhibitors or angiotensin-receptor blockers among outcome groups.

More pronounced laboratory abnormalities were found in patients who had a negative outcome, compared to patients who had a positive outcome: lower lymphocytes and higher C-reactive protein (CRP), procalcitonin, D-dimer, LDH, and NT-proBNP. We found no differences in the radiological distribution of pulmonary involvement in patients who had negative or positive outcomes, nor in the adopted medical treatment.

Data showed no difference in CPAP implementation in the 2 groups. However, prone positioning had been more frequently adopted in the group of patients who had a positive outcome, compared with patients who had a negative outcome. No differences of basal P/F were found in patients who had a negative or positive outcome, but the median P/F after 6 hours of prone position was significantly lower in patients who had a negative outcome. The delta P/F ratio did not differ in the 2 groups of patients.

Multivariate Analysis

A logistic regression model was created, including the variables significantly associated with outcomes in the univariate analysis (age, sex, history of diabetes, lymphocytes, CRP, procalcitonin, LDH, NT-proBNP, and D-dimer). In the multivariate analysis, independent predictors of a negative outcome were history of diabetes (odds ratio [OR], 8.22; 95% CI, 1.50-44.70; P =.015), high D-dimer values (OR, 1.28; CI, 1.04-1.57; P = .019), high LDH values (OR, 1.003; CI, 1.000-1.006; P = .039), and low lymphocytes count (OR, 0.996; CI, 0.993-0.999; P = .004).

Discussion

Role of Subintensive Units and Mortality

The novelty of our report is its attempt to investigate the specific group of COVID-19 patients admitted to a SICU. In Italy, SICUs receive acutely ill, spontaneously breathing patients who need (invasive) hemodynamic monitoring, vasoactive medication, renal replacement therapy, chest- tube placement, thrombolysis, and respiratory noninvasive support. The nurse-to-patient ratio is higher than for general wards (usually 1 nurse to every 4 or 5 patients), though lower than for ICUs. In northern Italy, a great number of COVID-19 patients have required this kind of high-intensity care during the pandemic: Noninvasive ventilation support had to be maintained for several days, pronation maneuvers required a high number of people 2 or 3 times a day, and strict monitoring had to be assured. The SICU setting allows patients to buy time as a bridge to progressive reduction of pulmonary involvement, sometimes preventing the need for intubation.

The high prevalence of negative outcomes in the SICU underlines the complexity of COVID-19 patients in this setting. In fact, published data about mortality for patients with severe COVID-19 pneumonia are similar to ours.^22,23

Clinical, Laboratory, and Imaging Data

Our analysis confirmed a high rate of comorbidities in COVID-19 patients²⁴ and their prognostic role with age.^25,26 A marked inflammatory milieu was a negative prognostic indicator, and associated concomitant bacterial superinfection could have led to a worse prognosis (procalcitonin was associated with negative outcomes).²⁷ The cardiovascular system was nevertheless stressed, as suggested by higher values of NT-proBNP in patients with negative outcomes, which could reflect sepsis-related systemic involvement.²⁸

It is known that the pulmonary damage caused by SARS-CoV-2 has a dynamic radiological and clinical course, with early areas of subsegmental consolidation, and bilateral ground-glass opacities predominating later in the course of the disease.²⁹ This could explain why in our population we found no specific radiological pattern leading to a worse outcome.

Medical Therapy

No specific pharmacological therapy was found to be associated with a positive outcome in our study, just like antiviral and immunomodulator therapies failed to demonstrate effectiveness in subsequent pandemic surges. The low statistical power of our study did not allow us to give insight into the effectiveness of steroids and heparin at any dosage.

PEEP Support and Prone Positioning

Continuous positive airway pressure was initiated in the majority of patients and maintained for several days. This was an absolute novelty, because we rarely had to keep patients in helmets for long. This was feasible thanks to the SICU’s high nurse-to-patient ratio and the possibility of providing monitored sedation. Patients who could no longer tolerate CPAP helmets or did not improve with CPAP support were evaluated with anesthetists for programming further management. No initial data on respiratory rate, level of hypoxemia, or oxygen support need (level of PEEP and Fio₂) could discriminate between outcomes.

Prone positioning during CPAP was implemented in 42% of our study population: P/F ratio amelioration after prone positioning was highly variable, ranging from very good P/F ratio improvements to few responses or no response. No significantly greater delta P/F ratio was seen after the first prone positioning cycle in patients who had a positive outcome, probably due to the small size of our population, but we observed a clear positive trend. Interestingly, patients showing a negative outcome had a lower percentage of long-term responses to prone positioning: 6 hours after resupination, they lost the benefit of prone positioning in terms of P/F ratio amelioration. Similarly, a greater number of patients tolerating prone positioning had a positive outcome. These data give insight on the possible benefits of prone positioning in a noninvasively supported cohort of patients, which has been mentioned in previous studies.^30,31

Outcomes and Variables Associated With Negative Outcomes

After correction for age and sex, we found in multiple regression analysis that higher D-dimer and LDH values, lymphopenia, and history of diabetes were independently associated with a worse outcome. Although our results had low statistical significance, we consider the trend of the obtained odds ratios important from a clinical point of view. These results could lead to greater attention being placed on COVID-19 patients who present with these characteristics upon their arrival to the ED because they have increased risk of death or intensive care need. Clinicians should consider SICU admission for these patients in order to guarantee closer monitoring and possibly more aggressive ventilatory treatments, earlier pronation, or earlier transfer to the ICU.

Limitations

The major limitation to our study is undoubtedly its statistical power, due to its relatively low patient population. Particularly, the small number of patients who underwent pronation did not allow speculation about the efficacy of this technique, although preliminary data seem promising. However, ours is among the first studies regarding patients with COVID-19 admitted to a SICU, and these preliminary data truthfully describe the Italian, and perhaps international, experience with the first surge of the pandemic.

Conclusions

Our data highlight the primary role of the SICU in COVID-19 in adequately treating critically ill patients who have high care needs different from intubation, and who require noninvasive ventilation for prolonged times as well as frequent pronation cycles. This setting of care may represent a valid, reliable, and effective option for critically ill respiratory patients. History of diabetes, lymphopenia, and high D-dimer and LDH values are independently associated with negative outcomes, and patients presenting with these characteristics should be strictly monitored.

Acknowledgments: The authors thank the Informatica System S.R.L., as well as Allessando Mendolia for the pro bono creation of the ISCovidCollect data collecting app.

Corresponding author: Sara Abram, MD, via Coppino, 12100 Cuneo, Italy; sara.abram84@gmail.com.

Disclosures: None.

From the Department of Emergency Medicine, Santa Croce e Carle Hospital, Cuneo, Italy (Drs. Abram, Tosello, Emanuele Bernardi, Allione, Cavalot, Dutto, Corsini, Martini, Sciolla, Sara Bernardi, and Lauria). From the School of Emergency Medicine, University of Turin, Turin, Italy (Drs. Paglietta and Giamello).

Objective: This retrospective and prospective cohort study was designed to describe the characteristics, treatments, and outcomes of patients with SARS-CoV-2 infection (COVID-19) admitted to subintensive care units (SICU) and to identify the variables associated with outcomes. SICUs have been extremely stressed during the pandemic, but most data regarding critically ill COVID-19 patients come from intensive care units (ICUs). Studies about COVID-19 patients in SICUs are lacking.

Setting and participants: The study included 88 COVID-19 patients admitted to our SICU in Cuneo, Italy, between March and May 2020.

Measurements: Clinical and ventilatory data were collected, and patients were divided by outcome. Multivariable logistic regression analysis examined the variables associated with negative outcomes (transfer to the ICU, palliation, or death in a SICU).

Results: A total of 60 patients (68%) had a positive outcome, and 28 patients (32%) had a negative outcome; 69 patients (78%) underwent continuous positive airway pressure (CPAP). Pronation (n = 37 [42%]) had been more frequently adopted in patients who had a positive outcome vs a negative outcome (n = 30 [50%] vs n = 7 [25%]; P = .048), and the median (interquartile range) Pao₂/Fio₂ ratio after 6 hours of prone positioning was lower in patients who had a negative outcome vs a positive outcome (144 [140-168] vs 249 [195-268], P = .006). Independent predictors of a negative outcome were diabetes (odds ratio [OR], 8.22; 95% CI, 1.50-44.70; P = .015), higher D-dimer (OR, 1.28; 95% CI, 1.04-1.57; P = .019), higher lactate dehydrogenase level (OR, 1.003; 95% CI, 1.000-1.006; P = .039), and lower lymphocytes count (OR, 0.996; 95% CI, 0.993-0.999; P = .004).

Conclusion: SICUs have a fundamental role in the treatment of critically ill patients with COVID-19, who require long-term CPAP and pronation cycles. Diabetes, lymphopenia, and high D-dimer and LDH levels are associated with negative outcomes.

Keywords: emergency medicine, noninvasive ventilation, prone position, continuous positive airway pressure.

The COVID-19 pandemic has led to large increases in hospital admissions. Subintensive care units (SICUs) are among the wards most under pressure worldwide,¹ dealing with the increased number of critically ill patients who need noninvasive ventilation, as well as serving as the best alternative to overfilled intensive care units (ICUs). In Italy, SICUs are playing a fundamental role in the management of COVID-19 patients, providing early treatment of respiratory failure by continuous noninvasive ventilation in order to reduce the need for intubation.^2-5 Nevertheless, the great majority of available data about critically ill COVID-19 patients comes from ICUs. Full studies about outcomes of patients in SICUs are lacking and need to be conducted.

We sought to evaluate the characteristics and outcomes of patients admitted to our SICU for COVID-19 to describe the treatments they needed and their impact on prognosis, and to identify the variables associated with patient outcomes.

Methods

Study Design

This cohort study used data from patients who were admitted in the very first weeks of the pandemic. Data were collected retrospectively as well as prospectively, since the ethical committee approved our project. The quality and quantity of data in the 2 groups were comparable.

Data were collected from electronic and written medical records gathered during the patient’s entire stay in our SICU. Data were entered in a database with limited and controlled access. This study complied with the Declaration of Helsinki and was approved by the local ethics committees (ID: MEDURG10).

Study Population

We studied 88 consecutive patients admitted to the SICU of the Santa Croce e Carle Teaching Hospital, Cuneo, Italy, for COVID-19, from March 8 to May 1, 2020. The diagnosis was based on acute respiratory failure associated with SARS-CoV-2 RNA detection on nasopharyngeal swab or tracheal aspirate and/or typical COVID-19 features on a pulmonary computed tomography (CT) scan.⁶ Exclusion criteria were age younger than 18 years and patient denial of permission to use their data for research purposes (the great majority of patients could actively give consent; for patients who were too sick to do so, family members were asked whether they were aware of any reason why the patient would deny consent).

The past medical history and recent symptoms description were obtained by manually reviewing medical records. Epidemiological exposure was defined as contact with SARS-CoV-2–positive people or staying in an epidemic outbreak area. Initial vital parameters, venous blood tests, arterial blood gas analysis, chest x-ray, as well as the result of the nasopharyngeal swab were gathered from the emergency department (ED) examination. (Additional swabs could be requested when the first one was negative but clinical suspicion for COVID-19 was high.) Upon admission to the SICU, a standardized panel of blood tests was performed, which was repeated the next day and then every 48 hours. Arterial blood gas analysis was performed when clinically indicated, at least twice a day, or following a scheduled time in patients undergoing pronation. Charlson Comorbidity Index⁷ and MuLBSTA score⁸ were calculated based on the collected data.

Imaging

Chest ultrasonography was performed in the ED at the time of hospitalization and once a day in the SICU. Pulmonary high-resolution computed tomography (HRCT) was performed when clinically indicated or when the results of nasopharyngeal swabs and/or x-ray results were discordant with COVID-19 clinical suspicion. Contrast CT was performed when pulmonary embolism was suspected.

Medical Therapy

Hydroxychloroquine, antiviral agents, tocilizumab, and ruxolitinib were used in the early phase of the pandemic, then were dismissed after evidence of no efficacy.^9-11 Steroids and low-molecular-weight heparin were used afterward. Enoxaparin was used at the standard prophylactic dosage, and 70% of the anticoagulant dosage was also adopted in patients with moderate-to-severe COVID-19 and D-dimer values >3 times the normal value.^12-14 Antibiotics were given when a bacterial superinfection was suspected.

Oxygen and Ventilatory Therapy

Oxygen support or noninvasive ventilation were started based on patients’ respiratory efficacy, estimated by respiratory rate and the ratio of partial pressure of arterial oxygen and fraction of inspired oxygen (P/F ratio).^15,16 Oxygen support was delivered through nasal cannula, Venturi mask, or reservoir mask. Noninvasive ventilation was performed by continuous positive airway pressure (CPAP) when the P/F ratio was <250 or the respiratory rate was >25 breaths per minute, using the helmet interface.^5,17 Prone positioning during CPAP^18-20 was adopted in patients meeting the acute respiratory distress syndrome (ARDS) criteria²¹ and having persistence of respiratory distress and P/F <300 after a 1-hour trial of CPAP.

The prone position was maintained based on patient tolerance. P/F ratio was measured before pronation (T0), after 1 hour of prone position (T1), before resupination (T2), and 6 hours after resupination (T3). With the same timing, the patient was asked to rate their comfort in each position, from 0 (lack of comfort) to 10 (optimal comfort). Delta P/F was defined as the difference between P/F at T3 and basal P/F at T0.

Outcomes

Positive outcomes were defined as patient discharge from the SICU or transfer to a lower-intensity care ward for treatment continuation. Negative outcomes were defined as need for transfer to the ICU, transfer to another ward for palliation, or death in the SICU.

Statistical Analysis

Continuous data are reported as median and interquartile range (IQR); normal distribution of variables was tested using the Shapiro-Wilk test. Categorical variables were reported as absolute number and percentage. The Mann-Whitney test was used to compare continuous variables between groups, and chi-square test with continuity correction was used for categorical variables. The variables that were most significantly associated with a negative outcome on the univariate analysis were included in a stepwise logistic regression analysis, in order to identify independent predictors of patient outcome. Statistical analysis was performed using JASP (JASP Team) software.

Study Population

Of the 88 patients included in the study, 70% were male; the median age was 66 years (IQR, 60-77). In most patients, the diagnosis of COVID-19 was derived from a positive SARS-CoV-2 nasopharyngeal swab. Six patients, however, maintained a negative swab at all determinations but had clinical and imaging features strongly suggesting COVID-19. No patients met the exclusion criteria. Most patients came from the ED (n = 58 [66%]) or general wards (n = 22 [25%]), while few were transferred from the ICU (n = 8 [9%]). The median length of stay in the SICU was 4 days (IQR, 2-7). An epidemiological link to affected persons or a known virus exposure was identifiable in 37 patients (42%).

Clinical, Laboratory, and Imaging Data

The clinical and anthropometric characteristics of patients are shown in Table 1. Hypertension and smoking habits were prevalent in our population, and the median Charlson Comorbidity Index was 3. Most patients experienced fever, dyspnea, and cough during the days before hospitalization.

Laboratory data showed a marked inflammatory milieu in all studied patients, both at baseline and after 24 and 72 hours. Lymphopenia was observed, along with a significant increase of lactate dehydrogenase (LDH), C-reactive protein (CPR), and D-dimer, and a mild increase of procalcitonin. N-terminal pro-brain natriuretic peptide (NT-proBNP) values were also increased, with normal troponin I values (Table 2).

Medical Therapy

Most patients (89%) received hydroxychloroquine, whereas steroids were used in one-third of the population (36%). Immunomodulators (tocilizumab and ruxolitinib) were restricted to 12 patients (14%). Empirical antiviral therapy was introduced in the first 41 patients (47%). Enoxaparin was the default agent for thromboembolism prophylaxis, and 6 patients (7%) received 70% of the anticoagulating dose.

Oxygen and Ventilatory Therapy

Basal median P/F ratio was 253 (IQR, 218-291), and respiratory rate at triage was 20 breaths/min (IQR, 16-28), underlining a moderate-to-severe respiratory insufficiency at presentation. A total of 69 patients (78%) underwent CPAP, with a median positive end-expiratory pressure (PEEP) of 10.0 cm H₂O (IQR, 7.5-10.0) and fraction of inspired oxygen (Fio₂) of 0.40 (IQR, 0.40-0.50). In 37 patients (42%) who received ongoing CPAP, prone positioning was adopted. In this subgroup, respiratory rate was not significantly different from baseline to resupination (24 vs 25 breaths/min). The median P/F improved from 197 (IQR, 154-236) at baseline to 217 (IQR, 180-262) after pronation (the duration of the prone position was variable, depending on patients’ tolerance: 1 to 6 hours or every pronation cycle). The median delta P/F ratio was 39.4 (IQR, –17.0 to 78.0).

Outcomes

A total of 28 patients (32%) had a negative outcome in the SICU: 8 patients (9%) died, having no clinical indication for higher-intensity care; 6 patients (7%) were transferred to general wards for palliation; and 14 patients (16%) needed an upgrade of cure intensity and were transferred to the ICU. Of these 14 patients, 9 died in the ICU. The total in-hospital mortality of COVID-19 patients, including patients transferred from the SICU to general wards in fair condition, was 27% (n = 24). Clinical, laboratory, and therapeutic characteristics between the 2 groups are shown in Table 4.

Patients who had a negative outcome were significantly older and had more comorbidities, as suggested by a significantly higher prevalence of diabetes and higher Charlson Comorbidity scores (reflecting the mortality risk based on age and comorbidities). The median MuLBSTA score, which estimates the 90-day mortality risk from viral pneumonia, was also higher in patients who had a negative outcome (9.33%). Symptom occurrence was not different in patients with a negative outcome (apart from cough, which was less frequent), but these patients underwent hospitalization earlier—since the appearance of their first COVID-19 symptoms—compared to patients who had a positive outcome. No difference was found in antihypertensive therapy with angiotensin-converting enzyme inhibitors or angiotensin-receptor blockers among outcome groups.

More pronounced laboratory abnormalities were found in patients who had a negative outcome, compared to patients who had a positive outcome: lower lymphocytes and higher C-reactive protein (CRP), procalcitonin, D-dimer, LDH, and NT-proBNP. We found no differences in the radiological distribution of pulmonary involvement in patients who had negative or positive outcomes, nor in the adopted medical treatment.

Data showed no difference in CPAP implementation in the 2 groups. However, prone positioning had been more frequently adopted in the group of patients who had a positive outcome, compared with patients who had a negative outcome. No differences of basal P/F were found in patients who had a negative or positive outcome, but the median P/F after 6 hours of prone position was significantly lower in patients who had a negative outcome. The delta P/F ratio did not differ in the 2 groups of patients.

Multivariate Analysis

A logistic regression model was created, including the variables significantly associated with outcomes in the univariate analysis (age, sex, history of diabetes, lymphocytes, CRP, procalcitonin, LDH, NT-proBNP, and D-dimer). In the multivariate analysis, independent predictors of a negative outcome were history of diabetes (odds ratio [OR], 8.22; 95% CI, 1.50-44.70; P =.015), high D-dimer values (OR, 1.28; CI, 1.04-1.57; P = .019), high LDH values (OR, 1.003; CI, 1.000-1.006; P = .039), and low lymphocytes count (OR, 0.996; CI, 0.993-0.999; P = .004).

Discussion

Role of Subintensive Units and Mortality

The novelty of our report is its attempt to investigate the specific group of COVID-19 patients admitted to a SICU. In Italy, SICUs receive acutely ill, spontaneously breathing patients who need (invasive) hemodynamic monitoring, vasoactive medication, renal replacement therapy, chest- tube placement, thrombolysis, and respiratory noninvasive support. The nurse-to-patient ratio is higher than for general wards (usually 1 nurse to every 4 or 5 patients), though lower than for ICUs. In northern Italy, a great number of COVID-19 patients have required this kind of high-intensity care during the pandemic: Noninvasive ventilation support had to be maintained for several days, pronation maneuvers required a high number of people 2 or 3 times a day, and strict monitoring had to be assured. The SICU setting allows patients to buy time as a bridge to progressive reduction of pulmonary involvement, sometimes preventing the need for intubation.

The high prevalence of negative outcomes in the SICU underlines the complexity of COVID-19 patients in this setting. In fact, published data about mortality for patients with severe COVID-19 pneumonia are similar to ours.^22,23

Clinical, Laboratory, and Imaging Data

Our analysis confirmed a high rate of comorbidities in COVID-19 patients²⁴ and their prognostic role with age.^25,26 A marked inflammatory milieu was a negative prognostic indicator, and associated concomitant bacterial superinfection could have led to a worse prognosis (procalcitonin was associated with negative outcomes).²⁷ The cardiovascular system was nevertheless stressed, as suggested by higher values of NT-proBNP in patients with negative outcomes, which could reflect sepsis-related systemic involvement.²⁸

It is known that the pulmonary damage caused by SARS-CoV-2 has a dynamic radiological and clinical course, with early areas of subsegmental consolidation, and bilateral ground-glass opacities predominating later in the course of the disease.²⁹ This could explain why in our population we found no specific radiological pattern leading to a worse outcome.

Medical Therapy

No specific pharmacological therapy was found to be associated with a positive outcome in our study, just like antiviral and immunomodulator therapies failed to demonstrate effectiveness in subsequent pandemic surges. The low statistical power of our study did not allow us to give insight into the effectiveness of steroids and heparin at any dosage.

PEEP Support and Prone Positioning

Continuous positive airway pressure was initiated in the majority of patients and maintained for several days. This was an absolute novelty, because we rarely had to keep patients in helmets for long. This was feasible thanks to the SICU’s high nurse-to-patient ratio and the possibility of providing monitored sedation. Patients who could no longer tolerate CPAP helmets or did not improve with CPAP support were evaluated with anesthetists for programming further management. No initial data on respiratory rate, level of hypoxemia, or oxygen support need (level of PEEP and Fio₂) could discriminate between outcomes.

Prone positioning during CPAP was implemented in 42% of our study population: P/F ratio amelioration after prone positioning was highly variable, ranging from very good P/F ratio improvements to few responses or no response. No significantly greater delta P/F ratio was seen after the first prone positioning cycle in patients who had a positive outcome, probably due to the small size of our population, but we observed a clear positive trend. Interestingly, patients showing a negative outcome had a lower percentage of long-term responses to prone positioning: 6 hours after resupination, they lost the benefit of prone positioning in terms of P/F ratio amelioration. Similarly, a greater number of patients tolerating prone positioning had a positive outcome. These data give insight on the possible benefits of prone positioning in a noninvasively supported cohort of patients, which has been mentioned in previous studies.^30,31

Outcomes and Variables Associated With Negative Outcomes

After correction for age and sex, we found in multiple regression analysis that higher D-dimer and LDH values, lymphopenia, and history of diabetes were independently associated with a worse outcome. Although our results had low statistical significance, we consider the trend of the obtained odds ratios important from a clinical point of view. These results could lead to greater attention being placed on COVID-19 patients who present with these characteristics upon their arrival to the ED because they have increased risk of death or intensive care need. Clinicians should consider SICU admission for these patients in order to guarantee closer monitoring and possibly more aggressive ventilatory treatments, earlier pronation, or earlier transfer to the ICU.

Limitations

The major limitation to our study is undoubtedly its statistical power, due to its relatively low patient population. Particularly, the small number of patients who underwent pronation did not allow speculation about the efficacy of this technique, although preliminary data seem promising. However, ours is among the first studies regarding patients with COVID-19 admitted to a SICU, and these preliminary data truthfully describe the Italian, and perhaps international, experience with the first surge of the pandemic.

Conclusions

Our data highlight the primary role of the SICU in COVID-19 in adequately treating critically ill patients who have high care needs different from intubation, and who require noninvasive ventilation for prolonged times as well as frequent pronation cycles. This setting of care may represent a valid, reliable, and effective option for critically ill respiratory patients. History of diabetes, lymphopenia, and high D-dimer and LDH values are independently associated with negative outcomes, and patients presenting with these characteristics should be strictly monitored.

Acknowledgments: The authors thank the Informatica System S.R.L., as well as Allessando Mendolia for the pro bono creation of the ISCovidCollect data collecting app.

Corresponding author: Sara Abram, MD, via Coppino, 12100 Cuneo, Italy; sara.abram84@gmail.com.

Disclosures: None.