Original Research

To the Editor:

Nail biopsy is an important surgical procedure for diagnosis of nail pathology. YouTube has become a potential instrument for physicians and patients to learn about medical procedures.^1,2 However, the sources, content, and quality of the information available have not been fully studied or characterized. Our objective was to analyze the efficiency of information and quality of YouTube videos on nail biopsies. We hypothesized that the quality of patient education and physician training videos would be unrepresentative on YouTube.

The term nail biopsy was searched on January 29, 2019, and filtered by relevance and rating using the default YouTube algorithm. Data were collected from the top 40 hits for the search term and filter coupling. All videos were viewed and sorted for nail biopsy procedures, and then those videos were categorized as being produced by a physician or other health care provider. The US medical board status of each physician videographer was determined using the American Board of Medical Specialties database.³ DISCERN criteria for assessing consumer health care information⁴ were used to interpret the videos by researchers (S.I. and S.R.L.) in this study.

From the queried search term collection, only 10 videos (1,023,423 combined views) were analyzed in this study (eTable). Although the other resulting videos were tagged as nail biopsy, they were excluded due to irrelevant content (eg, patient blogs, PowerPoints, nail avulsions). The mean age of the videos was 4 years (range, 4 days to 11 years), with a mean video length of 3.30 minutes (range, 49 seconds to 9.03 minutes). Four of 10 videos were performed for longitudinal melanonychia, and 5 of 10 videos were performed for melanonychia, clinically consistent with subungual hematoma. Dermatologists, plastic surgeons, and podiatrists produced the majority of the nail biopsy videos. The overall mean DISCERN rating was 1.60/5.00 (range, 1–3), meaning that the quality of content on nail biopsies was poor. This low DISCERN score signifies poor consumer health information. Video 2 (published in 2015) received a DISCERN score of 2 and received almost 1 million views, which is likely because the specific channel has a well-established subscriber pool (4.9 million subscribers). The highest DISCERN score of 3, demonstrating a tangential shave biopsy, was given to video 4 (published in 2010) and only received about 17,400 views (56 subscribers). Additionally, many videos lacked important information about the procedure. For instance, only 3 of 10 videos demonstrated the anesthetic technique and only 5 videos showed repair methods.

YouTube is an electronic learning source for general information; however, the content and quality of information on nail biopsy is not updated, reliable, or abundant. Patients undergoing nail biopsies are unlikely to find reliable and comprehensible information on YouTube; thus, there is a strong need for patient education in this area. In addition, physicians who did not learn to perform a nail biopsy during training are unlikely to learn this procedure from YouTube. Therefore, there is an unmet need for an outlet that will provide updated reliable content on nail biopsies geared toward both patients and physicians.

To the Editor:

Nail biopsy is an important surgical procedure for diagnosis of nail pathology. YouTube has become a potential instrument for physicians and patients to learn about medical procedures.^1,2 However, the sources, content, and quality of the information available have not been fully studied or characterized. Our objective was to analyze the efficiency of information and quality of YouTube videos on nail biopsies. We hypothesized that the quality of patient education and physician training videos would be unrepresentative on YouTube.

The term nail biopsy was searched on January 29, 2019, and filtered by relevance and rating using the default YouTube algorithm. Data were collected from the top 40 hits for the search term and filter coupling. All videos were viewed and sorted for nail biopsy procedures, and then those videos were categorized as being produced by a physician or other health care provider. The US medical board status of each physician videographer was determined using the American Board of Medical Specialties database.³ DISCERN criteria for assessing consumer health care information⁴ were used to interpret the videos by researchers (S.I. and S.R.L.) in this study.

From the queried search term collection, only 10 videos (1,023,423 combined views) were analyzed in this study (eTable). Although the other resulting videos were tagged as nail biopsy, they were excluded due to irrelevant content (eg, patient blogs, PowerPoints, nail avulsions). The mean age of the videos was 4 years (range, 4 days to 11 years), with a mean video length of 3.30 minutes (range, 49 seconds to 9.03 minutes). Four of 10 videos were performed for longitudinal melanonychia, and 5 of 10 videos were performed for melanonychia, clinically consistent with subungual hematoma. Dermatologists, plastic surgeons, and podiatrists produced the majority of the nail biopsy videos. The overall mean DISCERN rating was 1.60/5.00 (range, 1–3), meaning that the quality of content on nail biopsies was poor. This low DISCERN score signifies poor consumer health information. Video 2 (published in 2015) received a DISCERN score of 2 and received almost 1 million views, which is likely because the specific channel has a well-established subscriber pool (4.9 million subscribers). The highest DISCERN score of 3, demonstrating a tangential shave biopsy, was given to video 4 (published in 2010) and only received about 17,400 views (56 subscribers). Additionally, many videos lacked important information about the procedure. For instance, only 3 of 10 videos demonstrated the anesthetic technique and only 5 videos showed repair methods.

YouTube is an electronic learning source for general information; however, the content and quality of information on nail biopsy is not updated, reliable, or abundant. Patients undergoing nail biopsies are unlikely to find reliable and comprehensible information on YouTube; thus, there is a strong need for patient education in this area. In addition, physicians who did not learn to perform a nail biopsy during training are unlikely to learn this procedure from YouTube. Therefore, there is an unmet need for an outlet that will provide updated reliable content on nail biopsies geared toward both patients and physicians.

To the Editor:

Nail biopsy is an important surgical procedure for diagnosis of nail pathology. YouTube has become a potential instrument for physicians and patients to learn about medical procedures.^1,2 However, the sources, content, and quality of the information available have not been fully studied or characterized. Our objective was to analyze the efficiency of information and quality of YouTube videos on nail biopsies. We hypothesized that the quality of patient education and physician training videos would be unrepresentative on YouTube.

The term nail biopsy was searched on January 29, 2019, and filtered by relevance and rating using the default YouTube algorithm. Data were collected from the top 40 hits for the search term and filter coupling. All videos were viewed and sorted for nail biopsy procedures, and then those videos were categorized as being produced by a physician or other health care provider. The US medical board status of each physician videographer was determined using the American Board of Medical Specialties database.³ DISCERN criteria for assessing consumer health care information⁴ were used to interpret the videos by researchers (S.I. and S.R.L.) in this study.

From the queried search term collection, only 10 videos (1,023,423 combined views) were analyzed in this study (eTable). Although the other resulting videos were tagged as nail biopsy, they were excluded due to irrelevant content (eg, patient blogs, PowerPoints, nail avulsions). The mean age of the videos was 4 years (range, 4 days to 11 years), with a mean video length of 3.30 minutes (range, 49 seconds to 9.03 minutes). Four of 10 videos were performed for longitudinal melanonychia, and 5 of 10 videos were performed for melanonychia, clinically consistent with subungual hematoma. Dermatologists, plastic surgeons, and podiatrists produced the majority of the nail biopsy videos. The overall mean DISCERN rating was 1.60/5.00 (range, 1–3), meaning that the quality of content on nail biopsies was poor. This low DISCERN score signifies poor consumer health information. Video 2 (published in 2015) received a DISCERN score of 2 and received almost 1 million views, which is likely because the specific channel has a well-established subscriber pool (4.9 million subscribers). The highest DISCERN score of 3, demonstrating a tangential shave biopsy, was given to video 4 (published in 2010) and only received about 17,400 views (56 subscribers). Additionally, many videos lacked important information about the procedure. For instance, only 3 of 10 videos demonstrated the anesthetic technique and only 5 videos showed repair methods.

YouTube is an electronic learning source for general information; however, the content and quality of information on nail biopsy is not updated, reliable, or abundant. Patients undergoing nail biopsies are unlikely to find reliable and comprehensible information on YouTube; thus, there is a strong need for patient education in this area. In addition, physicians who did not learn to perform a nail biopsy during training are unlikely to learn this procedure from YouTube. Therefore, there is an unmet need for an outlet that will provide updated reliable content on nail biopsies geared toward both patients and physicians.

Psoriasis is a common chronic inflammatory disease affecting a diverse patient population, yet epidemiological and clinical data related to psoriasis in patients with skin of color are sparse. The Hispanic ethnic group includes a broad range of skin types and cultures. Prevalence of psoriasis in a Hispanic population has been reported as lower than in a white population¹; however, these data may be influenced by the finding that Hispanic patients are less likely to see a dermatologist when they have skin problems.² In addition, socioeconomic disparities and cultural variations among racial/ethnic groups may contribute to differences in access to care and thresholds for seeking care,³ leading to a tendency for more severe disease in skin of color and Hispanic ethnic groups.^4,5 Greater impairments in health-related quality of life have been reported in patients with skin of color and Hispanic racial/ethnic groups compared to white patients, independent of psoriasis severity.^4,6 Postinflammatory pigment alteration at the sites of resolving lesions, a common clinical feature in skin of color, may contribute to the impact of psoriasis on quality of life in patients with skin of color. Psoriasis in darker skin types also can present diagnostic challenges due to overlapping features with other papulosquamous disorders and less conspicuous erythema.⁷

We present a post hoc analysis of the treatment of moderate to severe psoriasis with a novel fixed-combination halobetasol propionate (HP) 0.01%–tazarotene (TAZ) 0.045% lotion in a Hispanic patient population. Historically, clinical trials for psoriasis have enrolled low proportions of Hispanic patients and other patients with skin of color; in this analysis, the Hispanic population (115/418) represented 28% of the total study population and provided valuable insights.

Methods

Study Design
Two phase 3 randomized controlled trials were conducted to demonstrate the efficacy and safety of HP/TAZ lotion. Patients with a clinical diagnosis of moderate or severe localized psoriasis (N=418) were randomized to receive HP/TAZ lotion or vehicle (2:1 ratio) once daily for 8 weeks with a 4-week posttreatment follow-up.^8,9 A post hoc analysis was conducted on data of the self-identified Hispanic population.

Assessments
Efficacy assessments included treatment success (at least a 2-grade improvement from baseline in the investigator global assessment [IGA] and a score of clear or almost clear) and impact on individual signs of psoriasis (at least a 2-grade improvement in erythema, plaque elevation, and scaling) at the target lesion. In addition, reduction in body surface area (BSA) was recorded, and an IGA×BSA score was calculated by multiplying IGA by BSA at each timepoint for each individual patient. A clinically meaningful improvement in disease severity (percentage of patients achieving a 75% reduction in IGA×BSA [IGA×BSA-75]) also was calculated.

Information on reported and observed adverse events (AEs) was obtained at each visit. The safety population included 112 participants (76 in the HP/TAZ group and 36 in the vehicle group).

Statistical Analysis
The statistical and analytical plan is detailed elsewhere⁹ and relevant to this post hoc analysis. No statistical analysis was carried out to compare data in the Hispanic population with either the overall study population or the non-Hispanic population.

Results

Overall, 115 Hispanic patients (27.5%) were enrolled (eFigure). Patients had a mean (standard deviation [SD]) age of 46.7 (13.12) years, and more than two-thirds were male (n=80, 69.6%).

Overall completion rates (80.0%) for Hispanic patients were similar to those in the overall study population, though there were more discontinuations in the vehicle group. The main reasons for treatment discontinuation among Hispanic patients were participant request (n=8, 7.0%), lost to follow-up (n=8, 7.0%), and AEs (n=4, 3.5%). Hispanic patients in this study had more severe disease—18.3% (n=21) had an IGA score of 4 compared to 13.5% (n=41) of non-Hispanic patients—and more severe erythema (19.1% vs 9.6%), plaque elevation (20.0% vs 10.2%), and scaling (15.7% vs 12.9%) compared to the non-Hispanic populations (Table).

 

Efficacy of HP/TAZ lotion in Hispanic patients was similar to the overall study populations,⁹ though maintenance of effect posttreatment appeared to be better. The incidence of treatment-related AEs also was lower.

Halobetasol propionate 0.01%–TAZ 0.045% lotion demonstrated statistically significant superiority based on treatment success compared to vehicle as early as week 4 (P=.034). By week 8, 39.3% of participants treated with HP/TAZ lotion achieved treatment success compared to 9.3% of participants in the vehicle group (P=.002)(Figure 1). Treatment success was maintained over the 4-week posttreatment period, whereby 40.5% of the HP/TAZ-treated participants were treatment successes at week 12 compared to only 4.1% of participants in the vehicle group (P<.001).

Improvements in psoriasis signs and symptoms at the target lesion were statistically significant compared to vehicle from week 2 (plaque elevation, P=.018) or week 4 (erythema, P=.004; scaling, P<.001)(Figure 2). By week 8, 46.8%, 58.1%, and 63.2% of participants showed at least a 2-grade improvement from baseline and were therefore treatment successes for erythema, plaque elevation, and scaling, respectively (all statistically significant [P<.001] compared to vehicle). The number of participants who achieved at least a 2-grade improvement in erythema with HP/TAZ lotion increased posttreatment from 46.8% to 53.0%.

Mean (SD) baseline BSA was 6.2 (3.07), and the mean (SD) size of the target lesion was 36.3 (21.85) cm². Overall, BSA also was significantly reduced in participants treated with HP/TAZ lotion compared to vehicle. At week 8, the mean percentage change from baseline was —40.7% compared to an increase (+10.1%) in the vehicle group (P=.002)(Figure 3). Improvements in BSA were maintained posttreatment, whereas in the vehicle group, mean (SD) BSA had increased to 6.1 (4.64).

Halobetasol propionate 0.01%–TAZ 0.045% lotion achieved a 50.5% reduction from baseline IGA×BSA by week 8 compared to an 8.5% increase with vehicle (P<.001)(Figure 4). Differences in treatment groups were significant from week 2 (P=.016). Efficacy was maintained posttreatment, with a 50.6% reduction from baseline IGA×BSA at week 12 compared to an increase of 13.6% in the vehicle group (P<.001). Again, although results were similar to the overall study population at week 8 (50.5% vs 51.9%), maintenance of effect was better posttreatment (50.6% vs 46.6%).¹⁰

A clinically meaningful effect (IGA×BSA-75) was achieved in 39.7% of Hispanic participants treated with HP/TAZ lotion compared to 8.1% of participants treated with vehicle (P<.001) at week 8. The benefits were significantly different from week 4 and more participants maintained a clinically meaningful effect posttreatment (43.1% vs 7.1%, P<.001)(Figure 5).

 

 

 

 

For Hispanic participants overall, 34 participants reported AEs: 26 (34.2%) treated with HP/TAZ lotion and 8 (22.2%) treated with vehicle (eTable). There was 1 (1.3%) serious AE in the HP/TAZ group. Most of the AEs were mild or moderate, with approximately half being related to study treatment. The most common treatment-related AEs in Hispanic participants treated with HP/TAZ lotion were contact dermatitis (n=3, 3.9%) and skin atrophy (n=3, 3.9%) compared to contact dermatitis (n=14, 7.2%) and application-site pain (n=7, 3.6%) in the non-Hispanic population. Pruritus was the most common AE in Hispanic participants treated with vehicle.

Comment

The large number of Hispanic patients in the 2 phase 3 trials^8,9 allowed for this valuable subgroup analysis on the topical treatment of Hispanic patients with plaque psoriasis. Validation of observed differences in maintenance of effect and tolerability warrant further study. Prior clinical studies in psoriasis have tended to enroll a small proportion of Hispanic patients without any post hoc analysis. For example, in a pooled analysis of 4 phase 3 trials with secukinumab, Hispanic patients accounted for only 16% of the overall population.¹¹ In our analysis, the Hispanic cohort represented 28% of the overall study population of 2 phase 3 studies investigating the efficacy, safety, and tolerability of HP/TAZ lotion in patients with moderate to severe psoriasis.^8,9 In addition, proportionately more Hispanic patients had severe disease (IGA of 4) or severe signs and symptoms of psoriasis (erythema, plaque elevation, and scaling) than the non-Hispanic population. This finding supports other studies that have suggested Hispanic patients with psoriasis tend to have more severe disease but also may reflect thresholds for seeking care.^3-5

Halobetasol propionate 0.01%–TAZ 0.045% lotion was significantly more effective than vehicle for all efficacy assessments. In general, efficacy results with HP/TAZ lotion were similar to those reported in the overall phase 3 study populations over the 8-week treatment period. The only noticeable difference was in the posttreatment period. In the overall study population, efficacy was maintained over the 4-week posttreatment period in the HP/TAZ group. In the Hispanic subpopulation, there appeared to be continued improvement in the number of participants achieving treatment success (IGA and erythema), clinically meaningful success, and further reductions in BSA. Although there is a paucity of studies evaluating psoriasis therapies in Hispanic populations, data on etanercept and secukinumab have been published.^6,11

Onset of effect also is an important aspect of treatment. In patients with skin of color, including patients of Hispanic ethnicity and higher Fitzpatrick skin phototypes, early clearance of lesions may help limit the severity and duration of postinflammatory pigment alteration. Improvements in IGA×BSA scores were significant compared to vehicle from week 2 (P=.016), and a clinically meaningful improvement with HP/TAZ lotion (IGA×BSA-75) was seen by week 4 (P=.024).

Halobetasol propionate 0.01%–TAZ 0.045% lotion was well tolerated, both in the 2 phase 3 studies and in the post hoc analysis of the Hispanic subpopulation. The incidence of skin atrophy (n=3, 3.9%) was more common vs the non-Hispanic population (n=2, 1.0%). Other common AEs—contact dermatitis, pruritus, and application-site pain—were more common in the non-Hispanic population.

A limitation of our analysis was that it was a post hoc analysis of the Hispanic participants. The phase 3 studies were not designed to specifically study the impact of treatment on ethnicity/race, though the number of Hispanic participants enrolled in the 2 studies was relatively high. The absence of Fitzpatrick skin phototypes in this data set is another limitation of this study.

Conclusion

Halobetasol propionate 0.01%–TAZ 0.045% lotion was associated with significant, rapid, and sustained reductions in disease severity in a Hispanic population with moderate to severe psoriasis that continued to show improvement posttreatment with good tolerability and safety.



Acknowledgments
We thank Brian Bulley, MSc (Konic Limited, United Kingdom), for assistance with the preparation of the manuscript. Ortho Dermatologics funded Konic’s activities pertaining to this manuscript.

Psoriasis is a common chronic inflammatory disease affecting a diverse patient population, yet epidemiological and clinical data related to psoriasis in patients with skin of color are sparse. The Hispanic ethnic group includes a broad range of skin types and cultures. Prevalence of psoriasis in a Hispanic population has been reported as lower than in a white population¹; however, these data may be influenced by the finding that Hispanic patients are less likely to see a dermatologist when they have skin problems.² In addition, socioeconomic disparities and cultural variations among racial/ethnic groups may contribute to differences in access to care and thresholds for seeking care,³ leading to a tendency for more severe disease in skin of color and Hispanic ethnic groups.^4,5 Greater impairments in health-related quality of life have been reported in patients with skin of color and Hispanic racial/ethnic groups compared to white patients, independent of psoriasis severity.^4,6 Postinflammatory pigment alteration at the sites of resolving lesions, a common clinical feature in skin of color, may contribute to the impact of psoriasis on quality of life in patients with skin of color. Psoriasis in darker skin types also can present diagnostic challenges due to overlapping features with other papulosquamous disorders and less conspicuous erythema.⁷

We present a post hoc analysis of the treatment of moderate to severe psoriasis with a novel fixed-combination halobetasol propionate (HP) 0.01%–tazarotene (TAZ) 0.045% lotion in a Hispanic patient population. Historically, clinical trials for psoriasis have enrolled low proportions of Hispanic patients and other patients with skin of color; in this analysis, the Hispanic population (115/418) represented 28% of the total study population and provided valuable insights.

Methods

Study Design
Two phase 3 randomized controlled trials were conducted to demonstrate the efficacy and safety of HP/TAZ lotion. Patients with a clinical diagnosis of moderate or severe localized psoriasis (N=418) were randomized to receive HP/TAZ lotion or vehicle (2:1 ratio) once daily for 8 weeks with a 4-week posttreatment follow-up.^8,9 A post hoc analysis was conducted on data of the self-identified Hispanic population.

Assessments
Efficacy assessments included treatment success (at least a 2-grade improvement from baseline in the investigator global assessment [IGA] and a score of clear or almost clear) and impact on individual signs of psoriasis (at least a 2-grade improvement in erythema, plaque elevation, and scaling) at the target lesion. In addition, reduction in body surface area (BSA) was recorded, and an IGA×BSA score was calculated by multiplying IGA by BSA at each timepoint for each individual patient. A clinically meaningful improvement in disease severity (percentage of patients achieving a 75% reduction in IGA×BSA [IGA×BSA-75]) also was calculated.

Information on reported and observed adverse events (AEs) was obtained at each visit. The safety population included 112 participants (76 in the HP/TAZ group and 36 in the vehicle group).

Statistical Analysis
The statistical and analytical plan is detailed elsewhere⁹ and relevant to this post hoc analysis. No statistical analysis was carried out to compare data in the Hispanic population with either the overall study population or the non-Hispanic population.

Results

Overall, 115 Hispanic patients (27.5%) were enrolled (eFigure). Patients had a mean (standard deviation [SD]) age of 46.7 (13.12) years, and more than two-thirds were male (n=80, 69.6%).

Overall completion rates (80.0%) for Hispanic patients were similar to those in the overall study population, though there were more discontinuations in the vehicle group. The main reasons for treatment discontinuation among Hispanic patients were participant request (n=8, 7.0%), lost to follow-up (n=8, 7.0%), and AEs (n=4, 3.5%). Hispanic patients in this study had more severe disease—18.3% (n=21) had an IGA score of 4 compared to 13.5% (n=41) of non-Hispanic patients—and more severe erythema (19.1% vs 9.6%), plaque elevation (20.0% vs 10.2%), and scaling (15.7% vs 12.9%) compared to the non-Hispanic populations (Table).

 

Efficacy of HP/TAZ lotion in Hispanic patients was similar to the overall study populations,⁹ though maintenance of effect posttreatment appeared to be better. The incidence of treatment-related AEs also was lower.

Halobetasol propionate 0.01%–TAZ 0.045% lotion demonstrated statistically significant superiority based on treatment success compared to vehicle as early as week 4 (P=.034). By week 8, 39.3% of participants treated with HP/TAZ lotion achieved treatment success compared to 9.3% of participants in the vehicle group (P=.002)(Figure 1). Treatment success was maintained over the 4-week posttreatment period, whereby 40.5% of the HP/TAZ-treated participants were treatment successes at week 12 compared to only 4.1% of participants in the vehicle group (P<.001).

Improvements in psoriasis signs and symptoms at the target lesion were statistically significant compared to vehicle from week 2 (plaque elevation, P=.018) or week 4 (erythema, P=.004; scaling, P<.001)(Figure 2). By week 8, 46.8%, 58.1%, and 63.2% of participants showed at least a 2-grade improvement from baseline and were therefore treatment successes for erythema, plaque elevation, and scaling, respectively (all statistically significant [P<.001] compared to vehicle). The number of participants who achieved at least a 2-grade improvement in erythema with HP/TAZ lotion increased posttreatment from 46.8% to 53.0%.

Mean (SD) baseline BSA was 6.2 (3.07), and the mean (SD) size of the target lesion was 36.3 (21.85) cm². Overall, BSA also was significantly reduced in participants treated with HP/TAZ lotion compared to vehicle. At week 8, the mean percentage change from baseline was —40.7% compared to an increase (+10.1%) in the vehicle group (P=.002)(Figure 3). Improvements in BSA were maintained posttreatment, whereas in the vehicle group, mean (SD) BSA had increased to 6.1 (4.64).

Halobetasol propionate 0.01%–TAZ 0.045% lotion achieved a 50.5% reduction from baseline IGA×BSA by week 8 compared to an 8.5% increase with vehicle (P<.001)(Figure 4). Differences in treatment groups were significant from week 2 (P=.016). Efficacy was maintained posttreatment, with a 50.6% reduction from baseline IGA×BSA at week 12 compared to an increase of 13.6% in the vehicle group (P<.001). Again, although results were similar to the overall study population at week 8 (50.5% vs 51.9%), maintenance of effect was better posttreatment (50.6% vs 46.6%).¹⁰

A clinically meaningful effect (IGA×BSA-75) was achieved in 39.7% of Hispanic participants treated with HP/TAZ lotion compared to 8.1% of participants treated with vehicle (P<.001) at week 8. The benefits were significantly different from week 4 and more participants maintained a clinically meaningful effect posttreatment (43.1% vs 7.1%, P<.001)(Figure 5).

 

 

 

 

For Hispanic participants overall, 34 participants reported AEs: 26 (34.2%) treated with HP/TAZ lotion and 8 (22.2%) treated with vehicle (eTable). There was 1 (1.3%) serious AE in the HP/TAZ group. Most of the AEs were mild or moderate, with approximately half being related to study treatment. The most common treatment-related AEs in Hispanic participants treated with HP/TAZ lotion were contact dermatitis (n=3, 3.9%) and skin atrophy (n=3, 3.9%) compared to contact dermatitis (n=14, 7.2%) and application-site pain (n=7, 3.6%) in the non-Hispanic population. Pruritus was the most common AE in Hispanic participants treated with vehicle.

Comment

The large number of Hispanic patients in the 2 phase 3 trials^8,9 allowed for this valuable subgroup analysis on the topical treatment of Hispanic patients with plaque psoriasis. Validation of observed differences in maintenance of effect and tolerability warrant further study. Prior clinical studies in psoriasis have tended to enroll a small proportion of Hispanic patients without any post hoc analysis. For example, in a pooled analysis of 4 phase 3 trials with secukinumab, Hispanic patients accounted for only 16% of the overall population.¹¹ In our analysis, the Hispanic cohort represented 28% of the overall study population of 2 phase 3 studies investigating the efficacy, safety, and tolerability of HP/TAZ lotion in patients with moderate to severe psoriasis.^8,9 In addition, proportionately more Hispanic patients had severe disease (IGA of 4) or severe signs and symptoms of psoriasis (erythema, plaque elevation, and scaling) than the non-Hispanic population. This finding supports other studies that have suggested Hispanic patients with psoriasis tend to have more severe disease but also may reflect thresholds for seeking care.^3-5

Halobetasol propionate 0.01%–TAZ 0.045% lotion was significantly more effective than vehicle for all efficacy assessments. In general, efficacy results with HP/TAZ lotion were similar to those reported in the overall phase 3 study populations over the 8-week treatment period. The only noticeable difference was in the posttreatment period. In the overall study population, efficacy was maintained over the 4-week posttreatment period in the HP/TAZ group. In the Hispanic subpopulation, there appeared to be continued improvement in the number of participants achieving treatment success (IGA and erythema), clinically meaningful success, and further reductions in BSA. Although there is a paucity of studies evaluating psoriasis therapies in Hispanic populations, data on etanercept and secukinumab have been published.^6,11

Onset of effect also is an important aspect of treatment. In patients with skin of color, including patients of Hispanic ethnicity and higher Fitzpatrick skin phototypes, early clearance of lesions may help limit the severity and duration of postinflammatory pigment alteration. Improvements in IGA×BSA scores were significant compared to vehicle from week 2 (P=.016), and a clinically meaningful improvement with HP/TAZ lotion (IGA×BSA-75) was seen by week 4 (P=.024).

Halobetasol propionate 0.01%–TAZ 0.045% lotion was well tolerated, both in the 2 phase 3 studies and in the post hoc analysis of the Hispanic subpopulation. The incidence of skin atrophy (n=3, 3.9%) was more common vs the non-Hispanic population (n=2, 1.0%). Other common AEs—contact dermatitis, pruritus, and application-site pain—were more common in the non-Hispanic population.

A limitation of our analysis was that it was a post hoc analysis of the Hispanic participants. The phase 3 studies were not designed to specifically study the impact of treatment on ethnicity/race, though the number of Hispanic participants enrolled in the 2 studies was relatively high. The absence of Fitzpatrick skin phototypes in this data set is another limitation of this study.

Conclusion

Halobetasol propionate 0.01%–TAZ 0.045% lotion was associated with significant, rapid, and sustained reductions in disease severity in a Hispanic population with moderate to severe psoriasis that continued to show improvement posttreatment with good tolerability and safety.



Acknowledgments
We thank Brian Bulley, MSc (Konic Limited, United Kingdom), for assistance with the preparation of the manuscript. Ortho Dermatologics funded Konic’s activities pertaining to this manuscript.

Psoriasis is a common chronic inflammatory disease affecting a diverse patient population, yet epidemiological and clinical data related to psoriasis in patients with skin of color are sparse. The Hispanic ethnic group includes a broad range of skin types and cultures. Prevalence of psoriasis in a Hispanic population has been reported as lower than in a white population¹; however, these data may be influenced by the finding that Hispanic patients are less likely to see a dermatologist when they have skin problems.² In addition, socioeconomic disparities and cultural variations among racial/ethnic groups may contribute to differences in access to care and thresholds for seeking care,³ leading to a tendency for more severe disease in skin of color and Hispanic ethnic groups.^4,5 Greater impairments in health-related quality of life have been reported in patients with skin of color and Hispanic racial/ethnic groups compared to white patients, independent of psoriasis severity.^4,6 Postinflammatory pigment alteration at the sites of resolving lesions, a common clinical feature in skin of color, may contribute to the impact of psoriasis on quality of life in patients with skin of color. Psoriasis in darker skin types also can present diagnostic challenges due to overlapping features with other papulosquamous disorders and less conspicuous erythema.⁷

We present a post hoc analysis of the treatment of moderate to severe psoriasis with a novel fixed-combination halobetasol propionate (HP) 0.01%–tazarotene (TAZ) 0.045% lotion in a Hispanic patient population. Historically, clinical trials for psoriasis have enrolled low proportions of Hispanic patients and other patients with skin of color; in this analysis, the Hispanic population (115/418) represented 28% of the total study population and provided valuable insights.

Methods

Study Design
Two phase 3 randomized controlled trials were conducted to demonstrate the efficacy and safety of HP/TAZ lotion. Patients with a clinical diagnosis of moderate or severe localized psoriasis (N=418) were randomized to receive HP/TAZ lotion or vehicle (2:1 ratio) once daily for 8 weeks with a 4-week posttreatment follow-up.^8,9 A post hoc analysis was conducted on data of the self-identified Hispanic population.

Assessments
Efficacy assessments included treatment success (at least a 2-grade improvement from baseline in the investigator global assessment [IGA] and a score of clear or almost clear) and impact on individual signs of psoriasis (at least a 2-grade improvement in erythema, plaque elevation, and scaling) at the target lesion. In addition, reduction in body surface area (BSA) was recorded, and an IGA×BSA score was calculated by multiplying IGA by BSA at each timepoint for each individual patient. A clinically meaningful improvement in disease severity (percentage of patients achieving a 75% reduction in IGA×BSA [IGA×BSA-75]) also was calculated.

Information on reported and observed adverse events (AEs) was obtained at each visit. The safety population included 112 participants (76 in the HP/TAZ group and 36 in the vehicle group).

Statistical Analysis
The statistical and analytical plan is detailed elsewhere⁹ and relevant to this post hoc analysis. No statistical analysis was carried out to compare data in the Hispanic population with either the overall study population or the non-Hispanic population.

Results

Overall, 115 Hispanic patients (27.5%) were enrolled (eFigure). Patients had a mean (standard deviation [SD]) age of 46.7 (13.12) years, and more than two-thirds were male (n=80, 69.6%).

Overall completion rates (80.0%) for Hispanic patients were similar to those in the overall study population, though there were more discontinuations in the vehicle group. The main reasons for treatment discontinuation among Hispanic patients were participant request (n=8, 7.0%), lost to follow-up (n=8, 7.0%), and AEs (n=4, 3.5%). Hispanic patients in this study had more severe disease—18.3% (n=21) had an IGA score of 4 compared to 13.5% (n=41) of non-Hispanic patients—and more severe erythema (19.1% vs 9.6%), plaque elevation (20.0% vs 10.2%), and scaling (15.7% vs 12.9%) compared to the non-Hispanic populations (Table).

 

Efficacy of HP/TAZ lotion in Hispanic patients was similar to the overall study populations,⁹ though maintenance of effect posttreatment appeared to be better. The incidence of treatment-related AEs also was lower.

Halobetasol propionate 0.01%–TAZ 0.045% lotion demonstrated statistically significant superiority based on treatment success compared to vehicle as early as week 4 (P=.034). By week 8, 39.3% of participants treated with HP/TAZ lotion achieved treatment success compared to 9.3% of participants in the vehicle group (P=.002)(Figure 1). Treatment success was maintained over the 4-week posttreatment period, whereby 40.5% of the HP/TAZ-treated participants were treatment successes at week 12 compared to only 4.1% of participants in the vehicle group (P<.001).

Improvements in psoriasis signs and symptoms at the target lesion were statistically significant compared to vehicle from week 2 (plaque elevation, P=.018) or week 4 (erythema, P=.004; scaling, P<.001)(Figure 2). By week 8, 46.8%, 58.1%, and 63.2% of participants showed at least a 2-grade improvement from baseline and were therefore treatment successes for erythema, plaque elevation, and scaling, respectively (all statistically significant [P<.001] compared to vehicle). The number of participants who achieved at least a 2-grade improvement in erythema with HP/TAZ lotion increased posttreatment from 46.8% to 53.0%.

Mean (SD) baseline BSA was 6.2 (3.07), and the mean (SD) size of the target lesion was 36.3 (21.85) cm². Overall, BSA also was significantly reduced in participants treated with HP/TAZ lotion compared to vehicle. At week 8, the mean percentage change from baseline was —40.7% compared to an increase (+10.1%) in the vehicle group (P=.002)(Figure 3). Improvements in BSA were maintained posttreatment, whereas in the vehicle group, mean (SD) BSA had increased to 6.1 (4.64).

Halobetasol propionate 0.01%–TAZ 0.045% lotion achieved a 50.5% reduction from baseline IGA×BSA by week 8 compared to an 8.5% increase with vehicle (P<.001)(Figure 4). Differences in treatment groups were significant from week 2 (P=.016). Efficacy was maintained posttreatment, with a 50.6% reduction from baseline IGA×BSA at week 12 compared to an increase of 13.6% in the vehicle group (P<.001). Again, although results were similar to the overall study population at week 8 (50.5% vs 51.9%), maintenance of effect was better posttreatment (50.6% vs 46.6%).¹⁰

A clinically meaningful effect (IGA×BSA-75) was achieved in 39.7% of Hispanic participants treated with HP/TAZ lotion compared to 8.1% of participants treated with vehicle (P<.001) at week 8. The benefits were significantly different from week 4 and more participants maintained a clinically meaningful effect posttreatment (43.1% vs 7.1%, P<.001)(Figure 5).

 

 

 

 

For Hispanic participants overall, 34 participants reported AEs: 26 (34.2%) treated with HP/TAZ lotion and 8 (22.2%) treated with vehicle (eTable). There was 1 (1.3%) serious AE in the HP/TAZ group. Most of the AEs were mild or moderate, with approximately half being related to study treatment. The most common treatment-related AEs in Hispanic participants treated with HP/TAZ lotion were contact dermatitis (n=3, 3.9%) and skin atrophy (n=3, 3.9%) compared to contact dermatitis (n=14, 7.2%) and application-site pain (n=7, 3.6%) in the non-Hispanic population. Pruritus was the most common AE in Hispanic participants treated with vehicle.

Comment

The large number of Hispanic patients in the 2 phase 3 trials^8,9 allowed for this valuable subgroup analysis on the topical treatment of Hispanic patients with plaque psoriasis. Validation of observed differences in maintenance of effect and tolerability warrant further study. Prior clinical studies in psoriasis have tended to enroll a small proportion of Hispanic patients without any post hoc analysis. For example, in a pooled analysis of 4 phase 3 trials with secukinumab, Hispanic patients accounted for only 16% of the overall population.¹¹ In our analysis, the Hispanic cohort represented 28% of the overall study population of 2 phase 3 studies investigating the efficacy, safety, and tolerability of HP/TAZ lotion in patients with moderate to severe psoriasis.^8,9 In addition, proportionately more Hispanic patients had severe disease (IGA of 4) or severe signs and symptoms of psoriasis (erythema, plaque elevation, and scaling) than the non-Hispanic population. This finding supports other studies that have suggested Hispanic patients with psoriasis tend to have more severe disease but also may reflect thresholds for seeking care.^3-5

Halobetasol propionate 0.01%–TAZ 0.045% lotion was significantly more effective than vehicle for all efficacy assessments. In general, efficacy results with HP/TAZ lotion were similar to those reported in the overall phase 3 study populations over the 8-week treatment period. The only noticeable difference was in the posttreatment period. In the overall study population, efficacy was maintained over the 4-week posttreatment period in the HP/TAZ group. In the Hispanic subpopulation, there appeared to be continued improvement in the number of participants achieving treatment success (IGA and erythema), clinically meaningful success, and further reductions in BSA. Although there is a paucity of studies evaluating psoriasis therapies in Hispanic populations, data on etanercept and secukinumab have been published.^6,11

Onset of effect also is an important aspect of treatment. In patients with skin of color, including patients of Hispanic ethnicity and higher Fitzpatrick skin phototypes, early clearance of lesions may help limit the severity and duration of postinflammatory pigment alteration. Improvements in IGA×BSA scores were significant compared to vehicle from week 2 (P=.016), and a clinically meaningful improvement with HP/TAZ lotion (IGA×BSA-75) was seen by week 4 (P=.024).

Halobetasol propionate 0.01%–TAZ 0.045% lotion was well tolerated, both in the 2 phase 3 studies and in the post hoc analysis of the Hispanic subpopulation. The incidence of skin atrophy (n=3, 3.9%) was more common vs the non-Hispanic population (n=2, 1.0%). Other common AEs—contact dermatitis, pruritus, and application-site pain—were more common in the non-Hispanic population.

A limitation of our analysis was that it was a post hoc analysis of the Hispanic participants. The phase 3 studies were not designed to specifically study the impact of treatment on ethnicity/race, though the number of Hispanic participants enrolled in the 2 studies was relatively high. The absence of Fitzpatrick skin phototypes in this data set is another limitation of this study.

Conclusion

Halobetasol propionate 0.01%–TAZ 0.045% lotion was associated with significant, rapid, and sustained reductions in disease severity in a Hispanic population with moderate to severe psoriasis that continued to show improvement posttreatment with good tolerability and safety.



Acknowledgments
We thank Brian Bulley, MSc (Konic Limited, United Kingdom), for assistance with the preparation of the manuscript. Ortho Dermatologics funded Konic’s activities pertaining to this manuscript.

Porphyria cutanea tarda (PCT) is the most common type of porphyria. The accumulation of porphyrin in various organ systems results from a deficiency of uroporphyrinogen decarboxylase (UROD).^1-3 Chronic hepatitis C virus (HCV) causes a hepatic decrease in hepcidin production, resulting in increased iron absorption. Iron loading and increased oxidative stress in the liver leads to nonporphyrin inhibition of UROD production and to oxidation of porphyrinogens to porphyrins.⁴ This in turn leads to accumulation of uroporphyrins and carboxylated metabolites that can be detected in urine.⁴

Signs of PCT include blisters, vesicles, and possibly milia developing on sun-exposed areas of the skin, such as the face, forearms, and dorsal hands.⁴ Case reports have demonstrated a resolution of PCT in patients with chronic HCV with treatment with direct-acting antivirals (DAAs), such as ledipasvir/sofosbuvir.^1,3 However, here we present 2 cases of patients who developed blistering diseases during treatment of chronic HCV with ledipasvir/sofosbuvir. Neither demonstrated complete resolution of symptoms during the treatment regimen.

Cases

Patient 1

A 63-year-old white male with a history of chronic HCV (genotype 1a), bipolar disorder, hyperlipidemia, tobacco dependence, and cirrhosis (F4 by elastography) presented with minimally to moderately painful blisters on his bilateral dorsal hands that had developed around weeks 8 to 9 of treatment with ledipasvir/sofosbuvir. The patient reported that no new blisters had appeared following completion of 12 weeks of treatment and that his current blisters were in various stages of healing. He reported alcohol use of 1 to 2 twelve-ounce beers daily and no history of dioxin exposure. His medications included doxepin, hydralazine, hydrochlorothiazide, quetiapine, folic acid, and thiamine. His hepatitis C viral load was 440,000 IU/mL prior to treatment. Tests for hepatitis B surface antigen and HIV antibodies were negative. His iron level was 135 µg/dL, total iron-binding capacity (TIBC) was 323 µg/dL, and ferritin was 299.0 ng/mL. His HFEgene was negative for mutations. Following 4 weeks of treatment with ledipasvir/sofosbuvir, a hepatitis C viral load was not detected.

A physical examination on presentation revealed erosions with overlying hemorrhagic crusts on the bilateral dorsal hands (Figure). 

The differential diagnosis included PCT, pseudo-PCT, bullous pemphigoid, bullous arthropod bite reaction, and epidermolysis bullosa acquisita. A punch biopsy of the lesion on the right dorsal hand demonstrated re-epithelialization of a previously formed subepidermal bullae deemed compatible with PCT or pseudo-PCT. A 24-hour high-performance liquid chromatography quantitative urine porphyrin showed greatly elevated levels of urine porphyrins, including uroporphyrins and heptacarboxylporphyrins, and slight elevations of hexcarboxyporphyrins, pentacarboxylporphyrins, and coproporphyrins indicating a diagnosis of PCT.

At the 4-month follow-up, the patient reported no new blister formations. A physical examination revealed well-healed scars and several clustered milia on bilateral dorsal hands with no active vesicles or bullae noted.

Patient 2

An African American male aged 63 years presented with a 1-month history of moderately painful blisters on his bilateral dorsal hands during treatment of chronic HCV (genotype 1a) with ledipasvir/sofosbuvir. His medical history included gout, tobacco and alcohol addiction, osteoarthritis, and hepatic fibrosis (F3 by elastography). The patient’s medications included allopurinol, lisinopril, and hydrochlorothiazide. He reported no history of dioxin exposure. On the day of presentation, he was on week 9 of the 12-week treatment ledipasvir/sofosbuvir regimen. Laboratory results included an initial HCV viral load of 1,618,605 IU/mL. Tests for hepatitis B surface antigen and HIV antibodies were negative. His iron was 191 µg/dL, TIBC 388 µg/dL, and ferritin 459.0 ng/mL. After 4 weeks of treatment, the patient’s hepatitis C viral load was undetectable.

 

A physical examination revealed several resolving erosions to his bilateral dorsal hands, some of which had overlying crusting along with one small hemorrhagic vesicle on the right dorsal hand. A punch biopsy of the hemorrhagic vesicle was performed and demonstrated a cell-poor subepidermal blister with festooning of the dermal papilla. A direct immunofluorescence study showed immunoglobulin (Ig) G fluorescence along the dermal-epidermal junction and within vessel walls in the superficial dermis. Weak IgM and C3 fluorescence also was noted within vessel walls in the superficial dermis. All of the patient findings and history were consistent with PCT, although pseudo-PCT also was a consideration. A 24-hour urine sample yielded negative results for porphobilinogen. Urine porphyrin test results were not available, leading to a presumptive histological diagnosis of PCT.

The patient completed 11 of the prescribed 12 weeks of ledipasvir/sofosbuvir. The blisters resolved shortly thereafter.

Discussion

PCT has a well-established association with chronic HCV infection.⁴ We present 2 cases of a blistering disease clinically and histologically compatible with PCT that developed in patients only after initiation of treatment for chronic HCV with ledipasvir/sofosbuvir. One case was confirmed as PCT on the basis of compatible histopathologic findings and a urine porphyrin assay that showed elevated levels of uroporphyrins and carboxylated metabolites. The second case was clinically and histologically suggestive of PCT but not confirmed by urine porphyrin testing. In both patients, after 8 to 9 weeks of a 12-week course of antiviral therapy, the blistering lesions were noted but appeared to be resolving, and no new lesions were noted after discontinuation of therapy. It appeared that the antiviral treatment temporally triggered the initiation of the blistering skin disease, and as the chronic HCV infection cleared after treatment, the blistering lesions also began to resolve.

Mechanistically, it is known that the virally-induced hepatic damage leads to inhibition of uroporphyrinogen decarboxylase, and the subsequent oxidation of porphyrinogens to porphyrins. Cofactors such as HIV infection also may contribute to development of PCT.⁵

De novo PCT has been documented during therapy using interferon and ribavirin.⁶ The hemolytic anemia and increased hepatic iron were implicated as potential etiologies.⁶ Patients with HCV and PCT treated with the newer direct-acting antiviral therapies have been described to have experienced improvement in PCT symptoms.³

Although there were rare reports of deterioration in renal and liver function,⁷ reactivation of HBV infection,⁸ and Stevens-Johnson syndrome⁹ with antiviral therapy, these complications were not observed in these patients. Both patients also had successful resolution of HCV infection, and by completion of the antiviral therapy, the blistering also resolved.

Conclusion

PCT is an extrahepatic manifestation of HCV infection. Health care providers should be aware of the association of chronic HCV infection with PCT. The findings of PCT should not result in the delay or discontinuation of antiviral therapy.

Porphyria cutanea tarda (PCT) is the most common type of porphyria. The accumulation of porphyrin in various organ systems results from a deficiency of uroporphyrinogen decarboxylase (UROD).^1-3 Chronic hepatitis C virus (HCV) causes a hepatic decrease in hepcidin production, resulting in increased iron absorption. Iron loading and increased oxidative stress in the liver leads to nonporphyrin inhibition of UROD production and to oxidation of porphyrinogens to porphyrins.⁴ This in turn leads to accumulation of uroporphyrins and carboxylated metabolites that can be detected in urine.⁴

Signs of PCT include blisters, vesicles, and possibly milia developing on sun-exposed areas of the skin, such as the face, forearms, and dorsal hands.⁴ Case reports have demonstrated a resolution of PCT in patients with chronic HCV with treatment with direct-acting antivirals (DAAs), such as ledipasvir/sofosbuvir.^1,3 However, here we present 2 cases of patients who developed blistering diseases during treatment of chronic HCV with ledipasvir/sofosbuvir. Neither demonstrated complete resolution of symptoms during the treatment regimen.

Cases

Patient 1

A 63-year-old white male with a history of chronic HCV (genotype 1a), bipolar disorder, hyperlipidemia, tobacco dependence, and cirrhosis (F4 by elastography) presented with minimally to moderately painful blisters on his bilateral dorsal hands that had developed around weeks 8 to 9 of treatment with ledipasvir/sofosbuvir. The patient reported that no new blisters had appeared following completion of 12 weeks of treatment and that his current blisters were in various stages of healing. He reported alcohol use of 1 to 2 twelve-ounce beers daily and no history of dioxin exposure. His medications included doxepin, hydralazine, hydrochlorothiazide, quetiapine, folic acid, and thiamine. His hepatitis C viral load was 440,000 IU/mL prior to treatment. Tests for hepatitis B surface antigen and HIV antibodies were negative. His iron level was 135 µg/dL, total iron-binding capacity (TIBC) was 323 µg/dL, and ferritin was 299.0 ng/mL. His HFEgene was negative for mutations. Following 4 weeks of treatment with ledipasvir/sofosbuvir, a hepatitis C viral load was not detected.

A physical examination on presentation revealed erosions with overlying hemorrhagic crusts on the bilateral dorsal hands (Figure). 

The differential diagnosis included PCT, pseudo-PCT, bullous pemphigoid, bullous arthropod bite reaction, and epidermolysis bullosa acquisita. A punch biopsy of the lesion on the right dorsal hand demonstrated re-epithelialization of a previously formed subepidermal bullae deemed compatible with PCT or pseudo-PCT. A 24-hour high-performance liquid chromatography quantitative urine porphyrin showed greatly elevated levels of urine porphyrins, including uroporphyrins and heptacarboxylporphyrins, and slight elevations of hexcarboxyporphyrins, pentacarboxylporphyrins, and coproporphyrins indicating a diagnosis of PCT.

At the 4-month follow-up, the patient reported no new blister formations. A physical examination revealed well-healed scars and several clustered milia on bilateral dorsal hands with no active vesicles or bullae noted.

Patient 2

An African American male aged 63 years presented with a 1-month history of moderately painful blisters on his bilateral dorsal hands during treatment of chronic HCV (genotype 1a) with ledipasvir/sofosbuvir. His medical history included gout, tobacco and alcohol addiction, osteoarthritis, and hepatic fibrosis (F3 by elastography). The patient’s medications included allopurinol, lisinopril, and hydrochlorothiazide. He reported no history of dioxin exposure. On the day of presentation, he was on week 9 of the 12-week treatment ledipasvir/sofosbuvir regimen. Laboratory results included an initial HCV viral load of 1,618,605 IU/mL. Tests for hepatitis B surface antigen and HIV antibodies were negative. His iron was 191 µg/dL, TIBC 388 µg/dL, and ferritin 459.0 ng/mL. After 4 weeks of treatment, the patient’s hepatitis C viral load was undetectable.

 

A physical examination revealed several resolving erosions to his bilateral dorsal hands, some of which had overlying crusting along with one small hemorrhagic vesicle on the right dorsal hand. A punch biopsy of the hemorrhagic vesicle was performed and demonstrated a cell-poor subepidermal blister with festooning of the dermal papilla. A direct immunofluorescence study showed immunoglobulin (Ig) G fluorescence along the dermal-epidermal junction and within vessel walls in the superficial dermis. Weak IgM and C3 fluorescence also was noted within vessel walls in the superficial dermis. All of the patient findings and history were consistent with PCT, although pseudo-PCT also was a consideration. A 24-hour urine sample yielded negative results for porphobilinogen. Urine porphyrin test results were not available, leading to a presumptive histological diagnosis of PCT.

The patient completed 11 of the prescribed 12 weeks of ledipasvir/sofosbuvir. The blisters resolved shortly thereafter.

Discussion

PCT has a well-established association with chronic HCV infection.⁴ We present 2 cases of a blistering disease clinically and histologically compatible with PCT that developed in patients only after initiation of treatment for chronic HCV with ledipasvir/sofosbuvir. One case was confirmed as PCT on the basis of compatible histopathologic findings and a urine porphyrin assay that showed elevated levels of uroporphyrins and carboxylated metabolites. The second case was clinically and histologically suggestive of PCT but not confirmed by urine porphyrin testing. In both patients, after 8 to 9 weeks of a 12-week course of antiviral therapy, the blistering lesions were noted but appeared to be resolving, and no new lesions were noted after discontinuation of therapy. It appeared that the antiviral treatment temporally triggered the initiation of the blistering skin disease, and as the chronic HCV infection cleared after treatment, the blistering lesions also began to resolve.

Mechanistically, it is known that the virally-induced hepatic damage leads to inhibition of uroporphyrinogen decarboxylase, and the subsequent oxidation of porphyrinogens to porphyrins. Cofactors such as HIV infection also may contribute to development of PCT.⁵

De novo PCT has been documented during therapy using interferon and ribavirin.⁶ The hemolytic anemia and increased hepatic iron were implicated as potential etiologies.⁶ Patients with HCV and PCT treated with the newer direct-acting antiviral therapies have been described to have experienced improvement in PCT symptoms.³

Although there were rare reports of deterioration in renal and liver function,⁷ reactivation of HBV infection,⁸ and Stevens-Johnson syndrome⁹ with antiviral therapy, these complications were not observed in these patients. Both patients also had successful resolution of HCV infection, and by completion of the antiviral therapy, the blistering also resolved.

Conclusion

PCT is an extrahepatic manifestation of HCV infection. Health care providers should be aware of the association of chronic HCV infection with PCT. The findings of PCT should not result in the delay or discontinuation of antiviral therapy.

Porphyria cutanea tarda (PCT) is the most common type of porphyria. The accumulation of porphyrin in various organ systems results from a deficiency of uroporphyrinogen decarboxylase (UROD).^1-3 Chronic hepatitis C virus (HCV) causes a hepatic decrease in hepcidin production, resulting in increased iron absorption. Iron loading and increased oxidative stress in the liver leads to nonporphyrin inhibition of UROD production and to oxidation of porphyrinogens to porphyrins.⁴ This in turn leads to accumulation of uroporphyrins and carboxylated metabolites that can be detected in urine.⁴

Signs of PCT include blisters, vesicles, and possibly milia developing on sun-exposed areas of the skin, such as the face, forearms, and dorsal hands.⁴ Case reports have demonstrated a resolution of PCT in patients with chronic HCV with treatment with direct-acting antivirals (DAAs), such as ledipasvir/sofosbuvir.^1,3 However, here we present 2 cases of patients who developed blistering diseases during treatment of chronic HCV with ledipasvir/sofosbuvir. Neither demonstrated complete resolution of symptoms during the treatment regimen.

Cases

Patient 1

A 63-year-old white male with a history of chronic HCV (genotype 1a), bipolar disorder, hyperlipidemia, tobacco dependence, and cirrhosis (F4 by elastography) presented with minimally to moderately painful blisters on his bilateral dorsal hands that had developed around weeks 8 to 9 of treatment with ledipasvir/sofosbuvir. The patient reported that no new blisters had appeared following completion of 12 weeks of treatment and that his current blisters were in various stages of healing. He reported alcohol use of 1 to 2 twelve-ounce beers daily and no history of dioxin exposure. His medications included doxepin, hydralazine, hydrochlorothiazide, quetiapine, folic acid, and thiamine. His hepatitis C viral load was 440,000 IU/mL prior to treatment. Tests for hepatitis B surface antigen and HIV antibodies were negative. His iron level was 135 µg/dL, total iron-binding capacity (TIBC) was 323 µg/dL, and ferritin was 299.0 ng/mL. His HFEgene was negative for mutations. Following 4 weeks of treatment with ledipasvir/sofosbuvir, a hepatitis C viral load was not detected.

A physical examination on presentation revealed erosions with overlying hemorrhagic crusts on the bilateral dorsal hands (Figure). 

The differential diagnosis included PCT, pseudo-PCT, bullous pemphigoid, bullous arthropod bite reaction, and epidermolysis bullosa acquisita. A punch biopsy of the lesion on the right dorsal hand demonstrated re-epithelialization of a previously formed subepidermal bullae deemed compatible with PCT or pseudo-PCT. A 24-hour high-performance liquid chromatography quantitative urine porphyrin showed greatly elevated levels of urine porphyrins, including uroporphyrins and heptacarboxylporphyrins, and slight elevations of hexcarboxyporphyrins, pentacarboxylporphyrins, and coproporphyrins indicating a diagnosis of PCT.

At the 4-month follow-up, the patient reported no new blister formations. A physical examination revealed well-healed scars and several clustered milia on bilateral dorsal hands with no active vesicles or bullae noted.

Patient 2

An African American male aged 63 years presented with a 1-month history of moderately painful blisters on his bilateral dorsal hands during treatment of chronic HCV (genotype 1a) with ledipasvir/sofosbuvir. His medical history included gout, tobacco and alcohol addiction, osteoarthritis, and hepatic fibrosis (F3 by elastography). The patient’s medications included allopurinol, lisinopril, and hydrochlorothiazide. He reported no history of dioxin exposure. On the day of presentation, he was on week 9 of the 12-week treatment ledipasvir/sofosbuvir regimen. Laboratory results included an initial HCV viral load of 1,618,605 IU/mL. Tests for hepatitis B surface antigen and HIV antibodies were negative. His iron was 191 µg/dL, TIBC 388 µg/dL, and ferritin 459.0 ng/mL. After 4 weeks of treatment, the patient’s hepatitis C viral load was undetectable.

 

A physical examination revealed several resolving erosions to his bilateral dorsal hands, some of which had overlying crusting along with one small hemorrhagic vesicle on the right dorsal hand. A punch biopsy of the hemorrhagic vesicle was performed and demonstrated a cell-poor subepidermal blister with festooning of the dermal papilla. A direct immunofluorescence study showed immunoglobulin (Ig) G fluorescence along the dermal-epidermal junction and within vessel walls in the superficial dermis. Weak IgM and C3 fluorescence also was noted within vessel walls in the superficial dermis. All of the patient findings and history were consistent with PCT, although pseudo-PCT also was a consideration. A 24-hour urine sample yielded negative results for porphobilinogen. Urine porphyrin test results were not available, leading to a presumptive histological diagnosis of PCT.

The patient completed 11 of the prescribed 12 weeks of ledipasvir/sofosbuvir. The blisters resolved shortly thereafter.

Discussion

PCT has a well-established association with chronic HCV infection.⁴ We present 2 cases of a blistering disease clinically and histologically compatible with PCT that developed in patients only after initiation of treatment for chronic HCV with ledipasvir/sofosbuvir. One case was confirmed as PCT on the basis of compatible histopathologic findings and a urine porphyrin assay that showed elevated levels of uroporphyrins and carboxylated metabolites. The second case was clinically and histologically suggestive of PCT but not confirmed by urine porphyrin testing. In both patients, after 8 to 9 weeks of a 12-week course of antiviral therapy, the blistering lesions were noted but appeared to be resolving, and no new lesions were noted after discontinuation of therapy. It appeared that the antiviral treatment temporally triggered the initiation of the blistering skin disease, and as the chronic HCV infection cleared after treatment, the blistering lesions also began to resolve.

Mechanistically, it is known that the virally-induced hepatic damage leads to inhibition of uroporphyrinogen decarboxylase, and the subsequent oxidation of porphyrinogens to porphyrins. Cofactors such as HIV infection also may contribute to development of PCT.⁵

De novo PCT has been documented during therapy using interferon and ribavirin.⁶ The hemolytic anemia and increased hepatic iron were implicated as potential etiologies.⁶ Patients with HCV and PCT treated with the newer direct-acting antiviral therapies have been described to have experienced improvement in PCT symptoms.³

Although there were rare reports of deterioration in renal and liver function,⁷ reactivation of HBV infection,⁸ and Stevens-Johnson syndrome⁹ with antiviral therapy, these complications were not observed in these patients. Both patients also had successful resolution of HCV infection, and by completion of the antiviral therapy, the blistering also resolved.

Conclusion

PCT is an extrahepatic manifestation of HCV infection. Health care providers should be aware of the association of chronic HCV infection with PCT. The findings of PCT should not result in the delay or discontinuation of antiviral therapy.

Sepsis is a major public health concern: 10% of patients with sepsis die, and mortality quadruples with progression to septic shock.¹ Systemic inflammatory response syndrome (SIRS) criteria, originally published in 1992, are commonly used to detect sepsis, but as early as 2001, these criteria were recognized as lacking specificity.² Nonetheless, the use of SIRS criteria has persisted in practice. Sepsis was redefined in Sepsis-3 (2016) to guide earlier and more appropriate identification and treatment, which has been shown to greatly improve patient outcomes.^1,3 Key recommendations in Sepsis 3 included eliminating SIRS criteria, defining organ dysfunction by the Sequential Organ Failure Assessment (SOFA) score, and introducing the quick SOFA (qSOFA) score.¹

The qSOFA combines 3 clinical variables to provide a rapid, simple bedside score that measures the likelihood of poor outcomes, such as admission to an intensive care unit (ICU) or mortality in adults with suspected infection.^1,3 The qSOFA score is intended to aid healthcare professionals in more timely stratification of those patients who need escalated care to prevent deterioration.¹ The assessment also has been explored as a screening tool for sepsis in clinical practice; however, limited data exists concerning the comparative utility of qSOFA and SIRS in this capacity, and study results are inconsistent.^4-6

The most important attribute of a screening tool is high sensitivity, but high specificity also is desired. The qSOFA could supplant SIRS as a screening tool for sepsis if it maintained similarly high sensitivity but achieved superior specificity. Therefore, our primary objective for this study was to determine the effectiveness of qSOFA as a screening assessment for sepsis in the setting of a general inpatient medicine service by contrasting the sensitivity and specificity of qSOFA with SIRS in predicting sepsis, using a retrospective chart review design.

Methods

Administrative data from the Department of Veterans Affairs (VA) Corporate Data Warehouse were accessed via the VA Informatics and Computing Infrastructure (VINCI) and used to identify VA inpatient admissions and obtain the laboratory and vital sign data necessary to calculate SIRS, qSOFA, and SOFA scores. The data were supplemented by manual review of VA health records to obtain information that was not readily available in administrative records, including septic shock outcomes and laboratory and vital sign data obtained in the ICU. This study was approved by the institutional review board at the University of Iowa and the research and development committee at the Iowa City VA Medical Center (ICVAMC).

Patients

The study population included veterans admitted to the nonsurgical medicine unit at ICVAMC between August 1, 2014 and August 1, 2016 who were transferred to an ICU after admission; direct ICU admissions were not included as the qSOFA has been shown in studies to be more beneficial and offer better predictive validity outside the ICU. Excluding these direct admissions prevented any potential skewing of the data. To control for possible selection bias, veterans also were excluded if they transferred from another facility, were admitted under observation status, or if they had been admitted within the prior 30 days. These patients may have been more critically ill than those who presented directly to our facility and any prior treatment could affect the clinical status of the patient and assessment for sepsis at the time of presentation to the VA. Veterans were further required to have evidence of suspected infection based on manual review of the health record, which was determined by receipt of an antibiotic relevant to the empiric treatment of sepsis within 48 hours of admission.

 

Sepsis and Septic Shock Assessment Tools

As outlined in the Sepsis-3 guidelines, sepsis was defined as suspected or confirmed infection with an acute change in the SOFA score of ≥ 2 points, which is assumed to be 0 in those not known to have preexisting dysfunction.¹ The SOFA score includes variables from the respiratory, coagulation, hepatic, cardiovascular, renal, and central nervous systems.¹ Septic shock was defined as vasopressor administration and a serum lactic acid level > 2 mmol/L occurring up to 24 hours apart and within 3 days of the first antibiotic dose administered.

The SIRS assessment includes 4 clinical variables (temperature, heart rate, respiratory rate, and white blood cell count) while qSOFA is comprised of 3 variables (respiratory rate, systolic blood pressure, and altered mental status).¹ With both assessments, a score ≥ 2 is considered positive, which indicates increased risk for sepsis in patients with suspected infection.¹ In keeping with existing studies, qSOFA and SIRS assessments were scored using maximum values found within 48 hours before and 24 hours after the first administered antibiotic dose.³

Outcomes

The primary outcome variable was the presence of sepsis in adults with evidence of infection within 48 hours of admission. Secondary outcome measures included 30-day mortality and septic shock.

Performance between the SIRS and qSOFA assessments was contrasted using sensitivity, specificity, and positive and negative predictive value measurements. Associations of qSOFA and SIRS with septic shock and 30-day mortality were evaluated using a 2-tailed Fisher’s exact test with a threshold of α = 0.05 to determine statistical significance.

Results

The study sample of 481 veterans had a mean age of 67.4 years, 94% were male, and 91.1% were white (Table 1). 

When predicting risk for sepsis, the qSOFA demonstrated lower sensitivity than SIRS (44.7% vs 80.0%) but higher specificity (83.6% vs 25.7%) and higher positive predictive value (75.5% vs 54.8%) than did SIRS (Table 2). Specificity and positive predictive value results indicated a good probability that veterans with positive qSOFA assessments actually had sepsis.

Scores for qSOFA, but not SIRS, were significantly associated with septic shock (Fisher’s exact test; qSOFA: P = .009; SIRS: P = .58) (Table 3). 

Both assessments were significantly associated with increased risk for 30-day mortality (Fisher’s exact test; qSOFA: P < .001; SIRS: P = .025). In an additional analysis, scores for SOFA were not significantly associated with septic shock (Fisher’s exact test, P = .13) but were significantly associated with an increased risk for 30-day mortality (Fisher’s exact test, P = .016) (Table 4).

Discussion

High sensitivity is critical for a sepsis screening tool. To be clinically useful, it has been suggested that biomarkers predicting poor outcomes for sepsis should have a sensitivity of > 80%.⁴ Although qSOFA demonstrated greater specificity than SIRS in our study (83.6% vs 25.7%), qSOFA showed lower sensitivity (44.7% vs 80.0%), which resulted in a greater potential for false negatives; 55.3% of those with sepsis would go undetected. Therefore, our study does not support qSOFA as a better screening assessment than SIRS for sepsis in the veteran population.

 

Most studies concur with our findings of low sensitivity and high specificity of qSOFA. In a systematic review and meta-analysis, Serafim and colleagues identified 10 studies published after Sepsis-3 that reported sensitivity or specificity of qSOFA and SIRS for sepsis diagnosis.⁵ Seven of the 10 studies reported sensitivities and favored SIRS in the diagnosis of sepsis (Relative risk: 1.32; 95% CI: 0.40-2.24; P < .0001; I² = 100%). The authors noted that substantial heterogeneity among studies, including differences in study design, sample size, and criteria for determination of infection, was an important limitation. In addition, most studies that contrast qSOFA and SIRS center on prognostic value in predicting mortality, rather than as a screening test for a diagnosis of sepsis.

We concluded SIRS was more sensitive and thus superior to qSOFA when used as a screening tool for sepsis but conceded that more prospective and homogenous investigations were necessary. To our knowledge, only 1 published study has deviated from this conclusion and reported comparable sensitivity between SIRS (92%) and qSOFA (90%).⁶ Our study adds to existing literature as it is the first conducted in a veteran population. Additionally, we performed our investigation in a general medicine population with methods similar to existing literature, including the key study validating clinical criteria for sepsis by Seymour and colleagues.³

Limitations

This study is not without limitations, including potential misclassification of cases if essential data points were not available during data collection via health record review or the data points were not representative of a true change from baseline (eg, the Glasgow Coma Scale score for altered mental status in the qSOFA or the SOFA score for organ dysfunction). Generalizability of the results also may be limited due to our retrospective, single-center design and characteristics typical of a veteran population (eg, older, white males). Additionally, many veterans were excluded from the study if they transferred from another facility. These veterans may have been more critically ill than those who presented directly to our facility, which possibly introduced selection bias.

Conclusion

Our findings do not support use of the qSOFA as a suitable replacement for SIRS as a sepsis screening tool among patients with suspected infection in the general medicine inpatient setting. The clinical concern with SIRS is that unfavorable specificity leads to unnecessary antibiotic exposure among patients who are falsely positive. While qSOFA has demonstrated higher specificity, its use would cause many sepsis cases to go undetected due to the technique’s low sensitivity. Frequent false negative qSOFA results could thus serve to impede, rather than enhance, early recognition and intervention for sepsis.

The ideal sepsis screening tool is rapid and possesses high sensitivity and specificity to promptly identify and manage sepsis and avert unfavorable outcomes such as septic shock and death. While the SIRS criteria do not satisfy these ideal features, its measurement characteristics are more suitable for the application of sepsis screening than the qSOFA and should thus remain the standard tool in this setting. Future prospectively designed studies with more uniform methodologies are necessary to ascertain the most effective approach to identify sepsis for which novel screening approaches with more clinically suitable measurement properties are greatly needed.

Acknowledgements
This research was supported by the Iowa City VA Health Care System, Department of Pharmacy Services. Additional support was provided by the Health Services Research and Development Service, Department of Veterans Affairs.

 

Sepsis is a major public health concern: 10% of patients with sepsis die, and mortality quadruples with progression to septic shock.¹ Systemic inflammatory response syndrome (SIRS) criteria, originally published in 1992, are commonly used to detect sepsis, but as early as 2001, these criteria were recognized as lacking specificity.² Nonetheless, the use of SIRS criteria has persisted in practice. Sepsis was redefined in Sepsis-3 (2016) to guide earlier and more appropriate identification and treatment, which has been shown to greatly improve patient outcomes.^1,3 Key recommendations in Sepsis 3 included eliminating SIRS criteria, defining organ dysfunction by the Sequential Organ Failure Assessment (SOFA) score, and introducing the quick SOFA (qSOFA) score.¹

The qSOFA combines 3 clinical variables to provide a rapid, simple bedside score that measures the likelihood of poor outcomes, such as admission to an intensive care unit (ICU) or mortality in adults with suspected infection.^1,3 The qSOFA score is intended to aid healthcare professionals in more timely stratification of those patients who need escalated care to prevent deterioration.¹ The assessment also has been explored as a screening tool for sepsis in clinical practice; however, limited data exists concerning the comparative utility of qSOFA and SIRS in this capacity, and study results are inconsistent.^4-6

The most important attribute of a screening tool is high sensitivity, but high specificity also is desired. The qSOFA could supplant SIRS as a screening tool for sepsis if it maintained similarly high sensitivity but achieved superior specificity. Therefore, our primary objective for this study was to determine the effectiveness of qSOFA as a screening assessment for sepsis in the setting of a general inpatient medicine service by contrasting the sensitivity and specificity of qSOFA with SIRS in predicting sepsis, using a retrospective chart review design.

Methods

Administrative data from the Department of Veterans Affairs (VA) Corporate Data Warehouse were accessed via the VA Informatics and Computing Infrastructure (VINCI) and used to identify VA inpatient admissions and obtain the laboratory and vital sign data necessary to calculate SIRS, qSOFA, and SOFA scores. The data were supplemented by manual review of VA health records to obtain information that was not readily available in administrative records, including septic shock outcomes and laboratory and vital sign data obtained in the ICU. This study was approved by the institutional review board at the University of Iowa and the research and development committee at the Iowa City VA Medical Center (ICVAMC).

Patients

The study population included veterans admitted to the nonsurgical medicine unit at ICVAMC between August 1, 2014 and August 1, 2016 who were transferred to an ICU after admission; direct ICU admissions were not included as the qSOFA has been shown in studies to be more beneficial and offer better predictive validity outside the ICU. Excluding these direct admissions prevented any potential skewing of the data. To control for possible selection bias, veterans also were excluded if they transferred from another facility, were admitted under observation status, or if they had been admitted within the prior 30 days. These patients may have been more critically ill than those who presented directly to our facility and any prior treatment could affect the clinical status of the patient and assessment for sepsis at the time of presentation to the VA. Veterans were further required to have evidence of suspected infection based on manual review of the health record, which was determined by receipt of an antibiotic relevant to the empiric treatment of sepsis within 48 hours of admission.

 

Sepsis and Septic Shock Assessment Tools

As outlined in the Sepsis-3 guidelines, sepsis was defined as suspected or confirmed infection with an acute change in the SOFA score of ≥ 2 points, which is assumed to be 0 in those not known to have preexisting dysfunction.¹ The SOFA score includes variables from the respiratory, coagulation, hepatic, cardiovascular, renal, and central nervous systems.¹ Septic shock was defined as vasopressor administration and a serum lactic acid level > 2 mmol/L occurring up to 24 hours apart and within 3 days of the first antibiotic dose administered.

The SIRS assessment includes 4 clinical variables (temperature, heart rate, respiratory rate, and white blood cell count) while qSOFA is comprised of 3 variables (respiratory rate, systolic blood pressure, and altered mental status).¹ With both assessments, a score ≥ 2 is considered positive, which indicates increased risk for sepsis in patients with suspected infection.¹ In keeping with existing studies, qSOFA and SIRS assessments were scored using maximum values found within 48 hours before and 24 hours after the first administered antibiotic dose.³

Outcomes

The primary outcome variable was the presence of sepsis in adults with evidence of infection within 48 hours of admission. Secondary outcome measures included 30-day mortality and septic shock.

Performance between the SIRS and qSOFA assessments was contrasted using sensitivity, specificity, and positive and negative predictive value measurements. Associations of qSOFA and SIRS with septic shock and 30-day mortality were evaluated using a 2-tailed Fisher’s exact test with a threshold of α = 0.05 to determine statistical significance.

Results

The study sample of 481 veterans had a mean age of 67.4 years, 94% were male, and 91.1% were white (Table 1). 

When predicting risk for sepsis, the qSOFA demonstrated lower sensitivity than SIRS (44.7% vs 80.0%) but higher specificity (83.6% vs 25.7%) and higher positive predictive value (75.5% vs 54.8%) than did SIRS (Table 2). Specificity and positive predictive value results indicated a good probability that veterans with positive qSOFA assessments actually had sepsis.

Scores for qSOFA, but not SIRS, were significantly associated with septic shock (Fisher’s exact test; qSOFA: P = .009; SIRS: P = .58) (Table 3). 

Both assessments were significantly associated with increased risk for 30-day mortality (Fisher’s exact test; qSOFA: P < .001; SIRS: P = .025). In an additional analysis, scores for SOFA were not significantly associated with septic shock (Fisher’s exact test, P = .13) but were significantly associated with an increased risk for 30-day mortality (Fisher’s exact test, P = .016) (Table 4).

Discussion

High sensitivity is critical for a sepsis screening tool. To be clinically useful, it has been suggested that biomarkers predicting poor outcomes for sepsis should have a sensitivity of > 80%.⁴ Although qSOFA demonstrated greater specificity than SIRS in our study (83.6% vs 25.7%), qSOFA showed lower sensitivity (44.7% vs 80.0%), which resulted in a greater potential for false negatives; 55.3% of those with sepsis would go undetected. Therefore, our study does not support qSOFA as a better screening assessment than SIRS for sepsis in the veteran population.

 

Most studies concur with our findings of low sensitivity and high specificity of qSOFA. In a systematic review and meta-analysis, Serafim and colleagues identified 10 studies published after Sepsis-3 that reported sensitivity or specificity of qSOFA and SIRS for sepsis diagnosis.⁵ Seven of the 10 studies reported sensitivities and favored SIRS in the diagnosis of sepsis (Relative risk: 1.32; 95% CI: 0.40-2.24; P < .0001; I² = 100%). The authors noted that substantial heterogeneity among studies, including differences in study design, sample size, and criteria for determination of infection, was an important limitation. In addition, most studies that contrast qSOFA and SIRS center on prognostic value in predicting mortality, rather than as a screening test for a diagnosis of sepsis.

We concluded SIRS was more sensitive and thus superior to qSOFA when used as a screening tool for sepsis but conceded that more prospective and homogenous investigations were necessary. To our knowledge, only 1 published study has deviated from this conclusion and reported comparable sensitivity between SIRS (92%) and qSOFA (90%).⁶ Our study adds to existing literature as it is the first conducted in a veteran population. Additionally, we performed our investigation in a general medicine population with methods similar to existing literature, including the key study validating clinical criteria for sepsis by Seymour and colleagues.³

Limitations

This study is not without limitations, including potential misclassification of cases if essential data points were not available during data collection via health record review or the data points were not representative of a true change from baseline (eg, the Glasgow Coma Scale score for altered mental status in the qSOFA or the SOFA score for organ dysfunction). Generalizability of the results also may be limited due to our retrospective, single-center design and characteristics typical of a veteran population (eg, older, white males). Additionally, many veterans were excluded from the study if they transferred from another facility. These veterans may have been more critically ill than those who presented directly to our facility, which possibly introduced selection bias.

Conclusion

Our findings do not support use of the qSOFA as a suitable replacement for SIRS as a sepsis screening tool among patients with suspected infection in the general medicine inpatient setting. The clinical concern with SIRS is that unfavorable specificity leads to unnecessary antibiotic exposure among patients who are falsely positive. While qSOFA has demonstrated higher specificity, its use would cause many sepsis cases to go undetected due to the technique’s low sensitivity. Frequent false negative qSOFA results could thus serve to impede, rather than enhance, early recognition and intervention for sepsis.

The ideal sepsis screening tool is rapid and possesses high sensitivity and specificity to promptly identify and manage sepsis and avert unfavorable outcomes such as septic shock and death. While the SIRS criteria do not satisfy these ideal features, its measurement characteristics are more suitable for the application of sepsis screening than the qSOFA and should thus remain the standard tool in this setting. Future prospectively designed studies with more uniform methodologies are necessary to ascertain the most effective approach to identify sepsis for which novel screening approaches with more clinically suitable measurement properties are greatly needed.

Acknowledgements
This research was supported by the Iowa City VA Health Care System, Department of Pharmacy Services. Additional support was provided by the Health Services Research and Development Service, Department of Veterans Affairs.

 

Sepsis is a major public health concern: 10% of patients with sepsis die, and mortality quadruples with progression to septic shock.¹ Systemic inflammatory response syndrome (SIRS) criteria, originally published in 1992, are commonly used to detect sepsis, but as early as 2001, these criteria were recognized as lacking specificity.² Nonetheless, the use of SIRS criteria has persisted in practice. Sepsis was redefined in Sepsis-3 (2016) to guide earlier and more appropriate identification and treatment, which has been shown to greatly improve patient outcomes.^1,3 Key recommendations in Sepsis 3 included eliminating SIRS criteria, defining organ dysfunction by the Sequential Organ Failure Assessment (SOFA) score, and introducing the quick SOFA (qSOFA) score.¹

The qSOFA combines 3 clinical variables to provide a rapid, simple bedside score that measures the likelihood of poor outcomes, such as admission to an intensive care unit (ICU) or mortality in adults with suspected infection.^1,3 The qSOFA score is intended to aid healthcare professionals in more timely stratification of those patients who need escalated care to prevent deterioration.¹ The assessment also has been explored as a screening tool for sepsis in clinical practice; however, limited data exists concerning the comparative utility of qSOFA and SIRS in this capacity, and study results are inconsistent.^4-6

The most important attribute of a screening tool is high sensitivity, but high specificity also is desired. The qSOFA could supplant SIRS as a screening tool for sepsis if it maintained similarly high sensitivity but achieved superior specificity. Therefore, our primary objective for this study was to determine the effectiveness of qSOFA as a screening assessment for sepsis in the setting of a general inpatient medicine service by contrasting the sensitivity and specificity of qSOFA with SIRS in predicting sepsis, using a retrospective chart review design.

Methods

Administrative data from the Department of Veterans Affairs (VA) Corporate Data Warehouse were accessed via the VA Informatics and Computing Infrastructure (VINCI) and used to identify VA inpatient admissions and obtain the laboratory and vital sign data necessary to calculate SIRS, qSOFA, and SOFA scores. The data were supplemented by manual review of VA health records to obtain information that was not readily available in administrative records, including septic shock outcomes and laboratory and vital sign data obtained in the ICU. This study was approved by the institutional review board at the University of Iowa and the research and development committee at the Iowa City VA Medical Center (ICVAMC).

Patients

The study population included veterans admitted to the nonsurgical medicine unit at ICVAMC between August 1, 2014 and August 1, 2016 who were transferred to an ICU after admission; direct ICU admissions were not included as the qSOFA has been shown in studies to be more beneficial and offer better predictive validity outside the ICU. Excluding these direct admissions prevented any potential skewing of the data. To control for possible selection bias, veterans also were excluded if they transferred from another facility, were admitted under observation status, or if they had been admitted within the prior 30 days. These patients may have been more critically ill than those who presented directly to our facility and any prior treatment could affect the clinical status of the patient and assessment for sepsis at the time of presentation to the VA. Veterans were further required to have evidence of suspected infection based on manual review of the health record, which was determined by receipt of an antibiotic relevant to the empiric treatment of sepsis within 48 hours of admission.

 

Sepsis and Septic Shock Assessment Tools

As outlined in the Sepsis-3 guidelines, sepsis was defined as suspected or confirmed infection with an acute change in the SOFA score of ≥ 2 points, which is assumed to be 0 in those not known to have preexisting dysfunction.¹ The SOFA score includes variables from the respiratory, coagulation, hepatic, cardiovascular, renal, and central nervous systems.¹ Septic shock was defined as vasopressor administration and a serum lactic acid level > 2 mmol/L occurring up to 24 hours apart and within 3 days of the first antibiotic dose administered.

The SIRS assessment includes 4 clinical variables (temperature, heart rate, respiratory rate, and white blood cell count) while qSOFA is comprised of 3 variables (respiratory rate, systolic blood pressure, and altered mental status).¹ With both assessments, a score ≥ 2 is considered positive, which indicates increased risk for sepsis in patients with suspected infection.¹ In keeping with existing studies, qSOFA and SIRS assessments were scored using maximum values found within 48 hours before and 24 hours after the first administered antibiotic dose.³

Outcomes

The primary outcome variable was the presence of sepsis in adults with evidence of infection within 48 hours of admission. Secondary outcome measures included 30-day mortality and septic shock.

Performance between the SIRS and qSOFA assessments was contrasted using sensitivity, specificity, and positive and negative predictive value measurements. Associations of qSOFA and SIRS with septic shock and 30-day mortality were evaluated using a 2-tailed Fisher’s exact test with a threshold of α = 0.05 to determine statistical significance.

Results

The study sample of 481 veterans had a mean age of 67.4 years, 94% were male, and 91.1% were white (Table 1). 

When predicting risk for sepsis, the qSOFA demonstrated lower sensitivity than SIRS (44.7% vs 80.0%) but higher specificity (83.6% vs 25.7%) and higher positive predictive value (75.5% vs 54.8%) than did SIRS (Table 2). Specificity and positive predictive value results indicated a good probability that veterans with positive qSOFA assessments actually had sepsis.

Scores for qSOFA, but not SIRS, were significantly associated with septic shock (Fisher’s exact test; qSOFA: P = .009; SIRS: P = .58) (Table 3). 

Both assessments were significantly associated with increased risk for 30-day mortality (Fisher’s exact test; qSOFA: P < .001; SIRS: P = .025). In an additional analysis, scores for SOFA were not significantly associated with septic shock (Fisher’s exact test, P = .13) but were significantly associated with an increased risk for 30-day mortality (Fisher’s exact test, P = .016) (Table 4).

Discussion

High sensitivity is critical for a sepsis screening tool. To be clinically useful, it has been suggested that biomarkers predicting poor outcomes for sepsis should have a sensitivity of > 80%.⁴ Although qSOFA demonstrated greater specificity than SIRS in our study (83.6% vs 25.7%), qSOFA showed lower sensitivity (44.7% vs 80.0%), which resulted in a greater potential for false negatives; 55.3% of those with sepsis would go undetected. Therefore, our study does not support qSOFA as a better screening assessment than SIRS for sepsis in the veteran population.

 

Most studies concur with our findings of low sensitivity and high specificity of qSOFA. In a systematic review and meta-analysis, Serafim and colleagues identified 10 studies published after Sepsis-3 that reported sensitivity or specificity of qSOFA and SIRS for sepsis diagnosis.⁵ Seven of the 10 studies reported sensitivities and favored SIRS in the diagnosis of sepsis (Relative risk: 1.32; 95% CI: 0.40-2.24; P < .0001; I² = 100%). The authors noted that substantial heterogeneity among studies, including differences in study design, sample size, and criteria for determination of infection, was an important limitation. In addition, most studies that contrast qSOFA and SIRS center on prognostic value in predicting mortality, rather than as a screening test for a diagnosis of sepsis.

We concluded SIRS was more sensitive and thus superior to qSOFA when used as a screening tool for sepsis but conceded that more prospective and homogenous investigations were necessary. To our knowledge, only 1 published study has deviated from this conclusion and reported comparable sensitivity between SIRS (92%) and qSOFA (90%).⁶ Our study adds to existing literature as it is the first conducted in a veteran population. Additionally, we performed our investigation in a general medicine population with methods similar to existing literature, including the key study validating clinical criteria for sepsis by Seymour and colleagues.³

Limitations

This study is not without limitations, including potential misclassification of cases if essential data points were not available during data collection via health record review or the data points were not representative of a true change from baseline (eg, the Glasgow Coma Scale score for altered mental status in the qSOFA or the SOFA score for organ dysfunction). Generalizability of the results also may be limited due to our retrospective, single-center design and characteristics typical of a veteran population (eg, older, white males). Additionally, many veterans were excluded from the study if they transferred from another facility. These veterans may have been more critically ill than those who presented directly to our facility, which possibly introduced selection bias.

Conclusion

Our findings do not support use of the qSOFA as a suitable replacement for SIRS as a sepsis screening tool among patients with suspected infection in the general medicine inpatient setting. The clinical concern with SIRS is that unfavorable specificity leads to unnecessary antibiotic exposure among patients who are falsely positive. While qSOFA has demonstrated higher specificity, its use would cause many sepsis cases to go undetected due to the technique’s low sensitivity. Frequent false negative qSOFA results could thus serve to impede, rather than enhance, early recognition and intervention for sepsis.

The ideal sepsis screening tool is rapid and possesses high sensitivity and specificity to promptly identify and manage sepsis and avert unfavorable outcomes such as septic shock and death. While the SIRS criteria do not satisfy these ideal features, its measurement characteristics are more suitable for the application of sepsis screening than the qSOFA and should thus remain the standard tool in this setting. Future prospectively designed studies with more uniform methodologies are necessary to ascertain the most effective approach to identify sepsis for which novel screening approaches with more clinically suitable measurement properties are greatly needed.

Acknowledgements
This research was supported by the Iowa City VA Health Care System, Department of Pharmacy Services. Additional support was provided by the Health Services Research and Development Service, Department of Veterans Affairs.

 

Population studies show high prevalence of chronic hepatitis C virus (HCV) infection among veterans, especially Vietnam War era veterans.^1,2 The development of safe and efficacious direct-acting antiviral (DAA) medications to treat HCV infection made the majority of those infected eligible for treatment. However, the large number of veterans needing DAA treatment stressed the resources of the US Department of Veterans Affairs (VA) health care system. This occurred while Congress was focused on reducing wait times for veterans receiving care at the VA.

Congress passed the Veterans Access, Choice, and Accountability Act (Choice) on August 7, 2014, leading to the creation of the Veterans Choice Program. Legislators felt there were inappropriate delays in care at the VA, and the Choice program was meant to address this problem and provide an “apples-to-apples comparison [of the VA] with non-VA hospitals.”³

Congress acknowledged the importance of curing HCV in the veteran population and allocated $1.5 billion for fiscal year (FY) 2016 for DAAs. The VA Central Office (VACO) carefully monitored these resources. The first policy memorandum from VACO for HCV care, issued on May 21, 2015, recommended that the sickest patients who will benefit from the treatment “receive priority over those who are less ill.”^4,5 Those who met criteria for advanced liver disease were prioritized for treatment at the VA, while those who did not meet criteria were given the option of receiving treatment through Choice, or waiting for a change in policy.⁶ Over time, revisions to the guidelines relaxed the criteria for VA treatment eligibility, and on February 24, 2016, all restrictions on HCV treatment at the VA were lifted.^7,8

The aim of this study was to provide a comparison of VA and non-VA care, specifically to determine whether care provided through Choice was timelier and more cost effective than care provided by the VA, and whether there was a quality difference. The high prevalence among veterans, well-established standards of care, and finite treatment course with clear indicators of success and failure makes HCV treatment an ideal disease with which to make this comparison.

Methods

We retrospectively analyzed the VA electronic health records of all veterans seen in the VA Loma Linda Healthcare System (VALLHCS) Hepatology clinic for chronic HCV infection during FY 2016 who were referred to Choice for HCV treatment. One hundred veterans met these criteria, encompassing the Choice population; 71 were seen at least once by a non-VA (Choice) health care provider (HCP) and 61 completed a treatment regimen through Choice. Treatment completion was defined as cessation of medication after the planned duration of therapy, or early termination of medication without resumption by that HCP. The Choice population was matched to an equal number of veterans who received HCV treatment from VALLHCS HCPs.

Data collected included age, gender, HCV genotype, determinants of liver fibrosis, and treatment success (defined as sustained virologic response at 12 weeks after the last dose of medication [SVR12]). Determinants of liver fibrosis included documented cirrhosis or complications of cirrhosis, Fibrosis-4 score (Fib-4), and platelet count.

Treatment failures were categorized as nonresponse (defined as detectable HCV viral load at the end of treatment), relapse (defined as an undetectable HCV viral load at the end of treatment with a subsequent positive test), and early termination (defined as a failure to complete the planned treatment regimen). Documented patient nonadherence, medical comorbidities that affected the treatment protocol, mental health diagnoses, and active social issues (defined as active or history of heavy alcohol use, active or history of illicit drug use, lack of social support, and homelessness) were noted.

Timeliness of delivery of care was measured in days. For the VA group, the wait time was defined as the date the consult for HCV treatment was placed to the date of the initial appointment with the HCV treatment provider. For the Choice group, the wait time was defined as the date the referral to the Choice program was made to the date of the initial appointment with the Choice HCP. Treatment regimens were evaluated for appropriateness based on guidelines from VACO and the American Association for the Study of Liver Diseases.^9-11

Tests performed by Choice providers were evaluated for whether they were relevant to HCV care and whether similar data already were available from VALLHCS. Tests that were not indicated were identified as unnecessary costs incurred by the Choice program, as were tests that had to be repeated at the VA because of a lack of documentation from the Choice provider. All medications given inappropriately were considered added costs. Medicare reimbursement rates for the most applicable Current Procedural Terminology (CPT) code and VA national contract pricing for medications were used for calculations. This study was approved by the VALLHCS institutional review board.

 

Statistical Analysis

IBM (Armonk, NY) Statistical Package for Social Sciences software was used to evaluate for differences in Fib-4, platelet count, prevalence of cirrhosis, prevalence of medical comorbidities, prevalence of mental health comorbidities, prevalence of the social issues defined in the Methods section, time from referral to time of appointment date, and SVR12 rate between the VA and Choice groups.

Exclusions

There were 15 veterans in the VA group who had a wait time of > 100 days. Of these, 5 (33%) were initially Choice referrals, but due to negative interactions with the Choice provider, the veterans returned to VALLHCS for care. Two of the 15 (13%) did not keep appointments and were lost to follow up. Six of the 15 (40%) had medical comorbidities that required more immediate attention, so HCV treatment initiation was deliberately moved back. The final 2 veterans scheduled their appointments unusually far apart, artificially increasing their wait time. Given that these were unique situations and some of the veterans received care from both Choice and VA providers, a decision was made to exclude these individuals from the study.

It has been shown that platelet count correlates with degree of liver fibrosis, a concept that is the basis for the Fib-4 scoring system.¹² Studies have shown that platelet count is a survival predictor in those with cirrhosis, and thrombocytopenia is a negative predictor of HCV treatment success using peginterferon and ribavirin.^13,14 Therefore, the VA memorandum automatically assigned the sickest individuals to the VA for HCV treatment. The goal of this study was to compare the impact of factors other than stage of fibrosis on HCV treatment success, which is why the 12 veterans with platelet count < 100,000 in the VA group were excluded. There were no veterans with platelet count < 100,000 in the Choice group.

When comparing SVR12 rates between the VA and Choice groups, every veteran treated at VALLHCS in FY 2016 was included, increasing the number in the VA group from 100 to 320 and therefore the power of this comparison.

Results

A summary of the statistical analysis can be found in Table 1. The genotype distribution was consistent with epidemiological studies, including those specific to veterans.^15,16 There were statistically significant differences (P < .001) in mean Fib-4 and mean platelet count. The VA group had a higher Fib-4 and lower platelet count. Seventy-four percent of the VA population was defined as cirrhotic, while only 3% of the Choice population met similar criteria (P < .001). The VA and Choice groups were similar in terms of age, gender, and genotype distribution (Table 2).

The VA group was found to have a higher prevalence of comorbidities that affected HCV treatment. These conditions included but were not limited to: chronic kidney disease that precluded the use of certain medications, any condition that required medication with a known interaction with DAAs (ie, proton pump inhibitors, statins, and amiodarone), and cirrhosis if it impacted the treatment regimen. The difference in the prevalence of mental health comorbidities was not significant (P = .39), but there was a higher prevalence of social issues among the VA group (P = .002).

The mean wait time from referral to appointment was 28.6 days for the VA group and 42.3 days for the Choice group (P < .001), indicating that a Choice referral took longer to complete than a referral within the VA for HCV treatment. Thirty of the 71 (42%) veterans seen by a Choice provider accrued extraneous cost, with a mean additional cost of $8,561.40 per veteran. In the Choice group, 61 veterans completed a treatment regimen with the Choice HCP. Fifty-five veterans completed treatment and had available SVR12 data (6 were lost to follow up without SVR12 testing) and 50 (91%) had confirmed SVR12. The charts of the 5 treatment failures were reviewed to discern the cause for failure. Two cases involved early termination of therapy, 3 involved relapse and 2 failed to comply with medication instructions. There was 1 case of the Choice HCP not addressing simultaneous use of ledipasvir and a proton pump inhibitor, potentially causing an interaction, and 1 case where both the VA and Choice providers failed to recognize indicators of cirrhosis, which impacted the regimen used.

In the VALLHCS group, records of 320 veterans who completed treatment and had SVR12 testing were reviewed. While the Choice memorandum was active, veterans selected to be treated at VALLHCS had advanced liver fibrosis or cirrhosis, medical and mental health comorbidities that increased the risk of treatment complications or were considered to have difficulty adhering to the medication regimen. For this group, 296 (93%) had confirmed SVR12. Eighteen of the 24 (75%) treatment failures were complicated by nonadherence, including all 13 cases of early termination. One patient died from complications of decompensated cirrhosis before completing treatment, and 1 did not receive HCV medications during a hospital admission due to poor coordination of care between the VA inpatient and outpatient pharmacy services, leading to multiple missed doses.

 

The difference in SVR12 rates (ie, treatment failure rates), between the VA and Choice groups was not statistically significant (P = .61). None of the specific reasons for treatment failure had a statistically significant difference between groups. A treatment failure analysis is shown in Table 3, and Table 4 indicates the breakdown of treatment regimens.

 

Discussion

The Veterans Health Administration (VHA) is the largest integrated health care system in the US, consisting of 152 medical centers and > 1,700 sites of care. The VA has the potential to meet the health care needs of 21.6 million veterans. About 9 million veterans are enrolled in the VA system and 5.9 million received health care through VHA.¹⁷ However, every medical service cannot realistically be made available at every facility, and some veterans have difficulty gaining access to VHA care; distance and wait times have been well-publicized issues that need further exploration.^18,19 The Choice program is an attempt to meet gaps in VA coverage using non-VA HCPs.

HCV infection is a specific diagnosis with national treatment guidelines and well-studied treatments; it can be cured, with an evidence-based definition of cure. The VACO policy memorandum to refer less sick veterans to Choice while treating sicker veterans at the VA provided the opportunity to directly compare the quality of the 2 programs. The SVR12 rates of VALLHCS and Choice providers were comparable to the national average at the time, and while the difference in SVR12 rate was not significant, VALLHCS treated a significantly higher number of patients with cirrhosis because of the referral criteria.²⁰

The significant difference in medical comorbidities between the VA and Choice groups was not surprising, partly because of the referral criteria. Cirrhosis can impact the treatment regimen, especially in regard to use of ribavirin. Since the presence of mental health comorbidities did not affect selection into the Choice group, it makes sense that there was no significant difference in prevalence between the groups.

VACO allowed veterans with HCV treatment plans that VA HCPs felt were too complicated for the Choice program to be treated by VHA HCPs.⁹ VALLHCS exercised this right for veterans at risk for nonadherence, because in HCV treatment, nonadherence leads to treatment failure and development of drug resistant virus strains. Therefore, veterans who would have difficulty traveling to VALLHCS to pick up medications, those who lacked means of communication (such as those who were homeless), and those who had active substance abuse were treated at the VA, where closer monitoring and immediate access to a wide range of services was possible. Studies have confirmed the impact of these types of issues on HCV treatment adherence and success.²¹ This explains the higher prevalence of social issues in the VA group.

For an internal referral for HCV treatment at VALLHCS, the hepatology provider submits a consult request to the HCV treatment provider, who works in the same office, making direct communication simple. The main administrative limiting factor to minimizing wait times is the number of HCPs, which is dependent on hiring allowances.

When a veteran is referred to Choice, the VA provider places a consult for non-VA care, which the VA Office of Community Care processes by compiling relevant documents and sending the package to Triwest Healthcare Alliance, a private insurance processor contracted with the VA. Triwest selects the Choice provider, often without any input from the VA, and arranges the veteran’s initial appointment.²² Geographic distance to the veteran’s address is the main selection criteria for Triwest. Documents sent between the Choice and VA HCPs go through the Office of Community Care and Triwest. This significantly increases the potential for delays and failed communication. Triwest had a comprehensive list of providers deemed to be qualified to treat HCV within the geographic catchment of VALLHCS. This list was reviewed, and all veterans referred to Choice had HCPs near their home address; therefore, availability of Choice HCPs was not an issue.

The VA can provide guidance on management of the veteran in the form of bundle packages containing a list of services for which the Choice provider is authorized to provide, and ones the Choice provider is not authorized to provide. Some Choice HCPs ordered tests that were not authorized for HCV treatment such as esophagogastroduodenoscopy, colonoscopy, and liver biopsy. In all, 17 of 71 (24%) veterans seen by Choice HCPs had tests performed or ordered that VA HCPs would not have obtained for the purpose of HCV treatment (Table 5).

In order to prevent veterans from receiving unnecessary tests, a VALLHCS hepatologist asked to be notified by VA administrators overseeing Choice referrals whenever a secondary authorization request (SAR) was submitted by a Choice HCP. This strategy is not standard VA practice, therefore at many VA sites these requested tests would have been performed by the Choice HCP, which is why SARs were factored into cost analysis.

SVR12 test results that were drawn too early and had to be repeated at VALLHCS were a cost unique to the Choice program. Duplicate tests, particularly imaging studies and blood work, were extraneous costs. The largest extraneous costs were treatment regimens prescribed by Choice HCPs that did not follow standard of care and required VA provider intervention. Thirty of the 71 (42%) veterans seen by a Choice provider accrued a mean $8,561.40 in extra costs. As a result, the Choice program cost VHA $250,000 more to provide care for 30 veterans (enough to pay for a physician’s annual salary).

Some inappropriate treatment regimens were the result of Choice HCP error, such as 1 case in which a veteran was inadvertently switched from ledipasvir/sofosbuvir to ombitasvir/paritaprevir/ritonavir/dasabuvir after week 4. The veteran had to start therapy over but still achieved SVR12. Other cases saw veterans receive regimens for which they had clear contraindications, such as creatinine clearance < 30 mL/min/1.73m² for sofosbuvir or a positive resistance panel for specific medications. Eleven of 62 (18%) veterans who were started on HCV treatment by a Choice HCP received a regimen not consistent with VA guidelines—an alarming result.

Follow up for veterans referred to Choice was extremely labor intensive, and assessment of personnel requirements in a Choice-based VA system must take this into consideration. The Choice HCP has no obligation to communicate with the VA HCP. At the time of chart review, 57 of 71 (80%) Choice veterans had inadequate documentation to make a confident assessment of the treatment outcome. Multiple calls to the offices of the Choice HCP were needed to acquire records, and veterans had to be tracked down for additional tests. Veterans often would complete treatment and stop following up with the Choice provider before SVR12 confirmation. The VA hepatology provider reviewing Choice referrals served as clinician, case manager, and clerk in order to get veterans to an appropriate end point in their hepatitis C treatment, with mostly unmeasured hours of work.

 

Limitations

The study population size was limited by the number of veterans able to complete treatment through Choice. The parameters in the VACO policy memos automatically selected the VA and Choice groups but made them clinically distinct populations. New treatment medications were released during the study period, which impacted management strategy. Occasionally, VA and non-VA HCPs preferred different treatment regimens, leading to variation in the distribution of regimens used despite similar genotype distribution (Tables 2 and 4). In addition, a retrospective study is at risk for recall bias. A prospective study randomizing veterans to the Choice and VA groups is an important future endeavor. Comparing veteran satisfaction for Choice and VA services is also crucial.

Conclusions

This study demonstrates that the VA was able to provide more cost-effective and more timely care for HCV treatment to a relatively sicker population with no reduction in treatment success when compared with non-VA HCPs through the Choice program. While the Choice program can help veterans receive services they may otherwise not have access to and reduce travel time, the current system introduces inefficiencies that delay care and decrease cost-effectiveness. The Choice HCP selection process is based on proximity rather than quality, which may place the veteran at risk for receiving substandard care. Large-scale quality of care studies that compare efficiency measures, clinical outcomes, patient demographics, travel distance, cost efficacy and patient satisfaction for veterans receiving similar services at a VA facility and through Choice should be performed to ensure that veterans receive the best care available.

Population studies show high prevalence of chronic hepatitis C virus (HCV) infection among veterans, especially Vietnam War era veterans.^1,2 The development of safe and efficacious direct-acting antiviral (DAA) medications to treat HCV infection made the majority of those infected eligible for treatment. However, the large number of veterans needing DAA treatment stressed the resources of the US Department of Veterans Affairs (VA) health care system. This occurred while Congress was focused on reducing wait times for veterans receiving care at the VA.

Congress passed the Veterans Access, Choice, and Accountability Act (Choice) on August 7, 2014, leading to the creation of the Veterans Choice Program. Legislators felt there were inappropriate delays in care at the VA, and the Choice program was meant to address this problem and provide an “apples-to-apples comparison [of the VA] with non-VA hospitals.”³

Congress acknowledged the importance of curing HCV in the veteran population and allocated $1.5 billion for fiscal year (FY) 2016 for DAAs. The VA Central Office (VACO) carefully monitored these resources. The first policy memorandum from VACO for HCV care, issued on May 21, 2015, recommended that the sickest patients who will benefit from the treatment “receive priority over those who are less ill.”^4,5 Those who met criteria for advanced liver disease were prioritized for treatment at the VA, while those who did not meet criteria were given the option of receiving treatment through Choice, or waiting for a change in policy.⁶ Over time, revisions to the guidelines relaxed the criteria for VA treatment eligibility, and on February 24, 2016, all restrictions on HCV treatment at the VA were lifted.^7,8

The aim of this study was to provide a comparison of VA and non-VA care, specifically to determine whether care provided through Choice was timelier and more cost effective than care provided by the VA, and whether there was a quality difference. The high prevalence among veterans, well-established standards of care, and finite treatment course with clear indicators of success and failure makes HCV treatment an ideal disease with which to make this comparison.

Methods

We retrospectively analyzed the VA electronic health records of all veterans seen in the VA Loma Linda Healthcare System (VALLHCS) Hepatology clinic for chronic HCV infection during FY 2016 who were referred to Choice for HCV treatment. One hundred veterans met these criteria, encompassing the Choice population; 71 were seen at least once by a non-VA (Choice) health care provider (HCP) and 61 completed a treatment regimen through Choice. Treatment completion was defined as cessation of medication after the planned duration of therapy, or early termination of medication without resumption by that HCP. The Choice population was matched to an equal number of veterans who received HCV treatment from VALLHCS HCPs.

Data collected included age, gender, HCV genotype, determinants of liver fibrosis, and treatment success (defined as sustained virologic response at 12 weeks after the last dose of medication [SVR12]). Determinants of liver fibrosis included documented cirrhosis or complications of cirrhosis, Fibrosis-4 score (Fib-4), and platelet count.

Treatment failures were categorized as nonresponse (defined as detectable HCV viral load at the end of treatment), relapse (defined as an undetectable HCV viral load at the end of treatment with a subsequent positive test), and early termination (defined as a failure to complete the planned treatment regimen). Documented patient nonadherence, medical comorbidities that affected the treatment protocol, mental health diagnoses, and active social issues (defined as active or history of heavy alcohol use, active or history of illicit drug use, lack of social support, and homelessness) were noted.

Timeliness of delivery of care was measured in days. For the VA group, the wait time was defined as the date the consult for HCV treatment was placed to the date of the initial appointment with the HCV treatment provider. For the Choice group, the wait time was defined as the date the referral to the Choice program was made to the date of the initial appointment with the Choice HCP. Treatment regimens were evaluated for appropriateness based on guidelines from VACO and the American Association for the Study of Liver Diseases.^9-11

Tests performed by Choice providers were evaluated for whether they were relevant to HCV care and whether similar data already were available from VALLHCS. Tests that were not indicated were identified as unnecessary costs incurred by the Choice program, as were tests that had to be repeated at the VA because of a lack of documentation from the Choice provider. All medications given inappropriately were considered added costs. Medicare reimbursement rates for the most applicable Current Procedural Terminology (CPT) code and VA national contract pricing for medications were used for calculations. This study was approved by the VALLHCS institutional review board.

 

Statistical Analysis

IBM (Armonk, NY) Statistical Package for Social Sciences software was used to evaluate for differences in Fib-4, platelet count, prevalence of cirrhosis, prevalence of medical comorbidities, prevalence of mental health comorbidities, prevalence of the social issues defined in the Methods section, time from referral to time of appointment date, and SVR12 rate between the VA and Choice groups.

Exclusions

There were 15 veterans in the VA group who had a wait time of > 100 days. Of these, 5 (33%) were initially Choice referrals, but due to negative interactions with the Choice provider, the veterans returned to VALLHCS for care. Two of the 15 (13%) did not keep appointments and were lost to follow up. Six of the 15 (40%) had medical comorbidities that required more immediate attention, so HCV treatment initiation was deliberately moved back. The final 2 veterans scheduled their appointments unusually far apart, artificially increasing their wait time. Given that these were unique situations and some of the veterans received care from both Choice and VA providers, a decision was made to exclude these individuals from the study.

It has been shown that platelet count correlates with degree of liver fibrosis, a concept that is the basis for the Fib-4 scoring system.¹² Studies have shown that platelet count is a survival predictor in those with cirrhosis, and thrombocytopenia is a negative predictor of HCV treatment success using peginterferon and ribavirin.^13,14 Therefore, the VA memorandum automatically assigned the sickest individuals to the VA for HCV treatment. The goal of this study was to compare the impact of factors other than stage of fibrosis on HCV treatment success, which is why the 12 veterans with platelet count < 100,000 in the VA group were excluded. There were no veterans with platelet count < 100,000 in the Choice group.

When comparing SVR12 rates between the VA and Choice groups, every veteran treated at VALLHCS in FY 2016 was included, increasing the number in the VA group from 100 to 320 and therefore the power of this comparison.

Results

A summary of the statistical analysis can be found in Table 1. The genotype distribution was consistent with epidemiological studies, including those specific to veterans.^15,16 There were statistically significant differences (P < .001) in mean Fib-4 and mean platelet count. The VA group had a higher Fib-4 and lower platelet count. Seventy-four percent of the VA population was defined as cirrhotic, while only 3% of the Choice population met similar criteria (P < .001). The VA and Choice groups were similar in terms of age, gender, and genotype distribution (Table 2).

The VA group was found to have a higher prevalence of comorbidities that affected HCV treatment. These conditions included but were not limited to: chronic kidney disease that precluded the use of certain medications, any condition that required medication with a known interaction with DAAs (ie, proton pump inhibitors, statins, and amiodarone), and cirrhosis if it impacted the treatment regimen. The difference in the prevalence of mental health comorbidities was not significant (P = .39), but there was a higher prevalence of social issues among the VA group (P = .002).

The mean wait time from referral to appointment was 28.6 days for the VA group and 42.3 days for the Choice group (P < .001), indicating that a Choice referral took longer to complete than a referral within the VA for HCV treatment. Thirty of the 71 (42%) veterans seen by a Choice provider accrued extraneous cost, with a mean additional cost of $8,561.40 per veteran. In the Choice group, 61 veterans completed a treatment regimen with the Choice HCP. Fifty-five veterans completed treatment and had available SVR12 data (6 were lost to follow up without SVR12 testing) and 50 (91%) had confirmed SVR12. The charts of the 5 treatment failures were reviewed to discern the cause for failure. Two cases involved early termination of therapy, 3 involved relapse and 2 failed to comply with medication instructions. There was 1 case of the Choice HCP not addressing simultaneous use of ledipasvir and a proton pump inhibitor, potentially causing an interaction, and 1 case where both the VA and Choice providers failed to recognize indicators of cirrhosis, which impacted the regimen used.

In the VALLHCS group, records of 320 veterans who completed treatment and had SVR12 testing were reviewed. While the Choice memorandum was active, veterans selected to be treated at VALLHCS had advanced liver fibrosis or cirrhosis, medical and mental health comorbidities that increased the risk of treatment complications or were considered to have difficulty adhering to the medication regimen. For this group, 296 (93%) had confirmed SVR12. Eighteen of the 24 (75%) treatment failures were complicated by nonadherence, including all 13 cases of early termination. One patient died from complications of decompensated cirrhosis before completing treatment, and 1 did not receive HCV medications during a hospital admission due to poor coordination of care between the VA inpatient and outpatient pharmacy services, leading to multiple missed doses.

 

The difference in SVR12 rates (ie, treatment failure rates), between the VA and Choice groups was not statistically significant (P = .61). None of the specific reasons for treatment failure had a statistically significant difference between groups. A treatment failure analysis is shown in Table 3, and Table 4 indicates the breakdown of treatment regimens.

 

Discussion

The Veterans Health Administration (VHA) is the largest integrated health care system in the US, consisting of 152 medical centers and > 1,700 sites of care. The VA has the potential to meet the health care needs of 21.6 million veterans. About 9 million veterans are enrolled in the VA system and 5.9 million received health care through VHA.¹⁷ However, every medical service cannot realistically be made available at every facility, and some veterans have difficulty gaining access to VHA care; distance and wait times have been well-publicized issues that need further exploration.^18,19 The Choice program is an attempt to meet gaps in VA coverage using non-VA HCPs.

HCV infection is a specific diagnosis with national treatment guidelines and well-studied treatments; it can be cured, with an evidence-based definition of cure. The VACO policy memorandum to refer less sick veterans to Choice while treating sicker veterans at the VA provided the opportunity to directly compare the quality of the 2 programs. The SVR12 rates of VALLHCS and Choice providers were comparable to the national average at the time, and while the difference in SVR12 rate was not significant, VALLHCS treated a significantly higher number of patients with cirrhosis because of the referral criteria.²⁰

The significant difference in medical comorbidities between the VA and Choice groups was not surprising, partly because of the referral criteria. Cirrhosis can impact the treatment regimen, especially in regard to use of ribavirin. Since the presence of mental health comorbidities did not affect selection into the Choice group, it makes sense that there was no significant difference in prevalence between the groups.

VACO allowed veterans with HCV treatment plans that VA HCPs felt were too complicated for the Choice program to be treated by VHA HCPs.⁹ VALLHCS exercised this right for veterans at risk for nonadherence, because in HCV treatment, nonadherence leads to treatment failure and development of drug resistant virus strains. Therefore, veterans who would have difficulty traveling to VALLHCS to pick up medications, those who lacked means of communication (such as those who were homeless), and those who had active substance abuse were treated at the VA, where closer monitoring and immediate access to a wide range of services was possible. Studies have confirmed the impact of these types of issues on HCV treatment adherence and success.²¹ This explains the higher prevalence of social issues in the VA group.

For an internal referral for HCV treatment at VALLHCS, the hepatology provider submits a consult request to the HCV treatment provider, who works in the same office, making direct communication simple. The main administrative limiting factor to minimizing wait times is the number of HCPs, which is dependent on hiring allowances.

When a veteran is referred to Choice, the VA provider places a consult for non-VA care, which the VA Office of Community Care processes by compiling relevant documents and sending the package to Triwest Healthcare Alliance, a private insurance processor contracted with the VA. Triwest selects the Choice provider, often without any input from the VA, and arranges the veteran’s initial appointment.²² Geographic distance to the veteran’s address is the main selection criteria for Triwest. Documents sent between the Choice and VA HCPs go through the Office of Community Care and Triwest. This significantly increases the potential for delays and failed communication. Triwest had a comprehensive list of providers deemed to be qualified to treat HCV within the geographic catchment of VALLHCS. This list was reviewed, and all veterans referred to Choice had HCPs near their home address; therefore, availability of Choice HCPs was not an issue.

The VA can provide guidance on management of the veteran in the form of bundle packages containing a list of services for which the Choice provider is authorized to provide, and ones the Choice provider is not authorized to provide. Some Choice HCPs ordered tests that were not authorized for HCV treatment such as esophagogastroduodenoscopy, colonoscopy, and liver biopsy. In all, 17 of 71 (24%) veterans seen by Choice HCPs had tests performed or ordered that VA HCPs would not have obtained for the purpose of HCV treatment (Table 5).

In order to prevent veterans from receiving unnecessary tests, a VALLHCS hepatologist asked to be notified by VA administrators overseeing Choice referrals whenever a secondary authorization request (SAR) was submitted by a Choice HCP. This strategy is not standard VA practice, therefore at many VA sites these requested tests would have been performed by the Choice HCP, which is why SARs were factored into cost analysis.

SVR12 test results that were drawn too early and had to be repeated at VALLHCS were a cost unique to the Choice program. Duplicate tests, particularly imaging studies and blood work, were extraneous costs. The largest extraneous costs were treatment regimens prescribed by Choice HCPs that did not follow standard of care and required VA provider intervention. Thirty of the 71 (42%) veterans seen by a Choice provider accrued a mean $8,561.40 in extra costs. As a result, the Choice program cost VHA $250,000 more to provide care for 30 veterans (enough to pay for a physician’s annual salary).

Some inappropriate treatment regimens were the result of Choice HCP error, such as 1 case in which a veteran was inadvertently switched from ledipasvir/sofosbuvir to ombitasvir/paritaprevir/ritonavir/dasabuvir after week 4. The veteran had to start therapy over but still achieved SVR12. Other cases saw veterans receive regimens for which they had clear contraindications, such as creatinine clearance < 30 mL/min/1.73m² for sofosbuvir or a positive resistance panel for specific medications. Eleven of 62 (18%) veterans who were started on HCV treatment by a Choice HCP received a regimen not consistent with VA guidelines—an alarming result.

Follow up for veterans referred to Choice was extremely labor intensive, and assessment of personnel requirements in a Choice-based VA system must take this into consideration. The Choice HCP has no obligation to communicate with the VA HCP. At the time of chart review, 57 of 71 (80%) Choice veterans had inadequate documentation to make a confident assessment of the treatment outcome. Multiple calls to the offices of the Choice HCP were needed to acquire records, and veterans had to be tracked down for additional tests. Veterans often would complete treatment and stop following up with the Choice provider before SVR12 confirmation. The VA hepatology provider reviewing Choice referrals served as clinician, case manager, and clerk in order to get veterans to an appropriate end point in their hepatitis C treatment, with mostly unmeasured hours of work.

 

Limitations

The study population size was limited by the number of veterans able to complete treatment through Choice. The parameters in the VACO policy memos automatically selected the VA and Choice groups but made them clinically distinct populations. New treatment medications were released during the study period, which impacted management strategy. Occasionally, VA and non-VA HCPs preferred different treatment regimens, leading to variation in the distribution of regimens used despite similar genotype distribution (Tables 2 and 4). In addition, a retrospective study is at risk for recall bias. A prospective study randomizing veterans to the Choice and VA groups is an important future endeavor. Comparing veteran satisfaction for Choice and VA services is also crucial.

Conclusions

This study demonstrates that the VA was able to provide more cost-effective and more timely care for HCV treatment to a relatively sicker population with no reduction in treatment success when compared with non-VA HCPs through the Choice program. While the Choice program can help veterans receive services they may otherwise not have access to and reduce travel time, the current system introduces inefficiencies that delay care and decrease cost-effectiveness. The Choice HCP selection process is based on proximity rather than quality, which may place the veteran at risk for receiving substandard care. Large-scale quality of care studies that compare efficiency measures, clinical outcomes, patient demographics, travel distance, cost efficacy and patient satisfaction for veterans receiving similar services at a VA facility and through Choice should be performed to ensure that veterans receive the best care available.

Population studies show high prevalence of chronic hepatitis C virus (HCV) infection among veterans, especially Vietnam War era veterans.^1,2 The development of safe and efficacious direct-acting antiviral (DAA) medications to treat HCV infection made the majority of those infected eligible for treatment. However, the large number of veterans needing DAA treatment stressed the resources of the US Department of Veterans Affairs (VA) health care system. This occurred while Congress was focused on reducing wait times for veterans receiving care at the VA.

Congress passed the Veterans Access, Choice, and Accountability Act (Choice) on August 7, 2014, leading to the creation of the Veterans Choice Program. Legislators felt there were inappropriate delays in care at the VA, and the Choice program was meant to address this problem and provide an “apples-to-apples comparison [of the VA] with non-VA hospitals.”³

Congress acknowledged the importance of curing HCV in the veteran population and allocated $1.5 billion for fiscal year (FY) 2016 for DAAs. The VA Central Office (VACO) carefully monitored these resources. The first policy memorandum from VACO for HCV care, issued on May 21, 2015, recommended that the sickest patients who will benefit from the treatment “receive priority over those who are less ill.”^4,5 Those who met criteria for advanced liver disease were prioritized for treatment at the VA, while those who did not meet criteria were given the option of receiving treatment through Choice, or waiting for a change in policy.⁶ Over time, revisions to the guidelines relaxed the criteria for VA treatment eligibility, and on February 24, 2016, all restrictions on HCV treatment at the VA were lifted.^7,8

The aim of this study was to provide a comparison of VA and non-VA care, specifically to determine whether care provided through Choice was timelier and more cost effective than care provided by the VA, and whether there was a quality difference. The high prevalence among veterans, well-established standards of care, and finite treatment course with clear indicators of success and failure makes HCV treatment an ideal disease with which to make this comparison.

Methods

We retrospectively analyzed the VA electronic health records of all veterans seen in the VA Loma Linda Healthcare System (VALLHCS) Hepatology clinic for chronic HCV infection during FY 2016 who were referred to Choice for HCV treatment. One hundred veterans met these criteria, encompassing the Choice population; 71 were seen at least once by a non-VA (Choice) health care provider (HCP) and 61 completed a treatment regimen through Choice. Treatment completion was defined as cessation of medication after the planned duration of therapy, or early termination of medication without resumption by that HCP. The Choice population was matched to an equal number of veterans who received HCV treatment from VALLHCS HCPs.

Data collected included age, gender, HCV genotype, determinants of liver fibrosis, and treatment success (defined as sustained virologic response at 12 weeks after the last dose of medication [SVR12]). Determinants of liver fibrosis included documented cirrhosis or complications of cirrhosis, Fibrosis-4 score (Fib-4), and platelet count.

Treatment failures were categorized as nonresponse (defined as detectable HCV viral load at the end of treatment), relapse (defined as an undetectable HCV viral load at the end of treatment with a subsequent positive test), and early termination (defined as a failure to complete the planned treatment regimen). Documented patient nonadherence, medical comorbidities that affected the treatment protocol, mental health diagnoses, and active social issues (defined as active or history of heavy alcohol use, active or history of illicit drug use, lack of social support, and homelessness) were noted.

Timeliness of delivery of care was measured in days. For the VA group, the wait time was defined as the date the consult for HCV treatment was placed to the date of the initial appointment with the HCV treatment provider. For the Choice group, the wait time was defined as the date the referral to the Choice program was made to the date of the initial appointment with the Choice HCP. Treatment regimens were evaluated for appropriateness based on guidelines from VACO and the American Association for the Study of Liver Diseases.^9-11

Tests performed by Choice providers were evaluated for whether they were relevant to HCV care and whether similar data already were available from VALLHCS. Tests that were not indicated were identified as unnecessary costs incurred by the Choice program, as were tests that had to be repeated at the VA because of a lack of documentation from the Choice provider. All medications given inappropriately were considered added costs. Medicare reimbursement rates for the most applicable Current Procedural Terminology (CPT) code and VA national contract pricing for medications were used for calculations. This study was approved by the VALLHCS institutional review board.

 

Statistical Analysis

IBM (Armonk, NY) Statistical Package for Social Sciences software was used to evaluate for differences in Fib-4, platelet count, prevalence of cirrhosis, prevalence of medical comorbidities, prevalence of mental health comorbidities, prevalence of the social issues defined in the Methods section, time from referral to time of appointment date, and SVR12 rate between the VA and Choice groups.

Exclusions

There were 15 veterans in the VA group who had a wait time of > 100 days. Of these, 5 (33%) were initially Choice referrals, but due to negative interactions with the Choice provider, the veterans returned to VALLHCS for care. Two of the 15 (13%) did not keep appointments and were lost to follow up. Six of the 15 (40%) had medical comorbidities that required more immediate attention, so HCV treatment initiation was deliberately moved back. The final 2 veterans scheduled their appointments unusually far apart, artificially increasing their wait time. Given that these were unique situations and some of the veterans received care from both Choice and VA providers, a decision was made to exclude these individuals from the study.

It has been shown that platelet count correlates with degree of liver fibrosis, a concept that is the basis for the Fib-4 scoring system.¹² Studies have shown that platelet count is a survival predictor in those with cirrhosis, and thrombocytopenia is a negative predictor of HCV treatment success using peginterferon and ribavirin.^13,14 Therefore, the VA memorandum automatically assigned the sickest individuals to the VA for HCV treatment. The goal of this study was to compare the impact of factors other than stage of fibrosis on HCV treatment success, which is why the 12 veterans with platelet count < 100,000 in the VA group were excluded. There were no veterans with platelet count < 100,000 in the Choice group.

When comparing SVR12 rates between the VA and Choice groups, every veteran treated at VALLHCS in FY 2016 was included, increasing the number in the VA group from 100 to 320 and therefore the power of this comparison.

Results

A summary of the statistical analysis can be found in Table 1. The genotype distribution was consistent with epidemiological studies, including those specific to veterans.^15,16 There were statistically significant differences (P < .001) in mean Fib-4 and mean platelet count. The VA group had a higher Fib-4 and lower platelet count. Seventy-four percent of the VA population was defined as cirrhotic, while only 3% of the Choice population met similar criteria (P < .001). The VA and Choice groups were similar in terms of age, gender, and genotype distribution (Table 2).

The VA group was found to have a higher prevalence of comorbidities that affected HCV treatment. These conditions included but were not limited to: chronic kidney disease that precluded the use of certain medications, any condition that required medication with a known interaction with DAAs (ie, proton pump inhibitors, statins, and amiodarone), and cirrhosis if it impacted the treatment regimen. The difference in the prevalence of mental health comorbidities was not significant (P = .39), but there was a higher prevalence of social issues among the VA group (P = .002).

The mean wait time from referral to appointment was 28.6 days for the VA group and 42.3 days for the Choice group (P < .001), indicating that a Choice referral took longer to complete than a referral within the VA for HCV treatment. Thirty of the 71 (42%) veterans seen by a Choice provider accrued extraneous cost, with a mean additional cost of $8,561.40 per veteran. In the Choice group, 61 veterans completed a treatment regimen with the Choice HCP. Fifty-five veterans completed treatment and had available SVR12 data (6 were lost to follow up without SVR12 testing) and 50 (91%) had confirmed SVR12. The charts of the 5 treatment failures were reviewed to discern the cause for failure. Two cases involved early termination of therapy, 3 involved relapse and 2 failed to comply with medication instructions. There was 1 case of the Choice HCP not addressing simultaneous use of ledipasvir and a proton pump inhibitor, potentially causing an interaction, and 1 case where both the VA and Choice providers failed to recognize indicators of cirrhosis, which impacted the regimen used.

In the VALLHCS group, records of 320 veterans who completed treatment and had SVR12 testing were reviewed. While the Choice memorandum was active, veterans selected to be treated at VALLHCS had advanced liver fibrosis or cirrhosis, medical and mental health comorbidities that increased the risk of treatment complications or were considered to have difficulty adhering to the medication regimen. For this group, 296 (93%) had confirmed SVR12. Eighteen of the 24 (75%) treatment failures were complicated by nonadherence, including all 13 cases of early termination. One patient died from complications of decompensated cirrhosis before completing treatment, and 1 did not receive HCV medications during a hospital admission due to poor coordination of care between the VA inpatient and outpatient pharmacy services, leading to multiple missed doses.

 

The difference in SVR12 rates (ie, treatment failure rates), between the VA and Choice groups was not statistically significant (P = .61). None of the specific reasons for treatment failure had a statistically significant difference between groups. A treatment failure analysis is shown in Table 3, and Table 4 indicates the breakdown of treatment regimens.

 

Discussion

The Veterans Health Administration (VHA) is the largest integrated health care system in the US, consisting of 152 medical centers and > 1,700 sites of care. The VA has the potential to meet the health care needs of 21.6 million veterans. About 9 million veterans are enrolled in the VA system and 5.9 million received health care through VHA.¹⁷ However, every medical service cannot realistically be made available at every facility, and some veterans have difficulty gaining access to VHA care; distance and wait times have been well-publicized issues that need further exploration.^18,19 The Choice program is an attempt to meet gaps in VA coverage using non-VA HCPs.

HCV infection is a specific diagnosis with national treatment guidelines and well-studied treatments; it can be cured, with an evidence-based definition of cure. The VACO policy memorandum to refer less sick veterans to Choice while treating sicker veterans at the VA provided the opportunity to directly compare the quality of the 2 programs. The SVR12 rates of VALLHCS and Choice providers were comparable to the national average at the time, and while the difference in SVR12 rate was not significant, VALLHCS treated a significantly higher number of patients with cirrhosis because of the referral criteria.²⁰

The significant difference in medical comorbidities between the VA and Choice groups was not surprising, partly because of the referral criteria. Cirrhosis can impact the treatment regimen, especially in regard to use of ribavirin. Since the presence of mental health comorbidities did not affect selection into the Choice group, it makes sense that there was no significant difference in prevalence between the groups.

VACO allowed veterans with HCV treatment plans that VA HCPs felt were too complicated for the Choice program to be treated by VHA HCPs.⁹ VALLHCS exercised this right for veterans at risk for nonadherence, because in HCV treatment, nonadherence leads to treatment failure and development of drug resistant virus strains. Therefore, veterans who would have difficulty traveling to VALLHCS to pick up medications, those who lacked means of communication (such as those who were homeless), and those who had active substance abuse were treated at the VA, where closer monitoring and immediate access to a wide range of services was possible. Studies have confirmed the impact of these types of issues on HCV treatment adherence and success.²¹ This explains the higher prevalence of social issues in the VA group.

For an internal referral for HCV treatment at VALLHCS, the hepatology provider submits a consult request to the HCV treatment provider, who works in the same office, making direct communication simple. The main administrative limiting factor to minimizing wait times is the number of HCPs, which is dependent on hiring allowances.

When a veteran is referred to Choice, the VA provider places a consult for non-VA care, which the VA Office of Community Care processes by compiling relevant documents and sending the package to Triwest Healthcare Alliance, a private insurance processor contracted with the VA. Triwest selects the Choice provider, often without any input from the VA, and arranges the veteran’s initial appointment.²² Geographic distance to the veteran’s address is the main selection criteria for Triwest. Documents sent between the Choice and VA HCPs go through the Office of Community Care and Triwest. This significantly increases the potential for delays and failed communication. Triwest had a comprehensive list of providers deemed to be qualified to treat HCV within the geographic catchment of VALLHCS. This list was reviewed, and all veterans referred to Choice had HCPs near their home address; therefore, availability of Choice HCPs was not an issue.

The VA can provide guidance on management of the veteran in the form of bundle packages containing a list of services for which the Choice provider is authorized to provide, and ones the Choice provider is not authorized to provide. Some Choice HCPs ordered tests that were not authorized for HCV treatment such as esophagogastroduodenoscopy, colonoscopy, and liver biopsy. In all, 17 of 71 (24%) veterans seen by Choice HCPs had tests performed or ordered that VA HCPs would not have obtained for the purpose of HCV treatment (Table 5).

In order to prevent veterans from receiving unnecessary tests, a VALLHCS hepatologist asked to be notified by VA administrators overseeing Choice referrals whenever a secondary authorization request (SAR) was submitted by a Choice HCP. This strategy is not standard VA practice, therefore at many VA sites these requested tests would have been performed by the Choice HCP, which is why SARs were factored into cost analysis.

SVR12 test results that were drawn too early and had to be repeated at VALLHCS were a cost unique to the Choice program. Duplicate tests, particularly imaging studies and blood work, were extraneous costs. The largest extraneous costs were treatment regimens prescribed by Choice HCPs that did not follow standard of care and required VA provider intervention. Thirty of the 71 (42%) veterans seen by a Choice provider accrued a mean $8,561.40 in extra costs. As a result, the Choice program cost VHA $250,000 more to provide care for 30 veterans (enough to pay for a physician’s annual salary).

Some inappropriate treatment regimens were the result of Choice HCP error, such as 1 case in which a veteran was inadvertently switched from ledipasvir/sofosbuvir to ombitasvir/paritaprevir/ritonavir/dasabuvir after week 4. The veteran had to start therapy over but still achieved SVR12. Other cases saw veterans receive regimens for which they had clear contraindications, such as creatinine clearance < 30 mL/min/1.73m² for sofosbuvir or a positive resistance panel for specific medications. Eleven of 62 (18%) veterans who were started on HCV treatment by a Choice HCP received a regimen not consistent with VA guidelines—an alarming result.

Follow up for veterans referred to Choice was extremely labor intensive, and assessment of personnel requirements in a Choice-based VA system must take this into consideration. The Choice HCP has no obligation to communicate with the VA HCP. At the time of chart review, 57 of 71 (80%) Choice veterans had inadequate documentation to make a confident assessment of the treatment outcome. Multiple calls to the offices of the Choice HCP were needed to acquire records, and veterans had to be tracked down for additional tests. Veterans often would complete treatment and stop following up with the Choice provider before SVR12 confirmation. The VA hepatology provider reviewing Choice referrals served as clinician, case manager, and clerk in order to get veterans to an appropriate end point in their hepatitis C treatment, with mostly unmeasured hours of work.

 

Limitations

The study population size was limited by the number of veterans able to complete treatment through Choice. The parameters in the VACO policy memos automatically selected the VA and Choice groups but made them clinically distinct populations. New treatment medications were released during the study period, which impacted management strategy. Occasionally, VA and non-VA HCPs preferred different treatment regimens, leading to variation in the distribution of regimens used despite similar genotype distribution (Tables 2 and 4). In addition, a retrospective study is at risk for recall bias. A prospective study randomizing veterans to the Choice and VA groups is an important future endeavor. Comparing veteran satisfaction for Choice and VA services is also crucial.

Conclusions

This study demonstrates that the VA was able to provide more cost-effective and more timely care for HCV treatment to a relatively sicker population with no reduction in treatment success when compared with non-VA HCPs through the Choice program. While the Choice program can help veterans receive services they may otherwise not have access to and reduce travel time, the current system introduces inefficiencies that delay care and decrease cost-effectiveness. The Choice HCP selection process is based on proximity rather than quality, which may place the veteran at risk for receiving substandard care. Large-scale quality of care studies that compare efficiency measures, clinical outcomes, patient demographics, travel distance, cost efficacy and patient satisfaction for veterans receiving similar services at a VA facility and through Choice should be performed to ensure that veterans receive the best care available.

Readmissions following hospitalization for exacerbations of chronic obstructive pulmonary disease (COPD) are common and economically burdensome.¹ The Affordable Care Act² outlined the Hospital Readmissions Reduction Program (HRRP),³ which aims to improve the quality of care and reduce the costs for patients with pneumonia, myocardial infarction, congestive heart failure, and COPD.³ With the implementation of the HRRP, readmission reduction has become a key priority of health systems.

Multiple approaches to reduce readmissions are published, with variable degrees of success across respiratory and all-cause rehospitalizations.⁴ Patient self-management programs are heterogenous with inconsistent utilization reductions.^5-7 While some transitional care programs demonstrate benefits,^8-10 one notable study of an intensive transitional care and self-management program showed increaseNod acute care utilization without improving health-related quality of life.^11-13 Another study of COPD comprehensive care management was stopped prematurely for increased mortality in the intervention group.¹⁴ Telehealth monitoring may predict exacerbations,^15,16 but inconsistent effects on quality of life and utilization are observed.^17,18 Pulmonary rehabilitation improves quality of life but not healthcare utilization.¹⁹ Dispensing respiratory medications at hospital discharge shows improved prescription fills and fewer readmissions,²⁰ further reinforced by inhaler training prior to discharge.²¹ Postdischarge oxygen therapy does not improve health-related quality of life or acute care utilization.²² The fact that these approaches have not reliably succeeded raises the need for further study on the drivers of readmissions in COPD. Previous studies found differences in factors associated with the timing of COPD readmissions and return diagnoses.^23,24 While HRRP is Medicare-specific, health systems likely use programs targeting their entire population when planning readmission reduction strategies. Previous analyses were primarily single-center studies²⁵ and Medicare²⁴ or private insurance claims.²⁶

In this analysis, we explore how comorbidity burden^27-29 may differentially influence readmissions for recurrent COPD exacerbations versus other diagnoses. Our approach uses a national all-payer sample that covers a diverse geographic area across the United States, providing robust estimates of factors influencing readmission and valuable insights for planning and implementing effective readmission reduction programs. By including data from a period that encompasses the implementation of HRRP, we also provide new information on the factors in the HRRP postimplementation that are not yet available in published literature.

 

METHODS

Data Source

The Nationwide Readmissions Database (NRD) is a nationally representative, all-payer, 100% sample of community acute care hospital discharges from multiple states.³⁰ We pooled COPD discharge records spanning 2010-2016, excluding those where the patients were not residents of the state in which they were hospitalized to minimize loss to follow-up.

Inclusion/Exclusion Criteria

Selection criteria mirrored the methodology used by the HRRP,^31,32 defining an index discharge as a patient ≥40 years of age with a qualifying COPD diagnosis (Appendix Tables 1-2), discharged alive, with at least 30 days elapsed since previous hospitalization. We excluded discharges against medical advice or those from a hospital with fewer than 25 COPD discharges in that calendar year as per HRRP,^31,32 as well as those involving lung transplants. In this pooled cross-sectional analysis, record identifiers were not reliably unique across years. We restricted to observations originating February-November because January stays may not have had the requisite HRRP 30-day washout period from last admission and December stays could not be tracked into the subsequent January.

Outcomes

We defined a readmission as subsequent hospitalization for any cause within 30 days of the index discharge, with exemptions defined by the HRRP (Appendix Figure 1).^31,32 We segmented the readmission outcome into two parts: those readmitted with diagnoses that met the COPD HRRP criteria versus for any other diagnoses. We also tabulated diagnosis-related groups (DRGs) coded for the readmission observation to capture attributable cause for rehospitalization.

Our main independent variable was the Elixhauser Comorbidity Index score,³³ constructed using adaptations of published software,^34,35 having previously validated its use for modeling COPD readmissions.³⁶ We involved covariates provided with the database, including sociodemographic variables (eg, age, sex, community characteristics, payer, and median income at patient’s ZIP code) and hospital characteristics (eg, size, ownership, teaching status). We constructed additional covariates to account for in-hospital events by aggregating ICD diagnosis and procedure codes (eg, mechanical ventilation), hospital discharge volume, and proportion of annual within-hospital Medicaid patient days as a surrogate marker for safety-net hospitals. A detailed explanation of database construction and selection criteria is found in the Supplemental Methods Appendix.

Statistical Analysis

We tabulated patient-level descriptive statistics across the three outcomes of interest (ie, not readmitted, readmitted for a stay that would have qualified as COPD-related by HRRP criteria and readmitted for any other diagnosis). Continuous variables were compared using Welch’s t-tests (ie, unequal variance) and categorical variables using Chi-squared tests. At the hospital level, we tabulated the proportions of hospitals within categories in key variables of interest and a sub-population readmission rate for that particular characteristic, compared using Chi-squared tests.

We fit a multilevel multinomial logistic regression with random intercepts at the hospital cluster level, with the tripartite readmission outcome described above with “not readmitted” as the reference group. We included fixed effects for year, Elixhauser score, and patient- and hospital-level covariates as described above. Time to readmission for each group was plotted to assess the time distribution for each outcome. In-hospital mortality during each readmission event was tabulated.

 

Sensitivity Analyses and Missing Data

We conducted sensitivity analyses to determine whether a lower age cutoff (≥18 years) affects modeling. We also tested the stability of our estimates across each individual year of the pooled analysis. To test the effect of time to differential readmission, we fit a Cox proportional hazards model within the readmitted patient subgroup with Huber-White standard errors clustered at the hospital level to estimate the differential hazard of readmission for COPD versus non-COPD diagnoses across the same variables of interest as a sensitivity analysis. We also tested using a liberal classification of readmission diagnoses by sorting into “respiratory” versus “nonrespiratory” returns, with DRGs 163 through 208 for “respiratory” versus all others, respectively. We tested the agreement between the HRRP ICD-based classification and DRG classification using Cohen’s kappa.

We designated a threshold of 10% missing data to necessitate imputation techniques, determined a priori for our main variables, none of which met this level. Complete case analyses were used for all models. Analyses were performed in Stata version 15.1 (StataCorp, College Station, Texas) with weighted estimates reported using patient-level survey weights for national representativeness.³⁷ The study protocol was reviewed by the institutional review board at the University of California, Los Angeles, and deemed exempt from oversight due to the publicly available, deidentified nature of the data (IRB# 18-001208).

RESULTS

Out of 104,897,595 hospitalizations in the NRD, a final sample of 1,622,983 COPD discharges was identified for our analysis (sample weighted effective population 3,743,164). The overall readmission rate was 17.25%, with 7.69% of patients readmitted for COPD and 9.56% readmitted for other diagnoses. Those with COPD readmissions were significantly younger with a lower proportion of Medicare and a higher proportion of Medicaid as the primary payer compared with those readmitted for all other causes (Table 1). Compared with non-COPD-readmitted patients, COPD-readmitted patients were more frequently discharged home without services and had shorter lengths of stay. Noninvasive ventilation was more common among COPD readmissions while mechanical ventilation and tracheostomy placement were less frequent compared with non-COPD readmissions. Compared with non-COPD-readmitted patients, COPD-readmitted patients had significantly lower mean Elixhauser Comorbidity Index scores (17.8 vs 22.8), rates of congestive heart failure (28.3% vs 38.6%), and renal failure (11.8% vs 21.5%; Appendix Table 3).

Readmission rates were significantly higher for non-COPD causes than for COPD causes across all hospital types by ownership, teaching status, or size (Table 2). Parallel patterns were observed for non-COPD and COPD readmissions across hospital types, with two key exceptions. Across categories of hospital ownership, for-profit hospitals had the highest rates for non-COPD readmissions, with no differences in hospital control for COPD rehospitalizations. While rates did not vary for non-COPD readmissions by within-hospital Medicaid prevalence, COPD readmission rates significantly increased as Medicaid-paid patient-days increased within hospitals.

The median time to non-COPD readmission was 13 days, whereas COPD readmission was 14 days. More COPD readmissions occurred in the first 2.4 days after discharge, after which the proportion of non-COPD cases readmitted consistently increased. Observed readmission rates for COPD and other diagnoses trended down over the study period (Figure 1A), as did mortality rates during readmission stays (Figure 1B). Sepsis, heart failure, and respiratory infections were seven of the top 10 ranked DRGs for the non-COPD rehospitalizations (Appendix Table 4). In trend analyses (Appendix Tables 5-8), sepsis and DRGs with major comorbidities increased in proportion each year across the study period, possibly reflecting changes in coding practices.³⁸

In our adjusted multinomial logistic regression model (Table 3), where the outcomes were not readmitted (reference category) versus readmitted for non-COPD diagnosis or for qualifying COPD diagnosis, the effect size of comorbidity, operationalized by change in the Elixhauser Comorbidity Index, was larger for non-COPD than non-COPD readmissions (odds ratio [OR] 1.19 vs 1.04 per one-half standard deviation of Elixhauser Index, an approximately 7.5 unit change in score). Increases in age were associated with higher non-COPD readmissions (OR 1.06 per 10 years) while actually protective against COPD readmissions (OR 0.89 per 10 years). Compared with Medicare patients, Medicaid patients had higher odds of COPD readmission (OR 1.10 vs 1.03) while the converse was observed in the privately insured (OR 0.65 vs 0.76). Transfers to postacute care facilities, referenced against discharges home, had a larger association with readmissions for non-COPD causes (OR 1.35 vs 1.00), whereas home-health had nearly equal adjusted readmission odds for each outcome (1.31 vs 1.30). Length of stay was associated with 1% greater odds per day for readmission for non-COPD causes than COPD returns. Regarding in-hospital events, odds of COPD readmission were higher for noninvasive ventilation (OR 1.37 vs 0.89) and mechanical ventilation (OR 0.87 vs 0.79, Appendix Table 9), which should be interpreted in the context that analyses were restricted to those discharged alive from their index admission, possibly biasing the true effect estimates due to competing risk of index in-hospital mortality.

In sensitivity analyses, we found no significant differences between our Cox proportional hazards model (Appendix Table 10) and our multinomial model. When we liberalized readmission outcome definitions to respiratory versus nonrespiratory DRGs, we observed 86% agreement between the HRRP and DRG classification systems (κ = 0.73, P < .001). Among the discordant observations, 13% of non-COPD readmissions under HRRP criteria were reclassified as respiratory by DRG and 1% of COPD readmissions under HRRP reclassified as nonrespiratory. When our multinomial model (Appendix Table 11) was re-fit using the DRG-based outcome, only slight changes in effect size occurred. For the Elixhauser Index, the OR for COPD by HRRP was slightly lower than that for respiratory DRGs (1.04 vs 1.05), parallel with the difference between non-COPD by HRRP and nonrespiratory DRG classification (1.19 vs 1.21, respectively). This result underscores the greater influence of comorbidity on non-COPD than COPD readmissions. Only one sign change was observed in those who underwent tracheostomy (OR 0.91 for “nonrespiratory” DRG vs 1.07 for “non-COPD” by HRRP), likely reflecting the shift of some non-COPD diagnoses into the respiratory categorization based on tracheostomy having its own DRG. We also evaluated the multinomial model without the Elixhauser Index (only covariates) and found minor adjustments to the effect sizes of the covariates, particularly for discharge disposition. However, no sign changes were observed for any of the odds ratios (Appendix Table 12). Readmission odds by the Elixhauser score for each condition were stable across years (Appendix Figure 2 & Appendix Table 13). Finally, including 18-39-year-old patients in the cohort did not substantially change our estimates (Appendix Table 14).

 

DISCUSSION

In this assessment of readmission odds following hospitalizations for COPD in a nationally representative all-payer sample, we demonstrate that 55% of rehospitalizations following COPD exacerbations are attributable to non-COPD diagnoses and describe the important role of comorbidity on influencing diagnoses at rehospitalization. These findings are consistent with a prior study of Medicare patients by Shah et al.²⁴ and expand upon the results of Jacobs et al. using a pre-HRRP sample of the NRD.²³ Our study offers an expanded analysis by including data spanning HRRP implementation, which went into effect for COPD in October 2014.³ Effect estimates were stable across all seven years of our study in sensitivity analyses, demonstrating the robustness of our findings. Our analysis also adds to the existing body of literature by assessing which factors are associated with readmissions related to ongoing COPD versus other diagnoses.

In our study, an increase in aggregated comorbidity by the Elixhauser Index was associated with a significantly higher readmission odds, with over four times the effect size for non-COPD than COPD returns. Comorbidity also moderated the effect of other factors, such as income and discharge disposition. While overall readmission rates declined across the course of the study period, the effect of comorbidity on readmission odds for both groups remained significant in annualized models. We also observed higher rates of nearly every individual Elixhauser component comorbidity in those readmitted for non-COPD causes compared with those readmitted for COPD causes. Taken together, these results underscore the need to account for comorbidities at the individual and composite levels when identifying those at highest risk for readmissions and necessitate a multidisciplinary approach to reduce risk for the multimorbid patient.

In a 2018 report, the American Thoracic Society highlighted the focus of programs on adherence to guidelines and reducing variability in COPD care as a potential pitfall in efforts to reduce COPD readmissions.³⁹ We demonstrate that a majority of patients who are readmitted return for diagnoses other than COPD. This finding further highlights that readmission reduction programs need not only focus on COPD control but on the overall management of the patient’s complex medical comorbidities. HRRP penalties are assessed for all-cause readmissions,^31,32 and attention to the entire range of diagnoses leading to return to hospital is important to reduce readmission rates and expenditures. Use of strategies such as multispecialty clinics or integrated practice units may be useful in mitigating risk in multimorbid COPD patients.

Other significant factors that deserve further investigation include the use of postacute care services, including home health and skilled nursing facilities. Both factors were associated with higher likelihood of returning for non-COPD than for COPD-related diagnoses. This finding may be collinear to some degree with comorbidity because complex patients are probably less likely to be discharged home directly. Interestingly, those discharged to a postacute care facility had substantially high odds of readmission for a non-COPD cause. Transitional care programs, including short stays in a nursing home, are often employed to mitigate the risk of adverse outcomes after discharge in sicker patients,⁴⁰ which may be insufficient based on these data.

We applied the HRRP criteria for coding a COPD-related admission to the readmission diagnoses, which is more stringent than using only a principal diagnosis or DRGs, to maintain the same standard for defining the index and readmission event. In the sensitivity analyses, we did not find significant differences in our estimates of comorbidity’s effect on outcomes using a more liberal DRG classification system.

We also used DRGs to classify the readmission causes in order to use the same grouping logic that a payer would use when determining the cause. When evaluating which DRG patients returned for following a COPD exacerbation, pneumonia or other respiratory infections make up 13.8%, which may represent the evolution of respiratory infections that provoked the original exacerbation. Heart failure comprised 9.1% of the non-COPD causes, with about one-third of our COPD cohort having known comorbid heart failure at the time of index admission, illustrating significant overlap between these two conditions. Heart failure and pneumonia are conditions of interest in the HRRP and would potentially garner their own penalties had sufficient time elapsed since a prior hospitalization. Among other causes in the top 20 return DRGs were esophagitis, gastritis, gastrointestinal bleeding, and psychoses, which may be potentially associated with the use of corticosteroids to treat a COPD exacerbation, as described in other population studies.^41,42 Lack of medication regimen data in our analysis precludes further attribution of these causes, but the potential associations are interesting and warrant additional study.

The structure of our data as pooled annual cross sections rather than a true longitudinal cohort limited us to use only 10 months (February to November) of index hospitalizations in order to stay aligned with HRRP policy inclusion criteria. As such, we may have missed some important observations during peak respiratory virus season. As in any administrative data analysis, we are limited to codes in the discharge records, which may not reflect the entire nature of a hospitalization. Administrative data are particularly problematic in identifying true COPD exacerbations, particularly with multiple comorbid cardiopulmonary conditions.^43,44 Validating coding algorithms for identifying COPD was beyond the scope of our evaluation, which purposefully used HRRP methodology. Further study thereof would be a useful endeavor, especially with transition to ICD-10, considering that previously published evaluation was limited to ICD-9.⁴⁴ Despite these limitations, we were left with a robust and representative national cohort, which is an acceptable tradeoff.

 

CONCLUSION

Our study highlights the importance of understanding comorbidity as a major determinant of readmissions following COPD exacerbations, particularly in distinguishing which patients will return for COPD versus non-COPD-related diagnoses. At the health system level, readmission programs should be designed with the multimorbid patient in mind. Engagement of care teams, facilitating communication, and shared decision making are strategies to mitigate readmission risk in addition to COPD-focused disease management.³⁹ These data highlight the need to use risk prediction tools in assigning resources to reduce readmissions,⁴⁵ as well as the need to move readmission reduction programs beyond COPD management alone. Developing such systems to prospectively identify which patients are at risk of returning for both COPD and non-COPD reasons may further elucidate readmission mitigation strategies and should be a subject of future prospective study.

Acknowledgments

Data were made available through the Agency for Healthcare Research and Quality’s Healthcare Utilization Project. A full list of partner organizations providing data for the Nationwide Readmission Database can be found at https://www.hcup-us.ahrq.gov/db/hcupdatapartners.jsp.

Prior Presentation

Portions of this work were presented in abstract form at the 2018 American Thoracic Society International Conference (May 2018, San Diego, CA). This manuscript is derived from the doctoral dissertation for the degree of PhD in Health Policy and Management of the corresponding author, conferred in June 2019.

Disclaimer

This article does not necessarily represent the views and policies of the Department of Veterans Affairs or the USPSTF.

 

Readmissions following hospitalization for exacerbations of chronic obstructive pulmonary disease (COPD) are common and economically burdensome.¹ The Affordable Care Act² outlined the Hospital Readmissions Reduction Program (HRRP),³ which aims to improve the quality of care and reduce the costs for patients with pneumonia, myocardial infarction, congestive heart failure, and COPD.³ With the implementation of the HRRP, readmission reduction has become a key priority of health systems.

Multiple approaches to reduce readmissions are published, with variable degrees of success across respiratory and all-cause rehospitalizations.⁴ Patient self-management programs are heterogenous with inconsistent utilization reductions.^5-7 While some transitional care programs demonstrate benefits,^8-10 one notable study of an intensive transitional care and self-management program showed increaseNod acute care utilization without improving health-related quality of life.^11-13 Another study of COPD comprehensive care management was stopped prematurely for increased mortality in the intervention group.¹⁴ Telehealth monitoring may predict exacerbations,^15,16 but inconsistent effects on quality of life and utilization are observed.^17,18 Pulmonary rehabilitation improves quality of life but not healthcare utilization.¹⁹ Dispensing respiratory medications at hospital discharge shows improved prescription fills and fewer readmissions,²⁰ further reinforced by inhaler training prior to discharge.²¹ Postdischarge oxygen therapy does not improve health-related quality of life or acute care utilization.²² The fact that these approaches have not reliably succeeded raises the need for further study on the drivers of readmissions in COPD. Previous studies found differences in factors associated with the timing of COPD readmissions and return diagnoses.^23,24 While HRRP is Medicare-specific, health systems likely use programs targeting their entire population when planning readmission reduction strategies. Previous analyses were primarily single-center studies²⁵ and Medicare²⁴ or private insurance claims.²⁶

In this analysis, we explore how comorbidity burden^27-29 may differentially influence readmissions for recurrent COPD exacerbations versus other diagnoses. Our approach uses a national all-payer sample that covers a diverse geographic area across the United States, providing robust estimates of factors influencing readmission and valuable insights for planning and implementing effective readmission reduction programs. By including data from a period that encompasses the implementation of HRRP, we also provide new information on the factors in the HRRP postimplementation that are not yet available in published literature.

 

METHODS

Data Source

The Nationwide Readmissions Database (NRD) is a nationally representative, all-payer, 100% sample of community acute care hospital discharges from multiple states.³⁰ We pooled COPD discharge records spanning 2010-2016, excluding those where the patients were not residents of the state in which they were hospitalized to minimize loss to follow-up.

Inclusion/Exclusion Criteria

Selection criteria mirrored the methodology used by the HRRP,^31,32 defining an index discharge as a patient ≥40 years of age with a qualifying COPD diagnosis (Appendix Tables 1-2), discharged alive, with at least 30 days elapsed since previous hospitalization. We excluded discharges against medical advice or those from a hospital with fewer than 25 COPD discharges in that calendar year as per HRRP,^31,32 as well as those involving lung transplants. In this pooled cross-sectional analysis, record identifiers were not reliably unique across years. We restricted to observations originating February-November because January stays may not have had the requisite HRRP 30-day washout period from last admission and December stays could not be tracked into the subsequent January.

Outcomes

We defined a readmission as subsequent hospitalization for any cause within 30 days of the index discharge, with exemptions defined by the HRRP (Appendix Figure 1).^31,32 We segmented the readmission outcome into two parts: those readmitted with diagnoses that met the COPD HRRP criteria versus for any other diagnoses. We also tabulated diagnosis-related groups (DRGs) coded for the readmission observation to capture attributable cause for rehospitalization.

Our main independent variable was the Elixhauser Comorbidity Index score,³³ constructed using adaptations of published software,^34,35 having previously validated its use for modeling COPD readmissions.³⁶ We involved covariates provided with the database, including sociodemographic variables (eg, age, sex, community characteristics, payer, and median income at patient’s ZIP code) and hospital characteristics (eg, size, ownership, teaching status). We constructed additional covariates to account for in-hospital events by aggregating ICD diagnosis and procedure codes (eg, mechanical ventilation), hospital discharge volume, and proportion of annual within-hospital Medicaid patient days as a surrogate marker for safety-net hospitals. A detailed explanation of database construction and selection criteria is found in the Supplemental Methods Appendix.

Statistical Analysis

We tabulated patient-level descriptive statistics across the three outcomes of interest (ie, not readmitted, readmitted for a stay that would have qualified as COPD-related by HRRP criteria and readmitted for any other diagnosis). Continuous variables were compared using Welch’s t-tests (ie, unequal variance) and categorical variables using Chi-squared tests. At the hospital level, we tabulated the proportions of hospitals within categories in key variables of interest and a sub-population readmission rate for that particular characteristic, compared using Chi-squared tests.

We fit a multilevel multinomial logistic regression with random intercepts at the hospital cluster level, with the tripartite readmission outcome described above with “not readmitted” as the reference group. We included fixed effects for year, Elixhauser score, and patient- and hospital-level covariates as described above. Time to readmission for each group was plotted to assess the time distribution for each outcome. In-hospital mortality during each readmission event was tabulated.

 

Sensitivity Analyses and Missing Data

We conducted sensitivity analyses to determine whether a lower age cutoff (≥18 years) affects modeling. We also tested the stability of our estimates across each individual year of the pooled analysis. To test the effect of time to differential readmission, we fit a Cox proportional hazards model within the readmitted patient subgroup with Huber-White standard errors clustered at the hospital level to estimate the differential hazard of readmission for COPD versus non-COPD diagnoses across the same variables of interest as a sensitivity analysis. We also tested using a liberal classification of readmission diagnoses by sorting into “respiratory” versus “nonrespiratory” returns, with DRGs 163 through 208 for “respiratory” versus all others, respectively. We tested the agreement between the HRRP ICD-based classification and DRG classification using Cohen’s kappa.

We designated a threshold of 10% missing data to necessitate imputation techniques, determined a priori for our main variables, none of which met this level. Complete case analyses were used for all models. Analyses were performed in Stata version 15.1 (StataCorp, College Station, Texas) with weighted estimates reported using patient-level survey weights for national representativeness.³⁷ The study protocol was reviewed by the institutional review board at the University of California, Los Angeles, and deemed exempt from oversight due to the publicly available, deidentified nature of the data (IRB# 18-001208).

RESULTS

Out of 104,897,595 hospitalizations in the NRD, a final sample of 1,622,983 COPD discharges was identified for our analysis (sample weighted effective population 3,743,164). The overall readmission rate was 17.25%, with 7.69% of patients readmitted for COPD and 9.56% readmitted for other diagnoses. Those with COPD readmissions were significantly younger with a lower proportion of Medicare and a higher proportion of Medicaid as the primary payer compared with those readmitted for all other causes (Table 1). Compared with non-COPD-readmitted patients, COPD-readmitted patients were more frequently discharged home without services and had shorter lengths of stay. Noninvasive ventilation was more common among COPD readmissions while mechanical ventilation and tracheostomy placement were less frequent compared with non-COPD readmissions. Compared with non-COPD-readmitted patients, COPD-readmitted patients had significantly lower mean Elixhauser Comorbidity Index scores (17.8 vs 22.8), rates of congestive heart failure (28.3% vs 38.6%), and renal failure (11.8% vs 21.5%; Appendix Table 3).

Readmission rates were significantly higher for non-COPD causes than for COPD causes across all hospital types by ownership, teaching status, or size (Table 2). Parallel patterns were observed for non-COPD and COPD readmissions across hospital types, with two key exceptions. Across categories of hospital ownership, for-profit hospitals had the highest rates for non-COPD readmissions, with no differences in hospital control for COPD rehospitalizations. While rates did not vary for non-COPD readmissions by within-hospital Medicaid prevalence, COPD readmission rates significantly increased as Medicaid-paid patient-days increased within hospitals.

The median time to non-COPD readmission was 13 days, whereas COPD readmission was 14 days. More COPD readmissions occurred in the first 2.4 days after discharge, after which the proportion of non-COPD cases readmitted consistently increased. Observed readmission rates for COPD and other diagnoses trended down over the study period (Figure 1A), as did mortality rates during readmission stays (Figure 1B). Sepsis, heart failure, and respiratory infections were seven of the top 10 ranked DRGs for the non-COPD rehospitalizations (Appendix Table 4). In trend analyses (Appendix Tables 5-8), sepsis and DRGs with major comorbidities increased in proportion each year across the study period, possibly reflecting changes in coding practices.³⁸

In our adjusted multinomial logistic regression model (Table 3), where the outcomes were not readmitted (reference category) versus readmitted for non-COPD diagnosis or for qualifying COPD diagnosis, the effect size of comorbidity, operationalized by change in the Elixhauser Comorbidity Index, was larger for non-COPD than non-COPD readmissions (odds ratio [OR] 1.19 vs 1.04 per one-half standard deviation of Elixhauser Index, an approximately 7.5 unit change in score). Increases in age were associated with higher non-COPD readmissions (OR 1.06 per 10 years) while actually protective against COPD readmissions (OR 0.89 per 10 years). Compared with Medicare patients, Medicaid patients had higher odds of COPD readmission (OR 1.10 vs 1.03) while the converse was observed in the privately insured (OR 0.65 vs 0.76). Transfers to postacute care facilities, referenced against discharges home, had a larger association with readmissions for non-COPD causes (OR 1.35 vs 1.00), whereas home-health had nearly equal adjusted readmission odds for each outcome (1.31 vs 1.30). Length of stay was associated with 1% greater odds per day for readmission for non-COPD causes than COPD returns. Regarding in-hospital events, odds of COPD readmission were higher for noninvasive ventilation (OR 1.37 vs 0.89) and mechanical ventilation (OR 0.87 vs 0.79, Appendix Table 9), which should be interpreted in the context that analyses were restricted to those discharged alive from their index admission, possibly biasing the true effect estimates due to competing risk of index in-hospital mortality.

In sensitivity analyses, we found no significant differences between our Cox proportional hazards model (Appendix Table 10) and our multinomial model. When we liberalized readmission outcome definitions to respiratory versus nonrespiratory DRGs, we observed 86% agreement between the HRRP and DRG classification systems (κ = 0.73, P < .001). Among the discordant observations, 13% of non-COPD readmissions under HRRP criteria were reclassified as respiratory by DRG and 1% of COPD readmissions under HRRP reclassified as nonrespiratory. When our multinomial model (Appendix Table 11) was re-fit using the DRG-based outcome, only slight changes in effect size occurred. For the Elixhauser Index, the OR for COPD by HRRP was slightly lower than that for respiratory DRGs (1.04 vs 1.05), parallel with the difference between non-COPD by HRRP and nonrespiratory DRG classification (1.19 vs 1.21, respectively). This result underscores the greater influence of comorbidity on non-COPD than COPD readmissions. Only one sign change was observed in those who underwent tracheostomy (OR 0.91 for “nonrespiratory” DRG vs 1.07 for “non-COPD” by HRRP), likely reflecting the shift of some non-COPD diagnoses into the respiratory categorization based on tracheostomy having its own DRG. We also evaluated the multinomial model without the Elixhauser Index (only covariates) and found minor adjustments to the effect sizes of the covariates, particularly for discharge disposition. However, no sign changes were observed for any of the odds ratios (Appendix Table 12). Readmission odds by the Elixhauser score for each condition were stable across years (Appendix Figure 2 & Appendix Table 13). Finally, including 18-39-year-old patients in the cohort did not substantially change our estimates (Appendix Table 14).

 

DISCUSSION

In this assessment of readmission odds following hospitalizations for COPD in a nationally representative all-payer sample, we demonstrate that 55% of rehospitalizations following COPD exacerbations are attributable to non-COPD diagnoses and describe the important role of comorbidity on influencing diagnoses at rehospitalization. These findings are consistent with a prior study of Medicare patients by Shah et al.²⁴ and expand upon the results of Jacobs et al. using a pre-HRRP sample of the NRD.²³ Our study offers an expanded analysis by including data spanning HRRP implementation, which went into effect for COPD in October 2014.³ Effect estimates were stable across all seven years of our study in sensitivity analyses, demonstrating the robustness of our findings. Our analysis also adds to the existing body of literature by assessing which factors are associated with readmissions related to ongoing COPD versus other diagnoses.

In our study, an increase in aggregated comorbidity by the Elixhauser Index was associated with a significantly higher readmission odds, with over four times the effect size for non-COPD than COPD returns. Comorbidity also moderated the effect of other factors, such as income and discharge disposition. While overall readmission rates declined across the course of the study period, the effect of comorbidity on readmission odds for both groups remained significant in annualized models. We also observed higher rates of nearly every individual Elixhauser component comorbidity in those readmitted for non-COPD causes compared with those readmitted for COPD causes. Taken together, these results underscore the need to account for comorbidities at the individual and composite levels when identifying those at highest risk for readmissions and necessitate a multidisciplinary approach to reduce risk for the multimorbid patient.

In a 2018 report, the American Thoracic Society highlighted the focus of programs on adherence to guidelines and reducing variability in COPD care as a potential pitfall in efforts to reduce COPD readmissions.³⁹ We demonstrate that a majority of patients who are readmitted return for diagnoses other than COPD. This finding further highlights that readmission reduction programs need not only focus on COPD control but on the overall management of the patient’s complex medical comorbidities. HRRP penalties are assessed for all-cause readmissions,^31,32 and attention to the entire range of diagnoses leading to return to hospital is important to reduce readmission rates and expenditures. Use of strategies such as multispecialty clinics or integrated practice units may be useful in mitigating risk in multimorbid COPD patients.

Other significant factors that deserve further investigation include the use of postacute care services, including home health and skilled nursing facilities. Both factors were associated with higher likelihood of returning for non-COPD than for COPD-related diagnoses. This finding may be collinear to some degree with comorbidity because complex patients are probably less likely to be discharged home directly. Interestingly, those discharged to a postacute care facility had substantially high odds of readmission for a non-COPD cause. Transitional care programs, including short stays in a nursing home, are often employed to mitigate the risk of adverse outcomes after discharge in sicker patients,⁴⁰ which may be insufficient based on these data.

We applied the HRRP criteria for coding a COPD-related admission to the readmission diagnoses, which is more stringent than using only a principal diagnosis or DRGs, to maintain the same standard for defining the index and readmission event. In the sensitivity analyses, we did not find significant differences in our estimates of comorbidity’s effect on outcomes using a more liberal DRG classification system.

We also used DRGs to classify the readmission causes in order to use the same grouping logic that a payer would use when determining the cause. When evaluating which DRG patients returned for following a COPD exacerbation, pneumonia or other respiratory infections make up 13.8%, which may represent the evolution of respiratory infections that provoked the original exacerbation. Heart failure comprised 9.1% of the non-COPD causes, with about one-third of our COPD cohort having known comorbid heart failure at the time of index admission, illustrating significant overlap between these two conditions. Heart failure and pneumonia are conditions of interest in the HRRP and would potentially garner their own penalties had sufficient time elapsed since a prior hospitalization. Among other causes in the top 20 return DRGs were esophagitis, gastritis, gastrointestinal bleeding, and psychoses, which may be potentially associated with the use of corticosteroids to treat a COPD exacerbation, as described in other population studies.^41,42 Lack of medication regimen data in our analysis precludes further attribution of these causes, but the potential associations are interesting and warrant additional study.

The structure of our data as pooled annual cross sections rather than a true longitudinal cohort limited us to use only 10 months (February to November) of index hospitalizations in order to stay aligned with HRRP policy inclusion criteria. As such, we may have missed some important observations during peak respiratory virus season. As in any administrative data analysis, we are limited to codes in the discharge records, which may not reflect the entire nature of a hospitalization. Administrative data are particularly problematic in identifying true COPD exacerbations, particularly with multiple comorbid cardiopulmonary conditions.^43,44 Validating coding algorithms for identifying COPD was beyond the scope of our evaluation, which purposefully used HRRP methodology. Further study thereof would be a useful endeavor, especially with transition to ICD-10, considering that previously published evaluation was limited to ICD-9.⁴⁴ Despite these limitations, we were left with a robust and representative national cohort, which is an acceptable tradeoff.

 

CONCLUSION

Our study highlights the importance of understanding comorbidity as a major determinant of readmissions following COPD exacerbations, particularly in distinguishing which patients will return for COPD versus non-COPD-related diagnoses. At the health system level, readmission programs should be designed with the multimorbid patient in mind. Engagement of care teams, facilitating communication, and shared decision making are strategies to mitigate readmission risk in addition to COPD-focused disease management.³⁹ These data highlight the need to use risk prediction tools in assigning resources to reduce readmissions,⁴⁵ as well as the need to move readmission reduction programs beyond COPD management alone. Developing such systems to prospectively identify which patients are at risk of returning for both COPD and non-COPD reasons may further elucidate readmission mitigation strategies and should be a subject of future prospective study.

Acknowledgments

Data were made available through the Agency for Healthcare Research and Quality’s Healthcare Utilization Project. A full list of partner organizations providing data for the Nationwide Readmission Database can be found at https://www.hcup-us.ahrq.gov/db/hcupdatapartners.jsp.

Prior Presentation

Portions of this work were presented in abstract form at the 2018 American Thoracic Society International Conference (May 2018, San Diego, CA). This manuscript is derived from the doctoral dissertation for the degree of PhD in Health Policy and Management of the corresponding author, conferred in June 2019.

Disclaimer

This article does not necessarily represent the views and policies of the Department of Veterans Affairs or the USPSTF.

 

Readmissions following hospitalization for exacerbations of chronic obstructive pulmonary disease (COPD) are common and economically burdensome.¹ The Affordable Care Act² outlined the Hospital Readmissions Reduction Program (HRRP),³ which aims to improve the quality of care and reduce the costs for patients with pneumonia, myocardial infarction, congestive heart failure, and COPD.³ With the implementation of the HRRP, readmission reduction has become a key priority of health systems.

Multiple approaches to reduce readmissions are published, with variable degrees of success across respiratory and all-cause rehospitalizations.⁴ Patient self-management programs are heterogenous with inconsistent utilization reductions.^5-7 While some transitional care programs demonstrate benefits,^8-10 one notable study of an intensive transitional care and self-management program showed increaseNod acute care utilization without improving health-related quality of life.^11-13 Another study of COPD comprehensive care management was stopped prematurely for increased mortality in the intervention group.¹⁴ Telehealth monitoring may predict exacerbations,^15,16 but inconsistent effects on quality of life and utilization are observed.^17,18 Pulmonary rehabilitation improves quality of life but not healthcare utilization.¹⁹ Dispensing respiratory medications at hospital discharge shows improved prescription fills and fewer readmissions,²⁰ further reinforced by inhaler training prior to discharge.²¹ Postdischarge oxygen therapy does not improve health-related quality of life or acute care utilization.²² The fact that these approaches have not reliably succeeded raises the need for further study on the drivers of readmissions in COPD. Previous studies found differences in factors associated with the timing of COPD readmissions and return diagnoses.^23,24 While HRRP is Medicare-specific, health systems likely use programs targeting their entire population when planning readmission reduction strategies. Previous analyses were primarily single-center studies²⁵ and Medicare²⁴ or private insurance claims.²⁶

In this analysis, we explore how comorbidity burden^27-29 may differentially influence readmissions for recurrent COPD exacerbations versus other diagnoses. Our approach uses a national all-payer sample that covers a diverse geographic area across the United States, providing robust estimates of factors influencing readmission and valuable insights for planning and implementing effective readmission reduction programs. By including data from a period that encompasses the implementation of HRRP, we also provide new information on the factors in the HRRP postimplementation that are not yet available in published literature.

 

METHODS

Data Source

The Nationwide Readmissions Database (NRD) is a nationally representative, all-payer, 100% sample of community acute care hospital discharges from multiple states.³⁰ We pooled COPD discharge records spanning 2010-2016, excluding those where the patients were not residents of the state in which they were hospitalized to minimize loss to follow-up.

Inclusion/Exclusion Criteria

Selection criteria mirrored the methodology used by the HRRP,^31,32 defining an index discharge as a patient ≥40 years of age with a qualifying COPD diagnosis (Appendix Tables 1-2), discharged alive, with at least 30 days elapsed since previous hospitalization. We excluded discharges against medical advice or those from a hospital with fewer than 25 COPD discharges in that calendar year as per HRRP,^31,32 as well as those involving lung transplants. In this pooled cross-sectional analysis, record identifiers were not reliably unique across years. We restricted to observations originating February-November because January stays may not have had the requisite HRRP 30-day washout period from last admission and December stays could not be tracked into the subsequent January.

Outcomes

We defined a readmission as subsequent hospitalization for any cause within 30 days of the index discharge, with exemptions defined by the HRRP (Appendix Figure 1).^31,32 We segmented the readmission outcome into two parts: those readmitted with diagnoses that met the COPD HRRP criteria versus for any other diagnoses. We also tabulated diagnosis-related groups (DRGs) coded for the readmission observation to capture attributable cause for rehospitalization.

Our main independent variable was the Elixhauser Comorbidity Index score,³³ constructed using adaptations of published software,^34,35 having previously validated its use for modeling COPD readmissions.³⁶ We involved covariates provided with the database, including sociodemographic variables (eg, age, sex, community characteristics, payer, and median income at patient’s ZIP code) and hospital characteristics (eg, size, ownership, teaching status). We constructed additional covariates to account for in-hospital events by aggregating ICD diagnosis and procedure codes (eg, mechanical ventilation), hospital discharge volume, and proportion of annual within-hospital Medicaid patient days as a surrogate marker for safety-net hospitals. A detailed explanation of database construction and selection criteria is found in the Supplemental Methods Appendix.

Statistical Analysis

We tabulated patient-level descriptive statistics across the three outcomes of interest (ie, not readmitted, readmitted for a stay that would have qualified as COPD-related by HRRP criteria and readmitted for any other diagnosis). Continuous variables were compared using Welch’s t-tests (ie, unequal variance) and categorical variables using Chi-squared tests. At the hospital level, we tabulated the proportions of hospitals within categories in key variables of interest and a sub-population readmission rate for that particular characteristic, compared using Chi-squared tests.

We fit a multilevel multinomial logistic regression with random intercepts at the hospital cluster level, with the tripartite readmission outcome described above with “not readmitted” as the reference group. We included fixed effects for year, Elixhauser score, and patient- and hospital-level covariates as described above. Time to readmission for each group was plotted to assess the time distribution for each outcome. In-hospital mortality during each readmission event was tabulated.

 

Sensitivity Analyses and Missing Data

We conducted sensitivity analyses to determine whether a lower age cutoff (≥18 years) affects modeling. We also tested the stability of our estimates across each individual year of the pooled analysis. To test the effect of time to differential readmission, we fit a Cox proportional hazards model within the readmitted patient subgroup with Huber-White standard errors clustered at the hospital level to estimate the differential hazard of readmission for COPD versus non-COPD diagnoses across the same variables of interest as a sensitivity analysis. We also tested using a liberal classification of readmission diagnoses by sorting into “respiratory” versus “nonrespiratory” returns, with DRGs 163 through 208 for “respiratory” versus all others, respectively. We tested the agreement between the HRRP ICD-based classification and DRG classification using Cohen’s kappa.

We designated a threshold of 10% missing data to necessitate imputation techniques, determined a priori for our main variables, none of which met this level. Complete case analyses were used for all models. Analyses were performed in Stata version 15.1 (StataCorp, College Station, Texas) with weighted estimates reported using patient-level survey weights for national representativeness.³⁷ The study protocol was reviewed by the institutional review board at the University of California, Los Angeles, and deemed exempt from oversight due to the publicly available, deidentified nature of the data (IRB# 18-001208).

RESULTS

Out of 104,897,595 hospitalizations in the NRD, a final sample of 1,622,983 COPD discharges was identified for our analysis (sample weighted effective population 3,743,164). The overall readmission rate was 17.25%, with 7.69% of patients readmitted for COPD and 9.56% readmitted for other diagnoses. Those with COPD readmissions were significantly younger with a lower proportion of Medicare and a higher proportion of Medicaid as the primary payer compared with those readmitted for all other causes (Table 1). Compared with non-COPD-readmitted patients, COPD-readmitted patients were more frequently discharged home without services and had shorter lengths of stay. Noninvasive ventilation was more common among COPD readmissions while mechanical ventilation and tracheostomy placement were less frequent compared with non-COPD readmissions. Compared with non-COPD-readmitted patients, COPD-readmitted patients had significantly lower mean Elixhauser Comorbidity Index scores (17.8 vs 22.8), rates of congestive heart failure (28.3% vs 38.6%), and renal failure (11.8% vs 21.5%; Appendix Table 3).

Readmission rates were significantly higher for non-COPD causes than for COPD causes across all hospital types by ownership, teaching status, or size (Table 2). Parallel patterns were observed for non-COPD and COPD readmissions across hospital types, with two key exceptions. Across categories of hospital ownership, for-profit hospitals had the highest rates for non-COPD readmissions, with no differences in hospital control for COPD rehospitalizations. While rates did not vary for non-COPD readmissions by within-hospital Medicaid prevalence, COPD readmission rates significantly increased as Medicaid-paid patient-days increased within hospitals.

The median time to non-COPD readmission was 13 days, whereas COPD readmission was 14 days. More COPD readmissions occurred in the first 2.4 days after discharge, after which the proportion of non-COPD cases readmitted consistently increased. Observed readmission rates for COPD and other diagnoses trended down over the study period (Figure 1A), as did mortality rates during readmission stays (Figure 1B). Sepsis, heart failure, and respiratory infections were seven of the top 10 ranked DRGs for the non-COPD rehospitalizations (Appendix Table 4). In trend analyses (Appendix Tables 5-8), sepsis and DRGs with major comorbidities increased in proportion each year across the study period, possibly reflecting changes in coding practices.³⁸

In our adjusted multinomial logistic regression model (Table 3), where the outcomes were not readmitted (reference category) versus readmitted for non-COPD diagnosis or for qualifying COPD diagnosis, the effect size of comorbidity, operationalized by change in the Elixhauser Comorbidity Index, was larger for non-COPD than non-COPD readmissions (odds ratio [OR] 1.19 vs 1.04 per one-half standard deviation of Elixhauser Index, an approximately 7.5 unit change in score). Increases in age were associated with higher non-COPD readmissions (OR 1.06 per 10 years) while actually protective against COPD readmissions (OR 0.89 per 10 years). Compared with Medicare patients, Medicaid patients had higher odds of COPD readmission (OR 1.10 vs 1.03) while the converse was observed in the privately insured (OR 0.65 vs 0.76). Transfers to postacute care facilities, referenced against discharges home, had a larger association with readmissions for non-COPD causes (OR 1.35 vs 1.00), whereas home-health had nearly equal adjusted readmission odds for each outcome (1.31 vs 1.30). Length of stay was associated with 1% greater odds per day for readmission for non-COPD causes than COPD returns. Regarding in-hospital events, odds of COPD readmission were higher for noninvasive ventilation (OR 1.37 vs 0.89) and mechanical ventilation (OR 0.87 vs 0.79, Appendix Table 9), which should be interpreted in the context that analyses were restricted to those discharged alive from their index admission, possibly biasing the true effect estimates due to competing risk of index in-hospital mortality.

In sensitivity analyses, we found no significant differences between our Cox proportional hazards model (Appendix Table 10) and our multinomial model. When we liberalized readmission outcome definitions to respiratory versus nonrespiratory DRGs, we observed 86% agreement between the HRRP and DRG classification systems (κ = 0.73, P < .001). Among the discordant observations, 13% of non-COPD readmissions under HRRP criteria were reclassified as respiratory by DRG and 1% of COPD readmissions under HRRP reclassified as nonrespiratory. When our multinomial model (Appendix Table 11) was re-fit using the DRG-based outcome, only slight changes in effect size occurred. For the Elixhauser Index, the OR for COPD by HRRP was slightly lower than that for respiratory DRGs (1.04 vs 1.05), parallel with the difference between non-COPD by HRRP and nonrespiratory DRG classification (1.19 vs 1.21, respectively). This result underscores the greater influence of comorbidity on non-COPD than COPD readmissions. Only one sign change was observed in those who underwent tracheostomy (OR 0.91 for “nonrespiratory” DRG vs 1.07 for “non-COPD” by HRRP), likely reflecting the shift of some non-COPD diagnoses into the respiratory categorization based on tracheostomy having its own DRG. We also evaluated the multinomial model without the Elixhauser Index (only covariates) and found minor adjustments to the effect sizes of the covariates, particularly for discharge disposition. However, no sign changes were observed for any of the odds ratios (Appendix Table 12). Readmission odds by the Elixhauser score for each condition were stable across years (Appendix Figure 2 & Appendix Table 13). Finally, including 18-39-year-old patients in the cohort did not substantially change our estimates (Appendix Table 14).

 

DISCUSSION

In this assessment of readmission odds following hospitalizations for COPD in a nationally representative all-payer sample, we demonstrate that 55% of rehospitalizations following COPD exacerbations are attributable to non-COPD diagnoses and describe the important role of comorbidity on influencing diagnoses at rehospitalization. These findings are consistent with a prior study of Medicare patients by Shah et al.²⁴ and expand upon the results of Jacobs et al. using a pre-HRRP sample of the NRD.²³ Our study offers an expanded analysis by including data spanning HRRP implementation, which went into effect for COPD in October 2014.³ Effect estimates were stable across all seven years of our study in sensitivity analyses, demonstrating the robustness of our findings. Our analysis also adds to the existing body of literature by assessing which factors are associated with readmissions related to ongoing COPD versus other diagnoses.

In our study, an increase in aggregated comorbidity by the Elixhauser Index was associated with a significantly higher readmission odds, with over four times the effect size for non-COPD than COPD returns. Comorbidity also moderated the effect of other factors, such as income and discharge disposition. While overall readmission rates declined across the course of the study period, the effect of comorbidity on readmission odds for both groups remained significant in annualized models. We also observed higher rates of nearly every individual Elixhauser component comorbidity in those readmitted for non-COPD causes compared with those readmitted for COPD causes. Taken together, these results underscore the need to account for comorbidities at the individual and composite levels when identifying those at highest risk for readmissions and necessitate a multidisciplinary approach to reduce risk for the multimorbid patient.

In a 2018 report, the American Thoracic Society highlighted the focus of programs on adherence to guidelines and reducing variability in COPD care as a potential pitfall in efforts to reduce COPD readmissions.³⁹ We demonstrate that a majority of patients who are readmitted return for diagnoses other than COPD. This finding further highlights that readmission reduction programs need not only focus on COPD control but on the overall management of the patient’s complex medical comorbidities. HRRP penalties are assessed for all-cause readmissions,^31,32 and attention to the entire range of diagnoses leading to return to hospital is important to reduce readmission rates and expenditures. Use of strategies such as multispecialty clinics or integrated practice units may be useful in mitigating risk in multimorbid COPD patients.

Other significant factors that deserve further investigation include the use of postacute care services, including home health and skilled nursing facilities. Both factors were associated with higher likelihood of returning for non-COPD than for COPD-related diagnoses. This finding may be collinear to some degree with comorbidity because complex patients are probably less likely to be discharged home directly. Interestingly, those discharged to a postacute care facility had substantially high odds of readmission for a non-COPD cause. Transitional care programs, including short stays in a nursing home, are often employed to mitigate the risk of adverse outcomes after discharge in sicker patients,⁴⁰ which may be insufficient based on these data.

We applied the HRRP criteria for coding a COPD-related admission to the readmission diagnoses, which is more stringent than using only a principal diagnosis or DRGs, to maintain the same standard for defining the index and readmission event. In the sensitivity analyses, we did not find significant differences in our estimates of comorbidity’s effect on outcomes using a more liberal DRG classification system.

We also used DRGs to classify the readmission causes in order to use the same grouping logic that a payer would use when determining the cause. When evaluating which DRG patients returned for following a COPD exacerbation, pneumonia or other respiratory infections make up 13.8%, which may represent the evolution of respiratory infections that provoked the original exacerbation. Heart failure comprised 9.1% of the non-COPD causes, with about one-third of our COPD cohort having known comorbid heart failure at the time of index admission, illustrating significant overlap between these two conditions. Heart failure and pneumonia are conditions of interest in the HRRP and would potentially garner their own penalties had sufficient time elapsed since a prior hospitalization. Among other causes in the top 20 return DRGs were esophagitis, gastritis, gastrointestinal bleeding, and psychoses, which may be potentially associated with the use of corticosteroids to treat a COPD exacerbation, as described in other population studies.^41,42 Lack of medication regimen data in our analysis precludes further attribution of these causes, but the potential associations are interesting and warrant additional study.

The structure of our data as pooled annual cross sections rather than a true longitudinal cohort limited us to use only 10 months (February to November) of index hospitalizations in order to stay aligned with HRRP policy inclusion criteria. As such, we may have missed some important observations during peak respiratory virus season. As in any administrative data analysis, we are limited to codes in the discharge records, which may not reflect the entire nature of a hospitalization. Administrative data are particularly problematic in identifying true COPD exacerbations, particularly with multiple comorbid cardiopulmonary conditions.^43,44 Validating coding algorithms for identifying COPD was beyond the scope of our evaluation, which purposefully used HRRP methodology. Further study thereof would be a useful endeavor, especially with transition to ICD-10, considering that previously published evaluation was limited to ICD-9.⁴⁴ Despite these limitations, we were left with a robust and representative national cohort, which is an acceptable tradeoff.

 

CONCLUSION

Our study highlights the importance of understanding comorbidity as a major determinant of readmissions following COPD exacerbations, particularly in distinguishing which patients will return for COPD versus non-COPD-related diagnoses. At the health system level, readmission programs should be designed with the multimorbid patient in mind. Engagement of care teams, facilitating communication, and shared decision making are strategies to mitigate readmission risk in addition to COPD-focused disease management.³⁹ These data highlight the need to use risk prediction tools in assigning resources to reduce readmissions,⁴⁵ as well as the need to move readmission reduction programs beyond COPD management alone. Developing such systems to prospectively identify which patients are at risk of returning for both COPD and non-COPD reasons may further elucidate readmission mitigation strategies and should be a subject of future prospective study.

Acknowledgments

Data were made available through the Agency for Healthcare Research and Quality’s Healthcare Utilization Project. A full list of partner organizations providing data for the Nationwide Readmission Database can be found at https://www.hcup-us.ahrq.gov/db/hcupdatapartners.jsp.

Prior Presentation

Portions of this work were presented in abstract form at the 2018 American Thoracic Society International Conference (May 2018, San Diego, CA). This manuscript is derived from the doctoral dissertation for the degree of PhD in Health Policy and Management of the corresponding author, conferred in June 2019.

Disclaimer

This article does not necessarily represent the views and policies of the Department of Veterans Affairs or the USPSTF.

 

Hip fractures are a significant cause of morbidity and mortality among elderly patients. Patients with fragility hip fractures often carry multiple comorbid diagnoses with a significant risk of perioperative complications. After hip fracture, 30-day mortality has been reported as 3.3% to 17.2% with one-year mortality as high as 50%.¹

Multidisciplinary care,^2-5 surgery within 24 hours (h),^6-12 use of regional peripheral nerve blocks,^13-16 restrictive blood transfusion strategies,^17,18 tranexamic acid (TXA) use,¹⁹ pharmacologic deep venous thrombosis (DVT) prophylaxis,²⁰ surgical site infection prevention protocols,²¹ early mobilization,²² and nutritional optimization^23-25 have been individually shown to improve outcomes in hip fracture patients.

Our program sought to define, standardize, and implement evidence-based best practices to improve clinical care and outcomes of patients with hip fractures. We convened a Center for Musculoskeletal Care (CMC) Hip Fracture Oversight Group that included surgeons and advanced practice providers from Orthopedics; physicians from Internal Medicine Hospitalist, Geriatrics, Emergency Medicine, and Anesthesia; and representatives from rehabilitation services, nursing, care management, pharmacy, and performance improvement. With clinical input from all involved services, we developed evidence-based protocols to standardize the care of patients with fragility hip fractures from the time of the patient’s evaluation in the emergency room to discharge and outpatient rehabilitation. The program was operationalized in February 2016.

This project was considered by the Yale University institutional review board (IRB) to be a quality improvement and, therefore, exempted from IRB approval.

MATERIALS AND METHODS

Yale-New Haven Hospital is composed of two main campuses. The York Street Campus (YSC) is the Level 1 Trauma Center. The St. Raphael’s Campus (SRC) houses the CMC nursing units for elective lower extremity arthroplasty and spine procedures. Prior to 2016, patients with hip fractures were cared for equally at both Yale-New Haven Hospital campuses. Patients were admitted to both medical and surgical services with no standardization of hip fracture care processes. Surgeons were assigned based on availability. Frequently, patients were added on to the operating room (OR) schedule and did not undergo surgery until off-hours and after a prolonged waiting period.

Medical comanagement of patients with fragility hip fractures at our institution predated the start of our CMC Integrated Fragility Hip Fracture Program (IFHFP). Comanagement was instituted in 2012 at YSC and in 2014 at SRC but without standardized protocols. The IFHFP began in February 2016 with the centralization of all patients with fragility hip fractures to the SRC at Yale-New Haven Hospital. Emergency medical services directed patients with suspected hip fractures to the designated campus. A dedicated hip fracture OR was allocated daily with a hip fracture surgeon assigned by a shared community and faculty surgeon call schedule. Patients were encouraged but not required to accept care from the on-call hip fracture surgical attending. Anesthesia was notified of the arrival of a patient with a hip fracture in the emergency department, and if the patient consented and qualified, a single-shot femoral nerve block was performed. Patients were screened for nasal staphylococcal colonization and treated with povidone-iodine nasal decolonization, chlorhexidine wash, and antibiotics determined by staphylococcal status and type of surgical procedure planned. Preoperative and postoperative order sets were implemented that dictated the care processes as outlined in Table 1. Surgeons determined the choice of operative intervention as per usual; this included internal fixation and partial or total hip replacement. Detailed medical and surgical protocols are included in Appendix A.

Since the initiation of the IFHFP on February 1, 2016, the program has continued to advance with our experience. We used the year preceding the start of the program as our baseline year (January 1, 2015, through December 31, 2015). The following years, 2016 and 2017, were a transition time during which our protocols were implemented. The intervention year was defined as January 1, 2018, through December 31, 2018. The outcomes during the intervention year were compared with the baseline year. It is important to note that our program has been in continuous evolution, including during the intervention year, with protocols created and refined as we gain experience.

Outcomes include 30-day mortality, transfusions, adverse effects of drugs, venous thromboembolic complications, sepsis, myocardial infarction, mechanical surgical fixation complications, length of stay, 30-day readmission rate, unexpected return to the OR, and time to operative intervention. Definitions of the outcome variables are reviewed in Appendix B.

 

RESULTS

There were 275 consecutive patients with hip fractures admitted to SRC in the baseline year (January 1, 2015 to December 31, 2015) and 434 patients with hip fractures admitted in the intervention year (January 1, 2018, to December 31, 2018) after consolidation of the program to the single Yale-New Haven Campus and implementation of standardized care processes. Patient demographic data including age, sex, ethnicity, body mass index, and American Society of Anesthesiologists physical status classification were evaluated for the baseline year and intervention year and reported in Table 2. There were no differences in the demographics of patients between baseline and intervention years.

From baseline year to intervention year, 30-day mortality, transfusion, adverse effects of drugs, length of stay, unexpected return to OR, and time to OR were all significantly reduced. Mortality within 30 days decreased from 8.0% to 2.8%. The results are displayed in Table 3. No significant difference was seen in the incidence of venous thromboembolism, sepsis, myocardial infarctions, readmission at 30 days, or mechanical surgical fixation complications.

The Figure shows the 30-day IFHFP mortality rate as reported on a monthly basis starting on January 1 of the baseline year, 2015, and continuing through December 31 of the intervention year, 2018. The process interventions are mapped according to the date of initiation. The median mortality rate (including all data from January 1, 2015, to December 31, 2018) is demonstrated as the dotted line. From May 2018 to December 2018, each monthly mortality rate was recorded below the four-year median, a visual demonstration of the statistical significance seen in our mortality reduction from 8.0% in the baseline year to 2.8% in the intervention year.

DISCUSSION

Patients with fragility hip fractures are a medically complex and vulnerable population. The goal of the CMC IFHFP was to standardize the care of these high-risk patients in an effort to reduce time to the OR, perioperative medical complications, time spent in the hospital, and ultimately mortality.

The barriers to implementing coordinated, multidisciplinary care are significant. In our case, we had a fragmented care model with fragility hip fracture patients cared for at two campuses, on different nursing units, with both community and faculty surgeons providing operative care, and with no predesignated primary team. We structured our program for equal sharing of call between community and faculty surgeons. However, there was distrust among the physician groups: Primary care physicians were concerned that their referral lines with orthopedic surgical colleagues would be fractured by the new shared call. Surgeons doubted that patients would be distributed equally among community and faculty practices. Hospitalists feared that comanagement would mean surgeons abdicating responsibility for care. Surgeons worried that routine medical involvement would delay surgery and prolong the length of stay with excessive testing. In order to achieve consensus, address concerns, and allay fears, we engaged the primary care and surgeon leadership for their support at the onset of the program and held monthly large group meetings and many smaller sessions to advance objectives. We meticulously tracked data and frequently reported out to the involved groups.

As it is well established that operative intervention on a hip fracture is best completed within 24 h to optimize a patient’s clinical outcomes, critical interventions were the designation of a hip fracture OR starting midday and expectation that surgery be performed the day after admission for medically cleared patients. Surgeons were able to book elective cases or outpatient clinic time in the morning. The morning hours prior to surgery allowed time for any final medical optimization, preoperative nursing care, and family discussions. Most surgeries were then completed by the primary OR staff during standard operating hours. Patients were out of the postanesthesia care unit and settled back on the orthopedic nursing unit in the early evening without a prolonged period of nil per os, bed rest, or sleep interruption.

While our protocol expected surgery the day after admission for medically cleared patients, we used surgery within 24 h as a simple metric to compare baseline with intervention outcomes. With our hip fracture OR block time beginning midday, the majority of our medically cleared hip fracture patients would receive surgical treatment within 24 h of admission. Our data show a significant improvement in timeliness of surgical intervention from 41.8% of patients to the OR within 24 h in 2015 to 55% in 2018. In 2017, we conducted an interval four-month audit involving a detailed chart review of all patients for whom surgery was delayed beyond 24 h from hospital admission. Chart review identified anticoagulation as the primary reason for surgical delay. Of patients who were eligible for surgery (medically stabilized and not therapeutically anticoagulated), 90% underwent surgery within 24 h during this four-month period in 2017. This compares to an overall rate of surgery within 24 h of 57% during the calendar year 2017. We did not perform a subgroup analysis of outcomes in patients with time to OR of 24-36 h. From this study, we are therefore unable to draw any conclusion regarding time to surgery and mortality.

Our dedicated OR hip fracture block time was changed from 7:30 am to 12:30 pm during 2016 per surgeon request (Figure). Patients admitted within the 24-hour time period from 7 am the day prior to 7 am the day of the OR block time undergo surgery in the 12:30 pm time slot. Any patient admitted from 7 am until 12:30 pm is not scheduled until the following day’s OR block time and would hence have a surgical delay of 30 h or more. To better understand the impact of the later OR block time, we included the outcome variable of time to OR of greater than 24 h but less than or equal to 36 h. We demonstrated a significant increase in the proportion of patients going to the OR in 24 h without an increase in patients waiting for 24 to 36 h for their surgery.

Transfusion rate reduction from 46.6% to 28.1% was achieved primarily by the implementation and strict enforcement of a policy to avoid transfusing asymptomatic patients with hemoglobin >7.0 g/dL. In addition, we recommended TXA using standard perioperative arthroplasty dosing of 1 g intravenously (IV) at the time of incision followed by 1 g IV 3 h later in the postanaesthesia care unit. However, adherence to TXA recommendations was poor. A year-long audit (February 2017 to February 2018) demonstrated that only 29% of patients undergoing hip fracture surgery received the recommended TXA. After the conclusion of the study period of this review, we revised our TXA protocol to include an infusion at the time of admission and subsequent perioperative doses. The expanded TXA protocol (with clear exclusion criteria) has been “hard-wired” into our electronic perioperative order sets. We are tracking TXA compliance on a weekly basis. We anticipate that earlier TXA administration and improved compliance will further reduce transfusion rates.

We reduced the adverse effects of medications with two initiatives: First, dedicated hip fracture order sets with medications selected and dosed specifically for the geriatric population were launched at the onset of the IFHFP in 2016. Second, in coordination with our regional anesthesia team, patients who met criteria underwent a single-shot femoral nerve block upon diagnosis of the hip fracture. Patients reported up to 24 h of nonnarcotic pain relief with the femoral nerve block.

Prior to the introduction of the IFHFP, surgeons determined DVT prophylaxis based on their personal preference. Many of our surgeons were concerned that standardizing DVT prophylaxis to enoxaparin would increase the risk of surgical site bleeding, hematoma, infection, and reoperation. With data tracking and periodic reporting, we were able to reassure our surgeons: We demonstrated a reduction in the rate of patients unexpectedly requiring a return to the OR from 5.1% in 2015 to 0% in 2018.

We did not find a significant difference in mechanical complications due to surgical fixation during the index admission. Most mechanical complications do not present within the index admission and, therefore, would not be identified by this metric. Furthermore, in this phase of the program, we did not seek to change or standardize intraoperative surgical processes outside of surgical site infection prevention measures. Surgical technical quality and variation among surgeons is an area of ongoing evaluation within our program. We have begun a surgical quality review process with an expert review of postoperative radiography, beginning with fixation of nondisplaced femoral neck fractures, feedback to surgeons, and tracking of mechanical complications beyond the index admission. The surgical quality outcomes will be presented in a future manuscript.

Anticoagulation use is common in patients with hip fractures because of the high prevalence of comorbid conditions such as atrial fibrillation and venous thromboembolic disease. Direct oral anticoagulants (DOACs) are now commonly used in place of the vitamin K antagonist, warfarin. Our inability to efficiently reverse the DOACs compounded by surgeon unfamiliarity with these agents led to extreme caution in the timing of OR, with most patients delayed a full 48 h from the last dose of their prescribed DOAC. After recognizing the impact of anticoagulation on the timing of surgery, we convened a multidisciplinary group to determine rational guidelines for the timing of surgery in patients on chronic anticoagulation based on the bleeding risk of the planned operative procedure. These guidelines were instituted in December 2018, so their impact is not reflected in this review.

Our results showing a reduction in length of stay and mortality cannot be explained by any one intervention. We propose that the standardization of all processes and protocols, the establishment of clear expectations among all the medical and nursing personnel, and the shorter time spent waiting for surgery all contributed to the length of stay reduction. Likewise, the decrease in time to OR, reduction in time spent in the hospital, fewer transfusions, adverse effects of medication, and surgical complications requiring a return to the OR have all likely contributed to the significant reduction in mortality. The efforts of the orthopedic nursing team certainly contributed: The CMC nurses led the efforts to standardize surgical site infection bundle care, reduce indwelling bladder catheter use, and together with physical therapy, mobilize patients out of bed for meals postoperatively. The strong focus on teamwork, data tracking, feedback and accountability, and the desire for continued improvement may have been the strongest drivers in this program’s success.

Our results showed a nonsignificant increase in 30-day readmission from 9.1% to 12.5%. One limitation of this study is that we did not track specific readmission diagnoses to better understand trends in diagnoses or indications for hospital readmission. Going forward, we are reviewing readmissions to better understand opportunities to improve our inpatient processes and transitions of care.

This an evolving project. We have expanded our use of TXA in an effort to further reduce transfusion rates. We have adjusted our protocols for patients admitted on DOACs and warfarin to allow more rapid surgical intervention. We have initiated a surgical quality review process in which surgical fixation is reviewed with timely feedback to the operating surgeon. We are working closely with the skilled nursing facilities to extend our rehabilitation and nursing care protocols beyond the acute care setting. We are measuring patient engagement with a brief discharge survey specific to the CMC IFHFP. We continue to seek feedback from our referring primary care physicians to improve communication at times of care transition.

One of the limitations of a quality improvement project such as this one is the inability to identify the effect of each individual intervention. We can conclude that the totality of the multidisciplinary project reduced mortality in our hip fracture population, but we cannot report the relative effect of each process change. Another center seeking to replicate this success cannot determine from this research how to prioritize their resources to achieve a similar outcome.

How we care for the fragility hip fracture patient after hospital discharge is critical and unaddressed in this current study. A limitation of our current program is the lack of consistent postdischarge bone health management—which we are working to address. Also related to postdischarge management, we have partnered with a network of preferred skilled nursing facilities to standardize the care and decrease the length of stay. These data will be published separately.

We understand that our experience at the CMC is unique and specific to our care environment. This is a single site study and may not be generalizable to other centers. Nonetheless, the principles of multidisciplinary care, evidence-based protocol development, technological integration of protocols through order sets, and data tracking with feedback and accountability are the essential elements of our success that can be generalized to other institutions.

 

CONCLUSIONS

The CMC at Yale School of Medicine and Yale-New Haven Hospital IFHFP provides a model for implementing well-documented evidence-based interventions to standardize the care of patients with fragility hip fractures. The IFHFP yielded reduced mortality, length of stay, blood transfusion utilization, adverse effects of medications, unexpected return to the OR, and time to the OR.

Acknowledgments

The authors thank the work of the Center for Musculoskeletal Care Hip Fracture Oversight Group, program surgeons, and community primary care leaders: Olukemi Akande, MD, Mark Altman, MD, Diren Arsoy, MD, John Aversa, MD, Michael Connair, MD, Leo Cooney, MD, Kenneth Donohue, MD, David Gibson, MD, Gail Haesche, RN, MS, ACM-RN, Carol Just, MSN, NEA-BC, RN, Patricia Kenyon, RN, ACM, Francis Lee MD, Michael Leslie, MD, Michael Lucchini, MD, Christopher Lynch, MD, Rowland Mayor, MD, Tara Messina, PT, Lorraine Novella, RN, Paul Oliver, PA-C, Vivek Parwani, MD, Joseph Quaranta, MD, Lee Rubin, MD, Derek Shia, MD, Jeff Sumner, MD, John Tarutis, Arya Varthi, MD, Anuruddha Walaliyadda, MD, Daniel Wiznia, MD, Shirvinda Wijesekera, MD, Joseph Wu, MD, Brad Yoo, MD, and James Yue, MD.

Hip fractures are a significant cause of morbidity and mortality among elderly patients. Patients with fragility hip fractures often carry multiple comorbid diagnoses with a significant risk of perioperative complications. After hip fracture, 30-day mortality has been reported as 3.3% to 17.2% with one-year mortality as high as 50%.¹

Multidisciplinary care,^2-5 surgery within 24 hours (h),^6-12 use of regional peripheral nerve blocks,^13-16 restrictive blood transfusion strategies,^17,18 tranexamic acid (TXA) use,¹⁹ pharmacologic deep venous thrombosis (DVT) prophylaxis,²⁰ surgical site infection prevention protocols,²¹ early mobilization,²² and nutritional optimization^23-25 have been individually shown to improve outcomes in hip fracture patients.

Our program sought to define, standardize, and implement evidence-based best practices to improve clinical care and outcomes of patients with hip fractures. We convened a Center for Musculoskeletal Care (CMC) Hip Fracture Oversight Group that included surgeons and advanced practice providers from Orthopedics; physicians from Internal Medicine Hospitalist, Geriatrics, Emergency Medicine, and Anesthesia; and representatives from rehabilitation services, nursing, care management, pharmacy, and performance improvement. With clinical input from all involved services, we developed evidence-based protocols to standardize the care of patients with fragility hip fractures from the time of the patient’s evaluation in the emergency room to discharge and outpatient rehabilitation. The program was operationalized in February 2016.

This project was considered by the Yale University institutional review board (IRB) to be a quality improvement and, therefore, exempted from IRB approval.

MATERIALS AND METHODS

Yale-New Haven Hospital is composed of two main campuses. The York Street Campus (YSC) is the Level 1 Trauma Center. The St. Raphael’s Campus (SRC) houses the CMC nursing units for elective lower extremity arthroplasty and spine procedures. Prior to 2016, patients with hip fractures were cared for equally at both Yale-New Haven Hospital campuses. Patients were admitted to both medical and surgical services with no standardization of hip fracture care processes. Surgeons were assigned based on availability. Frequently, patients were added on to the operating room (OR) schedule and did not undergo surgery until off-hours and after a prolonged waiting period.

Medical comanagement of patients with fragility hip fractures at our institution predated the start of our CMC Integrated Fragility Hip Fracture Program (IFHFP). Comanagement was instituted in 2012 at YSC and in 2014 at SRC but without standardized protocols. The IFHFP began in February 2016 with the centralization of all patients with fragility hip fractures to the SRC at Yale-New Haven Hospital. Emergency medical services directed patients with suspected hip fractures to the designated campus. A dedicated hip fracture OR was allocated daily with a hip fracture surgeon assigned by a shared community and faculty surgeon call schedule. Patients were encouraged but not required to accept care from the on-call hip fracture surgical attending. Anesthesia was notified of the arrival of a patient with a hip fracture in the emergency department, and if the patient consented and qualified, a single-shot femoral nerve block was performed. Patients were screened for nasal staphylococcal colonization and treated with povidone-iodine nasal decolonization, chlorhexidine wash, and antibiotics determined by staphylococcal status and type of surgical procedure planned. Preoperative and postoperative order sets were implemented that dictated the care processes as outlined in Table 1. Surgeons determined the choice of operative intervention as per usual; this included internal fixation and partial or total hip replacement. Detailed medical and surgical protocols are included in Appendix A.

Since the initiation of the IFHFP on February 1, 2016, the program has continued to advance with our experience. We used the year preceding the start of the program as our baseline year (January 1, 2015, through December 31, 2015). The following years, 2016 and 2017, were a transition time during which our protocols were implemented. The intervention year was defined as January 1, 2018, through December 31, 2018. The outcomes during the intervention year were compared with the baseline year. It is important to note that our program has been in continuous evolution, including during the intervention year, with protocols created and refined as we gain experience.

Outcomes include 30-day mortality, transfusions, adverse effects of drugs, venous thromboembolic complications, sepsis, myocardial infarction, mechanical surgical fixation complications, length of stay, 30-day readmission rate, unexpected return to the OR, and time to operative intervention. Definitions of the outcome variables are reviewed in Appendix B.

 

RESULTS

There were 275 consecutive patients with hip fractures admitted to SRC in the baseline year (January 1, 2015 to December 31, 2015) and 434 patients with hip fractures admitted in the intervention year (January 1, 2018, to December 31, 2018) after consolidation of the program to the single Yale-New Haven Campus and implementation of standardized care processes. Patient demographic data including age, sex, ethnicity, body mass index, and American Society of Anesthesiologists physical status classification were evaluated for the baseline year and intervention year and reported in Table 2. There were no differences in the demographics of patients between baseline and intervention years.

From baseline year to intervention year, 30-day mortality, transfusion, adverse effects of drugs, length of stay, unexpected return to OR, and time to OR were all significantly reduced. Mortality within 30 days decreased from 8.0% to 2.8%. The results are displayed in Table 3. No significant difference was seen in the incidence of venous thromboembolism, sepsis, myocardial infarctions, readmission at 30 days, or mechanical surgical fixation complications.

The Figure shows the 30-day IFHFP mortality rate as reported on a monthly basis starting on January 1 of the baseline year, 2015, and continuing through December 31 of the intervention year, 2018. The process interventions are mapped according to the date of initiation. The median mortality rate (including all data from January 1, 2015, to December 31, 2018) is demonstrated as the dotted line. From May 2018 to December 2018, each monthly mortality rate was recorded below the four-year median, a visual demonstration of the statistical significance seen in our mortality reduction from 8.0% in the baseline year to 2.8% in the intervention year.

DISCUSSION

Patients with fragility hip fractures are a medically complex and vulnerable population. The goal of the CMC IFHFP was to standardize the care of these high-risk patients in an effort to reduce time to the OR, perioperative medical complications, time spent in the hospital, and ultimately mortality.

The barriers to implementing coordinated, multidisciplinary care are significant. In our case, we had a fragmented care model with fragility hip fracture patients cared for at two campuses, on different nursing units, with both community and faculty surgeons providing operative care, and with no predesignated primary team. We structured our program for equal sharing of call between community and faculty surgeons. However, there was distrust among the physician groups: Primary care physicians were concerned that their referral lines with orthopedic surgical colleagues would be fractured by the new shared call. Surgeons doubted that patients would be distributed equally among community and faculty practices. Hospitalists feared that comanagement would mean surgeons abdicating responsibility for care. Surgeons worried that routine medical involvement would delay surgery and prolong the length of stay with excessive testing. In order to achieve consensus, address concerns, and allay fears, we engaged the primary care and surgeon leadership for their support at the onset of the program and held monthly large group meetings and many smaller sessions to advance objectives. We meticulously tracked data and frequently reported out to the involved groups.

As it is well established that operative intervention on a hip fracture is best completed within 24 h to optimize a patient’s clinical outcomes, critical interventions were the designation of a hip fracture OR starting midday and expectation that surgery be performed the day after admission for medically cleared patients. Surgeons were able to book elective cases or outpatient clinic time in the morning. The morning hours prior to surgery allowed time for any final medical optimization, preoperative nursing care, and family discussions. Most surgeries were then completed by the primary OR staff during standard operating hours. Patients were out of the postanesthesia care unit and settled back on the orthopedic nursing unit in the early evening without a prolonged period of nil per os, bed rest, or sleep interruption.

While our protocol expected surgery the day after admission for medically cleared patients, we used surgery within 24 h as a simple metric to compare baseline with intervention outcomes. With our hip fracture OR block time beginning midday, the majority of our medically cleared hip fracture patients would receive surgical treatment within 24 h of admission. Our data show a significant improvement in timeliness of surgical intervention from 41.8% of patients to the OR within 24 h in 2015 to 55% in 2018. In 2017, we conducted an interval four-month audit involving a detailed chart review of all patients for whom surgery was delayed beyond 24 h from hospital admission. Chart review identified anticoagulation as the primary reason for surgical delay. Of patients who were eligible for surgery (medically stabilized and not therapeutically anticoagulated), 90% underwent surgery within 24 h during this four-month period in 2017. This compares to an overall rate of surgery within 24 h of 57% during the calendar year 2017. We did not perform a subgroup analysis of outcomes in patients with time to OR of 24-36 h. From this study, we are therefore unable to draw any conclusion regarding time to surgery and mortality.

Our dedicated OR hip fracture block time was changed from 7:30 am to 12:30 pm during 2016 per surgeon request (Figure). Patients admitted within the 24-hour time period from 7 am the day prior to 7 am the day of the OR block time undergo surgery in the 12:30 pm time slot. Any patient admitted from 7 am until 12:30 pm is not scheduled until the following day’s OR block time and would hence have a surgical delay of 30 h or more. To better understand the impact of the later OR block time, we included the outcome variable of time to OR of greater than 24 h but less than or equal to 36 h. We demonstrated a significant increase in the proportion of patients going to the OR in 24 h without an increase in patients waiting for 24 to 36 h for their surgery.

Transfusion rate reduction from 46.6% to 28.1% was achieved primarily by the implementation and strict enforcement of a policy to avoid transfusing asymptomatic patients with hemoglobin >7.0 g/dL. In addition, we recommended TXA using standard perioperative arthroplasty dosing of 1 g intravenously (IV) at the time of incision followed by 1 g IV 3 h later in the postanaesthesia care unit. However, adherence to TXA recommendations was poor. A year-long audit (February 2017 to February 2018) demonstrated that only 29% of patients undergoing hip fracture surgery received the recommended TXA. After the conclusion of the study period of this review, we revised our TXA protocol to include an infusion at the time of admission and subsequent perioperative doses. The expanded TXA protocol (with clear exclusion criteria) has been “hard-wired” into our electronic perioperative order sets. We are tracking TXA compliance on a weekly basis. We anticipate that earlier TXA administration and improved compliance will further reduce transfusion rates.

We reduced the adverse effects of medications with two initiatives: First, dedicated hip fracture order sets with medications selected and dosed specifically for the geriatric population were launched at the onset of the IFHFP in 2016. Second, in coordination with our regional anesthesia team, patients who met criteria underwent a single-shot femoral nerve block upon diagnosis of the hip fracture. Patients reported up to 24 h of nonnarcotic pain relief with the femoral nerve block.

Prior to the introduction of the IFHFP, surgeons determined DVT prophylaxis based on their personal preference. Many of our surgeons were concerned that standardizing DVT prophylaxis to enoxaparin would increase the risk of surgical site bleeding, hematoma, infection, and reoperation. With data tracking and periodic reporting, we were able to reassure our surgeons: We demonstrated a reduction in the rate of patients unexpectedly requiring a return to the OR from 5.1% in 2015 to 0% in 2018.

We did not find a significant difference in mechanical complications due to surgical fixation during the index admission. Most mechanical complications do not present within the index admission and, therefore, would not be identified by this metric. Furthermore, in this phase of the program, we did not seek to change or standardize intraoperative surgical processes outside of surgical site infection prevention measures. Surgical technical quality and variation among surgeons is an area of ongoing evaluation within our program. We have begun a surgical quality review process with an expert review of postoperative radiography, beginning with fixation of nondisplaced femoral neck fractures, feedback to surgeons, and tracking of mechanical complications beyond the index admission. The surgical quality outcomes will be presented in a future manuscript.

Anticoagulation use is common in patients with hip fractures because of the high prevalence of comorbid conditions such as atrial fibrillation and venous thromboembolic disease. Direct oral anticoagulants (DOACs) are now commonly used in place of the vitamin K antagonist, warfarin. Our inability to efficiently reverse the DOACs compounded by surgeon unfamiliarity with these agents led to extreme caution in the timing of OR, with most patients delayed a full 48 h from the last dose of their prescribed DOAC. After recognizing the impact of anticoagulation on the timing of surgery, we convened a multidisciplinary group to determine rational guidelines for the timing of surgery in patients on chronic anticoagulation based on the bleeding risk of the planned operative procedure. These guidelines were instituted in December 2018, so their impact is not reflected in this review.

Our results showing a reduction in length of stay and mortality cannot be explained by any one intervention. We propose that the standardization of all processes and protocols, the establishment of clear expectations among all the medical and nursing personnel, and the shorter time spent waiting for surgery all contributed to the length of stay reduction. Likewise, the decrease in time to OR, reduction in time spent in the hospital, fewer transfusions, adverse effects of medication, and surgical complications requiring a return to the OR have all likely contributed to the significant reduction in mortality. The efforts of the orthopedic nursing team certainly contributed: The CMC nurses led the efforts to standardize surgical site infection bundle care, reduce indwelling bladder catheter use, and together with physical therapy, mobilize patients out of bed for meals postoperatively. The strong focus on teamwork, data tracking, feedback and accountability, and the desire for continued improvement may have been the strongest drivers in this program’s success.

Our results showed a nonsignificant increase in 30-day readmission from 9.1% to 12.5%. One limitation of this study is that we did not track specific readmission diagnoses to better understand trends in diagnoses or indications for hospital readmission. Going forward, we are reviewing readmissions to better understand opportunities to improve our inpatient processes and transitions of care.

This an evolving project. We have expanded our use of TXA in an effort to further reduce transfusion rates. We have adjusted our protocols for patients admitted on DOACs and warfarin to allow more rapid surgical intervention. We have initiated a surgical quality review process in which surgical fixation is reviewed with timely feedback to the operating surgeon. We are working closely with the skilled nursing facilities to extend our rehabilitation and nursing care protocols beyond the acute care setting. We are measuring patient engagement with a brief discharge survey specific to the CMC IFHFP. We continue to seek feedback from our referring primary care physicians to improve communication at times of care transition.

One of the limitations of a quality improvement project such as this one is the inability to identify the effect of each individual intervention. We can conclude that the totality of the multidisciplinary project reduced mortality in our hip fracture population, but we cannot report the relative effect of each process change. Another center seeking to replicate this success cannot determine from this research how to prioritize their resources to achieve a similar outcome.

How we care for the fragility hip fracture patient after hospital discharge is critical and unaddressed in this current study. A limitation of our current program is the lack of consistent postdischarge bone health management—which we are working to address. Also related to postdischarge management, we have partnered with a network of preferred skilled nursing facilities to standardize the care and decrease the length of stay. These data will be published separately.

We understand that our experience at the CMC is unique and specific to our care environment. This is a single site study and may not be generalizable to other centers. Nonetheless, the principles of multidisciplinary care, evidence-based protocol development, technological integration of protocols through order sets, and data tracking with feedback and accountability are the essential elements of our success that can be generalized to other institutions.

 

CONCLUSIONS

The CMC at Yale School of Medicine and Yale-New Haven Hospital IFHFP provides a model for implementing well-documented evidence-based interventions to standardize the care of patients with fragility hip fractures. The IFHFP yielded reduced mortality, length of stay, blood transfusion utilization, adverse effects of medications, unexpected return to the OR, and time to the OR.

Acknowledgments

The authors thank the work of the Center for Musculoskeletal Care Hip Fracture Oversight Group, program surgeons, and community primary care leaders: Olukemi Akande, MD, Mark Altman, MD, Diren Arsoy, MD, John Aversa, MD, Michael Connair, MD, Leo Cooney, MD, Kenneth Donohue, MD, David Gibson, MD, Gail Haesche, RN, MS, ACM-RN, Carol Just, MSN, NEA-BC, RN, Patricia Kenyon, RN, ACM, Francis Lee MD, Michael Leslie, MD, Michael Lucchini, MD, Christopher Lynch, MD, Rowland Mayor, MD, Tara Messina, PT, Lorraine Novella, RN, Paul Oliver, PA-C, Vivek Parwani, MD, Joseph Quaranta, MD, Lee Rubin, MD, Derek Shia, MD, Jeff Sumner, MD, John Tarutis, Arya Varthi, MD, Anuruddha Walaliyadda, MD, Daniel Wiznia, MD, Shirvinda Wijesekera, MD, Joseph Wu, MD, Brad Yoo, MD, and James Yue, MD.

Hip fractures are a significant cause of morbidity and mortality among elderly patients. Patients with fragility hip fractures often carry multiple comorbid diagnoses with a significant risk of perioperative complications. After hip fracture, 30-day mortality has been reported as 3.3% to 17.2% with one-year mortality as high as 50%.¹

Multidisciplinary care,^2-5 surgery within 24 hours (h),^6-12 use of regional peripheral nerve blocks,^13-16 restrictive blood transfusion strategies,^17,18 tranexamic acid (TXA) use,¹⁹ pharmacologic deep venous thrombosis (DVT) prophylaxis,²⁰ surgical site infection prevention protocols,²¹ early mobilization,²² and nutritional optimization^23-25 have been individually shown to improve outcomes in hip fracture patients.

Our program sought to define, standardize, and implement evidence-based best practices to improve clinical care and outcomes of patients with hip fractures. We convened a Center for Musculoskeletal Care (CMC) Hip Fracture Oversight Group that included surgeons and advanced practice providers from Orthopedics; physicians from Internal Medicine Hospitalist, Geriatrics, Emergency Medicine, and Anesthesia; and representatives from rehabilitation services, nursing, care management, pharmacy, and performance improvement. With clinical input from all involved services, we developed evidence-based protocols to standardize the care of patients with fragility hip fractures from the time of the patient’s evaluation in the emergency room to discharge and outpatient rehabilitation. The program was operationalized in February 2016.

This project was considered by the Yale University institutional review board (IRB) to be a quality improvement and, therefore, exempted from IRB approval.

MATERIALS AND METHODS

Yale-New Haven Hospital is composed of two main campuses. The York Street Campus (YSC) is the Level 1 Trauma Center. The St. Raphael’s Campus (SRC) houses the CMC nursing units for elective lower extremity arthroplasty and spine procedures. Prior to 2016, patients with hip fractures were cared for equally at both Yale-New Haven Hospital campuses. Patients were admitted to both medical and surgical services with no standardization of hip fracture care processes. Surgeons were assigned based on availability. Frequently, patients were added on to the operating room (OR) schedule and did not undergo surgery until off-hours and after a prolonged waiting period.

Medical comanagement of patients with fragility hip fractures at our institution predated the start of our CMC Integrated Fragility Hip Fracture Program (IFHFP). Comanagement was instituted in 2012 at YSC and in 2014 at SRC but without standardized protocols. The IFHFP began in February 2016 with the centralization of all patients with fragility hip fractures to the SRC at Yale-New Haven Hospital. Emergency medical services directed patients with suspected hip fractures to the designated campus. A dedicated hip fracture OR was allocated daily with a hip fracture surgeon assigned by a shared community and faculty surgeon call schedule. Patients were encouraged but not required to accept care from the on-call hip fracture surgical attending. Anesthesia was notified of the arrival of a patient with a hip fracture in the emergency department, and if the patient consented and qualified, a single-shot femoral nerve block was performed. Patients were screened for nasal staphylococcal colonization and treated with povidone-iodine nasal decolonization, chlorhexidine wash, and antibiotics determined by staphylococcal status and type of surgical procedure planned. Preoperative and postoperative order sets were implemented that dictated the care processes as outlined in Table 1. Surgeons determined the choice of operative intervention as per usual; this included internal fixation and partial or total hip replacement. Detailed medical and surgical protocols are included in Appendix A.

Since the initiation of the IFHFP on February 1, 2016, the program has continued to advance with our experience. We used the year preceding the start of the program as our baseline year (January 1, 2015, through December 31, 2015). The following years, 2016 and 2017, were a transition time during which our protocols were implemented. The intervention year was defined as January 1, 2018, through December 31, 2018. The outcomes during the intervention year were compared with the baseline year. It is important to note that our program has been in continuous evolution, including during the intervention year, with protocols created and refined as we gain experience.

Outcomes include 30-day mortality, transfusions, adverse effects of drugs, venous thromboembolic complications, sepsis, myocardial infarction, mechanical surgical fixation complications, length of stay, 30-day readmission rate, unexpected return to the OR, and time to operative intervention. Definitions of the outcome variables are reviewed in Appendix B.

 

RESULTS

There were 275 consecutive patients with hip fractures admitted to SRC in the baseline year (January 1, 2015 to December 31, 2015) and 434 patients with hip fractures admitted in the intervention year (January 1, 2018, to December 31, 2018) after consolidation of the program to the single Yale-New Haven Campus and implementation of standardized care processes. Patient demographic data including age, sex, ethnicity, body mass index, and American Society of Anesthesiologists physical status classification were evaluated for the baseline year and intervention year and reported in Table 2. There were no differences in the demographics of patients between baseline and intervention years.

From baseline year to intervention year, 30-day mortality, transfusion, adverse effects of drugs, length of stay, unexpected return to OR, and time to OR were all significantly reduced. Mortality within 30 days decreased from 8.0% to 2.8%. The results are displayed in Table 3. No significant difference was seen in the incidence of venous thromboembolism, sepsis, myocardial infarctions, readmission at 30 days, or mechanical surgical fixation complications.

The Figure shows the 30-day IFHFP mortality rate as reported on a monthly basis starting on January 1 of the baseline year, 2015, and continuing through December 31 of the intervention year, 2018. The process interventions are mapped according to the date of initiation. The median mortality rate (including all data from January 1, 2015, to December 31, 2018) is demonstrated as the dotted line. From May 2018 to December 2018, each monthly mortality rate was recorded below the four-year median, a visual demonstration of the statistical significance seen in our mortality reduction from 8.0% in the baseline year to 2.8% in the intervention year.

DISCUSSION

Patients with fragility hip fractures are a medically complex and vulnerable population. The goal of the CMC IFHFP was to standardize the care of these high-risk patients in an effort to reduce time to the OR, perioperative medical complications, time spent in the hospital, and ultimately mortality.

The barriers to implementing coordinated, multidisciplinary care are significant. In our case, we had a fragmented care model with fragility hip fracture patients cared for at two campuses, on different nursing units, with both community and faculty surgeons providing operative care, and with no predesignated primary team. We structured our program for equal sharing of call between community and faculty surgeons. However, there was distrust among the physician groups: Primary care physicians were concerned that their referral lines with orthopedic surgical colleagues would be fractured by the new shared call. Surgeons doubted that patients would be distributed equally among community and faculty practices. Hospitalists feared that comanagement would mean surgeons abdicating responsibility for care. Surgeons worried that routine medical involvement would delay surgery and prolong the length of stay with excessive testing. In order to achieve consensus, address concerns, and allay fears, we engaged the primary care and surgeon leadership for their support at the onset of the program and held monthly large group meetings and many smaller sessions to advance objectives. We meticulously tracked data and frequently reported out to the involved groups.

As it is well established that operative intervention on a hip fracture is best completed within 24 h to optimize a patient’s clinical outcomes, critical interventions were the designation of a hip fracture OR starting midday and expectation that surgery be performed the day after admission for medically cleared patients. Surgeons were able to book elective cases or outpatient clinic time in the morning. The morning hours prior to surgery allowed time for any final medical optimization, preoperative nursing care, and family discussions. Most surgeries were then completed by the primary OR staff during standard operating hours. Patients were out of the postanesthesia care unit and settled back on the orthopedic nursing unit in the early evening without a prolonged period of nil per os, bed rest, or sleep interruption.

While our protocol expected surgery the day after admission for medically cleared patients, we used surgery within 24 h as a simple metric to compare baseline with intervention outcomes. With our hip fracture OR block time beginning midday, the majority of our medically cleared hip fracture patients would receive surgical treatment within 24 h of admission. Our data show a significant improvement in timeliness of surgical intervention from 41.8% of patients to the OR within 24 h in 2015 to 55% in 2018. In 2017, we conducted an interval four-month audit involving a detailed chart review of all patients for whom surgery was delayed beyond 24 h from hospital admission. Chart review identified anticoagulation as the primary reason for surgical delay. Of patients who were eligible for surgery (medically stabilized and not therapeutically anticoagulated), 90% underwent surgery within 24 h during this four-month period in 2017. This compares to an overall rate of surgery within 24 h of 57% during the calendar year 2017. We did not perform a subgroup analysis of outcomes in patients with time to OR of 24-36 h. From this study, we are therefore unable to draw any conclusion regarding time to surgery and mortality.

Our dedicated OR hip fracture block time was changed from 7:30 am to 12:30 pm during 2016 per surgeon request (Figure). Patients admitted within the 24-hour time period from 7 am the day prior to 7 am the day of the OR block time undergo surgery in the 12:30 pm time slot. Any patient admitted from 7 am until 12:30 pm is not scheduled until the following day’s OR block time and would hence have a surgical delay of 30 h or more. To better understand the impact of the later OR block time, we included the outcome variable of time to OR of greater than 24 h but less than or equal to 36 h. We demonstrated a significant increase in the proportion of patients going to the OR in 24 h without an increase in patients waiting for 24 to 36 h for their surgery.

Transfusion rate reduction from 46.6% to 28.1% was achieved primarily by the implementation and strict enforcement of a policy to avoid transfusing asymptomatic patients with hemoglobin >7.0 g/dL. In addition, we recommended TXA using standard perioperative arthroplasty dosing of 1 g intravenously (IV) at the time of incision followed by 1 g IV 3 h later in the postanaesthesia care unit. However, adherence to TXA recommendations was poor. A year-long audit (February 2017 to February 2018) demonstrated that only 29% of patients undergoing hip fracture surgery received the recommended TXA. After the conclusion of the study period of this review, we revised our TXA protocol to include an infusion at the time of admission and subsequent perioperative doses. The expanded TXA protocol (with clear exclusion criteria) has been “hard-wired” into our electronic perioperative order sets. We are tracking TXA compliance on a weekly basis. We anticipate that earlier TXA administration and improved compliance will further reduce transfusion rates.

We reduced the adverse effects of medications with two initiatives: First, dedicated hip fracture order sets with medications selected and dosed specifically for the geriatric population were launched at the onset of the IFHFP in 2016. Second, in coordination with our regional anesthesia team, patients who met criteria underwent a single-shot femoral nerve block upon diagnosis of the hip fracture. Patients reported up to 24 h of nonnarcotic pain relief with the femoral nerve block.

Prior to the introduction of the IFHFP, surgeons determined DVT prophylaxis based on their personal preference. Many of our surgeons were concerned that standardizing DVT prophylaxis to enoxaparin would increase the risk of surgical site bleeding, hematoma, infection, and reoperation. With data tracking and periodic reporting, we were able to reassure our surgeons: We demonstrated a reduction in the rate of patients unexpectedly requiring a return to the OR from 5.1% in 2015 to 0% in 2018.

We did not find a significant difference in mechanical complications due to surgical fixation during the index admission. Most mechanical complications do not present within the index admission and, therefore, would not be identified by this metric. Furthermore, in this phase of the program, we did not seek to change or standardize intraoperative surgical processes outside of surgical site infection prevention measures. Surgical technical quality and variation among surgeons is an area of ongoing evaluation within our program. We have begun a surgical quality review process with an expert review of postoperative radiography, beginning with fixation of nondisplaced femoral neck fractures, feedback to surgeons, and tracking of mechanical complications beyond the index admission. The surgical quality outcomes will be presented in a future manuscript.

Anticoagulation use is common in patients with hip fractures because of the high prevalence of comorbid conditions such as atrial fibrillation and venous thromboembolic disease. Direct oral anticoagulants (DOACs) are now commonly used in place of the vitamin K antagonist, warfarin. Our inability to efficiently reverse the DOACs compounded by surgeon unfamiliarity with these agents led to extreme caution in the timing of OR, with most patients delayed a full 48 h from the last dose of their prescribed DOAC. After recognizing the impact of anticoagulation on the timing of surgery, we convened a multidisciplinary group to determine rational guidelines for the timing of surgery in patients on chronic anticoagulation based on the bleeding risk of the planned operative procedure. These guidelines were instituted in December 2018, so their impact is not reflected in this review.

Our results showing a reduction in length of stay and mortality cannot be explained by any one intervention. We propose that the standardization of all processes and protocols, the establishment of clear expectations among all the medical and nursing personnel, and the shorter time spent waiting for surgery all contributed to the length of stay reduction. Likewise, the decrease in time to OR, reduction in time spent in the hospital, fewer transfusions, adverse effects of medication, and surgical complications requiring a return to the OR have all likely contributed to the significant reduction in mortality. The efforts of the orthopedic nursing team certainly contributed: The CMC nurses led the efforts to standardize surgical site infection bundle care, reduce indwelling bladder catheter use, and together with physical therapy, mobilize patients out of bed for meals postoperatively. The strong focus on teamwork, data tracking, feedback and accountability, and the desire for continued improvement may have been the strongest drivers in this program’s success.

Our results showed a nonsignificant increase in 30-day readmission from 9.1% to 12.5%. One limitation of this study is that we did not track specific readmission diagnoses to better understand trends in diagnoses or indications for hospital readmission. Going forward, we are reviewing readmissions to better understand opportunities to improve our inpatient processes and transitions of care.

This an evolving project. We have expanded our use of TXA in an effort to further reduce transfusion rates. We have adjusted our protocols for patients admitted on DOACs and warfarin to allow more rapid surgical intervention. We have initiated a surgical quality review process in which surgical fixation is reviewed with timely feedback to the operating surgeon. We are working closely with the skilled nursing facilities to extend our rehabilitation and nursing care protocols beyond the acute care setting. We are measuring patient engagement with a brief discharge survey specific to the CMC IFHFP. We continue to seek feedback from our referring primary care physicians to improve communication at times of care transition.

One of the limitations of a quality improvement project such as this one is the inability to identify the effect of each individual intervention. We can conclude that the totality of the multidisciplinary project reduced mortality in our hip fracture population, but we cannot report the relative effect of each process change. Another center seeking to replicate this success cannot determine from this research how to prioritize their resources to achieve a similar outcome.

How we care for the fragility hip fracture patient after hospital discharge is critical and unaddressed in this current study. A limitation of our current program is the lack of consistent postdischarge bone health management—which we are working to address. Also related to postdischarge management, we have partnered with a network of preferred skilled nursing facilities to standardize the care and decrease the length of stay. These data will be published separately.

We understand that our experience at the CMC is unique and specific to our care environment. This is a single site study and may not be generalizable to other centers. Nonetheless, the principles of multidisciplinary care, evidence-based protocol development, technological integration of protocols through order sets, and data tracking with feedback and accountability are the essential elements of our success that can be generalized to other institutions.

 

CONCLUSIONS

The CMC at Yale School of Medicine and Yale-New Haven Hospital IFHFP provides a model for implementing well-documented evidence-based interventions to standardize the care of patients with fragility hip fractures. The IFHFP yielded reduced mortality, length of stay, blood transfusion utilization, adverse effects of medications, unexpected return to the OR, and time to the OR.

Acknowledgments

The authors thank the work of the Center for Musculoskeletal Care Hip Fracture Oversight Group, program surgeons, and community primary care leaders: Olukemi Akande, MD, Mark Altman, MD, Diren Arsoy, MD, John Aversa, MD, Michael Connair, MD, Leo Cooney, MD, Kenneth Donohue, MD, David Gibson, MD, Gail Haesche, RN, MS, ACM-RN, Carol Just, MSN, NEA-BC, RN, Patricia Kenyon, RN, ACM, Francis Lee MD, Michael Leslie, MD, Michael Lucchini, MD, Christopher Lynch, MD, Rowland Mayor, MD, Tara Messina, PT, Lorraine Novella, RN, Paul Oliver, PA-C, Vivek Parwani, MD, Joseph Quaranta, MD, Lee Rubin, MD, Derek Shia, MD, Jeff Sumner, MD, John Tarutis, Arya Varthi, MD, Anuruddha Walaliyadda, MD, Daniel Wiznia, MD, Shirvinda Wijesekera, MD, Joseph Wu, MD, Brad Yoo, MD, and James Yue, MD.

Examining disparities in health-related quality of life (HRQoL) outcomes in children provides a unique patient-centered perspective on pediatric health services equity.^1,2 Prior studies have demonstrated the relationship between minority race, low socioeconomic status, and lower maternal education with poorer HRQoL outcomes in children.^3-6 Some studies have also shown a dose-response relationship between social disadvantage markers and poorer child health status.^7,8 Furthermore, the associations between social disadvantage and poor access to care,^9-11 and between poor access to care and lower HRQoL, are also well established.^12-14

Examining HRQoL before and after hospitalization can further our understanding of how disparities in HRQoL may change once children engage with the medical system for an acute illness.¹⁵ Children requiring hospitalization constitute a useful population for examination of this question as they represent a group of children with variable social disadvantage markers and access to outpatient care.¹⁶ Although interventions to address social determinants of health for patients with social disadvantages have been associated with within-group improvements in HRQoL, none have assessed changes in disparities as an outcome.¹⁷ Furthermore, many of these studies were conducted in the outpatient setting,^18,19 whereas hospitalization provides an additional point of care to address the social determinants of health for vulnerable families.²⁰ Even for short hospitalizations, the 24/7 nature of hospital care provides the opportunity for frequent interactions with clinicians, nurses, and support staff to clarify illness-related questions, discuss other health concerns and unmet needs, and connect with social services or community resources. These opportunities may be particularly important for families with a higher number of social disadvantage markers and even more beneficial to those with difficulty accessing needed care from their primary medical home.

In this study, we focused on children with common respiratory illnesses (asthma, bronchiolitis, and pneumonia), which constitute the majority of childhood hospitalizations.²¹ Additionally, we only focused on the child’s physical functioning component of HRQoL because this component is most likely to improve after hospitalization for children with an acute respiratory illness.²² A prior study examining HRQoL before and after hospitalization demonstrated that most children return to and/or surpass their baseline physical functioning by 1 month after hospital discharge.²³

Our primary objective was to examine associations between several markers of social disadvantage, access to care, and child physical functioning before and after hospitalization for acute respiratory illness. Second, we aimed to understand if access to care (defined as perceived difficulty/delays getting care) acts as an independent predictor of improvement in physical functioning from baseline to follow-up and/or if it modifies the relationship between social disadvantage and improvement in physical functioning (Appendix Figure).

 

METHODS

 

Study Design and Population

 

This study was nested within a multicenter, prospective cohort study of children who were hospitalized for asthma, bronchiolitis, or pneumonia between July 2014 and June 2016 at one of five children’s hospitals in the Pediatric Research in Inpatient Settings Network.²⁴

We approached families for study participation within 72 hours of admission to the hospital using a standard protocol. Patients and their caregivers were eligible to participate in the study if the patient was 2 weeks to 16 years old and if the primary caregiver’s preferred language for medical communication was either English or Spanish. Patients with chronic medical conditions (except asthma), with moderate to severe developmental delay, with a history of prematurity <32 weeks, or who received care in the intensive care unit were excluded. Patients could only participate in the study once.

The study team set out to enroll an even number of patients across all three conditions. If a patient’s discharge diagnosis differed from their admission diagnosis (eg, from bronchiolitis to pneumonia), discharge diagnosis was used for condition group assignment. If the discharge diagnosis was not one of these three respiratory conditions, we excluded the patient from further analysis.

Data Collection

We collected data using two surveys. The first survey was administered within 72 hours of admission. This survey asked questions related to (1) caregiver-reported markers of social disadvantage, (2) caregiver perceptions of access to care, and (3) caregiver- and patient-reported assessments of physical functioning. The second survey was administered within 2 to 8 weeks after the patient’s discharge and included a second assessment of physical functioning.

Social Disadvantage

Patients were considered to have a marker of social disadvantage if their caregiver reported (1) being of non-White race and/or Hispanic ethnicity, (2) primarily speaking a language other than English at home and not speaking English very well (ie, limited English proficiency), (3) attaining at most a high school or equivalent degree, or (4) having a =/<$30,000 annual household income.

Access to Care

We used the following survey item from the 2009-2010 National Survey of Children with Special Health Care Needs²⁵ to measure caregiver perceptions of access to care: “In the last six months, did you have any difficulties or delays getting care for your child because there were waiting lists, backlogs, or other problems getting an appointment?” We narrowed the original assessment time frame from 12 months to 6 months to provide a more proximal assessment of access in relation to the hospitalization.

Child Physical Functioning

We assessed child physical functioning using the physical functioning domain of the Pediatric Quality of Life Inventory (PedsQL) 4.0 Generic Core Scales and PedsQL Infant Scales, which have been validated for use in the inpatient setting.²² Caregivers completed one of these scales based on their child’s age. Assenting patients 8 to 16 years old completed the self-report PedsQL 4.0 Generic Core Scales instrument. When completing the first PedsQL survey, caregivers and patients reflected on the previous month before their child (or they) became ill to obtain a baseline physical functioning assessment.²³ When completing the second PedsQL survey, caregivers and patients reflected on the past 7 days to obtain a follow-up assessment.

 

All study procedures were approved by the Western Institutional Review Board (IRB) or the participating hospitals’ IRB.

Statistical Analysis

Patients with no missing data for all four social disadvantage markers were categorized based on the number of markers they reported: none, one, two, or three or more markers. We combined patients with three and four social disadvantage markers into one group to maximize power for the analyses. We dichotomized the access to care variable and coded response options as “no difficulty/delays accessing care” if the caregiver chose “Never” and “any difficulty/delays accessing care” if they chose “Sometimes/Usually/Always.”

For each patient–caregiver dyad, PedsQL items were scored using a standard method in which higher scores reflected better functioning.²² A single set of PedsQL scores was used for each patient–caregiver dyad. We used self-reported patient scores if the patient completed the PedsQL instrument; otherwise, we used proxy-reported caregiver scores. Intraclass correlations between child self-report and parent proxy-report demonstrate moderate to good agreement above age 8 years.²⁶ We computed a change in the physical functioning score by subtracting the baseline score from the follow-up score. The minimal clinically important difference (MCID) for the PedsQL instrument is 4.5 points, which we used to identify clinically meaningful differences.¹³

Analysis of variance models were constructed to test for differences in mean baseline and follow-up PedsQL scores (dependent variable) between the following independent variables: (1) social disadvantage groups and (2) those who reported having any difficulty/delays accessing care compared with those who did not. Only patient–caregiver dyads with both baseline and follow-up assessments were included in these analyses. Mixed-effects linear regression models were constructed to identify clinically meaningful differences in PedsQL scores between groups (MCID =/> 4.5) with adjustment for patient age, gender, respiratory condition, days between surveys, and hospital site as fixed effects. Site-specific random effects were included to account for within-hospital clustering. A similarly adjusted mixed-effects linear regression model was constructed to examine whether having any difficulty/delays accessing care modified the association between social disadvantage and PedsQL change scores (eg, an improvement from baseline to follow-up).

Because 17% of respondents had missing data for at least one social disadvantage marker, sensitivity analyses were conducted using multiple imputation to account for missing social disadvantage markers using chained equations.²⁷ Sensitivity analyses were also conducted to adjust for severity of illness using vital sign data within the first 24 hours, which could only be validly captured on patients with asthma within our dataset. By restricting this latter analysis to patients with asthma, we were able to examine the relationships of interest in a population with chronic disease.

RESULTS

The study sample included 1,860 patients, of which 1,325 had both baseline and follow-up PedsQL data (71%). Descriptive statistics were similar between those who completed the baseline and follow-up surveys (Table 1).

Twenty-two percent of patients had >/=3 social disadvantages and 30% of caregivers reported having any difficulty/delays accessing care. The mean follow-up PedsQL score was higher than the baseline score (90.4 vs 82.5; Table 1).

 

Social Disadvantage Markers and PedsQL Scores

The number of social disadvantage markers was inversely related to mean baseline PedsQL scores, but there was no difference in mean follow-up PedsQL scores between social disadvantage groups (Table 2). In adjusted analyses, the mean baseline PedsQL score was −6.1 points (95% CI: −8.7, −3.5) lower for patients with >/= 3 social disadvantage markers compared with patients with no social disadvantage markers, which exceeded the scale’s MCID.

Difficulty/Delays Accessing Care and PedsQL Scores

Having any difficulty/delays accessing care was significantly associated with lower baseline and follow-up PedsQL scores (Table 2). In adjusted analyses, the difference in baseline scores was 5.2 points (95% CI: −7.2, −3.2), which exceedes the scale’s MCID.

Interaction Between Social Disadvantage Markers, Difficulty/Delays Accessing Care, and Change in PedsQL Scores from Baseline to Follow-Up

While having =/>2 social disadvantage markers and difficulty/delays accessing care were each positively associated with changes in PedsQL scores from baseline to follow-up (Table 3), only patients with =/> 3 social disadvantage markers exceeded the PedsQL MCID. In stratified analyses, patients with a combination of social disadvantage makers and difficulties/delays accessing care had lower baseline PedsQL scores and greater change in PedsQL scores from baseline to follow-up compared with those without difficulties/delays accessing care (Figure). However, having any difficulty/delays accessing care did not significantly modify the relationship between social disadvantage and change in PedsQL scores, as none of the interaction terms were significant (Table 3, Model 3).

Sensitivity Analysis

Baseline, follow-up, and change in PedsQL scores were similar to our main analysis after performing multiple imputation for missing social disadvantage markers (Supplemental Table 1). Findings were also similar for patients with a diagnosis of asthma only; however, changes in PedsQL scores were greater in magnitude (Appendix Table 2).

DISCUSSION

This study examined the relationship between social disadvantage and child physical functioning before and after hospitalization for acute respiratory illness. Study findings indicated that patients with higher numbers of social disadvantage markers reported lower PedsQL scores before hospitalization; however, differences in PedsQL scores were not apparent after hospitalization. Patients who experienced difficulty/delays accessing care also reported lower PedsQL scores at baseline. This difference was still significant but did not exceed the PedsQL MCID threshold after hospitalization. Difficulty/delays accessing care appeared to be an additional social disadvantage marker; however, it did not modify the relationship between social disadvantage and improvement in physical functioning.

The study findings at baseline are consistent with prior studies demonstrating a negative association between social disadvantage markers and HRQoL and a cumulative effect based on the number of social disadvantages.^3,4,7,8 This study adds to the existing literature by examining how this relationship changes after hospitalization. As evidenced by the lack of association between social disadvantage markers and follow-up PedsQL scores, our findings suggest that receipt of inpatient care improved perceptions of physical functioning to a greater extent for patients with more social disadvantage markers (especially patients with =/> 3 social disadvantage markers). There are several potential reasons for these findings.

 

One possibility is that caregivers and/or patients with more social disadvantage markers are more influenced by context when assessing physical functioning. This could lead to an underestimation of functioning when asked to recall baseline physical functioning at the time of acute illness and overestimation of functioning after recovery from an illness. This possibility is consistent with a form of response bias, extreme response tendencies, in which lower socioeconomic subgroups tend to choose the more extreme response options of a scale.²⁸ In the absence of longitudinal assessments of HRQoL across the care continuum over time, disentangling whether these differences are due to response bias or representative of true changes in physical functioning remains challenging.

Given that disparities in physical functioning at baseline were consistent with prior evidence, another possibility is that hospitalization provided an opportunity to address gaps in access and quality that may have existed for patients with social disadvantage in the community setting. The 24/7 nature of hospital care, usually from a multidisciplinary team of providers, lends itself to opportunities to receive intensive education related to the current illness or to address other health concerns that parents or providers identify during a hospital stay. For example, consistent and repetitive asthma education may be more beneficial to patients and families with more social disadvantage markers. The fact that the association between social disadvantage markers and change in physical functioning scores were greater for patients with asthma supports this reasoning. Hospital care may also provide an opportunity to address other unmet medical needs or psychosocial needs by providing efficient access to subspecialists, social workers, or interpreters. Further research is needed to elucidate whether families received additional services in the hospital setting that were not available to them prior to hospitalization, such as consistent interpreter use, social work engagement, and subspecialty/community referrals. Further studies should also determine whether the provision of equitable medical and social support services is associated with improvements in HRQoL disparities. Additionally, studies should examine whether physical functioning improvements following hospitalization return to baseline levels after a longer period of time and, if so, how we might sustain these reductions in HRQoL disparities. Such studies may identify tangible targets and interventions to reduce disparities in HRQoL for these children.

This study highlights the importance of assessing for difficulty/delays accessing care in addition to social disadvantage markers, as this was also a significant predictor of lower child physical functioning. Differences in PedsQL scores between those who reported any versus no difficulty/delays accessing care were more pronounced at baseline compared with follow-up. A possible reason for these findings is that receiving hospital care may have addressed some access to care issues that were present in the outpatient setting, which resulted in improved perceptions of physical functioning. For example, hospital care may mitigate access to care barriers such as limited after-hours clinic appointments, language barriers, and lack of insurance, thus providing some patients with an alternative pathway to address their health concerns. Alternatively, hospital staff may assist families in scheduling follow-up appointments with the patient’s primary medical home after discharge, which potentially reduced some access to care barriers. The question is whether these disparities will widen once again after a longer follow-up period if families continue facing barriers to accessing needed care in the outpatient setting.

 

The results of the effect modification analysis demonstrated that the association between social disadvantage and change in PedsQL scores from baseline to follow-up was not significantly different based on a child’s ability to access care. In our stratified analysis, difficulty/delays accessing care added to baseline disparities at each social disadvantage level but did not alter how perceptions of physical functioning change over time. Therefore, physical functioning improvements may rely more heavily on the type of care received within the hospital setting as opposed to accessing care in the first place. However, future studies should examine whether access to high-­quality care instead of simply measuring difficulty/delays in accessing care would lead to different results. Access to a comprehensive medical home may be a better measure to assess for effect modification because it measures features beyond access to care, such as continuity, comprehensiveness, communication, and coordination of outpatient care.^29-31

If additional studies find evidence that the nature of hospital care, an intensive 24/7 care setting, differentially benefits patients with higher social disadvantage markers (particularly those with =/> 3 markers and chronic illness), this would support the need for systematic screening for social disadvantages or difficulty/delays accessing care in the inpatient setting. Systematic screening could help ensure all patients who may benefit from additional services, such as intensive, culturally tailored education or connections to food, housing, or financial services, will in fact receive them, which may lead to sustained reductions in health disparities.²⁰ Further research into pairing validated screening tools with proven interventions is needed.³²

This study has additional limitations aside from those noted above. First, we did not reassess perceived or actual access to care after hospitalization, which may have allowed for analyses to examine access to care as a mediator between social disadvantage and lower child physical functioning. Second, this study included only English- and Spanish-speaking patients and families. Patients with less commonly spoken languages may experience more difficulty accessing or navigating the health system, which may further impact access to care and HRQoL. Third, we had a considerable amount of missing social disadvantage marker data (mainly income); however, our sensitivity analyses did not result in significantly different or clinically meaningful differences in our findings. Insurance status is more feasible to obtain from administrative data and could serve as a proxy for income or as an additional social disadvantage marker in future studies. Finally, we could calculate illness severity only for patients with asthma based on the available data; therefore, we could not adequately control for illness severity across all conditions.

CONCLUSIONS

Social disadvantage was associated with lower child physical functioning before hospitalization, but differences were not apparent after hospitalization for children with acute respiratory illness. Caregiver-perceived difficulty/delays accessing care was found to be an additional predictor of lower physical functioning at baseline but did not significantly alter the association between social disadvantage and improvement in physical functioning over time. Further studies are needed to understand how hospital care may differentially impact child physical functioning for patients with higher social disadvantage makers in order to sustain improvements in HRQoL disparities.

 

Acknowledgments

The authors thank the following individuals of the Pediatric Respiratory Illness Measurement System (PRIMES) study team for their contributions to this work: Karen M. Wilson, New York, New York; Ricardo A. Quinonez, Houston, Texas; Joyee G. Vachani, Houston, Texas; and Amy Tyler, Aurora, Colorado. We would also like to thank the Pediatric Research in Inpatient Settings Network for facilitating this work.

Examining disparities in health-related quality of life (HRQoL) outcomes in children provides a unique patient-centered perspective on pediatric health services equity.^1,2 Prior studies have demonstrated the relationship between minority race, low socioeconomic status, and lower maternal education with poorer HRQoL outcomes in children.^3-6 Some studies have also shown a dose-response relationship between social disadvantage markers and poorer child health status.^7,8 Furthermore, the associations between social disadvantage and poor access to care,^9-11 and between poor access to care and lower HRQoL, are also well established.^12-14

Examining HRQoL before and after hospitalization can further our understanding of how disparities in HRQoL may change once children engage with the medical system for an acute illness.¹⁵ Children requiring hospitalization constitute a useful population for examination of this question as they represent a group of children with variable social disadvantage markers and access to outpatient care.¹⁶ Although interventions to address social determinants of health for patients with social disadvantages have been associated with within-group improvements in HRQoL, none have assessed changes in disparities as an outcome.¹⁷ Furthermore, many of these studies were conducted in the outpatient setting,^18,19 whereas hospitalization provides an additional point of care to address the social determinants of health for vulnerable families.²⁰ Even for short hospitalizations, the 24/7 nature of hospital care provides the opportunity for frequent interactions with clinicians, nurses, and support staff to clarify illness-related questions, discuss other health concerns and unmet needs, and connect with social services or community resources. These opportunities may be particularly important for families with a higher number of social disadvantage markers and even more beneficial to those with difficulty accessing needed care from their primary medical home.

In this study, we focused on children with common respiratory illnesses (asthma, bronchiolitis, and pneumonia), which constitute the majority of childhood hospitalizations.²¹ Additionally, we only focused on the child’s physical functioning component of HRQoL because this component is most likely to improve after hospitalization for children with an acute respiratory illness.²² A prior study examining HRQoL before and after hospitalization demonstrated that most children return to and/or surpass their baseline physical functioning by 1 month after hospital discharge.²³

Our primary objective was to examine associations between several markers of social disadvantage, access to care, and child physical functioning before and after hospitalization for acute respiratory illness. Second, we aimed to understand if access to care (defined as perceived difficulty/delays getting care) acts as an independent predictor of improvement in physical functioning from baseline to follow-up and/or if it modifies the relationship between social disadvantage and improvement in physical functioning (Appendix Figure).

 

METHODS

 

Study Design and Population

 

This study was nested within a multicenter, prospective cohort study of children who were hospitalized for asthma, bronchiolitis, or pneumonia between July 2014 and June 2016 at one of five children’s hospitals in the Pediatric Research in Inpatient Settings Network.²⁴

We approached families for study participation within 72 hours of admission to the hospital using a standard protocol. Patients and their caregivers were eligible to participate in the study if the patient was 2 weeks to 16 years old and if the primary caregiver’s preferred language for medical communication was either English or Spanish. Patients with chronic medical conditions (except asthma), with moderate to severe developmental delay, with a history of prematurity <32 weeks, or who received care in the intensive care unit were excluded. Patients could only participate in the study once.

The study team set out to enroll an even number of patients across all three conditions. If a patient’s discharge diagnosis differed from their admission diagnosis (eg, from bronchiolitis to pneumonia), discharge diagnosis was used for condition group assignment. If the discharge diagnosis was not one of these three respiratory conditions, we excluded the patient from further analysis.

Data Collection

We collected data using two surveys. The first survey was administered within 72 hours of admission. This survey asked questions related to (1) caregiver-reported markers of social disadvantage, (2) caregiver perceptions of access to care, and (3) caregiver- and patient-reported assessments of physical functioning. The second survey was administered within 2 to 8 weeks after the patient’s discharge and included a second assessment of physical functioning.

Social Disadvantage

Patients were considered to have a marker of social disadvantage if their caregiver reported (1) being of non-White race and/or Hispanic ethnicity, (2) primarily speaking a language other than English at home and not speaking English very well (ie, limited English proficiency), (3) attaining at most a high school or equivalent degree, or (4) having a =/<$30,000 annual household income.

Access to Care

We used the following survey item from the 2009-2010 National Survey of Children with Special Health Care Needs²⁵ to measure caregiver perceptions of access to care: “In the last six months, did you have any difficulties or delays getting care for your child because there were waiting lists, backlogs, or other problems getting an appointment?” We narrowed the original assessment time frame from 12 months to 6 months to provide a more proximal assessment of access in relation to the hospitalization.

Child Physical Functioning

We assessed child physical functioning using the physical functioning domain of the Pediatric Quality of Life Inventory (PedsQL) 4.0 Generic Core Scales and PedsQL Infant Scales, which have been validated for use in the inpatient setting.²² Caregivers completed one of these scales based on their child’s age. Assenting patients 8 to 16 years old completed the self-report PedsQL 4.0 Generic Core Scales instrument. When completing the first PedsQL survey, caregivers and patients reflected on the previous month before their child (or they) became ill to obtain a baseline physical functioning assessment.²³ When completing the second PedsQL survey, caregivers and patients reflected on the past 7 days to obtain a follow-up assessment.

 

All study procedures were approved by the Western Institutional Review Board (IRB) or the participating hospitals’ IRB.

Statistical Analysis

Patients with no missing data for all four social disadvantage markers were categorized based on the number of markers they reported: none, one, two, or three or more markers. We combined patients with three and four social disadvantage markers into one group to maximize power for the analyses. We dichotomized the access to care variable and coded response options as “no difficulty/delays accessing care” if the caregiver chose “Never” and “any difficulty/delays accessing care” if they chose “Sometimes/Usually/Always.”

For each patient–caregiver dyad, PedsQL items were scored using a standard method in which higher scores reflected better functioning.²² A single set of PedsQL scores was used for each patient–caregiver dyad. We used self-reported patient scores if the patient completed the PedsQL instrument; otherwise, we used proxy-reported caregiver scores. Intraclass correlations between child self-report and parent proxy-report demonstrate moderate to good agreement above age 8 years.²⁶ We computed a change in the physical functioning score by subtracting the baseline score from the follow-up score. The minimal clinically important difference (MCID) for the PedsQL instrument is 4.5 points, which we used to identify clinically meaningful differences.¹³

Analysis of variance models were constructed to test for differences in mean baseline and follow-up PedsQL scores (dependent variable) between the following independent variables: (1) social disadvantage groups and (2) those who reported having any difficulty/delays accessing care compared with those who did not. Only patient–caregiver dyads with both baseline and follow-up assessments were included in these analyses. Mixed-effects linear regression models were constructed to identify clinically meaningful differences in PedsQL scores between groups (MCID =/> 4.5) with adjustment for patient age, gender, respiratory condition, days between surveys, and hospital site as fixed effects. Site-specific random effects were included to account for within-hospital clustering. A similarly adjusted mixed-effects linear regression model was constructed to examine whether having any difficulty/delays accessing care modified the association between social disadvantage and PedsQL change scores (eg, an improvement from baseline to follow-up).

Because 17% of respondents had missing data for at least one social disadvantage marker, sensitivity analyses were conducted using multiple imputation to account for missing social disadvantage markers using chained equations.²⁷ Sensitivity analyses were also conducted to adjust for severity of illness using vital sign data within the first 24 hours, which could only be validly captured on patients with asthma within our dataset. By restricting this latter analysis to patients with asthma, we were able to examine the relationships of interest in a population with chronic disease.

RESULTS

The study sample included 1,860 patients, of which 1,325 had both baseline and follow-up PedsQL data (71%). Descriptive statistics were similar between those who completed the baseline and follow-up surveys (Table 1).

Twenty-two percent of patients had >/=3 social disadvantages and 30% of caregivers reported having any difficulty/delays accessing care. The mean follow-up PedsQL score was higher than the baseline score (90.4 vs 82.5; Table 1).

 

Social Disadvantage Markers and PedsQL Scores

The number of social disadvantage markers was inversely related to mean baseline PedsQL scores, but there was no difference in mean follow-up PedsQL scores between social disadvantage groups (Table 2). In adjusted analyses, the mean baseline PedsQL score was −6.1 points (95% CI: −8.7, −3.5) lower for patients with >/= 3 social disadvantage markers compared with patients with no social disadvantage markers, which exceeded the scale’s MCID.

Difficulty/Delays Accessing Care and PedsQL Scores

Having any difficulty/delays accessing care was significantly associated with lower baseline and follow-up PedsQL scores (Table 2). In adjusted analyses, the difference in baseline scores was 5.2 points (95% CI: −7.2, −3.2), which exceedes the scale’s MCID.

Interaction Between Social Disadvantage Markers, Difficulty/Delays Accessing Care, and Change in PedsQL Scores from Baseline to Follow-Up

While having =/>2 social disadvantage markers and difficulty/delays accessing care were each positively associated with changes in PedsQL scores from baseline to follow-up (Table 3), only patients with =/> 3 social disadvantage markers exceeded the PedsQL MCID. In stratified analyses, patients with a combination of social disadvantage makers and difficulties/delays accessing care had lower baseline PedsQL scores and greater change in PedsQL scores from baseline to follow-up compared with those without difficulties/delays accessing care (Figure). However, having any difficulty/delays accessing care did not significantly modify the relationship between social disadvantage and change in PedsQL scores, as none of the interaction terms were significant (Table 3, Model 3).

Sensitivity Analysis

Baseline, follow-up, and change in PedsQL scores were similar to our main analysis after performing multiple imputation for missing social disadvantage markers (Supplemental Table 1). Findings were also similar for patients with a diagnosis of asthma only; however, changes in PedsQL scores were greater in magnitude (Appendix Table 2).

DISCUSSION

This study examined the relationship between social disadvantage and child physical functioning before and after hospitalization for acute respiratory illness. Study findings indicated that patients with higher numbers of social disadvantage markers reported lower PedsQL scores before hospitalization; however, differences in PedsQL scores were not apparent after hospitalization. Patients who experienced difficulty/delays accessing care also reported lower PedsQL scores at baseline. This difference was still significant but did not exceed the PedsQL MCID threshold after hospitalization. Difficulty/delays accessing care appeared to be an additional social disadvantage marker; however, it did not modify the relationship between social disadvantage and improvement in physical functioning.

The study findings at baseline are consistent with prior studies demonstrating a negative association between social disadvantage markers and HRQoL and a cumulative effect based on the number of social disadvantages.^3,4,7,8 This study adds to the existing literature by examining how this relationship changes after hospitalization. As evidenced by the lack of association between social disadvantage markers and follow-up PedsQL scores, our findings suggest that receipt of inpatient care improved perceptions of physical functioning to a greater extent for patients with more social disadvantage markers (especially patients with =/> 3 social disadvantage markers). There are several potential reasons for these findings.

 

One possibility is that caregivers and/or patients with more social disadvantage markers are more influenced by context when assessing physical functioning. This could lead to an underestimation of functioning when asked to recall baseline physical functioning at the time of acute illness and overestimation of functioning after recovery from an illness. This possibility is consistent with a form of response bias, extreme response tendencies, in which lower socioeconomic subgroups tend to choose the more extreme response options of a scale.²⁸ In the absence of longitudinal assessments of HRQoL across the care continuum over time, disentangling whether these differences are due to response bias or representative of true changes in physical functioning remains challenging.

Given that disparities in physical functioning at baseline were consistent with prior evidence, another possibility is that hospitalization provided an opportunity to address gaps in access and quality that may have existed for patients with social disadvantage in the community setting. The 24/7 nature of hospital care, usually from a multidisciplinary team of providers, lends itself to opportunities to receive intensive education related to the current illness or to address other health concerns that parents or providers identify during a hospital stay. For example, consistent and repetitive asthma education may be more beneficial to patients and families with more social disadvantage markers. The fact that the association between social disadvantage markers and change in physical functioning scores were greater for patients with asthma supports this reasoning. Hospital care may also provide an opportunity to address other unmet medical needs or psychosocial needs by providing efficient access to subspecialists, social workers, or interpreters. Further research is needed to elucidate whether families received additional services in the hospital setting that were not available to them prior to hospitalization, such as consistent interpreter use, social work engagement, and subspecialty/community referrals. Further studies should also determine whether the provision of equitable medical and social support services is associated with improvements in HRQoL disparities. Additionally, studies should examine whether physical functioning improvements following hospitalization return to baseline levels after a longer period of time and, if so, how we might sustain these reductions in HRQoL disparities. Such studies may identify tangible targets and interventions to reduce disparities in HRQoL for these children.

This study highlights the importance of assessing for difficulty/delays accessing care in addition to social disadvantage markers, as this was also a significant predictor of lower child physical functioning. Differences in PedsQL scores between those who reported any versus no difficulty/delays accessing care were more pronounced at baseline compared with follow-up. A possible reason for these findings is that receiving hospital care may have addressed some access to care issues that were present in the outpatient setting, which resulted in improved perceptions of physical functioning. For example, hospital care may mitigate access to care barriers such as limited after-hours clinic appointments, language barriers, and lack of insurance, thus providing some patients with an alternative pathway to address their health concerns. Alternatively, hospital staff may assist families in scheduling follow-up appointments with the patient’s primary medical home after discharge, which potentially reduced some access to care barriers. The question is whether these disparities will widen once again after a longer follow-up period if families continue facing barriers to accessing needed care in the outpatient setting.

 

The results of the effect modification analysis demonstrated that the association between social disadvantage and change in PedsQL scores from baseline to follow-up was not significantly different based on a child’s ability to access care. In our stratified analysis, difficulty/delays accessing care added to baseline disparities at each social disadvantage level but did not alter how perceptions of physical functioning change over time. Therefore, physical functioning improvements may rely more heavily on the type of care received within the hospital setting as opposed to accessing care in the first place. However, future studies should examine whether access to high-­quality care instead of simply measuring difficulty/delays in accessing care would lead to different results. Access to a comprehensive medical home may be a better measure to assess for effect modification because it measures features beyond access to care, such as continuity, comprehensiveness, communication, and coordination of outpatient care.^29-31

If additional studies find evidence that the nature of hospital care, an intensive 24/7 care setting, differentially benefits patients with higher social disadvantage markers (particularly those with =/> 3 markers and chronic illness), this would support the need for systematic screening for social disadvantages or difficulty/delays accessing care in the inpatient setting. Systematic screening could help ensure all patients who may benefit from additional services, such as intensive, culturally tailored education or connections to food, housing, or financial services, will in fact receive them, which may lead to sustained reductions in health disparities.²⁰ Further research into pairing validated screening tools with proven interventions is needed.³²

This study has additional limitations aside from those noted above. First, we did not reassess perceived or actual access to care after hospitalization, which may have allowed for analyses to examine access to care as a mediator between social disadvantage and lower child physical functioning. Second, this study included only English- and Spanish-speaking patients and families. Patients with less commonly spoken languages may experience more difficulty accessing or navigating the health system, which may further impact access to care and HRQoL. Third, we had a considerable amount of missing social disadvantage marker data (mainly income); however, our sensitivity analyses did not result in significantly different or clinically meaningful differences in our findings. Insurance status is more feasible to obtain from administrative data and could serve as a proxy for income or as an additional social disadvantage marker in future studies. Finally, we could calculate illness severity only for patients with asthma based on the available data; therefore, we could not adequately control for illness severity across all conditions.

CONCLUSIONS

Social disadvantage was associated with lower child physical functioning before hospitalization, but differences were not apparent after hospitalization for children with acute respiratory illness. Caregiver-perceived difficulty/delays accessing care was found to be an additional predictor of lower physical functioning at baseline but did not significantly alter the association between social disadvantage and improvement in physical functioning over time. Further studies are needed to understand how hospital care may differentially impact child physical functioning for patients with higher social disadvantage makers in order to sustain improvements in HRQoL disparities.

 

Acknowledgments

The authors thank the following individuals of the Pediatric Respiratory Illness Measurement System (PRIMES) study team for their contributions to this work: Karen M. Wilson, New York, New York; Ricardo A. Quinonez, Houston, Texas; Joyee G. Vachani, Houston, Texas; and Amy Tyler, Aurora, Colorado. We would also like to thank the Pediatric Research in Inpatient Settings Network for facilitating this work.

Examining disparities in health-related quality of life (HRQoL) outcomes in children provides a unique patient-centered perspective on pediatric health services equity.^1,2 Prior studies have demonstrated the relationship between minority race, low socioeconomic status, and lower maternal education with poorer HRQoL outcomes in children.^3-6 Some studies have also shown a dose-response relationship between social disadvantage markers and poorer child health status.^7,8 Furthermore, the associations between social disadvantage and poor access to care,^9-11 and between poor access to care and lower HRQoL, are also well established.^12-14

Examining HRQoL before and after hospitalization can further our understanding of how disparities in HRQoL may change once children engage with the medical system for an acute illness.¹⁵ Children requiring hospitalization constitute a useful population for examination of this question as they represent a group of children with variable social disadvantage markers and access to outpatient care.¹⁶ Although interventions to address social determinants of health for patients with social disadvantages have been associated with within-group improvements in HRQoL, none have assessed changes in disparities as an outcome.¹⁷ Furthermore, many of these studies were conducted in the outpatient setting,^18,19 whereas hospitalization provides an additional point of care to address the social determinants of health for vulnerable families.²⁰ Even for short hospitalizations, the 24/7 nature of hospital care provides the opportunity for frequent interactions with clinicians, nurses, and support staff to clarify illness-related questions, discuss other health concerns and unmet needs, and connect with social services or community resources. These opportunities may be particularly important for families with a higher number of social disadvantage markers and even more beneficial to those with difficulty accessing needed care from their primary medical home.

In this study, we focused on children with common respiratory illnesses (asthma, bronchiolitis, and pneumonia), which constitute the majority of childhood hospitalizations.²¹ Additionally, we only focused on the child’s physical functioning component of HRQoL because this component is most likely to improve after hospitalization for children with an acute respiratory illness.²² A prior study examining HRQoL before and after hospitalization demonstrated that most children return to and/or surpass their baseline physical functioning by 1 month after hospital discharge.²³

Our primary objective was to examine associations between several markers of social disadvantage, access to care, and child physical functioning before and after hospitalization for acute respiratory illness. Second, we aimed to understand if access to care (defined as perceived difficulty/delays getting care) acts as an independent predictor of improvement in physical functioning from baseline to follow-up and/or if it modifies the relationship between social disadvantage and improvement in physical functioning (Appendix Figure).

 

METHODS

 

Study Design and Population

 

This study was nested within a multicenter, prospective cohort study of children who were hospitalized for asthma, bronchiolitis, or pneumonia between July 2014 and June 2016 at one of five children’s hospitals in the Pediatric Research in Inpatient Settings Network.²⁴

We approached families for study participation within 72 hours of admission to the hospital using a standard protocol. Patients and their caregivers were eligible to participate in the study if the patient was 2 weeks to 16 years old and if the primary caregiver’s preferred language for medical communication was either English or Spanish. Patients with chronic medical conditions (except asthma), with moderate to severe developmental delay, with a history of prematurity <32 weeks, or who received care in the intensive care unit were excluded. Patients could only participate in the study once.

The study team set out to enroll an even number of patients across all three conditions. If a patient’s discharge diagnosis differed from their admission diagnosis (eg, from bronchiolitis to pneumonia), discharge diagnosis was used for condition group assignment. If the discharge diagnosis was not one of these three respiratory conditions, we excluded the patient from further analysis.

Data Collection

We collected data using two surveys. The first survey was administered within 72 hours of admission. This survey asked questions related to (1) caregiver-reported markers of social disadvantage, (2) caregiver perceptions of access to care, and (3) caregiver- and patient-reported assessments of physical functioning. The second survey was administered within 2 to 8 weeks after the patient’s discharge and included a second assessment of physical functioning.

Social Disadvantage

Patients were considered to have a marker of social disadvantage if their caregiver reported (1) being of non-White race and/or Hispanic ethnicity, (2) primarily speaking a language other than English at home and not speaking English very well (ie, limited English proficiency), (3) attaining at most a high school or equivalent degree, or (4) having a =/<$30,000 annual household income.

Access to Care

We used the following survey item from the 2009-2010 National Survey of Children with Special Health Care Needs²⁵ to measure caregiver perceptions of access to care: “In the last six months, did you have any difficulties or delays getting care for your child because there were waiting lists, backlogs, or other problems getting an appointment?” We narrowed the original assessment time frame from 12 months to 6 months to provide a more proximal assessment of access in relation to the hospitalization.

Child Physical Functioning

We assessed child physical functioning using the physical functioning domain of the Pediatric Quality of Life Inventory (PedsQL) 4.0 Generic Core Scales and PedsQL Infant Scales, which have been validated for use in the inpatient setting.²² Caregivers completed one of these scales based on their child’s age. Assenting patients 8 to 16 years old completed the self-report PedsQL 4.0 Generic Core Scales instrument. When completing the first PedsQL survey, caregivers and patients reflected on the previous month before their child (or they) became ill to obtain a baseline physical functioning assessment.²³ When completing the second PedsQL survey, caregivers and patients reflected on the past 7 days to obtain a follow-up assessment.

 

All study procedures were approved by the Western Institutional Review Board (IRB) or the participating hospitals’ IRB.

Statistical Analysis

Patients with no missing data for all four social disadvantage markers were categorized based on the number of markers they reported: none, one, two, or three or more markers. We combined patients with three and four social disadvantage markers into one group to maximize power for the analyses. We dichotomized the access to care variable and coded response options as “no difficulty/delays accessing care” if the caregiver chose “Never” and “any difficulty/delays accessing care” if they chose “Sometimes/Usually/Always.”

For each patient–caregiver dyad, PedsQL items were scored using a standard method in which higher scores reflected better functioning.²² A single set of PedsQL scores was used for each patient–caregiver dyad. We used self-reported patient scores if the patient completed the PedsQL instrument; otherwise, we used proxy-reported caregiver scores. Intraclass correlations between child self-report and parent proxy-report demonstrate moderate to good agreement above age 8 years.²⁶ We computed a change in the physical functioning score by subtracting the baseline score from the follow-up score. The minimal clinically important difference (MCID) for the PedsQL instrument is 4.5 points, which we used to identify clinically meaningful differences.¹³

Analysis of variance models were constructed to test for differences in mean baseline and follow-up PedsQL scores (dependent variable) between the following independent variables: (1) social disadvantage groups and (2) those who reported having any difficulty/delays accessing care compared with those who did not. Only patient–caregiver dyads with both baseline and follow-up assessments were included in these analyses. Mixed-effects linear regression models were constructed to identify clinically meaningful differences in PedsQL scores between groups (MCID =/> 4.5) with adjustment for patient age, gender, respiratory condition, days between surveys, and hospital site as fixed effects. Site-specific random effects were included to account for within-hospital clustering. A similarly adjusted mixed-effects linear regression model was constructed to examine whether having any difficulty/delays accessing care modified the association between social disadvantage and PedsQL change scores (eg, an improvement from baseline to follow-up).

Because 17% of respondents had missing data for at least one social disadvantage marker, sensitivity analyses were conducted using multiple imputation to account for missing social disadvantage markers using chained equations.²⁷ Sensitivity analyses were also conducted to adjust for severity of illness using vital sign data within the first 24 hours, which could only be validly captured on patients with asthma within our dataset. By restricting this latter analysis to patients with asthma, we were able to examine the relationships of interest in a population with chronic disease.

RESULTS

The study sample included 1,860 patients, of which 1,325 had both baseline and follow-up PedsQL data (71%). Descriptive statistics were similar between those who completed the baseline and follow-up surveys (Table 1).

Twenty-two percent of patients had >/=3 social disadvantages and 30% of caregivers reported having any difficulty/delays accessing care. The mean follow-up PedsQL score was higher than the baseline score (90.4 vs 82.5; Table 1).

 

Social Disadvantage Markers and PedsQL Scores

The number of social disadvantage markers was inversely related to mean baseline PedsQL scores, but there was no difference in mean follow-up PedsQL scores between social disadvantage groups (Table 2). In adjusted analyses, the mean baseline PedsQL score was −6.1 points (95% CI: −8.7, −3.5) lower for patients with >/= 3 social disadvantage markers compared with patients with no social disadvantage markers, which exceeded the scale’s MCID.

Difficulty/Delays Accessing Care and PedsQL Scores

Having any difficulty/delays accessing care was significantly associated with lower baseline and follow-up PedsQL scores (Table 2). In adjusted analyses, the difference in baseline scores was 5.2 points (95% CI: −7.2, −3.2), which exceedes the scale’s MCID.

Interaction Between Social Disadvantage Markers, Difficulty/Delays Accessing Care, and Change in PedsQL Scores from Baseline to Follow-Up

While having =/>2 social disadvantage markers and difficulty/delays accessing care were each positively associated with changes in PedsQL scores from baseline to follow-up (Table 3), only patients with =/> 3 social disadvantage markers exceeded the PedsQL MCID. In stratified analyses, patients with a combination of social disadvantage makers and difficulties/delays accessing care had lower baseline PedsQL scores and greater change in PedsQL scores from baseline to follow-up compared with those without difficulties/delays accessing care (Figure). However, having any difficulty/delays accessing care did not significantly modify the relationship between social disadvantage and change in PedsQL scores, as none of the interaction terms were significant (Table 3, Model 3).

Sensitivity Analysis

Baseline, follow-up, and change in PedsQL scores were similar to our main analysis after performing multiple imputation for missing social disadvantage markers (Supplemental Table 1). Findings were also similar for patients with a diagnosis of asthma only; however, changes in PedsQL scores were greater in magnitude (Appendix Table 2).

DISCUSSION

This study examined the relationship between social disadvantage and child physical functioning before and after hospitalization for acute respiratory illness. Study findings indicated that patients with higher numbers of social disadvantage markers reported lower PedsQL scores before hospitalization; however, differences in PedsQL scores were not apparent after hospitalization. Patients who experienced difficulty/delays accessing care also reported lower PedsQL scores at baseline. This difference was still significant but did not exceed the PedsQL MCID threshold after hospitalization. Difficulty/delays accessing care appeared to be an additional social disadvantage marker; however, it did not modify the relationship between social disadvantage and improvement in physical functioning.

The study findings at baseline are consistent with prior studies demonstrating a negative association between social disadvantage markers and HRQoL and a cumulative effect based on the number of social disadvantages.^3,4,7,8 This study adds to the existing literature by examining how this relationship changes after hospitalization. As evidenced by the lack of association between social disadvantage markers and follow-up PedsQL scores, our findings suggest that receipt of inpatient care improved perceptions of physical functioning to a greater extent for patients with more social disadvantage markers (especially patients with =/> 3 social disadvantage markers). There are several potential reasons for these findings.

 

One possibility is that caregivers and/or patients with more social disadvantage markers are more influenced by context when assessing physical functioning. This could lead to an underestimation of functioning when asked to recall baseline physical functioning at the time of acute illness and overestimation of functioning after recovery from an illness. This possibility is consistent with a form of response bias, extreme response tendencies, in which lower socioeconomic subgroups tend to choose the more extreme response options of a scale.²⁸ In the absence of longitudinal assessments of HRQoL across the care continuum over time, disentangling whether these differences are due to response bias or representative of true changes in physical functioning remains challenging.

Given that disparities in physical functioning at baseline were consistent with prior evidence, another possibility is that hospitalization provided an opportunity to address gaps in access and quality that may have existed for patients with social disadvantage in the community setting. The 24/7 nature of hospital care, usually from a multidisciplinary team of providers, lends itself to opportunities to receive intensive education related to the current illness or to address other health concerns that parents or providers identify during a hospital stay. For example, consistent and repetitive asthma education may be more beneficial to patients and families with more social disadvantage markers. The fact that the association between social disadvantage markers and change in physical functioning scores were greater for patients with asthma supports this reasoning. Hospital care may also provide an opportunity to address other unmet medical needs or psychosocial needs by providing efficient access to subspecialists, social workers, or interpreters. Further research is needed to elucidate whether families received additional services in the hospital setting that were not available to them prior to hospitalization, such as consistent interpreter use, social work engagement, and subspecialty/community referrals. Further studies should also determine whether the provision of equitable medical and social support services is associated with improvements in HRQoL disparities. Additionally, studies should examine whether physical functioning improvements following hospitalization return to baseline levels after a longer period of time and, if so, how we might sustain these reductions in HRQoL disparities. Such studies may identify tangible targets and interventions to reduce disparities in HRQoL for these children.

This study highlights the importance of assessing for difficulty/delays accessing care in addition to social disadvantage markers, as this was also a significant predictor of lower child physical functioning. Differences in PedsQL scores between those who reported any versus no difficulty/delays accessing care were more pronounced at baseline compared with follow-up. A possible reason for these findings is that receiving hospital care may have addressed some access to care issues that were present in the outpatient setting, which resulted in improved perceptions of physical functioning. For example, hospital care may mitigate access to care barriers such as limited after-hours clinic appointments, language barriers, and lack of insurance, thus providing some patients with an alternative pathway to address their health concerns. Alternatively, hospital staff may assist families in scheduling follow-up appointments with the patient’s primary medical home after discharge, which potentially reduced some access to care barriers. The question is whether these disparities will widen once again after a longer follow-up period if families continue facing barriers to accessing needed care in the outpatient setting.

 

The results of the effect modification analysis demonstrated that the association between social disadvantage and change in PedsQL scores from baseline to follow-up was not significantly different based on a child’s ability to access care. In our stratified analysis, difficulty/delays accessing care added to baseline disparities at each social disadvantage level but did not alter how perceptions of physical functioning change over time. Therefore, physical functioning improvements may rely more heavily on the type of care received within the hospital setting as opposed to accessing care in the first place. However, future studies should examine whether access to high-­quality care instead of simply measuring difficulty/delays in accessing care would lead to different results. Access to a comprehensive medical home may be a better measure to assess for effect modification because it measures features beyond access to care, such as continuity, comprehensiveness, communication, and coordination of outpatient care.^29-31

If additional studies find evidence that the nature of hospital care, an intensive 24/7 care setting, differentially benefits patients with higher social disadvantage markers (particularly those with =/> 3 markers and chronic illness), this would support the need for systematic screening for social disadvantages or difficulty/delays accessing care in the inpatient setting. Systematic screening could help ensure all patients who may benefit from additional services, such as intensive, culturally tailored education or connections to food, housing, or financial services, will in fact receive them, which may lead to sustained reductions in health disparities.²⁰ Further research into pairing validated screening tools with proven interventions is needed.³²

This study has additional limitations aside from those noted above. First, we did not reassess perceived or actual access to care after hospitalization, which may have allowed for analyses to examine access to care as a mediator between social disadvantage and lower child physical functioning. Second, this study included only English- and Spanish-speaking patients and families. Patients with less commonly spoken languages may experience more difficulty accessing or navigating the health system, which may further impact access to care and HRQoL. Third, we had a considerable amount of missing social disadvantage marker data (mainly income); however, our sensitivity analyses did not result in significantly different or clinically meaningful differences in our findings. Insurance status is more feasible to obtain from administrative data and could serve as a proxy for income or as an additional social disadvantage marker in future studies. Finally, we could calculate illness severity only for patients with asthma based on the available data; therefore, we could not adequately control for illness severity across all conditions.

CONCLUSIONS

Social disadvantage was associated with lower child physical functioning before hospitalization, but differences were not apparent after hospitalization for children with acute respiratory illness. Caregiver-perceived difficulty/delays accessing care was found to be an additional predictor of lower physical functioning at baseline but did not significantly alter the association between social disadvantage and improvement in physical functioning over time. Further studies are needed to understand how hospital care may differentially impact child physical functioning for patients with higher social disadvantage makers in order to sustain improvements in HRQoL disparities.

 

Acknowledgments

The authors thank the following individuals of the Pediatric Respiratory Illness Measurement System (PRIMES) study team for their contributions to this work: Karen M. Wilson, New York, New York; Ricardo A. Quinonez, Houston, Texas; Joyee G. Vachani, Houston, Texas; and Amy Tyler, Aurora, Colorado. We would also like to thank the Pediatric Research in Inpatient Settings Network for facilitating this work.

The patient-physician relationship is critical for ensuring the delivery of high-quality healthcare. Successful patient-physician relationships arise from shared trust, knowledge, mutual respect, and effective verbal and nonverbal communication. The ways in which patients experience healthcare and their satisfaction with physicians affect a myriad of important health outcomes, such as adherence to treatment and outcomes for conditions such as hypertension and diabetes mellitus.^1-5 One method for potentially enhancing patient satisfaction is through understanding how patients wish their physicians to dress^6-8 and tailoring attire to match these expectations. In addition to our systematic review,⁹ a recent large-scale, multicenter study in the United States revealed that most patients perceive physician attire as important, but that preferences for specific types of attire are contextual.^9,10 For example, elderly patients preferred physicians in formal attire and white coat, while scrubs with white coat or scrubs alone were preferred for emergency department (ED) physicians and surgeons, respectively. Moreover, regional variation regarding attire preference was also observed in the US, with preferences for more formal attire in the South and less formal in the Midwest.

Geographic variation, regarding patient preferences for physician dress, is perhaps even more relevant internationally. In particular, Japan is considered to have a highly contextualized culture that relies on nonverbal and implicit communication. However, medical professionals have no specific dress code and, thus, don many different kinds of attire. In part, this may be because it is not clear whether or how physician attire impacts patient satisfaction and perceived healthcare quality in Japan.^11-13 Although previous studies in Japan have suggested that physician attire has a considerable influence on patient satisfaction, these studies either involved a single department in one hospital or a small number of respondents.^14-17 Therefore, we performed a multicenter, cross-sectional study to understand patients’ preferences for physician attire in different clinical settings and in different geographic regions in Japan.

METHODS

Study Population

We conducted a cross-sectional, questionnaire-based study from 2015 to 2017, in four geographically diverse hospitals in Japan. Two of these hospitals, Tokyo Joto Hospital and Juntendo University Hospital, are located in eastern Japan whereas the others, Kurashiki Central Hospital and Akashi Medical Center, are in western Japan.

 

Questionnaires were printed and randomly distributed by research staff to outpatients in waiting rooms and inpatients in medical wards who were 20 years of age or older. We placed no restriction on ward site or time of questionnaire distribution. Research staff, including physicians, nurses, and medical clerks, were instructed to avoid guiding or influencing participants’ responses. Informed consent was obtained by the staff; only those who provided informed consent participated in the study. Respondents could request assistance with form completion from persons accompanying them if they had difficulties, such as physical, visual, or hearing impairments. All responses were collected anonymously. The study was approved by the ethics committees of all four hospitals.

Questionnaire

We used a modified version of the survey instrument from a prior study.¹⁰ The first section of the survey showed photographs of either a male or female physician with 7 unique forms of attire, including casual, casual with white coat, scrubs, scrubs with white coat, formal, formal with white coat, and business suit (Figure 1). Given the Japanese context of this study, the language was translated to Japanese and photographs of physicians of Japanese descent were used. Photographs were taken with attention paid to achieving constant facial expressions on the physicians as well as in other visual cues (eg, lighting, background, pose). The physician’s gender and attire in the first photograph seen by each respondent were randomized to prevent bias in ordering, priming, and anchoring; all other sections of the survey were identical.

Respondents were first asked to rate the standalone, randomized physician photograph using a 1-10 scale across five domains (ie, how knowledgeable, trustworthy, caring, and approachable the physician appeared and how comfortable the physician’s appearance made the respondent feel), with a score of 10 representing the highest rating. Respondents were subsequently given 7 photographs of the same physician wearing various forms of attire. Questions were asked regarding preference of attire in varied clinical settings (ie, primary care, ED, hospital, surgery, overall preference). To identify the influence of and respondent preferences for physician dress and white coats, a Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree) was employed. The scale was trichotomized into “disagree” (1, 2), “neither agree nor disagree” (3), and “agree” (4, 5) for analysis. Demographic data, including age, gender, education level, nationality (Japanese or non-Japanese), and number of physicians seen in the past year were collected.

Outcomes and Sample Size Calculation

The primary outcome of attire preference was calculated as the mean composite score of the five individual rating domains (ie, knowledgeable, trustworthy, caring, approachable, and comfortable), with the highest score representing the most preferred form of attire. We also assessed variation in preferences for physician attire by respondent characteristics, such as age and gender.

Sample size estimation was based on previous survey methodology.¹⁰ The Likert scale range for identifying influence of and respondent preferences for physician dress and white coats was 1-5 (“strongly disagree” to “strongly agree”). The scale range for measuring preferences for the randomized attire photograph was 1-10. An assumption of normality was made regarding responses on the 1-10 scale. An estimated standard deviation of 2.2 was assumed, based on prior findings.¹⁰ Based on these assumptions and the inclusion of at least 816 respondents (assuming a two-sided alpha error of 0.05), we expected to have 90% capacity to detect differences for effect sizes of 0.50 on the 1-10 scale.

 

Statistical Analyses

Paper-based survey data were entered independently and in duplicate by the study team. Respondents were not required to answer all questions; therefore, the denominator for each question varied. Data were reported as mean and standard deviation (SD) or percentages, where appropriate. Differences in the mean composite rating scores were assessed using one-way ANOVA with the Tukey method for pairwise comparisons. Differences in proportions for categorical data were compared using the Z-test. Chi-squared tests were used for bivariate comparisons between respondent age, gender, and level of education and corresponding respondent preferences. All analyses were performed using Stata 14 MP/SE (Stata Corp., College Station, Texas, USA).

RESULTS

Characteristics of Participants

Between December 1, 2015 and October 30, 2017, a total of 2,020 surveys were completed by patients across four academic hospitals in Japan. Of those, 1,960 patients (97.0%) completed the survey in its entirety. Approximately half of the respondents were 65 years of age or older (49%), of female gender (52%), and reported receiving care in the outpatient setting (53%). Regarding use of healthcare, 91% had seen more than one physician in the year preceding the time of survey completion (Table 1).

Ratings of Physician Attire

Compared with all forms of attire depicted in the survey’s first standalone photograph, respondents rated “casual attire with white coat” the highest (Figure 2). The mean composite score for “casual attire with white coat” was 7.1 (standard deviation [SD] = 1.8), and this attire was set as the referent group. Cronbach’s alpha, for the five items included in the composite score, was 0.95. However, “formal attire with white coat” was rated almost as highly as “casual attire with white coat” with an overall mean composite score of 7.0 (SD = 1.6).

Variation in Preference for Physician Attire by Clinical Setting

Preferences for physician attire varied by clinical care setting. Most respondents preferred “casual attire with white coat” or “formal attire with white coat” in both primary care and hospital settings, with a slight preference for “casual attire with white coat.” In contrast, respondents preferred “scrubs without white coat” in the ED and surgical settings. When asked about their overall preference, respondents reported they felt their physician should wear “formal attire with white coat” (35%) or “casual attire with white coat” (30%; Table 2). When comparing the group of photographs of physicians with white coats to the group without white coats (Figure 1), respondents preferred physicians wearing white coats overall and specifically when providing care in primary care and hospital settings. However, they preferred physicians without white coats when providing care in the ED (P < .001). With respect to surgeons, there was no statistically significant difference between preference for white coats and no white coats. These results were similar for photographs of both male and female physicians.

When asked whether physician dress was important to them and if physician attire influenced their satisfaction with the care received, 61% of participants agreed that physician dress was important, and 47% agreed that physician attire influenced satisfaction (Appendix Table 1). With respect to appropriateness of physicians dressing casually over the weekend in clinical settings, 52% responded that casual wear was inappropriate, while 31% had a neutral opinion.

Participants were asked whether physicians should wear a white coat in different clinical settings. Nearly two-thirds indicated a preference for white coats in the office and hospital (65% and 64%, respectively). Responses regarding whether emergency physicians should wear white coats were nearly equally divided (Agree, 37%; Disagree, 32%; Neither Agree nor Disagree, 31%). However, “scrubs without white coat” was most preferred (56%) when patients were given photographs of various attire and asked, “Which physician would you prefer to see when visiting the ER?” Responses to the question “Physicians should always wear a white coat when seeing patients in any setting” varied equally (Agree, 32%; Disagree, 34%; Neither Agree nor Disagree, 34%).

 

Variation in Preference for Physician Attire by Respondent Demographics

When comparing respondents by age, those 65 years or older preferred “formal attire with white coat” more so than respondents younger than 65 years (Appendix Table 2). This finding was identified in both primary care (36% vs 31%, P < .001) and hospital settings (37% vs 30%, P < .001). Additionally, physician attire had a greater impact on older respondents’ satisfaction and experience (Appendix Table 3). For example, 67% of respondents 65 years and older agreed that physician attire was important, and 54% agreed that attire influenced satisfaction. Conversely, for respondents younger than 65 years, the proportion agreeing with these statements was lower (56% and 41%, both P < .001). When comparing older and younger respondents, those 65 years and older more often preferred physicians wearing white coats in any setting (39% vs 26%, P < .001) and specifically in their office (68% vs 61%, P = .002), the ED (40% vs 34%, P < .001), and the hospital (69% vs 60%, P < .001).

When comparing male and female respondents, male respondents more often stated that physician dress was important to them (men, 64%; women, 58%; P = .002). When comparing responses to the question “Overall, which clothes do you feel a doctor should wear?”, between the eastern and western Japanese hospitals, preferences for physician attire varied.

Variation in Expectations Between Male and Female Physicians

When comparing the ratings of male and female physicians, female physicians were rated higher in how caring (P = .005) and approachable (P < .001) they appeared. However, there were no significant differences in the ratings of the three remaining domains (ie, knowledgeable, trustworthy, and comfortable) or the composite score.

DISCUSSION

This report is the first multicenter Japanese study to examine patients’ preferences for physician attire. Most Japanese respondents perceived that physician dress is important, and nearly half agreed that physician dress influences their satisfaction with care. Overall, “casual attire with white coat” and “formal attire with white coat” tended to be the preferred option for respondents; however, this varied widely across context of care delivery. “Scrubs without white coat” was the preferred attire for physicians in the ED and surgery department. Elderly patients preferred physicians in formal attire regardless of where care was being received. Collectively, these findings have important implications for how delivery of care in Japan is approached.

Since we employed the same methodology as previous studies conducted in the US¹⁰ and Switzerland,¹⁸ a notable strength of our approach is that comparisons among these countries can be drawn. For example, physician attire appears to hold greater importance in Japan than in the US and Switzerland. Among Japanese participants, 61% agreed that physician dress is important (US, 53%; Switzerland, 36%), and 47% agreed that physician dress influenced how satisfied they were with their care (US, 36%; Switzerland, 23%).¹⁰ This result supports the notion that nonverbal and implicit communications (such as physician dress) may carry more importance among Japanese people.^11-13

Regarding preference ratings for type of dress among respondents in Japan, “casual attire with white coat” received the highest mean composite score rating, with “formal attire with white coat” rated second overall. In contrast, US respondents rated “formal attire with white coat” highest and “scrubs with white coat” second.¹⁰ Our result runs counter to our expectation in that we expected Japanese respondents to prefer formal attire, since Japan is one of the most formal cultures in the world. One potential explanation for this difference is that the casual style chosen for this study was close to the smart casual style (slightly casual). Most hospitals and clinics in Japan do not allow physicians to wear jeans or polo shirts, which were chosen as the casual attire in the previous US study.

When examining various care settings and physician types, both Japanese and US respondents were more likely to prefer physicians wearing a white coat in the office or hospital.¹⁰ However, Japanese participants preferred both “casual attire with white coat” and “formal attire with white coat” equally in primary care or hospital settings. A smaller proportion of US respondents preferred “casual attire with white coat” in primary care (11%) and hospital settings (9%), but more preferred “formal attire with white coat” for primary care (44%) and hospital physicians (39%). In the ED setting, 32% of participants in Japan and 18% in the US disagreed with the idea that physicians should wear a white coat. Among Japanese participants, “scrubs without white coat” was rated highest for emergency physicians (56%) and surgeons (47%), while US preferences were 40% and 42%, respectively.¹⁰ One potential explanation is that scrubs-based attire became popular among Japanese ED and surgical contexts as a result of cultural influence and spread from western countries.^{19, 20}

With respect to perceptions regarding physician attire on weekends, 52% of participants considered it inappropriate for a physician to dress casually over the weekend, compared with only 30% in Switzerland and 21% in the US.^11,12 Given Japan’s level of formality and the fact that most Japanese physicians continue to work over the weekend,^21-23 Japanese patients tend to expect their physicians to dress in more formal attire during these times.

Previous studies in Japan have demonstrated that older patients gave low ratings to scrubs and high ratings to white coat with any attire,^15,17 and this was also the case in our study. Perhaps elderly patients reflect conservative values in their preferences of physician dress. Their perceptions may be less influenced by scenes portraying physicians in popular media when compared with the perceptions of younger patients. Though a 2015 systematic review and studies in other countries revealed white coats were preferred regardless of exact dress,^9,24-26 they also showed variation in preferences for physician attire. For example, patients in Saudi Arabia preferred white coat and traditional ethnic dress,²⁵ whereas mothers of pediatric patients in Saudi Arabia preferred scrubs for their pediatricians.²⁷ Therefore, it is recommended for internationally mobile physicians to choose their dress depending on a variety of factors including country, context, and patient age group.

Our study has limitations. First, because some physicians presented the surveys to the patients, participants may have responded differently. Second, participants may have identified photographs of the male physician model as their personal healthcare provider (one author, K.K.). To avoid this possible bias, we randomly distributed 14 different versions of physician photographs in the questionnaire. Third, although physician photographs were strictly controlled, the “formal attire and white coat” and “casual attire and white coat” photographs appeared similar, especially given that the white coats were buttoned. Also, the female physician depicted in the photographs did not have the scrub shirt tucked in, while the male physician did. These nuances may have affected participant ratings between groups. Fourth, we did not blind researchers or data collectors in the process of data collection and entry. Fifth, we asked participants to indicate their age using categories. The age group “35-54 years” covered a wide range of patients, and we may have obtained more granular detail if we had chosen different age groups. Sixth, our cohort included a higher proportion of older people who needed medical treatment for their comorbidities and who had not received high levels of education. This resulted in a seemingly high proportion of lower education levels in our cohort. Lastly, patient experience and satisfaction can be comprised not only by physician attire, but also physician behavior and attitude, which this survey could not elicit. Thus, additional studies are needed to identify and quantify all determinants of patient experience with their physicians.

In conclusion, patient preferences for physician attire were examined using a multicenter survey with a large sample size and robust survey methodology, thus overcoming weaknesses of previous studies into Japanese attire. Japanese patients perceive that physician attire is important and influences satisfaction with their care, more so than patients in other countries, like the US and Switzerland. Geography, settings of care, and patient age play a role in preferences. As a result, hospitals and health systems may use these findings to inform dress code policy based on patient population and context, recognizing that the appearance of their providers affects the patient-physician relationship. Future research should focus on better understanding the various cultural and societal customs that lead to patient expectations of physician attire.

 

Acknowledgments

The authors thank Drs. Fumi Takemoto, Masayuki Ueno, Kazuya Sakai, Saori Kinami, and Toshio Naito for their assistance with data collection at their respective sites. Additionally, the authors thank Dr. Yoko Kanamitsu for serving as a model for photographs.

The patient-physician relationship is critical for ensuring the delivery of high-quality healthcare. Successful patient-physician relationships arise from shared trust, knowledge, mutual respect, and effective verbal and nonverbal communication. The ways in which patients experience healthcare and their satisfaction with physicians affect a myriad of important health outcomes, such as adherence to treatment and outcomes for conditions such as hypertension and diabetes mellitus.^1-5 One method for potentially enhancing patient satisfaction is through understanding how patients wish their physicians to dress^6-8 and tailoring attire to match these expectations. In addition to our systematic review,⁹ a recent large-scale, multicenter study in the United States revealed that most patients perceive physician attire as important, but that preferences for specific types of attire are contextual.^9,10 For example, elderly patients preferred physicians in formal attire and white coat, while scrubs with white coat or scrubs alone were preferred for emergency department (ED) physicians and surgeons, respectively. Moreover, regional variation regarding attire preference was also observed in the US, with preferences for more formal attire in the South and less formal in the Midwest.

Geographic variation, regarding patient preferences for physician dress, is perhaps even more relevant internationally. In particular, Japan is considered to have a highly contextualized culture that relies on nonverbal and implicit communication. However, medical professionals have no specific dress code and, thus, don many different kinds of attire. In part, this may be because it is not clear whether or how physician attire impacts patient satisfaction and perceived healthcare quality in Japan.^11-13 Although previous studies in Japan have suggested that physician attire has a considerable influence on patient satisfaction, these studies either involved a single department in one hospital or a small number of respondents.^14-17 Therefore, we performed a multicenter, cross-sectional study to understand patients’ preferences for physician attire in different clinical settings and in different geographic regions in Japan.

METHODS

Study Population

We conducted a cross-sectional, questionnaire-based study from 2015 to 2017, in four geographically diverse hospitals in Japan. Two of these hospitals, Tokyo Joto Hospital and Juntendo University Hospital, are located in eastern Japan whereas the others, Kurashiki Central Hospital and Akashi Medical Center, are in western Japan.

 

Questionnaires were printed and randomly distributed by research staff to outpatients in waiting rooms and inpatients in medical wards who were 20 years of age or older. We placed no restriction on ward site or time of questionnaire distribution. Research staff, including physicians, nurses, and medical clerks, were instructed to avoid guiding or influencing participants’ responses. Informed consent was obtained by the staff; only those who provided informed consent participated in the study. Respondents could request assistance with form completion from persons accompanying them if they had difficulties, such as physical, visual, or hearing impairments. All responses were collected anonymously. The study was approved by the ethics committees of all four hospitals.

Questionnaire

We used a modified version of the survey instrument from a prior study.¹⁰ The first section of the survey showed photographs of either a male or female physician with 7 unique forms of attire, including casual, casual with white coat, scrubs, scrubs with white coat, formal, formal with white coat, and business suit (Figure 1). Given the Japanese context of this study, the language was translated to Japanese and photographs of physicians of Japanese descent were used. Photographs were taken with attention paid to achieving constant facial expressions on the physicians as well as in other visual cues (eg, lighting, background, pose). The physician’s gender and attire in the first photograph seen by each respondent were randomized to prevent bias in ordering, priming, and anchoring; all other sections of the survey were identical.

Respondents were first asked to rate the standalone, randomized physician photograph using a 1-10 scale across five domains (ie, how knowledgeable, trustworthy, caring, and approachable the physician appeared and how comfortable the physician’s appearance made the respondent feel), with a score of 10 representing the highest rating. Respondents were subsequently given 7 photographs of the same physician wearing various forms of attire. Questions were asked regarding preference of attire in varied clinical settings (ie, primary care, ED, hospital, surgery, overall preference). To identify the influence of and respondent preferences for physician dress and white coats, a Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree) was employed. The scale was trichotomized into “disagree” (1, 2), “neither agree nor disagree” (3), and “agree” (4, 5) for analysis. Demographic data, including age, gender, education level, nationality (Japanese or non-Japanese), and number of physicians seen in the past year were collected.

Outcomes and Sample Size Calculation

The primary outcome of attire preference was calculated as the mean composite score of the five individual rating domains (ie, knowledgeable, trustworthy, caring, approachable, and comfortable), with the highest score representing the most preferred form of attire. We also assessed variation in preferences for physician attire by respondent characteristics, such as age and gender.

Sample size estimation was based on previous survey methodology.¹⁰ The Likert scale range for identifying influence of and respondent preferences for physician dress and white coats was 1-5 (“strongly disagree” to “strongly agree”). The scale range for measuring preferences for the randomized attire photograph was 1-10. An assumption of normality was made regarding responses on the 1-10 scale. An estimated standard deviation of 2.2 was assumed, based on prior findings.¹⁰ Based on these assumptions and the inclusion of at least 816 respondents (assuming a two-sided alpha error of 0.05), we expected to have 90% capacity to detect differences for effect sizes of 0.50 on the 1-10 scale.

 

Statistical Analyses

Paper-based survey data were entered independently and in duplicate by the study team. Respondents were not required to answer all questions; therefore, the denominator for each question varied. Data were reported as mean and standard deviation (SD) or percentages, where appropriate. Differences in the mean composite rating scores were assessed using one-way ANOVA with the Tukey method for pairwise comparisons. Differences in proportions for categorical data were compared using the Z-test. Chi-squared tests were used for bivariate comparisons between respondent age, gender, and level of education and corresponding respondent preferences. All analyses were performed using Stata 14 MP/SE (Stata Corp., College Station, Texas, USA).

RESULTS

Characteristics of Participants

Between December 1, 2015 and October 30, 2017, a total of 2,020 surveys were completed by patients across four academic hospitals in Japan. Of those, 1,960 patients (97.0%) completed the survey in its entirety. Approximately half of the respondents were 65 years of age or older (49%), of female gender (52%), and reported receiving care in the outpatient setting (53%). Regarding use of healthcare, 91% had seen more than one physician in the year preceding the time of survey completion (Table 1).

Ratings of Physician Attire

Compared with all forms of attire depicted in the survey’s first standalone photograph, respondents rated “casual attire with white coat” the highest (Figure 2). The mean composite score for “casual attire with white coat” was 7.1 (standard deviation [SD] = 1.8), and this attire was set as the referent group. Cronbach’s alpha, for the five items included in the composite score, was 0.95. However, “formal attire with white coat” was rated almost as highly as “casual attire with white coat” with an overall mean composite score of 7.0 (SD = 1.6).

Variation in Preference for Physician Attire by Clinical Setting

Preferences for physician attire varied by clinical care setting. Most respondents preferred “casual attire with white coat” or “formal attire with white coat” in both primary care and hospital settings, with a slight preference for “casual attire with white coat.” In contrast, respondents preferred “scrubs without white coat” in the ED and surgical settings. When asked about their overall preference, respondents reported they felt their physician should wear “formal attire with white coat” (35%) or “casual attire with white coat” (30%; Table 2). When comparing the group of photographs of physicians with white coats to the group without white coats (Figure 1), respondents preferred physicians wearing white coats overall and specifically when providing care in primary care and hospital settings. However, they preferred physicians without white coats when providing care in the ED (P < .001). With respect to surgeons, there was no statistically significant difference between preference for white coats and no white coats. These results were similar for photographs of both male and female physicians.

When asked whether physician dress was important to them and if physician attire influenced their satisfaction with the care received, 61% of participants agreed that physician dress was important, and 47% agreed that physician attire influenced satisfaction (Appendix Table 1). With respect to appropriateness of physicians dressing casually over the weekend in clinical settings, 52% responded that casual wear was inappropriate, while 31% had a neutral opinion.

Participants were asked whether physicians should wear a white coat in different clinical settings. Nearly two-thirds indicated a preference for white coats in the office and hospital (65% and 64%, respectively). Responses regarding whether emergency physicians should wear white coats were nearly equally divided (Agree, 37%; Disagree, 32%; Neither Agree nor Disagree, 31%). However, “scrubs without white coat” was most preferred (56%) when patients were given photographs of various attire and asked, “Which physician would you prefer to see when visiting the ER?” Responses to the question “Physicians should always wear a white coat when seeing patients in any setting” varied equally (Agree, 32%; Disagree, 34%; Neither Agree nor Disagree, 34%).

 

Variation in Preference for Physician Attire by Respondent Demographics

When comparing respondents by age, those 65 years or older preferred “formal attire with white coat” more so than respondents younger than 65 years (Appendix Table 2). This finding was identified in both primary care (36% vs 31%, P < .001) and hospital settings (37% vs 30%, P < .001). Additionally, physician attire had a greater impact on older respondents’ satisfaction and experience (Appendix Table 3). For example, 67% of respondents 65 years and older agreed that physician attire was important, and 54% agreed that attire influenced satisfaction. Conversely, for respondents younger than 65 years, the proportion agreeing with these statements was lower (56% and 41%, both P < .001). When comparing older and younger respondents, those 65 years and older more often preferred physicians wearing white coats in any setting (39% vs 26%, P < .001) and specifically in their office (68% vs 61%, P = .002), the ED (40% vs 34%, P < .001), and the hospital (69% vs 60%, P < .001).

When comparing male and female respondents, male respondents more often stated that physician dress was important to them (men, 64%; women, 58%; P = .002). When comparing responses to the question “Overall, which clothes do you feel a doctor should wear?”, between the eastern and western Japanese hospitals, preferences for physician attire varied.

Variation in Expectations Between Male and Female Physicians

When comparing the ratings of male and female physicians, female physicians were rated higher in how caring (P = .005) and approachable (P < .001) they appeared. However, there were no significant differences in the ratings of the three remaining domains (ie, knowledgeable, trustworthy, and comfortable) or the composite score.

DISCUSSION

This report is the first multicenter Japanese study to examine patients’ preferences for physician attire. Most Japanese respondents perceived that physician dress is important, and nearly half agreed that physician dress influences their satisfaction with care. Overall, “casual attire with white coat” and “formal attire with white coat” tended to be the preferred option for respondents; however, this varied widely across context of care delivery. “Scrubs without white coat” was the preferred attire for physicians in the ED and surgery department. Elderly patients preferred physicians in formal attire regardless of where care was being received. Collectively, these findings have important implications for how delivery of care in Japan is approached.

Since we employed the same methodology as previous studies conducted in the US¹⁰ and Switzerland,¹⁸ a notable strength of our approach is that comparisons among these countries can be drawn. For example, physician attire appears to hold greater importance in Japan than in the US and Switzerland. Among Japanese participants, 61% agreed that physician dress is important (US, 53%; Switzerland, 36%), and 47% agreed that physician dress influenced how satisfied they were with their care (US, 36%; Switzerland, 23%).¹⁰ This result supports the notion that nonverbal and implicit communications (such as physician dress) may carry more importance among Japanese people.^11-13

Regarding preference ratings for type of dress among respondents in Japan, “casual attire with white coat” received the highest mean composite score rating, with “formal attire with white coat” rated second overall. In contrast, US respondents rated “formal attire with white coat” highest and “scrubs with white coat” second.¹⁰ Our result runs counter to our expectation in that we expected Japanese respondents to prefer formal attire, since Japan is one of the most formal cultures in the world. One potential explanation for this difference is that the casual style chosen for this study was close to the smart casual style (slightly casual). Most hospitals and clinics in Japan do not allow physicians to wear jeans or polo shirts, which were chosen as the casual attire in the previous US study.

When examining various care settings and physician types, both Japanese and US respondents were more likely to prefer physicians wearing a white coat in the office or hospital.¹⁰ However, Japanese participants preferred both “casual attire with white coat” and “formal attire with white coat” equally in primary care or hospital settings. A smaller proportion of US respondents preferred “casual attire with white coat” in primary care (11%) and hospital settings (9%), but more preferred “formal attire with white coat” for primary care (44%) and hospital physicians (39%). In the ED setting, 32% of participants in Japan and 18% in the US disagreed with the idea that physicians should wear a white coat. Among Japanese participants, “scrubs without white coat” was rated highest for emergency physicians (56%) and surgeons (47%), while US preferences were 40% and 42%, respectively.¹⁰ One potential explanation is that scrubs-based attire became popular among Japanese ED and surgical contexts as a result of cultural influence and spread from western countries.^{19, 20}

With respect to perceptions regarding physician attire on weekends, 52% of participants considered it inappropriate for a physician to dress casually over the weekend, compared with only 30% in Switzerland and 21% in the US.^11,12 Given Japan’s level of formality and the fact that most Japanese physicians continue to work over the weekend,^21-23 Japanese patients tend to expect their physicians to dress in more formal attire during these times.

Previous studies in Japan have demonstrated that older patients gave low ratings to scrubs and high ratings to white coat with any attire,^15,17 and this was also the case in our study. Perhaps elderly patients reflect conservative values in their preferences of physician dress. Their perceptions may be less influenced by scenes portraying physicians in popular media when compared with the perceptions of younger patients. Though a 2015 systematic review and studies in other countries revealed white coats were preferred regardless of exact dress,^9,24-26 they also showed variation in preferences for physician attire. For example, patients in Saudi Arabia preferred white coat and traditional ethnic dress,²⁵ whereas mothers of pediatric patients in Saudi Arabia preferred scrubs for their pediatricians.²⁷ Therefore, it is recommended for internationally mobile physicians to choose their dress depending on a variety of factors including country, context, and patient age group.

Our study has limitations. First, because some physicians presented the surveys to the patients, participants may have responded differently. Second, participants may have identified photographs of the male physician model as their personal healthcare provider (one author, K.K.). To avoid this possible bias, we randomly distributed 14 different versions of physician photographs in the questionnaire. Third, although physician photographs were strictly controlled, the “formal attire and white coat” and “casual attire and white coat” photographs appeared similar, especially given that the white coats were buttoned. Also, the female physician depicted in the photographs did not have the scrub shirt tucked in, while the male physician did. These nuances may have affected participant ratings between groups. Fourth, we did not blind researchers or data collectors in the process of data collection and entry. Fifth, we asked participants to indicate their age using categories. The age group “35-54 years” covered a wide range of patients, and we may have obtained more granular detail if we had chosen different age groups. Sixth, our cohort included a higher proportion of older people who needed medical treatment for their comorbidities and who had not received high levels of education. This resulted in a seemingly high proportion of lower education levels in our cohort. Lastly, patient experience and satisfaction can be comprised not only by physician attire, but also physician behavior and attitude, which this survey could not elicit. Thus, additional studies are needed to identify and quantify all determinants of patient experience with their physicians.

In conclusion, patient preferences for physician attire were examined using a multicenter survey with a large sample size and robust survey methodology, thus overcoming weaknesses of previous studies into Japanese attire. Japanese patients perceive that physician attire is important and influences satisfaction with their care, more so than patients in other countries, like the US and Switzerland. Geography, settings of care, and patient age play a role in preferences. As a result, hospitals and health systems may use these findings to inform dress code policy based on patient population and context, recognizing that the appearance of their providers affects the patient-physician relationship. Future research should focus on better understanding the various cultural and societal customs that lead to patient expectations of physician attire.

 

Acknowledgments

The authors thank Drs. Fumi Takemoto, Masayuki Ueno, Kazuya Sakai, Saori Kinami, and Toshio Naito for their assistance with data collection at their respective sites. Additionally, the authors thank Dr. Yoko Kanamitsu for serving as a model for photographs.

The patient-physician relationship is critical for ensuring the delivery of high-quality healthcare. Successful patient-physician relationships arise from shared trust, knowledge, mutual respect, and effective verbal and nonverbal communication. The ways in which patients experience healthcare and their satisfaction with physicians affect a myriad of important health outcomes, such as adherence to treatment and outcomes for conditions such as hypertension and diabetes mellitus.^1-5 One method for potentially enhancing patient satisfaction is through understanding how patients wish their physicians to dress^6-8 and tailoring attire to match these expectations. In addition to our systematic review,⁹ a recent large-scale, multicenter study in the United States revealed that most patients perceive physician attire as important, but that preferences for specific types of attire are contextual.^9,10 For example, elderly patients preferred physicians in formal attire and white coat, while scrubs with white coat or scrubs alone were preferred for emergency department (ED) physicians and surgeons, respectively. Moreover, regional variation regarding attire preference was also observed in the US, with preferences for more formal attire in the South and less formal in the Midwest.

Geographic variation, regarding patient preferences for physician dress, is perhaps even more relevant internationally. In particular, Japan is considered to have a highly contextualized culture that relies on nonverbal and implicit communication. However, medical professionals have no specific dress code and, thus, don many different kinds of attire. In part, this may be because it is not clear whether or how physician attire impacts patient satisfaction and perceived healthcare quality in Japan.^11-13 Although previous studies in Japan have suggested that physician attire has a considerable influence on patient satisfaction, these studies either involved a single department in one hospital or a small number of respondents.^14-17 Therefore, we performed a multicenter, cross-sectional study to understand patients’ preferences for physician attire in different clinical settings and in different geographic regions in Japan.

METHODS

Study Population

We conducted a cross-sectional, questionnaire-based study from 2015 to 2017, in four geographically diverse hospitals in Japan. Two of these hospitals, Tokyo Joto Hospital and Juntendo University Hospital, are located in eastern Japan whereas the others, Kurashiki Central Hospital and Akashi Medical Center, are in western Japan.

 

Questionnaires were printed and randomly distributed by research staff to outpatients in waiting rooms and inpatients in medical wards who were 20 years of age or older. We placed no restriction on ward site or time of questionnaire distribution. Research staff, including physicians, nurses, and medical clerks, were instructed to avoid guiding or influencing participants’ responses. Informed consent was obtained by the staff; only those who provided informed consent participated in the study. Respondents could request assistance with form completion from persons accompanying them if they had difficulties, such as physical, visual, or hearing impairments. All responses were collected anonymously. The study was approved by the ethics committees of all four hospitals.

Questionnaire

We used a modified version of the survey instrument from a prior study.¹⁰ The first section of the survey showed photographs of either a male or female physician with 7 unique forms of attire, including casual, casual with white coat, scrubs, scrubs with white coat, formal, formal with white coat, and business suit (Figure 1). Given the Japanese context of this study, the language was translated to Japanese and photographs of physicians of Japanese descent were used. Photographs were taken with attention paid to achieving constant facial expressions on the physicians as well as in other visual cues (eg, lighting, background, pose). The physician’s gender and attire in the first photograph seen by each respondent were randomized to prevent bias in ordering, priming, and anchoring; all other sections of the survey were identical.

Respondents were first asked to rate the standalone, randomized physician photograph using a 1-10 scale across five domains (ie, how knowledgeable, trustworthy, caring, and approachable the physician appeared and how comfortable the physician’s appearance made the respondent feel), with a score of 10 representing the highest rating. Respondents were subsequently given 7 photographs of the same physician wearing various forms of attire. Questions were asked regarding preference of attire in varied clinical settings (ie, primary care, ED, hospital, surgery, overall preference). To identify the influence of and respondent preferences for physician dress and white coats, a Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree) was employed. The scale was trichotomized into “disagree” (1, 2), “neither agree nor disagree” (3), and “agree” (4, 5) for analysis. Demographic data, including age, gender, education level, nationality (Japanese or non-Japanese), and number of physicians seen in the past year were collected.

Outcomes and Sample Size Calculation

The primary outcome of attire preference was calculated as the mean composite score of the five individual rating domains (ie, knowledgeable, trustworthy, caring, approachable, and comfortable), with the highest score representing the most preferred form of attire. We also assessed variation in preferences for physician attire by respondent characteristics, such as age and gender.

Sample size estimation was based on previous survey methodology.¹⁰ The Likert scale range for identifying influence of and respondent preferences for physician dress and white coats was 1-5 (“strongly disagree” to “strongly agree”). The scale range for measuring preferences for the randomized attire photograph was 1-10. An assumption of normality was made regarding responses on the 1-10 scale. An estimated standard deviation of 2.2 was assumed, based on prior findings.¹⁰ Based on these assumptions and the inclusion of at least 816 respondents (assuming a two-sided alpha error of 0.05), we expected to have 90% capacity to detect differences for effect sizes of 0.50 on the 1-10 scale.

 

Statistical Analyses

Paper-based survey data were entered independently and in duplicate by the study team. Respondents were not required to answer all questions; therefore, the denominator for each question varied. Data were reported as mean and standard deviation (SD) or percentages, where appropriate. Differences in the mean composite rating scores were assessed using one-way ANOVA with the Tukey method for pairwise comparisons. Differences in proportions for categorical data were compared using the Z-test. Chi-squared tests were used for bivariate comparisons between respondent age, gender, and level of education and corresponding respondent preferences. All analyses were performed using Stata 14 MP/SE (Stata Corp., College Station, Texas, USA).

RESULTS

Characteristics of Participants

Between December 1, 2015 and October 30, 2017, a total of 2,020 surveys were completed by patients across four academic hospitals in Japan. Of those, 1,960 patients (97.0%) completed the survey in its entirety. Approximately half of the respondents were 65 years of age or older (49%), of female gender (52%), and reported receiving care in the outpatient setting (53%). Regarding use of healthcare, 91% had seen more than one physician in the year preceding the time of survey completion (Table 1).

Ratings of Physician Attire

Compared with all forms of attire depicted in the survey’s first standalone photograph, respondents rated “casual attire with white coat” the highest (Figure 2). The mean composite score for “casual attire with white coat” was 7.1 (standard deviation [SD] = 1.8), and this attire was set as the referent group. Cronbach’s alpha, for the five items included in the composite score, was 0.95. However, “formal attire with white coat” was rated almost as highly as “casual attire with white coat” with an overall mean composite score of 7.0 (SD = 1.6).

Variation in Preference for Physician Attire by Clinical Setting

Preferences for physician attire varied by clinical care setting. Most respondents preferred “casual attire with white coat” or “formal attire with white coat” in both primary care and hospital settings, with a slight preference for “casual attire with white coat.” In contrast, respondents preferred “scrubs without white coat” in the ED and surgical settings. When asked about their overall preference, respondents reported they felt their physician should wear “formal attire with white coat” (35%) or “casual attire with white coat” (30%; Table 2). When comparing the group of photographs of physicians with white coats to the group without white coats (Figure 1), respondents preferred physicians wearing white coats overall and specifically when providing care in primary care and hospital settings. However, they preferred physicians without white coats when providing care in the ED (P < .001). With respect to surgeons, there was no statistically significant difference between preference for white coats and no white coats. These results were similar for photographs of both male and female physicians.

When asked whether physician dress was important to them and if physician attire influenced their satisfaction with the care received, 61% of participants agreed that physician dress was important, and 47% agreed that physician attire influenced satisfaction (Appendix Table 1). With respect to appropriateness of physicians dressing casually over the weekend in clinical settings, 52% responded that casual wear was inappropriate, while 31% had a neutral opinion.

Participants were asked whether physicians should wear a white coat in different clinical settings. Nearly two-thirds indicated a preference for white coats in the office and hospital (65% and 64%, respectively). Responses regarding whether emergency physicians should wear white coats were nearly equally divided (Agree, 37%; Disagree, 32%; Neither Agree nor Disagree, 31%). However, “scrubs without white coat” was most preferred (56%) when patients were given photographs of various attire and asked, “Which physician would you prefer to see when visiting the ER?” Responses to the question “Physicians should always wear a white coat when seeing patients in any setting” varied equally (Agree, 32%; Disagree, 34%; Neither Agree nor Disagree, 34%).

 

Variation in Preference for Physician Attire by Respondent Demographics

When comparing respondents by age, those 65 years or older preferred “formal attire with white coat” more so than respondents younger than 65 years (Appendix Table 2). This finding was identified in both primary care (36% vs 31%, P < .001) and hospital settings (37% vs 30%, P < .001). Additionally, physician attire had a greater impact on older respondents’ satisfaction and experience (Appendix Table 3). For example, 67% of respondents 65 years and older agreed that physician attire was important, and 54% agreed that attire influenced satisfaction. Conversely, for respondents younger than 65 years, the proportion agreeing with these statements was lower (56% and 41%, both P < .001). When comparing older and younger respondents, those 65 years and older more often preferred physicians wearing white coats in any setting (39% vs 26%, P < .001) and specifically in their office (68% vs 61%, P = .002), the ED (40% vs 34%, P < .001), and the hospital (69% vs 60%, P < .001).

When comparing male and female respondents, male respondents more often stated that physician dress was important to them (men, 64%; women, 58%; P = .002). When comparing responses to the question “Overall, which clothes do you feel a doctor should wear?”, between the eastern and western Japanese hospitals, preferences for physician attire varied.

Variation in Expectations Between Male and Female Physicians

When comparing the ratings of male and female physicians, female physicians were rated higher in how caring (P = .005) and approachable (P < .001) they appeared. However, there were no significant differences in the ratings of the three remaining domains (ie, knowledgeable, trustworthy, and comfortable) or the composite score.

DISCUSSION

This report is the first multicenter Japanese study to examine patients’ preferences for physician attire. Most Japanese respondents perceived that physician dress is important, and nearly half agreed that physician dress influences their satisfaction with care. Overall, “casual attire with white coat” and “formal attire with white coat” tended to be the preferred option for respondents; however, this varied widely across context of care delivery. “Scrubs without white coat” was the preferred attire for physicians in the ED and surgery department. Elderly patients preferred physicians in formal attire regardless of where care was being received. Collectively, these findings have important implications for how delivery of care in Japan is approached.

Since we employed the same methodology as previous studies conducted in the US¹⁰ and Switzerland,¹⁸ a notable strength of our approach is that comparisons among these countries can be drawn. For example, physician attire appears to hold greater importance in Japan than in the US and Switzerland. Among Japanese participants, 61% agreed that physician dress is important (US, 53%; Switzerland, 36%), and 47% agreed that physician dress influenced how satisfied they were with their care (US, 36%; Switzerland, 23%).¹⁰ This result supports the notion that nonverbal and implicit communications (such as physician dress) may carry more importance among Japanese people.^11-13

Regarding preference ratings for type of dress among respondents in Japan, “casual attire with white coat” received the highest mean composite score rating, with “formal attire with white coat” rated second overall. In contrast, US respondents rated “formal attire with white coat” highest and “scrubs with white coat” second.¹⁰ Our result runs counter to our expectation in that we expected Japanese respondents to prefer formal attire, since Japan is one of the most formal cultures in the world. One potential explanation for this difference is that the casual style chosen for this study was close to the smart casual style (slightly casual). Most hospitals and clinics in Japan do not allow physicians to wear jeans or polo shirts, which were chosen as the casual attire in the previous US study.

When examining various care settings and physician types, both Japanese and US respondents were more likely to prefer physicians wearing a white coat in the office or hospital.¹⁰ However, Japanese participants preferred both “casual attire with white coat” and “formal attire with white coat” equally in primary care or hospital settings. A smaller proportion of US respondents preferred “casual attire with white coat” in primary care (11%) and hospital settings (9%), but more preferred “formal attire with white coat” for primary care (44%) and hospital physicians (39%). In the ED setting, 32% of participants in Japan and 18% in the US disagreed with the idea that physicians should wear a white coat. Among Japanese participants, “scrubs without white coat” was rated highest for emergency physicians (56%) and surgeons (47%), while US preferences were 40% and 42%, respectively.¹⁰ One potential explanation is that scrubs-based attire became popular among Japanese ED and surgical contexts as a result of cultural influence and spread from western countries.^{19, 20}

With respect to perceptions regarding physician attire on weekends, 52% of participants considered it inappropriate for a physician to dress casually over the weekend, compared with only 30% in Switzerland and 21% in the US.^11,12 Given Japan’s level of formality and the fact that most Japanese physicians continue to work over the weekend,^21-23 Japanese patients tend to expect their physicians to dress in more formal attire during these times.

Previous studies in Japan have demonstrated that older patients gave low ratings to scrubs and high ratings to white coat with any attire,^15,17 and this was also the case in our study. Perhaps elderly patients reflect conservative values in their preferences of physician dress. Their perceptions may be less influenced by scenes portraying physicians in popular media when compared with the perceptions of younger patients. Though a 2015 systematic review and studies in other countries revealed white coats were preferred regardless of exact dress,^9,24-26 they also showed variation in preferences for physician attire. For example, patients in Saudi Arabia preferred white coat and traditional ethnic dress,²⁵ whereas mothers of pediatric patients in Saudi Arabia preferred scrubs for their pediatricians.²⁷ Therefore, it is recommended for internationally mobile physicians to choose their dress depending on a variety of factors including country, context, and patient age group.

Our study has limitations. First, because some physicians presented the surveys to the patients, participants may have responded differently. Second, participants may have identified photographs of the male physician model as their personal healthcare provider (one author, K.K.). To avoid this possible bias, we randomly distributed 14 different versions of physician photographs in the questionnaire. Third, although physician photographs were strictly controlled, the “formal attire and white coat” and “casual attire and white coat” photographs appeared similar, especially given that the white coats were buttoned. Also, the female physician depicted in the photographs did not have the scrub shirt tucked in, while the male physician did. These nuances may have affected participant ratings between groups. Fourth, we did not blind researchers or data collectors in the process of data collection and entry. Fifth, we asked participants to indicate their age using categories. The age group “35-54 years” covered a wide range of patients, and we may have obtained more granular detail if we had chosen different age groups. Sixth, our cohort included a higher proportion of older people who needed medical treatment for their comorbidities and who had not received high levels of education. This resulted in a seemingly high proportion of lower education levels in our cohort. Lastly, patient experience and satisfaction can be comprised not only by physician attire, but also physician behavior and attitude, which this survey could not elicit. Thus, additional studies are needed to identify and quantify all determinants of patient experience with their physicians.

In conclusion, patient preferences for physician attire were examined using a multicenter survey with a large sample size and robust survey methodology, thus overcoming weaknesses of previous studies into Japanese attire. Japanese patients perceive that physician attire is important and influences satisfaction with their care, more so than patients in other countries, like the US and Switzerland. Geography, settings of care, and patient age play a role in preferences. As a result, hospitals and health systems may use these findings to inform dress code policy based on patient population and context, recognizing that the appearance of their providers affects the patient-physician relationship. Future research should focus on better understanding the various cultural and societal customs that lead to patient expectations of physician attire.

 

Acknowledgments

The authors thank Drs. Fumi Takemoto, Masayuki Ueno, Kazuya Sakai, Saori Kinami, and Toshio Naito for their assistance with data collection at their respective sites. Additionally, the authors thank Dr. Yoko Kanamitsu for serving as a model for photographs.