Bipolar Disorder

Women with bipolar disorder were three times more likely than were healthy controls to experience polycystic ovarian syndrome, based on data from nearly 250 individuals.

Previous studies suggest that the prevalence of polycystic ovarian syndrome (PCOS) is higher in bipolar disorder (BD) patients compared with individuals not diagnosed with BD, wrote Jieyu Liu, PhD, of the Second Xiangya Hospital of Central South University, Hunan, China, and colleagues.

However, studies have been limited to drug-treated BD patients, and data on the effects of BD on the development of PCOS are limited, they said. Data from previous studies also indicate that serum testosterone levels, serum androstenedione levels, and polycystic ovarian morphology (PCOM) are increased in BD patients compared with women without BD.

In a study published in the Journal of Affective Disorders, the researchers recruited 72 BD patients on long-term medication, 72 drug-naive patients, and 98 healthy controls between March 2022 and November 2022.

PCOM was assessed using ≥ 8 MHz transvaginal transducers to determine the number of follicles and ovarian volume. PCOS was then defined using the Rotterdam criteria, in which patients met two of three qualifications: oligoovulation or anovulation; hyperandrogenemia; or PCOM (excluding other endocrine diseases).

In a multivariate analysis, drug-naive women with BD had significantly higher rates of PCOS compared with healthy controls (odds ratio 3.02). The drug-naive BD patients also had a greater prevalence of oligoamenorrhea compared with healthy controls (36.36% vs. 12.12%) and higher levels of anti-mullerian hormone, luteinizing hormone, and follicle stimulating hormone compared to the controls.

A further regression analysis showed that those on long-term valproate treatment had the highest risk (OR 3.89) and the prevalence of PCOS was significantly higher among patients treated with valproate compared with drug-naive patients (53.3% vs. 30.6%). Younger age and the presence of insulin resistance also were associated with increased risk of PCOS (OR 0.37 and OR 1.73, respectively).

“Unexpectedly, no significant differences in serum androgen levels, including TT, FAI, androstenedione, and [dehydroepiandrosterone sulfate] levels, were observed between drug-naive BD patients and the HCs,” the researchers wrote in their discussion. This difference may stem from multiple causes including demographic variables, inclusion of PCOM as a diagnostic criterion, and the impact of genetic and environmental factors, they said.

The findings were limited by several factors including the small study population, which prevented conclusions of causality and comparison of the effects of different mood stabilizers on PCOS, the researchers noted. Other limitations included the relatively homogeneous population from a single region in China, and the inability to account for the effects of diet and lifestyle.

More research is needed to explore the impact of mediations, but the results suggest that BD patients are susceptible to PCOS; therefore, they should evaluate their reproductive health before starting any medication, and review reproductive health regularly, the researchers concluded.

The study was supported by the National Natural Science Foundation of China. The researchers had no financial conflicts to disclose.

Women with bipolar disorder were three times more likely than were healthy controls to experience polycystic ovarian syndrome, based on data from nearly 250 individuals.

Previous studies suggest that the prevalence of polycystic ovarian syndrome (PCOS) is higher in bipolar disorder (BD) patients compared with individuals not diagnosed with BD, wrote Jieyu Liu, PhD, of the Second Xiangya Hospital of Central South University, Hunan, China, and colleagues.

However, studies have been limited to drug-treated BD patients, and data on the effects of BD on the development of PCOS are limited, they said. Data from previous studies also indicate that serum testosterone levels, serum androstenedione levels, and polycystic ovarian morphology (PCOM) are increased in BD patients compared with women without BD.

In a study published in the Journal of Affective Disorders, the researchers recruited 72 BD patients on long-term medication, 72 drug-naive patients, and 98 healthy controls between March 2022 and November 2022.

PCOM was assessed using ≥ 8 MHz transvaginal transducers to determine the number of follicles and ovarian volume. PCOS was then defined using the Rotterdam criteria, in which patients met two of three qualifications: oligoovulation or anovulation; hyperandrogenemia; or PCOM (excluding other endocrine diseases).

In a multivariate analysis, drug-naive women with BD had significantly higher rates of PCOS compared with healthy controls (odds ratio 3.02). The drug-naive BD patients also had a greater prevalence of oligoamenorrhea compared with healthy controls (36.36% vs. 12.12%) and higher levels of anti-mullerian hormone, luteinizing hormone, and follicle stimulating hormone compared to the controls.

A further regression analysis showed that those on long-term valproate treatment had the highest risk (OR 3.89) and the prevalence of PCOS was significantly higher among patients treated with valproate compared with drug-naive patients (53.3% vs. 30.6%). Younger age and the presence of insulin resistance also were associated with increased risk of PCOS (OR 0.37 and OR 1.73, respectively).

“Unexpectedly, no significant differences in serum androgen levels, including TT, FAI, androstenedione, and [dehydroepiandrosterone sulfate] levels, were observed between drug-naive BD patients and the HCs,” the researchers wrote in their discussion. This difference may stem from multiple causes including demographic variables, inclusion of PCOM as a diagnostic criterion, and the impact of genetic and environmental factors, they said.

The findings were limited by several factors including the small study population, which prevented conclusions of causality and comparison of the effects of different mood stabilizers on PCOS, the researchers noted. Other limitations included the relatively homogeneous population from a single region in China, and the inability to account for the effects of diet and lifestyle.

More research is needed to explore the impact of mediations, but the results suggest that BD patients are susceptible to PCOS; therefore, they should evaluate their reproductive health before starting any medication, and review reproductive health regularly, the researchers concluded.

The study was supported by the National Natural Science Foundation of China. The researchers had no financial conflicts to disclose.

Women with bipolar disorder were three times more likely than were healthy controls to experience polycystic ovarian syndrome, based on data from nearly 250 individuals.

Previous studies suggest that the prevalence of polycystic ovarian syndrome (PCOS) is higher in bipolar disorder (BD) patients compared with individuals not diagnosed with BD, wrote Jieyu Liu, PhD, of the Second Xiangya Hospital of Central South University, Hunan, China, and colleagues.

However, studies have been limited to drug-treated BD patients, and data on the effects of BD on the development of PCOS are limited, they said. Data from previous studies also indicate that serum testosterone levels, serum androstenedione levels, and polycystic ovarian morphology (PCOM) are increased in BD patients compared with women without BD.

In a study published in the Journal of Affective Disorders, the researchers recruited 72 BD patients on long-term medication, 72 drug-naive patients, and 98 healthy controls between March 2022 and November 2022.

PCOM was assessed using ≥ 8 MHz transvaginal transducers to determine the number of follicles and ovarian volume. PCOS was then defined using the Rotterdam criteria, in which patients met two of three qualifications: oligoovulation or anovulation; hyperandrogenemia; or PCOM (excluding other endocrine diseases).

In a multivariate analysis, drug-naive women with BD had significantly higher rates of PCOS compared with healthy controls (odds ratio 3.02). The drug-naive BD patients also had a greater prevalence of oligoamenorrhea compared with healthy controls (36.36% vs. 12.12%) and higher levels of anti-mullerian hormone, luteinizing hormone, and follicle stimulating hormone compared to the controls.

A further regression analysis showed that those on long-term valproate treatment had the highest risk (OR 3.89) and the prevalence of PCOS was significantly higher among patients treated with valproate compared with drug-naive patients (53.3% vs. 30.6%). Younger age and the presence of insulin resistance also were associated with increased risk of PCOS (OR 0.37 and OR 1.73, respectively).

“Unexpectedly, no significant differences in serum androgen levels, including TT, FAI, androstenedione, and [dehydroepiandrosterone sulfate] levels, were observed between drug-naive BD patients and the HCs,” the researchers wrote in their discussion. This difference may stem from multiple causes including demographic variables, inclusion of PCOM as a diagnostic criterion, and the impact of genetic and environmental factors, they said.

The findings were limited by several factors including the small study population, which prevented conclusions of causality and comparison of the effects of different mood stabilizers on PCOS, the researchers noted. Other limitations included the relatively homogeneous population from a single region in China, and the inability to account for the effects of diet and lifestyle.

More research is needed to explore the impact of mediations, but the results suggest that BD patients are susceptible to PCOS; therefore, they should evaluate their reproductive health before starting any medication, and review reproductive health regularly, the researchers concluded.

The study was supported by the National Natural Science Foundation of China. The researchers had no financial conflicts to disclose.

Mental health conditions are the leading cause of pregnancy-related death in Illinois (40%) and across the United States (21%).^1,2There is increasing recognition in primary care that major depressive disorder (MDD) often co-occurs with other mental health conditions. Funding bodies, such as the Agency for Healthcare Research and Quality³ and the Health Resources and Service Administration,⁴ have spotlights on improving screening and access to care for depression and substance use disorders (SUDs). However, the needs of individuals with multiple mental health conditions still often go unrecognized and unaddressed in perinatal health settings.

The U.S. Preventive Services Task Force recommends that all adults be screened for depression, alcohol use, and drug use, and will be recommending screening for anxiety.^5,6 The American College of Obstetrics and Gynecology recommends screening for perinatal mental health conditions including depression, anxiety, bipolar disorder, acute postpartum psychosis, and suicidality; however, despite these recommendations, screening and treatment for comorbid mental health disorders during pregnancy and the postpartum is not standard practice.⁷

Addressing perinatal mental health is critical because untreated mental health conditions during the perinatal period can cause long-term adverse psychiatric and medical outcomes for the birthing person, the baby, and the family.⁸ This commentary highlights the importance of recognizing and screening for perinatal mental health comorbidities, improving referral rates for mental health treatment, and raising awareness of the importance of addressing rural perinatal mental health.
 

Perinatal mental health comorbidities

Major depressive disorder is the most common mental health condition during the perinatal period⁹ and is often comorbid.^10-12 In “Perinatal mental health in low-income urban and rural patients: The importance of screening for comorbidities,” Craemer et al.¹³ reported that nearly half of the perinatal patients who screened positive for MDD also screened positive for at least one other mental health condition, among them general anxiety disorder (GAD), SUD, posttraumatic stress disorder (PTSD), and suicidality.

Many (9%) of the perinatal patients with MDD had a severe comorbidity profile characterized by four diagnoses – MDD, GAD, SUD, and PTSD. In routine medical care these comorbidities often go undetected even though the risk to mothers and babies increases with more severe mental health symptoms.⁸

The high frequency of perinatal mental health comorbidities Craemer et al.¹³ found demonstrates a compelling need for comorbid mental health screening during the perinatal period, particularly for low-income Black, Hispanic, and rural birthing persons. Positive screens for perinatal mental health disorders may reflect the onset of these disorders in pregnancy or the postpartum, or preexisting disorders that have gone undetected or untreated before pregnancy.

For many patients, the perinatal period is the first time they are screened for any mental health disorder; typically, they are screened solely for depression. Screening alone can have a positive impact on perinatal mental health. In fact, the USPSTF found that programs to screen perinatal patients, with or without treatment-related support, resulted in a 2%-9% absolute reduction in depression prevalence.¹⁴ However, screening for MDD is too infrequent for many reasons, including the logistics of integrating screening into the clinic workflow and limited provider availability, time, and training in mental health.

We recommend screening perinatal patients for mental health comorbidities. This recommendation may seem impractical given the lack of screening tools for comorbid mental health conditions; however, the Computerized Adaptive Test for Mental Health (CAT-MH), the validated tool^15-17 used in this study, is an ideal option. CAT-MH is uniquely capable of screening for MDD, GAD, PTSD, SUD, and suicidality in one platform and is routinely used in diverse settings including the Veterans Administration,¹⁸ foster care,¹⁹ and universities.²⁰ The main limitation of this more comprehensive screening is that it takes about 10 minutes per patient. However, CAT-MH is self-administered and can be done in the waiting room or on a mobile device prior to a clinic visit.

CAT-MH can also be easily integrated into clinical workflow when added to the Electronic Medical Record²¹, and is a more comprehensive tool than existing perinatal depression tools such as the Perinatal Health Questionaire-9 (PHQ-9) and Edinburgh Perinatal Depression Scale (EPDS).²² Another limitation is cost – currently $5.00 per assessment – however, this is less than routine blood work.²³ If CAT-MH is not an option, we recommend a stepped approach of screening for GAD when perinatal patients screen positive for MDD, as this is the most common comorbidity profile. The GAD-7 is a free and widely available tool.²⁴

Barriers to care

In Craemer et al,¹³ nearly two-thirds (64.9%) of perinatal patients with a positive screen did not receive a referral to follow-up care or a medication prescription. These low referral rates may reflect a variety of widely recognized barriers to care, including lack of referral options, provider and/or patient reluctance to pursue referrals, barriers to insurance coverage, or inadequate behavioral health infrastructure to ensure referral and diagnostic follow-up.

Further, rural residing perinatal patients are an underserved population that need more resources and screening. Despite an on-site behavioral specialist at the rural clinic, Craemer et al¹³ found a stark disparity in referral rates: referrals to treatment for a positive diagnosis was over two times less at the rural clinic (23.9%), compared with the urban clinics (51.6%). The most common treatment offered at the rural clinic was a prescription for medication (17.4%), while referral to follow-up care was the most common at the urban clinics (35.5%). Rural areas not only have a shortage of health care providers, but community members seeking mental health care often encounter greater stigma, compared with urban residents.^25,26

These data highlight an unmet need for referrals to treatment for patients in rural communities, particularly in Illinois where the pregnancy-related mortality ratio attributable to mental health conditions is three times greater in rural areas, compared with those residing in urban Cook County (Chicago).² Increasing access and availability to mental health treatment and prevention resources in Illinois, especially in rural areas, is an opportunity to prevent pregnancy-related mortality attributable to mental health conditions.

Overall, there is a critical need for screening for perinatal mental health comorbidities, increased attention to low rates of referral to mental health treatment, and investing in rural perinatal mental health. Addressing perinatal mental health disorders is key to decreasing the burden of maternal mortality, particularly in Illinois.

Ms. Craemer and Ms. Sayah are senior research specialists at the Center for Research on Women & Gender, University of Illinois at Chicago. Dr. Duffecy is a professor of clinical psychiatry at the University of Illinois at Chicago. Dr. Geller is a professor of obstetrics & gynecology and director of the Center for Research on Women & Gender, University of Illinois at Chicago. Dr. Maki is a professor of psychiatry, psychology, and obstetrics & gynecology at the University of Illinois at Chicago.

References

1. Trost S et al. Pregnancy-related deaths: Data from maternal mortality review committees in 36 states, 2017-2019. Atlanta: Centers for Disease Control and Prevention, U.S. Department of Health & Human Services, 2022.

2. Illinois Department of Public Health. Illinois maternal morbidity and mortality report 2016-2017. 2021.

3. AHRQ. Funding opportunities to address opioid and other substance use disorders. Updated 2023.

4. HRSA. Screening and treatment for maternal mental health and substance use disorders.

5. U.S. Preventive Services Task Force. Recommendations for primary care practice. Accessed May 26, 2023.

6. U.S. Preventive Services Task Force. Draft recommendation statement: Anxiety in adults: Screening. 2022.

7. ACOG. Screening and diagnosis of mental health conditions during pregnancy and postpartum. Clinical Practice Guideline. Number 4. 2023 June.

8. Meltzer-Brody S and Stuebe A. The long-term psychiatric and medical prognosis of perinatal mental illness. Best Pract Res Clin Obstet Gynaecol. 2014 Jan. doi: 10.1016/j.bpobgyn.2013.08.009.

9. Van Niel MS and Payne JL. Perinatal depression: A review. Cleve Clin J Med. 2020 May. doi: 10.3949/ccjm.87a.19054.

10. Wisner KL et al. Onset timing, thoughts of self-harm, and diagnoses in postpartum women with screen-positive depression findings. 2013 May. doi: 10.1001/jamapsychiatry.2013.87.

11. Falah-Hassani K et al. The prevalence of antenatal and postnatal co-morbid anxiety and depression: A meta-analysis. Psychol Med. 2017 Sep. doi: 10.1017/S0033291717000617.

12. Pentecost R et al. Scoping review of the associations between perinatal substance use and perinatal depression and anxiety. J Obstet Gynecol Neonatal Nurs. 2021 Jul. doi: 10.1016/j.jogn.2021.02.008.

13. Craemer KA et al. Perinatal mental health in low-income urban and rural patients: The importance of screening for comorbidities. Gen Hosp Psychiatry. 2023 Jul-Aug. doi: 10.1016/j.genhosppsych.2023.05.007.

14. O’Connor E et al. Primary care screening for and treatment of depression in pregnant and postpartum women: Evidence report and systematic review for the U.S. Preventive Services Task Force. JAMA. 2016 Jan 26. doi: 10.1001/jama.2015.18948.

15. Kozhimannil KB et al. Racial and ethnic disparities in postpartum depression care among low-income women. Psychiatr Serv. 2011 Jun. doi: 10.1176/ps.62.6.pss6206_0619.

16. Wenzel ES et al. Depression and anxiety symptoms across pregnancy and the postpartum in low-income Black and Latina women. Arch Womens Ment Health. 2021 Dec. doi: 10.1007/s00737-021-01139-y.

17. Gibbons RD et al. Development of a computerized adaptive substance use disorder scale for screening and measurement: The CAT‐SUD. Addiction. 2020 Jul. doi: 10.1111/add.14938.

18. Brenner LA et al. Validation of a computerized adaptive test suicide scale (CAT-SS) among united states military veterans. PloS One. 2022 Jan 21. doi: 10.1371/journal.pone.0261920.

19. The Center for State Child Welfare Data. Using technology to diagnose and report on behavioral health challenges facing foster youth. 2018.

20. Kim JJ et al. The experience of depression, anxiety, and mania among perinatal women. Arch Womens Ment Health. 2016 Oct. doi: 10.1007/s00737-016-0632-6.

21. Tepper MC et al. Toward population health: Using a learning behavioral health system and measurement-based care to improve access, care, outcomes, and disparities. Community Ment Health J. 2022 Nov. doi: 10.1007/s10597-022-00957-3.

22. Wenzel E et al. Using computerised adaptive tests to screen for perinatal depression in underserved women of colour. Evid Based Ment Health. 2022 Feb. doi: 10.1136/ebmental-2021-300262.

23. Sanger-Katz M. They want it to be secret: How a common blood test can cost $11 or almost $1,000. New York Times. 2019 Apr 19.

24. Spitzer RL et al. A brief measure for assessing generalized anxiety disorder: The GAD-7. Arch Intern Med. 2006 May 22. doi: 10.1001/archinte.166.10.1092.

25. Mollard E et al. An integrative review of postpartum depression in rural US communities. Arch Psychiatr Nurs. 2016 Jun. doi: 10.1016/j.apnu.2015.12.003.

26. Anglim AJ and Radke SM. Rural maternal health care outcomes, drivers, and patient perspectives. Clin Obstet Gynecol. 2022 Dec 1. doi: 10.1097/GRF.0000000000000753.

Mental health conditions are the leading cause of pregnancy-related death in Illinois (40%) and across the United States (21%).^1,2There is increasing recognition in primary care that major depressive disorder (MDD) often co-occurs with other mental health conditions. Funding bodies, such as the Agency for Healthcare Research and Quality³ and the Health Resources and Service Administration,⁴ have spotlights on improving screening and access to care for depression and substance use disorders (SUDs). However, the needs of individuals with multiple mental health conditions still often go unrecognized and unaddressed in perinatal health settings.

The U.S. Preventive Services Task Force recommends that all adults be screened for depression, alcohol use, and drug use, and will be recommending screening for anxiety.^5,6 The American College of Obstetrics and Gynecology recommends screening for perinatal mental health conditions including depression, anxiety, bipolar disorder, acute postpartum psychosis, and suicidality; however, despite these recommendations, screening and treatment for comorbid mental health disorders during pregnancy and the postpartum is not standard practice.⁷

Addressing perinatal mental health is critical because untreated mental health conditions during the perinatal period can cause long-term adverse psychiatric and medical outcomes for the birthing person, the baby, and the family.⁸ This commentary highlights the importance of recognizing and screening for perinatal mental health comorbidities, improving referral rates for mental health treatment, and raising awareness of the importance of addressing rural perinatal mental health.
 

Perinatal mental health comorbidities

Major depressive disorder is the most common mental health condition during the perinatal period⁹ and is often comorbid.^10-12 In “Perinatal mental health in low-income urban and rural patients: The importance of screening for comorbidities,” Craemer et al.¹³ reported that nearly half of the perinatal patients who screened positive for MDD also screened positive for at least one other mental health condition, among them general anxiety disorder (GAD), SUD, posttraumatic stress disorder (PTSD), and suicidality.

Many (9%) of the perinatal patients with MDD had a severe comorbidity profile characterized by four diagnoses – MDD, GAD, SUD, and PTSD. In routine medical care these comorbidities often go undetected even though the risk to mothers and babies increases with more severe mental health symptoms.⁸

The high frequency of perinatal mental health comorbidities Craemer et al.¹³ found demonstrates a compelling need for comorbid mental health screening during the perinatal period, particularly for low-income Black, Hispanic, and rural birthing persons. Positive screens for perinatal mental health disorders may reflect the onset of these disorders in pregnancy or the postpartum, or preexisting disorders that have gone undetected or untreated before pregnancy.

For many patients, the perinatal period is the first time they are screened for any mental health disorder; typically, they are screened solely for depression. Screening alone can have a positive impact on perinatal mental health. In fact, the USPSTF found that programs to screen perinatal patients, with or without treatment-related support, resulted in a 2%-9% absolute reduction in depression prevalence.¹⁴ However, screening for MDD is too infrequent for many reasons, including the logistics of integrating screening into the clinic workflow and limited provider availability, time, and training in mental health.

We recommend screening perinatal patients for mental health comorbidities. This recommendation may seem impractical given the lack of screening tools for comorbid mental health conditions; however, the Computerized Adaptive Test for Mental Health (CAT-MH), the validated tool^15-17 used in this study, is an ideal option. CAT-MH is uniquely capable of screening for MDD, GAD, PTSD, SUD, and suicidality in one platform and is routinely used in diverse settings including the Veterans Administration,¹⁸ foster care,¹⁹ and universities.²⁰ The main limitation of this more comprehensive screening is that it takes about 10 minutes per patient. However, CAT-MH is self-administered and can be done in the waiting room or on a mobile device prior to a clinic visit.

CAT-MH can also be easily integrated into clinical workflow when added to the Electronic Medical Record²¹, and is a more comprehensive tool than existing perinatal depression tools such as the Perinatal Health Questionaire-9 (PHQ-9) and Edinburgh Perinatal Depression Scale (EPDS).²² Another limitation is cost – currently $5.00 per assessment – however, this is less than routine blood work.²³ If CAT-MH is not an option, we recommend a stepped approach of screening for GAD when perinatal patients screen positive for MDD, as this is the most common comorbidity profile. The GAD-7 is a free and widely available tool.²⁴

Barriers to care

In Craemer et al,¹³ nearly two-thirds (64.9%) of perinatal patients with a positive screen did not receive a referral to follow-up care or a medication prescription. These low referral rates may reflect a variety of widely recognized barriers to care, including lack of referral options, provider and/or patient reluctance to pursue referrals, barriers to insurance coverage, or inadequate behavioral health infrastructure to ensure referral and diagnostic follow-up.

Further, rural residing perinatal patients are an underserved population that need more resources and screening. Despite an on-site behavioral specialist at the rural clinic, Craemer et al¹³ found a stark disparity in referral rates: referrals to treatment for a positive diagnosis was over two times less at the rural clinic (23.9%), compared with the urban clinics (51.6%). The most common treatment offered at the rural clinic was a prescription for medication (17.4%), while referral to follow-up care was the most common at the urban clinics (35.5%). Rural areas not only have a shortage of health care providers, but community members seeking mental health care often encounter greater stigma, compared with urban residents.^25,26

These data highlight an unmet need for referrals to treatment for patients in rural communities, particularly in Illinois where the pregnancy-related mortality ratio attributable to mental health conditions is three times greater in rural areas, compared with those residing in urban Cook County (Chicago).² Increasing access and availability to mental health treatment and prevention resources in Illinois, especially in rural areas, is an opportunity to prevent pregnancy-related mortality attributable to mental health conditions.

Overall, there is a critical need for screening for perinatal mental health comorbidities, increased attention to low rates of referral to mental health treatment, and investing in rural perinatal mental health. Addressing perinatal mental health disorders is key to decreasing the burden of maternal mortality, particularly in Illinois.

Ms. Craemer and Ms. Sayah are senior research specialists at the Center for Research on Women & Gender, University of Illinois at Chicago. Dr. Duffecy is a professor of clinical psychiatry at the University of Illinois at Chicago. Dr. Geller is a professor of obstetrics & gynecology and director of the Center for Research on Women & Gender, University of Illinois at Chicago. Dr. Maki is a professor of psychiatry, psychology, and obstetrics & gynecology at the University of Illinois at Chicago.

References

1. Trost S et al. Pregnancy-related deaths: Data from maternal mortality review committees in 36 states, 2017-2019. Atlanta: Centers for Disease Control and Prevention, U.S. Department of Health & Human Services, 2022.

2. Illinois Department of Public Health. Illinois maternal morbidity and mortality report 2016-2017. 2021.

3. AHRQ. Funding opportunities to address opioid and other substance use disorders. Updated 2023.

4. HRSA. Screening and treatment for maternal mental health and substance use disorders.

5. U.S. Preventive Services Task Force. Recommendations for primary care practice. Accessed May 26, 2023.

6. U.S. Preventive Services Task Force. Draft recommendation statement: Anxiety in adults: Screening. 2022.

7. ACOG. Screening and diagnosis of mental health conditions during pregnancy and postpartum. Clinical Practice Guideline. Number 4. 2023 June.

8. Meltzer-Brody S and Stuebe A. The long-term psychiatric and medical prognosis of perinatal mental illness. Best Pract Res Clin Obstet Gynaecol. 2014 Jan. doi: 10.1016/j.bpobgyn.2013.08.009.

9. Van Niel MS and Payne JL. Perinatal depression: A review. Cleve Clin J Med. 2020 May. doi: 10.3949/ccjm.87a.19054.

10. Wisner KL et al. Onset timing, thoughts of self-harm, and diagnoses in postpartum women with screen-positive depression findings. 2013 May. doi: 10.1001/jamapsychiatry.2013.87.

11. Falah-Hassani K et al. The prevalence of antenatal and postnatal co-morbid anxiety and depression: A meta-analysis. Psychol Med. 2017 Sep. doi: 10.1017/S0033291717000617.

12. Pentecost R et al. Scoping review of the associations between perinatal substance use and perinatal depression and anxiety. J Obstet Gynecol Neonatal Nurs. 2021 Jul. doi: 10.1016/j.jogn.2021.02.008.

13. Craemer KA et al. Perinatal mental health in low-income urban and rural patients: The importance of screening for comorbidities. Gen Hosp Psychiatry. 2023 Jul-Aug. doi: 10.1016/j.genhosppsych.2023.05.007.

14. O’Connor E et al. Primary care screening for and treatment of depression in pregnant and postpartum women: Evidence report and systematic review for the U.S. Preventive Services Task Force. JAMA. 2016 Jan 26. doi: 10.1001/jama.2015.18948.

15. Kozhimannil KB et al. Racial and ethnic disparities in postpartum depression care among low-income women. Psychiatr Serv. 2011 Jun. doi: 10.1176/ps.62.6.pss6206_0619.

16. Wenzel ES et al. Depression and anxiety symptoms across pregnancy and the postpartum in low-income Black and Latina women. Arch Womens Ment Health. 2021 Dec. doi: 10.1007/s00737-021-01139-y.

17. Gibbons RD et al. Development of a computerized adaptive substance use disorder scale for screening and measurement: The CAT‐SUD. Addiction. 2020 Jul. doi: 10.1111/add.14938.

18. Brenner LA et al. Validation of a computerized adaptive test suicide scale (CAT-SS) among united states military veterans. PloS One. 2022 Jan 21. doi: 10.1371/journal.pone.0261920.

19. The Center for State Child Welfare Data. Using technology to diagnose and report on behavioral health challenges facing foster youth. 2018.

20. Kim JJ et al. The experience of depression, anxiety, and mania among perinatal women. Arch Womens Ment Health. 2016 Oct. doi: 10.1007/s00737-016-0632-6.

21. Tepper MC et al. Toward population health: Using a learning behavioral health system and measurement-based care to improve access, care, outcomes, and disparities. Community Ment Health J. 2022 Nov. doi: 10.1007/s10597-022-00957-3.

22. Wenzel E et al. Using computerised adaptive tests to screen for perinatal depression in underserved women of colour. Evid Based Ment Health. 2022 Feb. doi: 10.1136/ebmental-2021-300262.

23. Sanger-Katz M. They want it to be secret: How a common blood test can cost $11 or almost $1,000. New York Times. 2019 Apr 19.

24. Spitzer RL et al. A brief measure for assessing generalized anxiety disorder: The GAD-7. Arch Intern Med. 2006 May 22. doi: 10.1001/archinte.166.10.1092.

25. Mollard E et al. An integrative review of postpartum depression in rural US communities. Arch Psychiatr Nurs. 2016 Jun. doi: 10.1016/j.apnu.2015.12.003.

26. Anglim AJ and Radke SM. Rural maternal health care outcomes, drivers, and patient perspectives. Clin Obstet Gynecol. 2022 Dec 1. doi: 10.1097/GRF.0000000000000753.

Mental health conditions are the leading cause of pregnancy-related death in Illinois (40%) and across the United States (21%).^1,2There is increasing recognition in primary care that major depressive disorder (MDD) often co-occurs with other mental health conditions. Funding bodies, such as the Agency for Healthcare Research and Quality³ and the Health Resources and Service Administration,⁴ have spotlights on improving screening and access to care for depression and substance use disorders (SUDs). However, the needs of individuals with multiple mental health conditions still often go unrecognized and unaddressed in perinatal health settings.

The U.S. Preventive Services Task Force recommends that all adults be screened for depression, alcohol use, and drug use, and will be recommending screening for anxiety.^5,6 The American College of Obstetrics and Gynecology recommends screening for perinatal mental health conditions including depression, anxiety, bipolar disorder, acute postpartum psychosis, and suicidality; however, despite these recommendations, screening and treatment for comorbid mental health disorders during pregnancy and the postpartum is not standard practice.⁷

Addressing perinatal mental health is critical because untreated mental health conditions during the perinatal period can cause long-term adverse psychiatric and medical outcomes for the birthing person, the baby, and the family.⁸ This commentary highlights the importance of recognizing and screening for perinatal mental health comorbidities, improving referral rates for mental health treatment, and raising awareness of the importance of addressing rural perinatal mental health.
 

Perinatal mental health comorbidities

Major depressive disorder is the most common mental health condition during the perinatal period⁹ and is often comorbid.^10-12 In “Perinatal mental health in low-income urban and rural patients: The importance of screening for comorbidities,” Craemer et al.¹³ reported that nearly half of the perinatal patients who screened positive for MDD also screened positive for at least one other mental health condition, among them general anxiety disorder (GAD), SUD, posttraumatic stress disorder (PTSD), and suicidality.

Many (9%) of the perinatal patients with MDD had a severe comorbidity profile characterized by four diagnoses – MDD, GAD, SUD, and PTSD. In routine medical care these comorbidities often go undetected even though the risk to mothers and babies increases with more severe mental health symptoms.⁸

The high frequency of perinatal mental health comorbidities Craemer et al.¹³ found demonstrates a compelling need for comorbid mental health screening during the perinatal period, particularly for low-income Black, Hispanic, and rural birthing persons. Positive screens for perinatal mental health disorders may reflect the onset of these disorders in pregnancy or the postpartum, or preexisting disorders that have gone undetected or untreated before pregnancy.

For many patients, the perinatal period is the first time they are screened for any mental health disorder; typically, they are screened solely for depression. Screening alone can have a positive impact on perinatal mental health. In fact, the USPSTF found that programs to screen perinatal patients, with or without treatment-related support, resulted in a 2%-9% absolute reduction in depression prevalence.¹⁴ However, screening for MDD is too infrequent for many reasons, including the logistics of integrating screening into the clinic workflow and limited provider availability, time, and training in mental health.

We recommend screening perinatal patients for mental health comorbidities. This recommendation may seem impractical given the lack of screening tools for comorbid mental health conditions; however, the Computerized Adaptive Test for Mental Health (CAT-MH), the validated tool^15-17 used in this study, is an ideal option. CAT-MH is uniquely capable of screening for MDD, GAD, PTSD, SUD, and suicidality in one platform and is routinely used in diverse settings including the Veterans Administration,¹⁸ foster care,¹⁹ and universities.²⁰ The main limitation of this more comprehensive screening is that it takes about 10 minutes per patient. However, CAT-MH is self-administered and can be done in the waiting room or on a mobile device prior to a clinic visit.

CAT-MH can also be easily integrated into clinical workflow when added to the Electronic Medical Record²¹, and is a more comprehensive tool than existing perinatal depression tools such as the Perinatal Health Questionaire-9 (PHQ-9) and Edinburgh Perinatal Depression Scale (EPDS).²² Another limitation is cost – currently $5.00 per assessment – however, this is less than routine blood work.²³ If CAT-MH is not an option, we recommend a stepped approach of screening for GAD when perinatal patients screen positive for MDD, as this is the most common comorbidity profile. The GAD-7 is a free and widely available tool.²⁴

Barriers to care

In Craemer et al,¹³ nearly two-thirds (64.9%) of perinatal patients with a positive screen did not receive a referral to follow-up care or a medication prescription. These low referral rates may reflect a variety of widely recognized barriers to care, including lack of referral options, provider and/or patient reluctance to pursue referrals, barriers to insurance coverage, or inadequate behavioral health infrastructure to ensure referral and diagnostic follow-up.

Further, rural residing perinatal patients are an underserved population that need more resources and screening. Despite an on-site behavioral specialist at the rural clinic, Craemer et al¹³ found a stark disparity in referral rates: referrals to treatment for a positive diagnosis was over two times less at the rural clinic (23.9%), compared with the urban clinics (51.6%). The most common treatment offered at the rural clinic was a prescription for medication (17.4%), while referral to follow-up care was the most common at the urban clinics (35.5%). Rural areas not only have a shortage of health care providers, but community members seeking mental health care often encounter greater stigma, compared with urban residents.^25,26

These data highlight an unmet need for referrals to treatment for patients in rural communities, particularly in Illinois where the pregnancy-related mortality ratio attributable to mental health conditions is three times greater in rural areas, compared with those residing in urban Cook County (Chicago).² Increasing access and availability to mental health treatment and prevention resources in Illinois, especially in rural areas, is an opportunity to prevent pregnancy-related mortality attributable to mental health conditions.

Overall, there is a critical need for screening for perinatal mental health comorbidities, increased attention to low rates of referral to mental health treatment, and investing in rural perinatal mental health. Addressing perinatal mental health disorders is key to decreasing the burden of maternal mortality, particularly in Illinois.

Ms. Craemer and Ms. Sayah are senior research specialists at the Center for Research on Women & Gender, University of Illinois at Chicago. Dr. Duffecy is a professor of clinical psychiatry at the University of Illinois at Chicago. Dr. Geller is a professor of obstetrics & gynecology and director of the Center for Research on Women & Gender, University of Illinois at Chicago. Dr. Maki is a professor of psychiatry, psychology, and obstetrics & gynecology at the University of Illinois at Chicago.

References

1. Trost S et al. Pregnancy-related deaths: Data from maternal mortality review committees in 36 states, 2017-2019. Atlanta: Centers for Disease Control and Prevention, U.S. Department of Health & Human Services, 2022.

2. Illinois Department of Public Health. Illinois maternal morbidity and mortality report 2016-2017. 2021.

3. AHRQ. Funding opportunities to address opioid and other substance use disorders. Updated 2023.

4. HRSA. Screening and treatment for maternal mental health and substance use disorders.

5. U.S. Preventive Services Task Force. Recommendations for primary care practice. Accessed May 26, 2023.

6. U.S. Preventive Services Task Force. Draft recommendation statement: Anxiety in adults: Screening. 2022.

7. ACOG. Screening and diagnosis of mental health conditions during pregnancy and postpartum. Clinical Practice Guideline. Number 4. 2023 June.

8. Meltzer-Brody S and Stuebe A. The long-term psychiatric and medical prognosis of perinatal mental illness. Best Pract Res Clin Obstet Gynaecol. 2014 Jan. doi: 10.1016/j.bpobgyn.2013.08.009.

9. Van Niel MS and Payne JL. Perinatal depression: A review. Cleve Clin J Med. 2020 May. doi: 10.3949/ccjm.87a.19054.

10. Wisner KL et al. Onset timing, thoughts of self-harm, and diagnoses in postpartum women with screen-positive depression findings. 2013 May. doi: 10.1001/jamapsychiatry.2013.87.

11. Falah-Hassani K et al. The prevalence of antenatal and postnatal co-morbid anxiety and depression: A meta-analysis. Psychol Med. 2017 Sep. doi: 10.1017/S0033291717000617.

12. Pentecost R et al. Scoping review of the associations between perinatal substance use and perinatal depression and anxiety. J Obstet Gynecol Neonatal Nurs. 2021 Jul. doi: 10.1016/j.jogn.2021.02.008.

13. Craemer KA et al. Perinatal mental health in low-income urban and rural patients: The importance of screening for comorbidities. Gen Hosp Psychiatry. 2023 Jul-Aug. doi: 10.1016/j.genhosppsych.2023.05.007.

14. O’Connor E et al. Primary care screening for and treatment of depression in pregnant and postpartum women: Evidence report and systematic review for the U.S. Preventive Services Task Force. JAMA. 2016 Jan 26. doi: 10.1001/jama.2015.18948.

15. Kozhimannil KB et al. Racial and ethnic disparities in postpartum depression care among low-income women. Psychiatr Serv. 2011 Jun. doi: 10.1176/ps.62.6.pss6206_0619.

16. Wenzel ES et al. Depression and anxiety symptoms across pregnancy and the postpartum in low-income Black and Latina women. Arch Womens Ment Health. 2021 Dec. doi: 10.1007/s00737-021-01139-y.

17. Gibbons RD et al. Development of a computerized adaptive substance use disorder scale for screening and measurement: The CAT‐SUD. Addiction. 2020 Jul. doi: 10.1111/add.14938.

18. Brenner LA et al. Validation of a computerized adaptive test suicide scale (CAT-SS) among united states military veterans. PloS One. 2022 Jan 21. doi: 10.1371/journal.pone.0261920.

19. The Center for State Child Welfare Data. Using technology to diagnose and report on behavioral health challenges facing foster youth. 2018.

20. Kim JJ et al. The experience of depression, anxiety, and mania among perinatal women. Arch Womens Ment Health. 2016 Oct. doi: 10.1007/s00737-016-0632-6.

21. Tepper MC et al. Toward population health: Using a learning behavioral health system and measurement-based care to improve access, care, outcomes, and disparities. Community Ment Health J. 2022 Nov. doi: 10.1007/s10597-022-00957-3.

22. Wenzel E et al. Using computerised adaptive tests to screen for perinatal depression in underserved women of colour. Evid Based Ment Health. 2022 Feb. doi: 10.1136/ebmental-2021-300262.

23. Sanger-Katz M. They want it to be secret: How a common blood test can cost $11 or almost $1,000. New York Times. 2019 Apr 19.

24. Spitzer RL et al. A brief measure for assessing generalized anxiety disorder: The GAD-7. Arch Intern Med. 2006 May 22. doi: 10.1001/archinte.166.10.1092.

25. Mollard E et al. An integrative review of postpartum depression in rural US communities. Arch Psychiatr Nurs. 2016 Jun. doi: 10.1016/j.apnu.2015.12.003.

26. Anglim AJ and Radke SM. Rural maternal health care outcomes, drivers, and patient perspectives. Clin Obstet Gynecol. 2022 Dec 1. doi: 10.1097/GRF.0000000000000753.

TOPLINE:

A blood test that measures biomarkers linked to manic symptoms can accurately identify patients with bipolar disorder (BD) who were previously misdiagnosed with major depressive disorder (MDD), new research shows. Investigators state that the test could identify up to 30% of patients with BD when used on its own and could be even more effective when combined with a standardized psychometric assessment.

METHODOLOGY:

• In the proof-of-concept study, investigators sought to identify biomarkers to accurately identify BD, which is frequently misdiagnosed as MDD because of overlapping symptoms and the lack of objective diagnostic tools.
• The study included 241 participants (70% female; mean age, 28 years) from the U.K.-based Delta Study who had been diagnosed with MDD within the past 5 years and had depressive symptoms as assessed with the Patient Health Questionnaire-9 (score ≥ 5).
• Participants completed an online questionnaire that included questions from the Mood Disorder Questionnaire and the Warwick-Edinburgh Mental Well-Being Scale and were asked to return a dried blood spot (DBS) fasting blood sample.
• Investigators analyzed the DBS samples for 630 metabolites and contacted participants by phone to establish diagnoses at 6 and 12 months using the World Health Organization World Mental Health Composite International Diagnostic Interview.

TAKEAWAY:

• Investigators used a panel of 17 biomarkers to correctly identify 67 (27.8%) participants with BD who had been previously misdiagnosed with MDD. They confirmed MDD in the remaining 174 patients.
• The biomarkers used in the test were correlated primarily with lifetime manic symptoms and were validated in a separate group of 30 patients.
• The identified biomarker panel provided a mean cross-validated area under the receiver operating characteristic curve of 0.71 (P < .001), with ceramide d18:0/24:1 emerging as the strongest biomarker.
• Combining biomarker readouts with patient-reported data significantly improved the performance of diagnostic models based on extensive demographic data and information from the Patient Health Questionnaire and Mood Disorder Questionnaire (P = .03 for all).

IN PRACTICE:

“The added value of biomarkers was particularly evident in scenarios where data on psychiatric symptoms were unavailable and at intermediate diagnostic thresholds, suggesting that biomarker tests may especially benefit patients who do not report their symptoms and whose diagnoses are uncertain,” the authors write.

SOURCE:

Jakub Tomasik, PhD, of the University of Cambridge (England), led the study, which was published online in JAMA Psychiatry. Stanley Medical Research Institute and Psyomics funded the study.

LIMITATIONS:

Data on confounding factors such as diet and blood pressure were missing. In addition, investigators noted that the sample mostly comprised White Internet users and was not representative of all individuals with BD.

Dr. Tomasik has a patent pending for DBS blood biomarkers. Other disclosures are noted in the original article.

A version of this article first appeared on Medscape.com.

TOPLINE:

A blood test that measures biomarkers linked to manic symptoms can accurately identify patients with bipolar disorder (BD) who were previously misdiagnosed with major depressive disorder (MDD), new research shows. Investigators state that the test could identify up to 30% of patients with BD when used on its own and could be even more effective when combined with a standardized psychometric assessment.

METHODOLOGY:

• In the proof-of-concept study, investigators sought to identify biomarkers to accurately identify BD, which is frequently misdiagnosed as MDD because of overlapping symptoms and the lack of objective diagnostic tools.
• The study included 241 participants (70% female; mean age, 28 years) from the U.K.-based Delta Study who had been diagnosed with MDD within the past 5 years and had depressive symptoms as assessed with the Patient Health Questionnaire-9 (score ≥ 5).
• Participants completed an online questionnaire that included questions from the Mood Disorder Questionnaire and the Warwick-Edinburgh Mental Well-Being Scale and were asked to return a dried blood spot (DBS) fasting blood sample.
• Investigators analyzed the DBS samples for 630 metabolites and contacted participants by phone to establish diagnoses at 6 and 12 months using the World Health Organization World Mental Health Composite International Diagnostic Interview.

TAKEAWAY:

• Investigators used a panel of 17 biomarkers to correctly identify 67 (27.8%) participants with BD who had been previously misdiagnosed with MDD. They confirmed MDD in the remaining 174 patients.
• The biomarkers used in the test were correlated primarily with lifetime manic symptoms and were validated in a separate group of 30 patients.
• The identified biomarker panel provided a mean cross-validated area under the receiver operating characteristic curve of 0.71 (P < .001), with ceramide d18:0/24:1 emerging as the strongest biomarker.
• Combining biomarker readouts with patient-reported data significantly improved the performance of diagnostic models based on extensive demographic data and information from the Patient Health Questionnaire and Mood Disorder Questionnaire (P = .03 for all).

IN PRACTICE:

“The added value of biomarkers was particularly evident in scenarios where data on psychiatric symptoms were unavailable and at intermediate diagnostic thresholds, suggesting that biomarker tests may especially benefit patients who do not report their symptoms and whose diagnoses are uncertain,” the authors write.

SOURCE:

Jakub Tomasik, PhD, of the University of Cambridge (England), led the study, which was published online in JAMA Psychiatry. Stanley Medical Research Institute and Psyomics funded the study.

LIMITATIONS:

Data on confounding factors such as diet and blood pressure were missing. In addition, investigators noted that the sample mostly comprised White Internet users and was not representative of all individuals with BD.

Dr. Tomasik has a patent pending for DBS blood biomarkers. Other disclosures are noted in the original article.

A version of this article first appeared on Medscape.com.

TOPLINE:

A blood test that measures biomarkers linked to manic symptoms can accurately identify patients with bipolar disorder (BD) who were previously misdiagnosed with major depressive disorder (MDD), new research shows. Investigators state that the test could identify up to 30% of patients with BD when used on its own and could be even more effective when combined with a standardized psychometric assessment.

METHODOLOGY:

• In the proof-of-concept study, investigators sought to identify biomarkers to accurately identify BD, which is frequently misdiagnosed as MDD because of overlapping symptoms and the lack of objective diagnostic tools.
• The study included 241 participants (70% female; mean age, 28 years) from the U.K.-based Delta Study who had been diagnosed with MDD within the past 5 years and had depressive symptoms as assessed with the Patient Health Questionnaire-9 (score ≥ 5).
• Participants completed an online questionnaire that included questions from the Mood Disorder Questionnaire and the Warwick-Edinburgh Mental Well-Being Scale and were asked to return a dried blood spot (DBS) fasting blood sample.
• Investigators analyzed the DBS samples for 630 metabolites and contacted participants by phone to establish diagnoses at 6 and 12 months using the World Health Organization World Mental Health Composite International Diagnostic Interview.

TAKEAWAY:

• Investigators used a panel of 17 biomarkers to correctly identify 67 (27.8%) participants with BD who had been previously misdiagnosed with MDD. They confirmed MDD in the remaining 174 patients.
• The biomarkers used in the test were correlated primarily with lifetime manic symptoms and were validated in a separate group of 30 patients.
• The identified biomarker panel provided a mean cross-validated area under the receiver operating characteristic curve of 0.71 (P < .001), with ceramide d18:0/24:1 emerging as the strongest biomarker.
• Combining biomarker readouts with patient-reported data significantly improved the performance of diagnostic models based on extensive demographic data and information from the Patient Health Questionnaire and Mood Disorder Questionnaire (P = .03 for all).

IN PRACTICE:

“The added value of biomarkers was particularly evident in scenarios where data on psychiatric symptoms were unavailable and at intermediate diagnostic thresholds, suggesting that biomarker tests may especially benefit patients who do not report their symptoms and whose diagnoses are uncertain,” the authors write.

SOURCE:

Jakub Tomasik, PhD, of the University of Cambridge (England), led the study, which was published online in JAMA Psychiatry. Stanley Medical Research Institute and Psyomics funded the study.

LIMITATIONS:

Data on confounding factors such as diet and blood pressure were missing. In addition, investigators noted that the sample mostly comprised White Internet users and was not representative of all individuals with BD.

Dr. Tomasik has a patent pending for DBS blood biomarkers. Other disclosures are noted in the original article.

A version of this article first appeared on Medscape.com.

TOPLINE:

U.S. adults with psychiatric illness experienced fewer disruptions in receiving psychotherapy following the transition to virtual psychiatric care that accompanied the onset of the COVID-19 pandemic, a large study has shown.

METHODOLOGY:

• Retrospective study using electronic health records and insurance claims data from three large U.S. health systems.
• Sample included 110,089 patients with mental health conditions who attended at least two psychotherapy visits during the 9 months before and 9 months after the onset of COVID-19, defined in this study as March 14, 2020.
• Outcome was disruption in psychotherapy, defined as a gap of more than 45 days between visits.

TAKEAWAY:

• Before the pandemic, 96.9% of psychotherapy visits were in person and 35.4% were followed by a gap of more than 45 days.
• After the onset of the pandemic, more than half of visits (51.8%) were virtual, and only 17.9% were followed by a gap of more than 45 days.
• Prior to the pandemic, the median time between visits was 27 days, and after the pandemic, it dropped to 14 days, suggesting individuals were more likely to return for additional psychotherapy after the widespread shift to virtual care.
• Over the entire study period, individuals with depressive, anxiety, or bipolar disorders were more likely to maintain consistent psychotherapy visits, whereas those with schizophrenia, ADHD, autism, conduct or disruptive disorders, dementia, or personality disorders were more likely to have a disruption in their visits.

IN PRACTICE:

“These findings support continued use of virtual psychotherapy as an option for care when appropriate infrastructure is in place. In addition, these findings support the continuation of policies that provide access to and coverage for virtual psychotherapy,” the authors write.

SOURCE:

The study, led by Brian K. Ahmedani, PhD, with the Center for Health Policy and Health Services Research, Henry Ford Health, Detroit, was published online  in Psychiatric Services.

LIMITATIONS:

The study was conducted in three large health systems with virtual care infrastructure already in place. Researchers did not examine use of virtual care for medication management or for types of care other than psychotherapy, which may present different challenges.

DISCLOSURES:

The study was supported by the National Institute of Mental Health. The authors have no relevant disclosures.

A version of this article first appeared on Medscape.com.

TOPLINE:

U.S. adults with psychiatric illness experienced fewer disruptions in receiving psychotherapy following the transition to virtual psychiatric care that accompanied the onset of the COVID-19 pandemic, a large study has shown.

METHODOLOGY:

• Retrospective study using electronic health records and insurance claims data from three large U.S. health systems.
• Sample included 110,089 patients with mental health conditions who attended at least two psychotherapy visits during the 9 months before and 9 months after the onset of COVID-19, defined in this study as March 14, 2020.
• Outcome was disruption in psychotherapy, defined as a gap of more than 45 days between visits.

TAKEAWAY:

• Before the pandemic, 96.9% of psychotherapy visits were in person and 35.4% were followed by a gap of more than 45 days.
• After the onset of the pandemic, more than half of visits (51.8%) were virtual, and only 17.9% were followed by a gap of more than 45 days.
• Prior to the pandemic, the median time between visits was 27 days, and after the pandemic, it dropped to 14 days, suggesting individuals were more likely to return for additional psychotherapy after the widespread shift to virtual care.
• Over the entire study period, individuals with depressive, anxiety, or bipolar disorders were more likely to maintain consistent psychotherapy visits, whereas those with schizophrenia, ADHD, autism, conduct or disruptive disorders, dementia, or personality disorders were more likely to have a disruption in their visits.

IN PRACTICE:

“These findings support continued use of virtual psychotherapy as an option for care when appropriate infrastructure is in place. In addition, these findings support the continuation of policies that provide access to and coverage for virtual psychotherapy,” the authors write.

SOURCE:

The study, led by Brian K. Ahmedani, PhD, with the Center for Health Policy and Health Services Research, Henry Ford Health, Detroit, was published online  in Psychiatric Services.

LIMITATIONS:

The study was conducted in three large health systems with virtual care infrastructure already in place. Researchers did not examine use of virtual care for medication management or for types of care other than psychotherapy, which may present different challenges.

DISCLOSURES:

The study was supported by the National Institute of Mental Health. The authors have no relevant disclosures.

A version of this article first appeared on Medscape.com.

TOPLINE:

U.S. adults with psychiatric illness experienced fewer disruptions in receiving psychotherapy following the transition to virtual psychiatric care that accompanied the onset of the COVID-19 pandemic, a large study has shown.

METHODOLOGY:

• Retrospective study using electronic health records and insurance claims data from three large U.S. health systems.
• Sample included 110,089 patients with mental health conditions who attended at least two psychotherapy visits during the 9 months before and 9 months after the onset of COVID-19, defined in this study as March 14, 2020.
• Outcome was disruption in psychotherapy, defined as a gap of more than 45 days between visits.

TAKEAWAY:

• Before the pandemic, 96.9% of psychotherapy visits were in person and 35.4% were followed by a gap of more than 45 days.
• After the onset of the pandemic, more than half of visits (51.8%) were virtual, and only 17.9% were followed by a gap of more than 45 days.
• Prior to the pandemic, the median time between visits was 27 days, and after the pandemic, it dropped to 14 days, suggesting individuals were more likely to return for additional psychotherapy after the widespread shift to virtual care.
• Over the entire study period, individuals with depressive, anxiety, or bipolar disorders were more likely to maintain consistent psychotherapy visits, whereas those with schizophrenia, ADHD, autism, conduct or disruptive disorders, dementia, or personality disorders were more likely to have a disruption in their visits.

IN PRACTICE:

“These findings support continued use of virtual psychotherapy as an option for care when appropriate infrastructure is in place. In addition, these findings support the continuation of policies that provide access to and coverage for virtual psychotherapy,” the authors write.

SOURCE:

The study, led by Brian K. Ahmedani, PhD, with the Center for Health Policy and Health Services Research, Henry Ford Health, Detroit, was published online  in Psychiatric Services.

LIMITATIONS:

The study was conducted in three large health systems with virtual care infrastructure already in place. Researchers did not examine use of virtual care for medication management or for types of care other than psychotherapy, which may present different challenges.

DISCLOSURES:

The study was supported by the National Institute of Mental Health. The authors have no relevant disclosures.

A version of this article first appeared on Medscape.com.

BARCELONA – Electrodermal activity (EDA) measured via a smart bracelet/wristband may help predict and track changes in mood and more rapidly assess treatment response in patients with bipolar disorder (BD), early research suggests.

In a small observational pilot study, researchers found the E4 wristband (Empatica Inc) was able to detect fluctuations in mood.

The results highlight the potential of EDA to serve as an objective BD biomarker, noted the investigators, led by Diego Hidalgo-Mazzei, MD, PhD, Bipolar and Depressive Disorders Unit, University of Barcelona.

The findings were presented at the 36th European College of Neuropsychopharmacology (ECNP) Congress.
 

A need for objective markers

The evaluation of BD currently consists of clinical interviews, questionnaires, and scales, which largely rely on physician assessment, highlighting the need for objective biomarkers.

Previous studies show that EDA, which tracks changes in the skin due to sweat gland activity in response to psychological stimuli, is reduced in unipolar depression.

The researchers hypothesized that EDA could be a biomarker of mood changes in patients with BD. They recruited 38 patients experiencing manic (n = 12) or depressive (n = 9) episodes or who were euthymic (n = 17) and compared their responses with those of 19 healthy control persons.

Study participants were asked to wear the wristband continuously for approximately 48 hours to measure EDA, motion-based activity, blood volume pulse, and skin temperature.

The 48-hour monitoring session was determined by the battery life of the device, Dr. Hidalgo-Mazzei said in an interview.

The acute-phase patients in the study had three sessions at different time points – one during the acute state, another when the clinician determined there was a response to treatment, and again at remission. Euthymic patients and healthy control persons had a single monitoring session.

Dr. Hidalgo-Mazzei said the study’s protocol is unique because it involves unusually long sessions with the device. In this setup, each sensor collects a sample every second, resulting in highly detailed and granular data.

“At the end, it is a trade-off, as handling such an enormous amount of data for each session requires equally large preprocessing, computing power, and analysis,” he said.

Dr. Hidalgo-Mazzei characterized compliance with the device as “outstanding” for the majority of study participants.

Results showed that mean EDA was notably and significantly lower in BD patients during depressive episodes in comparison with those in other groups. Patients with depression also had significantly less frequent EDA peaks per minute (P = .001 for both).

There were also significant differences in EDA measures between baseline and after treatment in the acute BD groups.

Patients with depression had significant increases in mean EDA (P = .033), EDA peaks per minute (P = .002), and the mean amplitude of EDA peaks (P = .001) from baseline, while manic patients experienced a decrease in the mean amplitude of EDA peaks (P = .001).

It is important for the patient and doctor to know how and when mood fluctuations take place, said Dr. Hidalgo-Mazzei, because treatment for manic and depressive states differ.

“Until now, these mood swings have mostly been diagnosed subjectively, through interview with doctors or by questionnaires, and this had led to real difficulties.

“Arriving at the correct drug is difficult, with only around 30% to 40% of treated individuals having the expected response. We hope that the additional information these systems can provide will give us greater certainty in treating patients.”

However, Dr. Hidalgo-Mazzei said that is still a long way off, noting that this is an exploratory, observational study.

“We need to look at a larger sample and use machine learning to analyze all the biomarkers collected by the wearers to confirm the findings,” he said.
 

A true biomarker?

In a comment, Joseph F. Goldberg, MD, clinical professor of psychiatry at Icahn School of Medicine at Mount Sinai, New York, said the study is an “interesting use of this technology to differentiate physiological correlates of mood states.”

However, he said the findings are limited and preliminary because the sample sizes were small and the measures weren’t repeated.

In addition, medications or other factors that may influence electrophysiologic activity, such as anxiety or panic, were not considered, and Dr. Goldberg noted the researchers did not compare the results with those in patients with other diagnoses.

“So, I don’t think one could call this a biomarker in the sense of having diagnostic specificity,” he said, making the comparison with body temperature, which “goes up in an infection; but fever alone doesn’t tell us much about the nature or cause of a presumed infection. More studies are needed before generalizable conclusion can be drawn.”

Also commenting on the research, Paolo Ossola, MD, PhD, assistant professor of psychiatry, department of medicine and surgery, University of Parma, Italy, described the study as exploratory but preliminary.

He said the researchers have “laid the foundation for a new approach to diagnosing and treating bipolar disorders.

“The shift from the subjective to the biological level could also promote understanding of the underlying mechanistic dynamics of mood swings.”

The study was funded by the Instituto de Salud Carlos III and a Baszucki Brain Research Fund grant from the Milken Foundation. The authors have disclosed no relevant financial relationships.
 

A version of this article first appeared on Medscape.com.

BARCELONA – Electrodermal activity (EDA) measured via a smart bracelet/wristband may help predict and track changes in mood and more rapidly assess treatment response in patients with bipolar disorder (BD), early research suggests.

In a small observational pilot study, researchers found the E4 wristband (Empatica Inc) was able to detect fluctuations in mood.

The results highlight the potential of EDA to serve as an objective BD biomarker, noted the investigators, led by Diego Hidalgo-Mazzei, MD, PhD, Bipolar and Depressive Disorders Unit, University of Barcelona.

The findings were presented at the 36th European College of Neuropsychopharmacology (ECNP) Congress.
 

A need for objective markers

The evaluation of BD currently consists of clinical interviews, questionnaires, and scales, which largely rely on physician assessment, highlighting the need for objective biomarkers.

Previous studies show that EDA, which tracks changes in the skin due to sweat gland activity in response to psychological stimuli, is reduced in unipolar depression.

The researchers hypothesized that EDA could be a biomarker of mood changes in patients with BD. They recruited 38 patients experiencing manic (n = 12) or depressive (n = 9) episodes or who were euthymic (n = 17) and compared their responses with those of 19 healthy control persons.

Study participants were asked to wear the wristband continuously for approximately 48 hours to measure EDA, motion-based activity, blood volume pulse, and skin temperature.

The 48-hour monitoring session was determined by the battery life of the device, Dr. Hidalgo-Mazzei said in an interview.

The acute-phase patients in the study had three sessions at different time points – one during the acute state, another when the clinician determined there was a response to treatment, and again at remission. Euthymic patients and healthy control persons had a single monitoring session.

Dr. Hidalgo-Mazzei said the study’s protocol is unique because it involves unusually long sessions with the device. In this setup, each sensor collects a sample every second, resulting in highly detailed and granular data.

“At the end, it is a trade-off, as handling such an enormous amount of data for each session requires equally large preprocessing, computing power, and analysis,” he said.

Dr. Hidalgo-Mazzei characterized compliance with the device as “outstanding” for the majority of study participants.

Results showed that mean EDA was notably and significantly lower in BD patients during depressive episodes in comparison with those in other groups. Patients with depression also had significantly less frequent EDA peaks per minute (P = .001 for both).

There were also significant differences in EDA measures between baseline and after treatment in the acute BD groups.

Patients with depression had significant increases in mean EDA (P = .033), EDA peaks per minute (P = .002), and the mean amplitude of EDA peaks (P = .001) from baseline, while manic patients experienced a decrease in the mean amplitude of EDA peaks (P = .001).

It is important for the patient and doctor to know how and when mood fluctuations take place, said Dr. Hidalgo-Mazzei, because treatment for manic and depressive states differ.

“Until now, these mood swings have mostly been diagnosed subjectively, through interview with doctors or by questionnaires, and this had led to real difficulties.

“Arriving at the correct drug is difficult, with only around 30% to 40% of treated individuals having the expected response. We hope that the additional information these systems can provide will give us greater certainty in treating patients.”

However, Dr. Hidalgo-Mazzei said that is still a long way off, noting that this is an exploratory, observational study.

“We need to look at a larger sample and use machine learning to analyze all the biomarkers collected by the wearers to confirm the findings,” he said.
 

A true biomarker?

In a comment, Joseph F. Goldberg, MD, clinical professor of psychiatry at Icahn School of Medicine at Mount Sinai, New York, said the study is an “interesting use of this technology to differentiate physiological correlates of mood states.”

However, he said the findings are limited and preliminary because the sample sizes were small and the measures weren’t repeated.

In addition, medications or other factors that may influence electrophysiologic activity, such as anxiety or panic, were not considered, and Dr. Goldberg noted the researchers did not compare the results with those in patients with other diagnoses.

“So, I don’t think one could call this a biomarker in the sense of having diagnostic specificity,” he said, making the comparison with body temperature, which “goes up in an infection; but fever alone doesn’t tell us much about the nature or cause of a presumed infection. More studies are needed before generalizable conclusion can be drawn.”

Also commenting on the research, Paolo Ossola, MD, PhD, assistant professor of psychiatry, department of medicine and surgery, University of Parma, Italy, described the study as exploratory but preliminary.

He said the researchers have “laid the foundation for a new approach to diagnosing and treating bipolar disorders.

“The shift from the subjective to the biological level could also promote understanding of the underlying mechanistic dynamics of mood swings.”

The study was funded by the Instituto de Salud Carlos III and a Baszucki Brain Research Fund grant from the Milken Foundation. The authors have disclosed no relevant financial relationships.
 

A version of this article first appeared on Medscape.com.

BARCELONA – Electrodermal activity (EDA) measured via a smart bracelet/wristband may help predict and track changes in mood and more rapidly assess treatment response in patients with bipolar disorder (BD), early research suggests.

In a small observational pilot study, researchers found the E4 wristband (Empatica Inc) was able to detect fluctuations in mood.

The results highlight the potential of EDA to serve as an objective BD biomarker, noted the investigators, led by Diego Hidalgo-Mazzei, MD, PhD, Bipolar and Depressive Disorders Unit, University of Barcelona.

The findings were presented at the 36th European College of Neuropsychopharmacology (ECNP) Congress.
 

A need for objective markers

The evaluation of BD currently consists of clinical interviews, questionnaires, and scales, which largely rely on physician assessment, highlighting the need for objective biomarkers.

Previous studies show that EDA, which tracks changes in the skin due to sweat gland activity in response to psychological stimuli, is reduced in unipolar depression.

The researchers hypothesized that EDA could be a biomarker of mood changes in patients with BD. They recruited 38 patients experiencing manic (n = 12) or depressive (n = 9) episodes or who were euthymic (n = 17) and compared their responses with those of 19 healthy control persons.

Study participants were asked to wear the wristband continuously for approximately 48 hours to measure EDA, motion-based activity, blood volume pulse, and skin temperature.

The 48-hour monitoring session was determined by the battery life of the device, Dr. Hidalgo-Mazzei said in an interview.

The acute-phase patients in the study had three sessions at different time points – one during the acute state, another when the clinician determined there was a response to treatment, and again at remission. Euthymic patients and healthy control persons had a single monitoring session.

Dr. Hidalgo-Mazzei said the study’s protocol is unique because it involves unusually long sessions with the device. In this setup, each sensor collects a sample every second, resulting in highly detailed and granular data.

“At the end, it is a trade-off, as handling such an enormous amount of data for each session requires equally large preprocessing, computing power, and analysis,” he said.

Dr. Hidalgo-Mazzei characterized compliance with the device as “outstanding” for the majority of study participants.

Results showed that mean EDA was notably and significantly lower in BD patients during depressive episodes in comparison with those in other groups. Patients with depression also had significantly less frequent EDA peaks per minute (P = .001 for both).

There were also significant differences in EDA measures between baseline and after treatment in the acute BD groups.

Patients with depression had significant increases in mean EDA (P = .033), EDA peaks per minute (P = .002), and the mean amplitude of EDA peaks (P = .001) from baseline, while manic patients experienced a decrease in the mean amplitude of EDA peaks (P = .001).

It is important for the patient and doctor to know how and when mood fluctuations take place, said Dr. Hidalgo-Mazzei, because treatment for manic and depressive states differ.

“Until now, these mood swings have mostly been diagnosed subjectively, through interview with doctors or by questionnaires, and this had led to real difficulties.

“Arriving at the correct drug is difficult, with only around 30% to 40% of treated individuals having the expected response. We hope that the additional information these systems can provide will give us greater certainty in treating patients.”

However, Dr. Hidalgo-Mazzei said that is still a long way off, noting that this is an exploratory, observational study.

“We need to look at a larger sample and use machine learning to analyze all the biomarkers collected by the wearers to confirm the findings,” he said.
 

A true biomarker?

In a comment, Joseph F. Goldberg, MD, clinical professor of psychiatry at Icahn School of Medicine at Mount Sinai, New York, said the study is an “interesting use of this technology to differentiate physiological correlates of mood states.”

However, he said the findings are limited and preliminary because the sample sizes were small and the measures weren’t repeated.

In addition, medications or other factors that may influence electrophysiologic activity, such as anxiety or panic, were not considered, and Dr. Goldberg noted the researchers did not compare the results with those in patients with other diagnoses.

“So, I don’t think one could call this a biomarker in the sense of having diagnostic specificity,” he said, making the comparison with body temperature, which “goes up in an infection; but fever alone doesn’t tell us much about the nature or cause of a presumed infection. More studies are needed before generalizable conclusion can be drawn.”

Also commenting on the research, Paolo Ossola, MD, PhD, assistant professor of psychiatry, department of medicine and surgery, University of Parma, Italy, described the study as exploratory but preliminary.

He said the researchers have “laid the foundation for a new approach to diagnosing and treating bipolar disorders.

“The shift from the subjective to the biological level could also promote understanding of the underlying mechanistic dynamics of mood swings.”

The study was funded by the Instituto de Salud Carlos III and a Baszucki Brain Research Fund grant from the Milken Foundation. The authors have disclosed no relevant financial relationships.
 

A version of this article first appeared on Medscape.com.

Evaluation of temperament in mental health outpatients showed a significant association between the irritable temperament type and a diagnosis of bipolar I and bipolar II disorders, based on data from more than 1,700 individuals.

When German psychiatrist Emil Kraepelin (1856-1926) studied emotions in patients with affective disorders, he identified four temperaments: the depressive (DT), the hyperthymic (HT), the irritable (IT), and the cyclothymic (CT). Subsequent researchers later identified an anxious temperament (AT).

“The notion that temperaments can be useful in predicting bipolar disorders sparked a plethora of research,” wrote Elie G. Karam, MD, of Saint George Hospital, Beirut, and colleagues. In particular, the cyclothymic (CT) and irritable (IT) temperament types have been targeted in studies of patients with bipolar disorders, but previous studies of temperament and bipolar have been limited by methodological issues, they said.

In a study published in European Psychiatry, the researchers reviewed data from 1,723 consecutive adult outpatients who presented to a university-based mental health clinic with various symptoms between January 2014 and September 2019.

Patients were assessed using the Hypomania Checklist-32 (HCL-32) and the Temperament Evaluation of Memphis, Pisa, Paris, and San Diego-Auto-questionnaire (TEMPS-A), then were diagnosed by psychiatrists using DSM-5 criteria. Patients with any bipolar types as defined by the DSM-5 underwent simple and multiple binary logistic regression analyses. The analysis included continuous scores and categorical normalized z-scores.

A total of 369 patients had confirmed DSM-5 diagnosis of bipolar disorder (52 with type I, 176 with type II, 102 with other specified bipolar and related disorder, and 39 with substance- or medication-induced bipolar disorder. The mean age of the participants was 38 years, and 54% were female.

In a bivariate analysis, all continuous temperament scores were significant predictors of bipolar disorder; all except AT remained significant in multivariate analysis. Increasing scores of IT, CT, and HT were associated with bipolar disorder, but increasing scores of DT were reflective of lower chance of bipolar disorder, the researchers noted.

In multivariate analysis of categorical normalized z-scores, IT and CT were significant predictors of bipolar disorder. At the highest point, CT was the stronger predictor, compared with IT (odds ratio, 3.84 vs. 2.55); having a higher DT score significantly reduced the odds of bipolar disorder (OR, 0.50).

However, “after adjusting for the presence of all temperaments as well as age and gender, only IT remained a significant predictor of patients with bipolar I disorder with adjusted OR of 1.19,” the researchers wrote.

“Correlations among temperaments were solid whether looking at patients with bipolarity or not, further emphasizing the necessity of controlling for them,” the researchers wrote in their discussion.

The findings were limited by several factors including the lack of structured interviews, the use of an outpatient-only sample, and the small number of bipolar I patients, the researchers noted.

However, the result suggest that IT can serve as a predictor of bipolar I and bipolar II disorders they said. Given the underdiagnosis of bipolar disorder in many studies, the incorporation of temperaments into the assessment of patients and research participants alike is likely to help us detect the presence of bipolarity more readily and quite importantly help us in our quest to understand their genesis,” they concluded.

The study was supported in part by anonymous private unrestricted donations to IDRAAC, Lebanon, and by Eli Lilly. The researchers had no financial conflicts to disclose.

Evaluation of temperament in mental health outpatients showed a significant association between the irritable temperament type and a diagnosis of bipolar I and bipolar II disorders, based on data from more than 1,700 individuals.

When German psychiatrist Emil Kraepelin (1856-1926) studied emotions in patients with affective disorders, he identified four temperaments: the depressive (DT), the hyperthymic (HT), the irritable (IT), and the cyclothymic (CT). Subsequent researchers later identified an anxious temperament (AT).

“The notion that temperaments can be useful in predicting bipolar disorders sparked a plethora of research,” wrote Elie G. Karam, MD, of Saint George Hospital, Beirut, and colleagues. In particular, the cyclothymic (CT) and irritable (IT) temperament types have been targeted in studies of patients with bipolar disorders, but previous studies of temperament and bipolar have been limited by methodological issues, they said.

In a study published in European Psychiatry, the researchers reviewed data from 1,723 consecutive adult outpatients who presented to a university-based mental health clinic with various symptoms between January 2014 and September 2019.

Patients were assessed using the Hypomania Checklist-32 (HCL-32) and the Temperament Evaluation of Memphis, Pisa, Paris, and San Diego-Auto-questionnaire (TEMPS-A), then were diagnosed by psychiatrists using DSM-5 criteria. Patients with any bipolar types as defined by the DSM-5 underwent simple and multiple binary logistic regression analyses. The analysis included continuous scores and categorical normalized z-scores.

A total of 369 patients had confirmed DSM-5 diagnosis of bipolar disorder (52 with type I, 176 with type II, 102 with other specified bipolar and related disorder, and 39 with substance- or medication-induced bipolar disorder. The mean age of the participants was 38 years, and 54% were female.

In a bivariate analysis, all continuous temperament scores were significant predictors of bipolar disorder; all except AT remained significant in multivariate analysis. Increasing scores of IT, CT, and HT were associated with bipolar disorder, but increasing scores of DT were reflective of lower chance of bipolar disorder, the researchers noted.

In multivariate analysis of categorical normalized z-scores, IT and CT were significant predictors of bipolar disorder. At the highest point, CT was the stronger predictor, compared with IT (odds ratio, 3.84 vs. 2.55); having a higher DT score significantly reduced the odds of bipolar disorder (OR, 0.50).

However, “after adjusting for the presence of all temperaments as well as age and gender, only IT remained a significant predictor of patients with bipolar I disorder with adjusted OR of 1.19,” the researchers wrote.

“Correlations among temperaments were solid whether looking at patients with bipolarity or not, further emphasizing the necessity of controlling for them,” the researchers wrote in their discussion.

The findings were limited by several factors including the lack of structured interviews, the use of an outpatient-only sample, and the small number of bipolar I patients, the researchers noted.

However, the result suggest that IT can serve as a predictor of bipolar I and bipolar II disorders they said. Given the underdiagnosis of bipolar disorder in many studies, the incorporation of temperaments into the assessment of patients and research participants alike is likely to help us detect the presence of bipolarity more readily and quite importantly help us in our quest to understand their genesis,” they concluded.

The study was supported in part by anonymous private unrestricted donations to IDRAAC, Lebanon, and by Eli Lilly. The researchers had no financial conflicts to disclose.

Evaluation of temperament in mental health outpatients showed a significant association between the irritable temperament type and a diagnosis of bipolar I and bipolar II disorders, based on data from more than 1,700 individuals.

When German psychiatrist Emil Kraepelin (1856-1926) studied emotions in patients with affective disorders, he identified four temperaments: the depressive (DT), the hyperthymic (HT), the irritable (IT), and the cyclothymic (CT). Subsequent researchers later identified an anxious temperament (AT).

“The notion that temperaments can be useful in predicting bipolar disorders sparked a plethora of research,” wrote Elie G. Karam, MD, of Saint George Hospital, Beirut, and colleagues. In particular, the cyclothymic (CT) and irritable (IT) temperament types have been targeted in studies of patients with bipolar disorders, but previous studies of temperament and bipolar have been limited by methodological issues, they said.

In a study published in European Psychiatry, the researchers reviewed data from 1,723 consecutive adult outpatients who presented to a university-based mental health clinic with various symptoms between January 2014 and September 2019.

Patients were assessed using the Hypomania Checklist-32 (HCL-32) and the Temperament Evaluation of Memphis, Pisa, Paris, and San Diego-Auto-questionnaire (TEMPS-A), then were diagnosed by psychiatrists using DSM-5 criteria. Patients with any bipolar types as defined by the DSM-5 underwent simple and multiple binary logistic regression analyses. The analysis included continuous scores and categorical normalized z-scores.

A total of 369 patients had confirmed DSM-5 diagnosis of bipolar disorder (52 with type I, 176 with type II, 102 with other specified bipolar and related disorder, and 39 with substance- or medication-induced bipolar disorder. The mean age of the participants was 38 years, and 54% were female.

In a bivariate analysis, all continuous temperament scores were significant predictors of bipolar disorder; all except AT remained significant in multivariate analysis. Increasing scores of IT, CT, and HT were associated with bipolar disorder, but increasing scores of DT were reflective of lower chance of bipolar disorder, the researchers noted.

In multivariate analysis of categorical normalized z-scores, IT and CT were significant predictors of bipolar disorder. At the highest point, CT was the stronger predictor, compared with IT (odds ratio, 3.84 vs. 2.55); having a higher DT score significantly reduced the odds of bipolar disorder (OR, 0.50).

However, “after adjusting for the presence of all temperaments as well as age and gender, only IT remained a significant predictor of patients with bipolar I disorder with adjusted OR of 1.19,” the researchers wrote.

“Correlations among temperaments were solid whether looking at patients with bipolarity or not, further emphasizing the necessity of controlling for them,” the researchers wrote in their discussion.

The findings were limited by several factors including the lack of structured interviews, the use of an outpatient-only sample, and the small number of bipolar I patients, the researchers noted.

However, the result suggest that IT can serve as a predictor of bipolar I and bipolar II disorders they said. Given the underdiagnosis of bipolar disorder in many studies, the incorporation of temperaments into the assessment of patients and research participants alike is likely to help us detect the presence of bipolarity more readily and quite importantly help us in our quest to understand their genesis,” they concluded.

The study was supported in part by anonymous private unrestricted donations to IDRAAC, Lebanon, and by Eli Lilly. The researchers had no financial conflicts to disclose.

Use of dialectical behavior therapy significantly reduced suicide attempts in adolescents with bipolar disorder, compared with standard of care, based on data from 100 individuals aged 12-18 years.

University of Pittsburgh

Bipolar spectrum disorder (BP) is known to substantially increase the risk for suicide in youth, but no psychosocial intervention for this population has targeted suicidal behavior in particular, wrote Tina R. Goldstein, PhD, of the University of Pittsburgh, and colleagues.

Dialectical behavior therapy (DBT) had shown effectiveness for decreasing suicide attempts in adults with borderline personality disorder, and previous studies of DBT have shown reduced suicidal ideation, self-harm, and suicide attempts in suicidal adolescents, but these studies have mainly excluded BP teens, the researchers said.

In a study published in JAMA Psychiatry, the researchers recruited adolescents aged 12-18 years with a diagnosis of BP who were treated at an outpatient clinic between November 2014 and September 2019. Of these, 47 were randomized to 1 year of DBT (a total of 36 sessions) and 53 to standard of care (SOC) psychotherapy. All participants also received medication using a flexible algorithm.

The primary outcomes were suicide attempts over a 1-year period and measurements of mood symptoms and states, specifically depression and hypomania/mania. Secondary analyses included the effect of DBT on individuals with a history of suicide attempt and on improving emotion dysregulation. The mean age of the participants was 16.1 years; 85 were female, and 74% were White.

Participants in both DBT and SOC groups reported similar rates of suicide attempt rates at study enrollment based on the Adolescent Longitudinal Follow-Up Evaluation (ALIFE) with a mean of 2.0 and 1.8 attempts, respectively (P = .80). Based on the Columbia–Suicide Severity Rating Scale Pediatric Version (C-SSRS), participants in the DBT group had slightly more suicide attempts than the SOC group at study enrollment, with a mean of 1.4 and 0.6 attempts, respectively (P = .02).

Controlling for baseline attempts, participants in the DBT group had significantly fewer suicide attempts over the study period, compared with the SOC group as measured by both ALIFE (mean 0.2 vs. 1.1) and C-SSRS (mean 0.04 vs. 0.10, P = .03 for both measures). The incidence rate ratios for reduced suicide attempts were 0.32 for ALIFE and 0.13 for C-SSRS, both significant in favor of DBT, compared with SOC.

Overall, both groups showed similarly significant improvement on measures of mood symptoms and episodes over the study period. The standardized depression rating scale slope was –0.17 and the standardized mania rating scale slope was –0.24.

DBT was significantly more effective than SOC psychotherapy at decreasing suicide attempts over 1 year (ALIFE: incidence rate ratio, 0.32; 95% CI, 0.11-0.96; C-SSRS: IRR, 0.13; 95% CI, 0.02-0.78).

On further analysis, the decrease in suicide attempts in the DBT group was greater over time and among those with a lifetime history of suicide attempts (IRR, 0.23). “Decreased risk of suicide attempt in DBT was mediated by improvement in emotion dysregulation, particularly for those with high baseline emotion dysregulation,” the researchers wrote in their discussion.

The findings were limited by several factors including the mainly female, non-Hispanic White study population, and controlled clinical setting, the researchers noted. Data from a forthcoming community implementation field trial will address some generalizability issues, although more work is needed to address disparities in BP diagnosis and treatment, they added.

However, the results support the potential of DBT for mood management and for reducing suicide attempts in a high-risk adolescent population, especially those with high levels of emotional dysregulation, on par with other established psychosocial treatments, the researchers concluded.
 

More options needed to manage increased risk

“It was important to conduct this study at this time because, while still relatively rare, bipolar spectrum disorders in adolescents confer increased risk for suicide,” Peter L. Loper Jr., MD, of the University of South Carolina, Columbia, said in an interview. The complexity of BP and the increased risk of suicide in these patients challenge clinicians to identify robust evidence-based interventions beyond pharmacotherapy that mitigate this risk, said Dr. Loper, who is triple board certified in pediatrics, general psychiatry, and child & adolescent psychiatry, but was not involved in the study.

The current study findings were not surprising, because DBT has proven effective in decreasing suicidal ideation and suicide attempts in other high-risk adolescent patient populations, Dr. Loper said. “Given the therapeutic content of DBT, with emphasis on mindfulness, distress tolerance, social skills, and emotional regulation, I think it is reasonable to hypothesize that DBT might be a globally applicable intervention, independent of mental health diagnosis or etiology of suicidal ideation,” he said.

The take-home message for clinicians is that the results support the efficacy of DBT as an intervention for adolescents with BP and suicidal ideation, self-injurious behavior, or suicide attempts, said Dr. Loper. For these patients, given their increased suicide risk, “DBT should certainly be recommended as a component of their treatment plan,” he said.

However, barriers to the use of DBT in clinical practice exist, notably access and cost, Dr. Loper noted. “I think that the most prominent barrier in accessing DBT in clinical practice is the availability of certified, structured DBT treatment programs, and particularly those willing to provide services to adolescents,” he said. “Additionally, certified DBT programs, which are the gold standard, are often not covered by third-party payers, making cost yet another potential barrier.”

Looking ahead, Dr. Loper agreed with the study authors that additional research with a more diverse patient population representative of adolescents with bipolar spectrum disorder “is a crucial area of focus.”

The study was funded by the National Institutes of Mental Health through a grant to Dr. Goldstein, who also disclosed royalties from Guilford Press unrelated to the current study. Dr. Loper had no financial conflicts to disclose.
 

Use of dialectical behavior therapy significantly reduced suicide attempts in adolescents with bipolar disorder, compared with standard of care, based on data from 100 individuals aged 12-18 years.

University of Pittsburgh

Bipolar spectrum disorder (BP) is known to substantially increase the risk for suicide in youth, but no psychosocial intervention for this population has targeted suicidal behavior in particular, wrote Tina R. Goldstein, PhD, of the University of Pittsburgh, and colleagues.

Dialectical behavior therapy (DBT) had shown effectiveness for decreasing suicide attempts in adults with borderline personality disorder, and previous studies of DBT have shown reduced suicidal ideation, self-harm, and suicide attempts in suicidal adolescents, but these studies have mainly excluded BP teens, the researchers said.

In a study published in JAMA Psychiatry, the researchers recruited adolescents aged 12-18 years with a diagnosis of BP who were treated at an outpatient clinic between November 2014 and September 2019. Of these, 47 were randomized to 1 year of DBT (a total of 36 sessions) and 53 to standard of care (SOC) psychotherapy. All participants also received medication using a flexible algorithm.

The primary outcomes were suicide attempts over a 1-year period and measurements of mood symptoms and states, specifically depression and hypomania/mania. Secondary analyses included the effect of DBT on individuals with a history of suicide attempt and on improving emotion dysregulation. The mean age of the participants was 16.1 years; 85 were female, and 74% were White.

Participants in both DBT and SOC groups reported similar rates of suicide attempt rates at study enrollment based on the Adolescent Longitudinal Follow-Up Evaluation (ALIFE) with a mean of 2.0 and 1.8 attempts, respectively (P = .80). Based on the Columbia–Suicide Severity Rating Scale Pediatric Version (C-SSRS), participants in the DBT group had slightly more suicide attempts than the SOC group at study enrollment, with a mean of 1.4 and 0.6 attempts, respectively (P = .02).

Controlling for baseline attempts, participants in the DBT group had significantly fewer suicide attempts over the study period, compared with the SOC group as measured by both ALIFE (mean 0.2 vs. 1.1) and C-SSRS (mean 0.04 vs. 0.10, P = .03 for both measures). The incidence rate ratios for reduced suicide attempts were 0.32 for ALIFE and 0.13 for C-SSRS, both significant in favor of DBT, compared with SOC.

Overall, both groups showed similarly significant improvement on measures of mood symptoms and episodes over the study period. The standardized depression rating scale slope was –0.17 and the standardized mania rating scale slope was –0.24.

DBT was significantly more effective than SOC psychotherapy at decreasing suicide attempts over 1 year (ALIFE: incidence rate ratio, 0.32; 95% CI, 0.11-0.96; C-SSRS: IRR, 0.13; 95% CI, 0.02-0.78).

On further analysis, the decrease in suicide attempts in the DBT group was greater over time and among those with a lifetime history of suicide attempts (IRR, 0.23). “Decreased risk of suicide attempt in DBT was mediated by improvement in emotion dysregulation, particularly for those with high baseline emotion dysregulation,” the researchers wrote in their discussion.

The findings were limited by several factors including the mainly female, non-Hispanic White study population, and controlled clinical setting, the researchers noted. Data from a forthcoming community implementation field trial will address some generalizability issues, although more work is needed to address disparities in BP diagnosis and treatment, they added.

However, the results support the potential of DBT for mood management and for reducing suicide attempts in a high-risk adolescent population, especially those with high levels of emotional dysregulation, on par with other established psychosocial treatments, the researchers concluded.
 

More options needed to manage increased risk

“It was important to conduct this study at this time because, while still relatively rare, bipolar spectrum disorders in adolescents confer increased risk for suicide,” Peter L. Loper Jr., MD, of the University of South Carolina, Columbia, said in an interview. The complexity of BP and the increased risk of suicide in these patients challenge clinicians to identify robust evidence-based interventions beyond pharmacotherapy that mitigate this risk, said Dr. Loper, who is triple board certified in pediatrics, general psychiatry, and child & adolescent psychiatry, but was not involved in the study.

The current study findings were not surprising, because DBT has proven effective in decreasing suicidal ideation and suicide attempts in other high-risk adolescent patient populations, Dr. Loper said. “Given the therapeutic content of DBT, with emphasis on mindfulness, distress tolerance, social skills, and emotional regulation, I think it is reasonable to hypothesize that DBT might be a globally applicable intervention, independent of mental health diagnosis or etiology of suicidal ideation,” he said.

The take-home message for clinicians is that the results support the efficacy of DBT as an intervention for adolescents with BP and suicidal ideation, self-injurious behavior, or suicide attempts, said Dr. Loper. For these patients, given their increased suicide risk, “DBT should certainly be recommended as a component of their treatment plan,” he said.

However, barriers to the use of DBT in clinical practice exist, notably access and cost, Dr. Loper noted. “I think that the most prominent barrier in accessing DBT in clinical practice is the availability of certified, structured DBT treatment programs, and particularly those willing to provide services to adolescents,” he said. “Additionally, certified DBT programs, which are the gold standard, are often not covered by third-party payers, making cost yet another potential barrier.”

Looking ahead, Dr. Loper agreed with the study authors that additional research with a more diverse patient population representative of adolescents with bipolar spectrum disorder “is a crucial area of focus.”

The study was funded by the National Institutes of Mental Health through a grant to Dr. Goldstein, who also disclosed royalties from Guilford Press unrelated to the current study. Dr. Loper had no financial conflicts to disclose.
 

Use of dialectical behavior therapy significantly reduced suicide attempts in adolescents with bipolar disorder, compared with standard of care, based on data from 100 individuals aged 12-18 years.

University of Pittsburgh

Bipolar spectrum disorder (BP) is known to substantially increase the risk for suicide in youth, but no psychosocial intervention for this population has targeted suicidal behavior in particular, wrote Tina R. Goldstein, PhD, of the University of Pittsburgh, and colleagues.

Dialectical behavior therapy (DBT) had shown effectiveness for decreasing suicide attempts in adults with borderline personality disorder, and previous studies of DBT have shown reduced suicidal ideation, self-harm, and suicide attempts in suicidal adolescents, but these studies have mainly excluded BP teens, the researchers said.

In a study published in JAMA Psychiatry, the researchers recruited adolescents aged 12-18 years with a diagnosis of BP who were treated at an outpatient clinic between November 2014 and September 2019. Of these, 47 were randomized to 1 year of DBT (a total of 36 sessions) and 53 to standard of care (SOC) psychotherapy. All participants also received medication using a flexible algorithm.

The primary outcomes were suicide attempts over a 1-year period and measurements of mood symptoms and states, specifically depression and hypomania/mania. Secondary analyses included the effect of DBT on individuals with a history of suicide attempt and on improving emotion dysregulation. The mean age of the participants was 16.1 years; 85 were female, and 74% were White.

Participants in both DBT and SOC groups reported similar rates of suicide attempt rates at study enrollment based on the Adolescent Longitudinal Follow-Up Evaluation (ALIFE) with a mean of 2.0 and 1.8 attempts, respectively (P = .80). Based on the Columbia–Suicide Severity Rating Scale Pediatric Version (C-SSRS), participants in the DBT group had slightly more suicide attempts than the SOC group at study enrollment, with a mean of 1.4 and 0.6 attempts, respectively (P = .02).

Controlling for baseline attempts, participants in the DBT group had significantly fewer suicide attempts over the study period, compared with the SOC group as measured by both ALIFE (mean 0.2 vs. 1.1) and C-SSRS (mean 0.04 vs. 0.10, P = .03 for both measures). The incidence rate ratios for reduced suicide attempts were 0.32 for ALIFE and 0.13 for C-SSRS, both significant in favor of DBT, compared with SOC.

Overall, both groups showed similarly significant improvement on measures of mood symptoms and episodes over the study period. The standardized depression rating scale slope was –0.17 and the standardized mania rating scale slope was –0.24.

DBT was significantly more effective than SOC psychotherapy at decreasing suicide attempts over 1 year (ALIFE: incidence rate ratio, 0.32; 95% CI, 0.11-0.96; C-SSRS: IRR, 0.13; 95% CI, 0.02-0.78).

On further analysis, the decrease in suicide attempts in the DBT group was greater over time and among those with a lifetime history of suicide attempts (IRR, 0.23). “Decreased risk of suicide attempt in DBT was mediated by improvement in emotion dysregulation, particularly for those with high baseline emotion dysregulation,” the researchers wrote in their discussion.

The findings were limited by several factors including the mainly female, non-Hispanic White study population, and controlled clinical setting, the researchers noted. Data from a forthcoming community implementation field trial will address some generalizability issues, although more work is needed to address disparities in BP diagnosis and treatment, they added.

However, the results support the potential of DBT for mood management and for reducing suicide attempts in a high-risk adolescent population, especially those with high levels of emotional dysregulation, on par with other established psychosocial treatments, the researchers concluded.
 

More options needed to manage increased risk

“It was important to conduct this study at this time because, while still relatively rare, bipolar spectrum disorders in adolescents confer increased risk for suicide,” Peter L. Loper Jr., MD, of the University of South Carolina, Columbia, said in an interview. The complexity of BP and the increased risk of suicide in these patients challenge clinicians to identify robust evidence-based interventions beyond pharmacotherapy that mitigate this risk, said Dr. Loper, who is triple board certified in pediatrics, general psychiatry, and child & adolescent psychiatry, but was not involved in the study.

The current study findings were not surprising, because DBT has proven effective in decreasing suicidal ideation and suicide attempts in other high-risk adolescent patient populations, Dr. Loper said. “Given the therapeutic content of DBT, with emphasis on mindfulness, distress tolerance, social skills, and emotional regulation, I think it is reasonable to hypothesize that DBT might be a globally applicable intervention, independent of mental health diagnosis or etiology of suicidal ideation,” he said.

The take-home message for clinicians is that the results support the efficacy of DBT as an intervention for adolescents with BP and suicidal ideation, self-injurious behavior, or suicide attempts, said Dr. Loper. For these patients, given their increased suicide risk, “DBT should certainly be recommended as a component of their treatment plan,” he said.

However, barriers to the use of DBT in clinical practice exist, notably access and cost, Dr. Loper noted. “I think that the most prominent barrier in accessing DBT in clinical practice is the availability of certified, structured DBT treatment programs, and particularly those willing to provide services to adolescents,” he said. “Additionally, certified DBT programs, which are the gold standard, are often not covered by third-party payers, making cost yet another potential barrier.”

Looking ahead, Dr. Loper agreed with the study authors that additional research with a more diverse patient population representative of adolescents with bipolar spectrum disorder “is a crucial area of focus.”

The study was funded by the National Institutes of Mental Health through a grant to Dr. Goldstein, who also disclosed royalties from Guilford Press unrelated to the current study. Dr. Loper had no financial conflicts to disclose.
 

Suicide is not a trivial matter – it upends families, robs partners of a loved one, prevents children from having a parent, and can destroy a parent’s most cherished being. It is not surprising that societies have repeatedly made it a goal to study and reduce suicide within their populations.

The suicide rate in the United States is trending upward, from about 10 per 100,000 in 2000 to about 15 per 100,000 in more recent reports. The increasing suicide rates have been accompanied by increasing distress among many strata of society. From a public health level, analysts are not just witnessing increasing suicide rates, but a shocking rise in all “deaths of despair,”¹ among which suicide can be considered the ultimate example.

On an individual level, many know someone who has died of suicide or suffered from a serious suicide attempt. From the public health level to the individual level, advocacy has called for various interventions in the field of psychiatry to remedy this tragic problem.

Psychiatrists have been firsthand witnesses to this increasing demand for suicide interventions. When in residency, the norm was to perform a suicide risk assessment at the time of admission to the hospital and again at the time of discharge. As the years passed, the new normal within psychiatric hospitals has shifted to asking about suicidality on a daily basis.

In what seems to us like an escalating arms race, the emerging standard of care at many facilities is now not only for daily suicide risk assessments by each psychiatrist, but also to require nurses to ask about suicidality during every 8-hour shift – in addition to documented inquiries about suicidality by other allied staff on the psychiatric unit. As a result, it is not uncommon for a patient hospitalized at an academic center to receive more than half a dozen suicide risk assessments in a day (first by the medical student, at least once – often more than once – by the resident, again by the attending psychiatrist, then the social worker and three nurses in 24 hours).

One of the concerns about such an approach is the lack of logic inherent to many risk assessment tools and symptom scales. Many of us are familiar with the Patient Health Questionnaire (PHQ-9) to assess depression.² The PHQ-9 asks to consider “over the last 2 weeks, how often have you ...” in relation to nine symptoms associated with depression. It has always defied reason to perform a PHQ-9 every day and expect the answers to change from “nearly every day” to “not at all,” considering only 1 day has passed since the last time the patient has answered the questions. Yet daily, or near daily, PHQ-9 scores are a frequently used tool of tracking symptom improvement in response to treatments, such as electroconvulsive therapy, performed multiple times a week.

One can argue that the patient’s perspective on how symptomatic he or she has been over the past 2 weeks may change rapidly with alleviation of a depressed mood. However, the PHQ-9 is both reported to be, and often regarded as, an objective score. If one wishes to utilize it as such, the defense of its use should not be that it is a subjective report with just as much utility as “Rate your depression on a scale of 0-27.”

Similarly, many suicide scales were intended to assess thoughts of suicide in the past month³ or have been re-tooled to address this particular concern by asking “since the last contact.”⁴ It is baffling to see a chart with many dozens of suicide risk assessments with at times widely differing answers, yet all measuring thoughts of suicide in the past month. Is one to expect the answer to “How many times have you had these thoughts [of suicide ideation]? (1) Less than once a week (2) Once a week ...” to change between 8 a.m. and noon? Furthermore, for the purpose of assessing acute risk of suicidality in the immediate future, to only consider symptoms since the last contact – or past 2 weeks, past month, etc. – is of unclear significance.
 

Provider liability

Another concern is the liability placed on providers. A common problem encountered in the inpatient setting is insurance companies refusing to reimburse a hospital stay for depressed patients denying suicidality.

Any provider in the position of caring for such a patient must ask: What is the likelihood of someone providing a false negative – a false denial of suicidality? Is the likelihood of a suicidal person denying suicidality different if asked 5 or 10 or more times in a day? There are innumerable instances where a patient at a very high risk of self-harm has denied suicidality, been discharged from the hospital, and suffered terrible consequences. Ethically, the psychiatrist aware of this risk is no more at ease discharging these patients, whether it is one suicide risk scale or a dozen that suggests a patient is at low risk.

Alternatively, it may feel untenable from a medicolegal perspective for a psychiatrist to discharge a patient denying suicidality when the chart includes over a dozen previously documented elevated suicide risk assessments in the past 72 hours. By placing the job of suicide risk assessment in the hands of providers of varying levels of training and responsibility, a situation is created in which the seasoned psychiatrist who would otherwise be comfortable discharging a patient feels unable to do so because every other note-writer in the record – from the triage nurse to the medical assistant to the sitter in the emergency department – has recorded the patient as high risk for suicide. When put in such a position, the thought often occurs that systems of care, rather than individual providers, are protected most by ever escalating requirements for suicide risk documentation. To make a clinical decision contrary to the body of suicide risk documentation puts the provider at risk of being scapegoated by the system of care, which can point to its illogical and ineffective, though profusely documented, suicide prevention protocols.
 

Limitations of risk assessments

Considering the ongoing rise in the use of suicide risk assessments, one would expect that the evidence for their efficacy was robust and well established. Yet a thorough review of suicide risk assessments funded by the MacArthur Foundation, which examined decades of research, came to disheartening conclusions: “predictive ability has not improved over the past 50 years”; “no risk factor category or subcategory is substantially stronger than any other”; and “predicting solely according to base rates may be comparable to prediction with current risk factors.”⁵

Those findings were consistent with the conclusions of many other studies, which have summarized the utility of suicide risk assessments as follows: “occurrence of suicide is too low to identify those individuals who are likely to die by suicide”;⁶ “suicide prediction models produce accurate overall classification models, but their accuracy of predicting a future event is near zero”;⁷ “risk stratification is too inaccurate to be clinically useful and might even be harmful”;⁸ “suicide risk prediction [lacks] any items or information that to a useful degree permit the identification of persons who will complete suicide”;⁹ “existing suicide prediction tools have little current clinical value”;¹⁰ “our current preoccupation with risk assessment has ... created a mythology with no evidence to support it.”¹¹ And that’s to cite just a few.

Sadly, we have known about the limitations of suicide risk assessments for many decades. In 1983 a large VA prospective study, which aimed to identify veterans who will die by suicide, examined 4,800 patients with a wide range of instruments and measures.¹² This study concluded that “discriminant analysis was clearly inadequate in correctly classifying the subjects. For an event as rare as suicide, our predictive tools and guides are simply not equal to the task.” The authors described the feelings of many in stating “courts and public opinion expect physicians to be able to pick out the particular persons who will later commit suicide. Although we may reconstruct causal chains and motives, we do not possess the tools to predict suicides.”

Yet, even several decades prior, in 1954, Dr. Albert Rosen performed an elegant statistical analysis and predicted that, considering the low base rate of suicide, suicide risk assessments are “of no practical value, for it would be impossible to treat the prodigious number of false positives.”¹³ It seems that we continue to be unable to accept Dr. Rosen’s premonition despite decades of confirmatory evidence.
 

“Quantity over quality”

Regardless of those sobering reports, the field of psychiatry is seemingly doubling down on efforts to predict and prevent suicide deaths, and the way it is doing so has very questionable validity.

One can reasonably argue that the periodic performance of a suicide risk assessment may have clinical utility in reminding us of modifiable risk factors such as intoxication, social isolation, and access to lethal means. One can also reasonably argue that these risk assessments may provide useful education to patients and their families on epidemiological risk factors such as gender, age, and marital status. But our pursuit of serial suicide risk assessments throughout the day is encouraging providers to focus on a particular risk factor that changes from moment to moment and has particularly low validity, that being self-reported suicidality.

Reported suicidality is one of the few risk factors that can change from shift to shift. But 80% of people who die by suicide had not previously expressed suicidality, and 98.3% of people who have endorsed suicidality do not die by suicide.¹⁴ While the former statistic may improve with increased assessment, the later will likely worsen.

Suicide is not a trivial matter. We admire those that study it and advocate for better interventions. We have compassion for those who have suffered the loss of a loved one to suicide. Our patients have died as a result of the human limitations surrounding suicide prevention. Recognizing the weight of suicide and making an effort to avoid minimizing its immense consequences drive our desire to be honest with ourselves, our patients and their families, and society. That includes the unfortunate truth regarding the current state of the evidence and our ability to enact change.

It is our concern that the rising fascination with repeated suicide risk assessment is misguided in its current form and serves the purpose of appeasing administrators more than reflecting a scientific understanding of the literature. More sadly, we are concerned that this “quantity-over-quality” approach is yet another barrier to practicing what may be one of the few interventions with any hope of meaningfully impacting a patient’s risk of suicide in the clinical setting – spending time connecting with our patients.

Dr. Badre is a clinical and forensic psychiatrist in San Diego. He holds teaching positions at the University of California, San Diego, and the University of San Diego. He teaches medical education, psychopharmacology, ethics in psychiatry, and correctional care. Dr. Badre can be reached at his website, BadreMD.com. Dr. Compton is a member of the psychiatry faculty at University of California, San Diego. His background includes medical education, mental health advocacy, work with underserved populations, and brain cancer research. Dr. Badre and Dr. Compton have no conflicts of interest.

References

1. Joint Economic Committee. (2019). Long Term Trends in Deaths of Despair. SCP Report 4-19.

2. Kroenke K and Spitzer RL. The PHQ-9: A new depression diagnostic and severity measure. Psychiatr Ann. 2013;32(9):509-15. doi: 10.3928/0048-5713-20020901-06.

3. Columbia-Suicide Severity Rating Scale (C-SSRS) Full Lifetime/Recent.

4. Columbia-Suicide Severity Rating Scale (C-SSRS) Full Since Last Contact.

5. Franklin JC et al. Risk factors for suicidal thoughts and behaviors: A meta-analysis of 50 years of research. Psychol Bull. 2017 Feb;143(2):187-232. doi: 10.1037/bul0000084.

6. Beautrais AL. Further suicidal behavior among medically serious suicide attempters. Suicide Life Threat Behav. 2004 Spring;34(1):1-11. doi: 10.1521/suli.34.1.1.27772.

7. Belsher BE. Prediction models for suicide attempts and deaths: A systematic review and simulation. JAMA Psychiatry. 2019 Jun 1;76(6):642-651. doi: 10.1001/jamapsychiatry.2019.0174.

8. Carter G et al. Royal Australian and New Zealand College of Psychiatrists clinical practice guideline for the management of deliberate self-harm. Aust N Z J Psychiatry. 2016 Oct;50(10):939-1000. doi: 10.1177/0004867416661039.

9. Fosse R et al. Predictors of suicide in the patient population admitted to a locked-door psychiatric acute ward. PLoS One. 2017 Mar 16;12(3):e0173958. doi: 10.1371/journal.pone.0173958.

10. Kessler RC et al. Suicide prediction models: A critical review of recent research with recommendations for the way forward. Mol Psychiatry. 2020 Jan;25(1):168-79. doi: 10.1038/s41380-019-0531-0.

11. Mulder R. Problems with suicide risk assessment. Aust N Z J Psychiatry. 2011 Aug;45(8):605-7. doi: 10.3109/00048674.2011.594786.

12. Pokorny AD. Prediction of suicide in psychiatric patients: Report of a prospective study. Arch Gen Psychiatry. 1983 Mar;40(3):249-57. doi: 10.1001/archpsyc.1983.01790030019002.

13. Rosen A. Detection of suicidal patients: An example of some limitations in the prediction of infrequent events. J Consult Psychol. 1954 Dec;18(6):397-403. doi: 10.1037/h0058579.

14. McHugh CM et al. (2019). Association between suicidal ideation and suicide: Meta-analyses of odds ratios, sensitivity, specificity and positive predictive value. BJPsych Open. 2019 Mar;5(2):e18. doi: 10.1192/bjo.2018.88.

Suicide is not a trivial matter – it upends families, robs partners of a loved one, prevents children from having a parent, and can destroy a parent’s most cherished being. It is not surprising that societies have repeatedly made it a goal to study and reduce suicide within their populations.

The suicide rate in the United States is trending upward, from about 10 per 100,000 in 2000 to about 15 per 100,000 in more recent reports. The increasing suicide rates have been accompanied by increasing distress among many strata of society. From a public health level, analysts are not just witnessing increasing suicide rates, but a shocking rise in all “deaths of despair,”¹ among which suicide can be considered the ultimate example.

On an individual level, many know someone who has died of suicide or suffered from a serious suicide attempt. From the public health level to the individual level, advocacy has called for various interventions in the field of psychiatry to remedy this tragic problem.

Psychiatrists have been firsthand witnesses to this increasing demand for suicide interventions. When in residency, the norm was to perform a suicide risk assessment at the time of admission to the hospital and again at the time of discharge. As the years passed, the new normal within psychiatric hospitals has shifted to asking about suicidality on a daily basis.

In what seems to us like an escalating arms race, the emerging standard of care at many facilities is now not only for daily suicide risk assessments by each psychiatrist, but also to require nurses to ask about suicidality during every 8-hour shift – in addition to documented inquiries about suicidality by other allied staff on the psychiatric unit. As a result, it is not uncommon for a patient hospitalized at an academic center to receive more than half a dozen suicide risk assessments in a day (first by the medical student, at least once – often more than once – by the resident, again by the attending psychiatrist, then the social worker and three nurses in 24 hours).

One of the concerns about such an approach is the lack of logic inherent to many risk assessment tools and symptom scales. Many of us are familiar with the Patient Health Questionnaire (PHQ-9) to assess depression.² The PHQ-9 asks to consider “over the last 2 weeks, how often have you ...” in relation to nine symptoms associated with depression. It has always defied reason to perform a PHQ-9 every day and expect the answers to change from “nearly every day” to “not at all,” considering only 1 day has passed since the last time the patient has answered the questions. Yet daily, or near daily, PHQ-9 scores are a frequently used tool of tracking symptom improvement in response to treatments, such as electroconvulsive therapy, performed multiple times a week.

One can argue that the patient’s perspective on how symptomatic he or she has been over the past 2 weeks may change rapidly with alleviation of a depressed mood. However, the PHQ-9 is both reported to be, and often regarded as, an objective score. If one wishes to utilize it as such, the defense of its use should not be that it is a subjective report with just as much utility as “Rate your depression on a scale of 0-27.”

Similarly, many suicide scales were intended to assess thoughts of suicide in the past month³ or have been re-tooled to address this particular concern by asking “since the last contact.”⁴ It is baffling to see a chart with many dozens of suicide risk assessments with at times widely differing answers, yet all measuring thoughts of suicide in the past month. Is one to expect the answer to “How many times have you had these thoughts [of suicide ideation]? (1) Less than once a week (2) Once a week ...” to change between 8 a.m. and noon? Furthermore, for the purpose of assessing acute risk of suicidality in the immediate future, to only consider symptoms since the last contact – or past 2 weeks, past month, etc. – is of unclear significance.
 

Provider liability

Another concern is the liability placed on providers. A common problem encountered in the inpatient setting is insurance companies refusing to reimburse a hospital stay for depressed patients denying suicidality.

Any provider in the position of caring for such a patient must ask: What is the likelihood of someone providing a false negative – a false denial of suicidality? Is the likelihood of a suicidal person denying suicidality different if asked 5 or 10 or more times in a day? There are innumerable instances where a patient at a very high risk of self-harm has denied suicidality, been discharged from the hospital, and suffered terrible consequences. Ethically, the psychiatrist aware of this risk is no more at ease discharging these patients, whether it is one suicide risk scale or a dozen that suggests a patient is at low risk.

Alternatively, it may feel untenable from a medicolegal perspective for a psychiatrist to discharge a patient denying suicidality when the chart includes over a dozen previously documented elevated suicide risk assessments in the past 72 hours. By placing the job of suicide risk assessment in the hands of providers of varying levels of training and responsibility, a situation is created in which the seasoned psychiatrist who would otherwise be comfortable discharging a patient feels unable to do so because every other note-writer in the record – from the triage nurse to the medical assistant to the sitter in the emergency department – has recorded the patient as high risk for suicide. When put in such a position, the thought often occurs that systems of care, rather than individual providers, are protected most by ever escalating requirements for suicide risk documentation. To make a clinical decision contrary to the body of suicide risk documentation puts the provider at risk of being scapegoated by the system of care, which can point to its illogical and ineffective, though profusely documented, suicide prevention protocols.
 

Limitations of risk assessments

Considering the ongoing rise in the use of suicide risk assessments, one would expect that the evidence for their efficacy was robust and well established. Yet a thorough review of suicide risk assessments funded by the MacArthur Foundation, which examined decades of research, came to disheartening conclusions: “predictive ability has not improved over the past 50 years”; “no risk factor category or subcategory is substantially stronger than any other”; and “predicting solely according to base rates may be comparable to prediction with current risk factors.”⁵

Those findings were consistent with the conclusions of many other studies, which have summarized the utility of suicide risk assessments as follows: “occurrence of suicide is too low to identify those individuals who are likely to die by suicide”;⁶ “suicide prediction models produce accurate overall classification models, but their accuracy of predicting a future event is near zero”;⁷ “risk stratification is too inaccurate to be clinically useful and might even be harmful”;⁸ “suicide risk prediction [lacks] any items or information that to a useful degree permit the identification of persons who will complete suicide”;⁹ “existing suicide prediction tools have little current clinical value”;¹⁰ “our current preoccupation with risk assessment has ... created a mythology with no evidence to support it.”¹¹ And that’s to cite just a few.

Sadly, we have known about the limitations of suicide risk assessments for many decades. In 1983 a large VA prospective study, which aimed to identify veterans who will die by suicide, examined 4,800 patients with a wide range of instruments and measures.¹² This study concluded that “discriminant analysis was clearly inadequate in correctly classifying the subjects. For an event as rare as suicide, our predictive tools and guides are simply not equal to the task.” The authors described the feelings of many in stating “courts and public opinion expect physicians to be able to pick out the particular persons who will later commit suicide. Although we may reconstruct causal chains and motives, we do not possess the tools to predict suicides.”

Yet, even several decades prior, in 1954, Dr. Albert Rosen performed an elegant statistical analysis and predicted that, considering the low base rate of suicide, suicide risk assessments are “of no practical value, for it would be impossible to treat the prodigious number of false positives.”¹³ It seems that we continue to be unable to accept Dr. Rosen’s premonition despite decades of confirmatory evidence.
 

“Quantity over quality”

Regardless of those sobering reports, the field of psychiatry is seemingly doubling down on efforts to predict and prevent suicide deaths, and the way it is doing so has very questionable validity.

One can reasonably argue that the periodic performance of a suicide risk assessment may have clinical utility in reminding us of modifiable risk factors such as intoxication, social isolation, and access to lethal means. One can also reasonably argue that these risk assessments may provide useful education to patients and their families on epidemiological risk factors such as gender, age, and marital status. But our pursuit of serial suicide risk assessments throughout the day is encouraging providers to focus on a particular risk factor that changes from moment to moment and has particularly low validity, that being self-reported suicidality.

Reported suicidality is one of the few risk factors that can change from shift to shift. But 80% of people who die by suicide had not previously expressed suicidality, and 98.3% of people who have endorsed suicidality do not die by suicide.¹⁴ While the former statistic may improve with increased assessment, the later will likely worsen.

Suicide is not a trivial matter. We admire those that study it and advocate for better interventions. We have compassion for those who have suffered the loss of a loved one to suicide. Our patients have died as a result of the human limitations surrounding suicide prevention. Recognizing the weight of suicide and making an effort to avoid minimizing its immense consequences drive our desire to be honest with ourselves, our patients and their families, and society. That includes the unfortunate truth regarding the current state of the evidence and our ability to enact change.

It is our concern that the rising fascination with repeated suicide risk assessment is misguided in its current form and serves the purpose of appeasing administrators more than reflecting a scientific understanding of the literature. More sadly, we are concerned that this “quantity-over-quality” approach is yet another barrier to practicing what may be one of the few interventions with any hope of meaningfully impacting a patient’s risk of suicide in the clinical setting – spending time connecting with our patients.

Dr. Badre is a clinical and forensic psychiatrist in San Diego. He holds teaching positions at the University of California, San Diego, and the University of San Diego. He teaches medical education, psychopharmacology, ethics in psychiatry, and correctional care. Dr. Badre can be reached at his website, BadreMD.com. Dr. Compton is a member of the psychiatry faculty at University of California, San Diego. His background includes medical education, mental health advocacy, work with underserved populations, and brain cancer research. Dr. Badre and Dr. Compton have no conflicts of interest.

References

1. Joint Economic Committee. (2019). Long Term Trends in Deaths of Despair. SCP Report 4-19.

2. Kroenke K and Spitzer RL. The PHQ-9: A new depression diagnostic and severity measure. Psychiatr Ann. 2013;32(9):509-15. doi: 10.3928/0048-5713-20020901-06.

3. Columbia-Suicide Severity Rating Scale (C-SSRS) Full Lifetime/Recent.

4. Columbia-Suicide Severity Rating Scale (C-SSRS) Full Since Last Contact.

5. Franklin JC et al. Risk factors for suicidal thoughts and behaviors: A meta-analysis of 50 years of research. Psychol Bull. 2017 Feb;143(2):187-232. doi: 10.1037/bul0000084.

6. Beautrais AL. Further suicidal behavior among medically serious suicide attempters. Suicide Life Threat Behav. 2004 Spring;34(1):1-11. doi: 10.1521/suli.34.1.1.27772.

7. Belsher BE. Prediction models for suicide attempts and deaths: A systematic review and simulation. JAMA Psychiatry. 2019 Jun 1;76(6):642-651. doi: 10.1001/jamapsychiatry.2019.0174.

8. Carter G et al. Royal Australian and New Zealand College of Psychiatrists clinical practice guideline for the management of deliberate self-harm. Aust N Z J Psychiatry. 2016 Oct;50(10):939-1000. doi: 10.1177/0004867416661039.

9. Fosse R et al. Predictors of suicide in the patient population admitted to a locked-door psychiatric acute ward. PLoS One. 2017 Mar 16;12(3):e0173958. doi: 10.1371/journal.pone.0173958.

10. Kessler RC et al. Suicide prediction models: A critical review of recent research with recommendations for the way forward. Mol Psychiatry. 2020 Jan;25(1):168-79. doi: 10.1038/s41380-019-0531-0.

11. Mulder R. Problems with suicide risk assessment. Aust N Z J Psychiatry. 2011 Aug;45(8):605-7. doi: 10.3109/00048674.2011.594786.

12. Pokorny AD. Prediction of suicide in psychiatric patients: Report of a prospective study. Arch Gen Psychiatry. 1983 Mar;40(3):249-57. doi: 10.1001/archpsyc.1983.01790030019002.

13. Rosen A. Detection of suicidal patients: An example of some limitations in the prediction of infrequent events. J Consult Psychol. 1954 Dec;18(6):397-403. doi: 10.1037/h0058579.

14. McHugh CM et al. (2019). Association between suicidal ideation and suicide: Meta-analyses of odds ratios, sensitivity, specificity and positive predictive value. BJPsych Open. 2019 Mar;5(2):e18. doi: 10.1192/bjo.2018.88.

Suicide is not a trivial matter – it upends families, robs partners of a loved one, prevents children from having a parent, and can destroy a parent’s most cherished being. It is not surprising that societies have repeatedly made it a goal to study and reduce suicide within their populations.

The suicide rate in the United States is trending upward, from about 10 per 100,000 in 2000 to about 15 per 100,000 in more recent reports. The increasing suicide rates have been accompanied by increasing distress among many strata of society. From a public health level, analysts are not just witnessing increasing suicide rates, but a shocking rise in all “deaths of despair,”¹ among which suicide can be considered the ultimate example.

On an individual level, many know someone who has died of suicide or suffered from a serious suicide attempt. From the public health level to the individual level, advocacy has called for various interventions in the field of psychiatry to remedy this tragic problem.

Psychiatrists have been firsthand witnesses to this increasing demand for suicide interventions. When in residency, the norm was to perform a suicide risk assessment at the time of admission to the hospital and again at the time of discharge. As the years passed, the new normal within psychiatric hospitals has shifted to asking about suicidality on a daily basis.

In what seems to us like an escalating arms race, the emerging standard of care at many facilities is now not only for daily suicide risk assessments by each psychiatrist, but also to require nurses to ask about suicidality during every 8-hour shift – in addition to documented inquiries about suicidality by other allied staff on the psychiatric unit. As a result, it is not uncommon for a patient hospitalized at an academic center to receive more than half a dozen suicide risk assessments in a day (first by the medical student, at least once – often more than once – by the resident, again by the attending psychiatrist, then the social worker and three nurses in 24 hours).

One of the concerns about such an approach is the lack of logic inherent to many risk assessment tools and symptom scales. Many of us are familiar with the Patient Health Questionnaire (PHQ-9) to assess depression.² The PHQ-9 asks to consider “over the last 2 weeks, how often have you ...” in relation to nine symptoms associated with depression. It has always defied reason to perform a PHQ-9 every day and expect the answers to change from “nearly every day” to “not at all,” considering only 1 day has passed since the last time the patient has answered the questions. Yet daily, or near daily, PHQ-9 scores are a frequently used tool of tracking symptom improvement in response to treatments, such as electroconvulsive therapy, performed multiple times a week.

One can argue that the patient’s perspective on how symptomatic he or she has been over the past 2 weeks may change rapidly with alleviation of a depressed mood. However, the PHQ-9 is both reported to be, and often regarded as, an objective score. If one wishes to utilize it as such, the defense of its use should not be that it is a subjective report with just as much utility as “Rate your depression on a scale of 0-27.”

Similarly, many suicide scales were intended to assess thoughts of suicide in the past month³ or have been re-tooled to address this particular concern by asking “since the last contact.”⁴ It is baffling to see a chart with many dozens of suicide risk assessments with at times widely differing answers, yet all measuring thoughts of suicide in the past month. Is one to expect the answer to “How many times have you had these thoughts [of suicide ideation]? (1) Less than once a week (2) Once a week ...” to change between 8 a.m. and noon? Furthermore, for the purpose of assessing acute risk of suicidality in the immediate future, to only consider symptoms since the last contact – or past 2 weeks, past month, etc. – is of unclear significance.
 

Provider liability

Another concern is the liability placed on providers. A common problem encountered in the inpatient setting is insurance companies refusing to reimburse a hospital stay for depressed patients denying suicidality.

Any provider in the position of caring for such a patient must ask: What is the likelihood of someone providing a false negative – a false denial of suicidality? Is the likelihood of a suicidal person denying suicidality different if asked 5 or 10 or more times in a day? There are innumerable instances where a patient at a very high risk of self-harm has denied suicidality, been discharged from the hospital, and suffered terrible consequences. Ethically, the psychiatrist aware of this risk is no more at ease discharging these patients, whether it is one suicide risk scale or a dozen that suggests a patient is at low risk.

Alternatively, it may feel untenable from a medicolegal perspective for a psychiatrist to discharge a patient denying suicidality when the chart includes over a dozen previously documented elevated suicide risk assessments in the past 72 hours. By placing the job of suicide risk assessment in the hands of providers of varying levels of training and responsibility, a situation is created in which the seasoned psychiatrist who would otherwise be comfortable discharging a patient feels unable to do so because every other note-writer in the record – from the triage nurse to the medical assistant to the sitter in the emergency department – has recorded the patient as high risk for suicide. When put in such a position, the thought often occurs that systems of care, rather than individual providers, are protected most by ever escalating requirements for suicide risk documentation. To make a clinical decision contrary to the body of suicide risk documentation puts the provider at risk of being scapegoated by the system of care, which can point to its illogical and ineffective, though profusely documented, suicide prevention protocols.
 

Limitations of risk assessments

Considering the ongoing rise in the use of suicide risk assessments, one would expect that the evidence for their efficacy was robust and well established. Yet a thorough review of suicide risk assessments funded by the MacArthur Foundation, which examined decades of research, came to disheartening conclusions: “predictive ability has not improved over the past 50 years”; “no risk factor category or subcategory is substantially stronger than any other”; and “predicting solely according to base rates may be comparable to prediction with current risk factors.”⁵

Those findings were consistent with the conclusions of many other studies, which have summarized the utility of suicide risk assessments as follows: “occurrence of suicide is too low to identify those individuals who are likely to die by suicide”;⁶ “suicide prediction models produce accurate overall classification models, but their accuracy of predicting a future event is near zero”;⁷ “risk stratification is too inaccurate to be clinically useful and might even be harmful”;⁸ “suicide risk prediction [lacks] any items or information that to a useful degree permit the identification of persons who will complete suicide”;⁹ “existing suicide prediction tools have little current clinical value”;¹⁰ “our current preoccupation with risk assessment has ... created a mythology with no evidence to support it.”¹¹ And that’s to cite just a few.

Sadly, we have known about the limitations of suicide risk assessments for many decades. In 1983 a large VA prospective study, which aimed to identify veterans who will die by suicide, examined 4,800 patients with a wide range of instruments and measures.¹² This study concluded that “discriminant analysis was clearly inadequate in correctly classifying the subjects. For an event as rare as suicide, our predictive tools and guides are simply not equal to the task.” The authors described the feelings of many in stating “courts and public opinion expect physicians to be able to pick out the particular persons who will later commit suicide. Although we may reconstruct causal chains and motives, we do not possess the tools to predict suicides.”

Yet, even several decades prior, in 1954, Dr. Albert Rosen performed an elegant statistical analysis and predicted that, considering the low base rate of suicide, suicide risk assessments are “of no practical value, for it would be impossible to treat the prodigious number of false positives.”¹³ It seems that we continue to be unable to accept Dr. Rosen’s premonition despite decades of confirmatory evidence.
 

“Quantity over quality”

Regardless of those sobering reports, the field of psychiatry is seemingly doubling down on efforts to predict and prevent suicide deaths, and the way it is doing so has very questionable validity.

One can reasonably argue that the periodic performance of a suicide risk assessment may have clinical utility in reminding us of modifiable risk factors such as intoxication, social isolation, and access to lethal means. One can also reasonably argue that these risk assessments may provide useful education to patients and their families on epidemiological risk factors such as gender, age, and marital status. But our pursuit of serial suicide risk assessments throughout the day is encouraging providers to focus on a particular risk factor that changes from moment to moment and has particularly low validity, that being self-reported suicidality.

Reported suicidality is one of the few risk factors that can change from shift to shift. But 80% of people who die by suicide had not previously expressed suicidality, and 98.3% of people who have endorsed suicidality do not die by suicide.¹⁴ While the former statistic may improve with increased assessment, the later will likely worsen.

Suicide is not a trivial matter. We admire those that study it and advocate for better interventions. We have compassion for those who have suffered the loss of a loved one to suicide. Our patients have died as a result of the human limitations surrounding suicide prevention. Recognizing the weight of suicide and making an effort to avoid minimizing its immense consequences drive our desire to be honest with ourselves, our patients and their families, and society. That includes the unfortunate truth regarding the current state of the evidence and our ability to enact change.

It is our concern that the rising fascination with repeated suicide risk assessment is misguided in its current form and serves the purpose of appeasing administrators more than reflecting a scientific understanding of the literature. More sadly, we are concerned that this “quantity-over-quality” approach is yet another barrier to practicing what may be one of the few interventions with any hope of meaningfully impacting a patient’s risk of suicide in the clinical setting – spending time connecting with our patients.

Dr. Badre is a clinical and forensic psychiatrist in San Diego. He holds teaching positions at the University of California, San Diego, and the University of San Diego. He teaches medical education, psychopharmacology, ethics in psychiatry, and correctional care. Dr. Badre can be reached at his website, BadreMD.com. Dr. Compton is a member of the psychiatry faculty at University of California, San Diego. His background includes medical education, mental health advocacy, work with underserved populations, and brain cancer research. Dr. Badre and Dr. Compton have no conflicts of interest.

References

1. Joint Economic Committee. (2019). Long Term Trends in Deaths of Despair. SCP Report 4-19.

2. Kroenke K and Spitzer RL. The PHQ-9: A new depression diagnostic and severity measure. Psychiatr Ann. 2013;32(9):509-15. doi: 10.3928/0048-5713-20020901-06.

3. Columbia-Suicide Severity Rating Scale (C-SSRS) Full Lifetime/Recent.

4. Columbia-Suicide Severity Rating Scale (C-SSRS) Full Since Last Contact.

5. Franklin JC et al. Risk factors for suicidal thoughts and behaviors: A meta-analysis of 50 years of research. Psychol Bull. 2017 Feb;143(2):187-232. doi: 10.1037/bul0000084.

6. Beautrais AL. Further suicidal behavior among medically serious suicide attempters. Suicide Life Threat Behav. 2004 Spring;34(1):1-11. doi: 10.1521/suli.34.1.1.27772.

7. Belsher BE. Prediction models for suicide attempts and deaths: A systematic review and simulation. JAMA Psychiatry. 2019 Jun 1;76(6):642-651. doi: 10.1001/jamapsychiatry.2019.0174.

8. Carter G et al. Royal Australian and New Zealand College of Psychiatrists clinical practice guideline for the management of deliberate self-harm. Aust N Z J Psychiatry. 2016 Oct;50(10):939-1000. doi: 10.1177/0004867416661039.

9. Fosse R et al. Predictors of suicide in the patient population admitted to a locked-door psychiatric acute ward. PLoS One. 2017 Mar 16;12(3):e0173958. doi: 10.1371/journal.pone.0173958.

10. Kessler RC et al. Suicide prediction models: A critical review of recent research with recommendations for the way forward. Mol Psychiatry. 2020 Jan;25(1):168-79. doi: 10.1038/s41380-019-0531-0.

11. Mulder R. Problems with suicide risk assessment. Aust N Z J Psychiatry. 2011 Aug;45(8):605-7. doi: 10.3109/00048674.2011.594786.

12. Pokorny AD. Prediction of suicide in psychiatric patients: Report of a prospective study. Arch Gen Psychiatry. 1983 Mar;40(3):249-57. doi: 10.1001/archpsyc.1983.01790030019002.

13. Rosen A. Detection of suicidal patients: An example of some limitations in the prediction of infrequent events. J Consult Psychol. 1954 Dec;18(6):397-403. doi: 10.1037/h0058579.

14. McHugh CM et al. (2019). Association between suicidal ideation and suicide: Meta-analyses of odds ratios, sensitivity, specificity and positive predictive value. BJPsych Open. 2019 Mar;5(2):e18. doi: 10.1192/bjo.2018.88.

Eating disorders are a common comorbidity in bipolar disorder patients, especially those with type II, based on data from more than 2,000 individuals.

Previous research of bipolar disorder (BD) shows a high rate of comorbidities with other psychiatric disorders, including eating disorders (EDs), Valentin Flaudias, PhD, of Nantes (France) University and colleagues wrote.

Valentin Flaudias

“There is growing evidence that, compared with individuals with BD alone, individuals with both BD and EDs have a more severe clinical profile, including increased mood instability, alcohol use disorders, anxiety disorders, more depressive episodes, more rapid cycling, increased suicidality, and poorer response to medication,” but studies of BD type-specific ED prevalence have been inconsistent, they said.

In a study published in the Journal of Affective Disorders, the researchers reviewed data from 2,929 outpatients who underwent assessments for BD at 1 of 12 psychiatric centers in France. Of these, 1,505 met criteria for type I and 1,424 met criteria for type II. The post hoc analysis included identification of lifetime prevalence of ED. Diagnosis was based on the DSM-4-TR and the researchers considered three ED types: anorexia nervosa (AN), bulimia nervosa (BN), and binge-eating disorder (BED). Subtypes of BD were type I and type II. DSM not otherwise specified diagnoses for BD and EDs were excluded. The mean age of the participants was 40.5 years, and 61% were women.

A total of 479 individuals met criteria for comorbid EDs (16.4%). ED prevalence was significantly higher in BD type II patients than in BD type I patients (20.6 % vs. 12.4 %, P < .001). The overall breakdown according to ED subtype was 30% for AN, 13% for BN, and 56% for BED. The researchers found no significant differences in patients with AN, BN, or BED according to BD subtype.

In a multivariate analysis, BD patients with ED were more likely than those without ED to be women (77% vs. 55%), especially those with AN (95% vs. 82%).

BD patients with ED also tended to be younger than those without ED (37 years vs. 41 years) and reported more frequent suicide attempts (50% vs. 35%). Younger age and more frequent suicide attempts were further significant among BD patients with AN, compared with those with BED, but BD patients with BED reported higher levels of childhood trauma.

BD patients with ED also reported higher levels of depressive symptoms than those without ED, although history of psychosis was less frequent among BD patients with AN and BED compared with BD patients without EDs.

Overall, “after controlling for other variables, the independent factors differentiating BD patients with versus without ED were primarily younger age, female gender, abnormal BMI, increased affective lability and higher comorbidity with anxiety disorders,” the researchers wrote. In addition, presence of EDs except for AN was associated with decreased current functioning.

The findings were limited by several factors including the cross-sectional design, lack of a control group of non-BD individuals, and the consideration of ED over a lifetime, and small number of BN cases, the researchers noted.

However, the results suggest a high prevalence of ED in BD patients and highlight the need to screen BD patients for ED and provide integrated care. More research is needed to explore the evolution of the two conditions as comorbidities and to examine subtypes and of both conditions and their interactions, they concluded.

The study was supported by the FondaMental Foundation, French National Institute for Health and Medical Research, Public Hospitals of Paris, and the French National Research Agency’s Investment for the Future program. The researchers had no financial conflicts to disclose.

Eating disorders are a common comorbidity in bipolar disorder patients, especially those with type II, based on data from more than 2,000 individuals.

Previous research of bipolar disorder (BD) shows a high rate of comorbidities with other psychiatric disorders, including eating disorders (EDs), Valentin Flaudias, PhD, of Nantes (France) University and colleagues wrote.

Valentin Flaudias

“There is growing evidence that, compared with individuals with BD alone, individuals with both BD and EDs have a more severe clinical profile, including increased mood instability, alcohol use disorders, anxiety disorders, more depressive episodes, more rapid cycling, increased suicidality, and poorer response to medication,” but studies of BD type-specific ED prevalence have been inconsistent, they said.

In a study published in the Journal of Affective Disorders, the researchers reviewed data from 2,929 outpatients who underwent assessments for BD at 1 of 12 psychiatric centers in France. Of these, 1,505 met criteria for type I and 1,424 met criteria for type II. The post hoc analysis included identification of lifetime prevalence of ED. Diagnosis was based on the DSM-4-TR and the researchers considered three ED types: anorexia nervosa (AN), bulimia nervosa (BN), and binge-eating disorder (BED). Subtypes of BD were type I and type II. DSM not otherwise specified diagnoses for BD and EDs were excluded. The mean age of the participants was 40.5 years, and 61% were women.

A total of 479 individuals met criteria for comorbid EDs (16.4%). ED prevalence was significantly higher in BD type II patients than in BD type I patients (20.6 % vs. 12.4 %, P < .001). The overall breakdown according to ED subtype was 30% for AN, 13% for BN, and 56% for BED. The researchers found no significant differences in patients with AN, BN, or BED according to BD subtype.

In a multivariate analysis, BD patients with ED were more likely than those without ED to be women (77% vs. 55%), especially those with AN (95% vs. 82%).

BD patients with ED also tended to be younger than those without ED (37 years vs. 41 years) and reported more frequent suicide attempts (50% vs. 35%). Younger age and more frequent suicide attempts were further significant among BD patients with AN, compared with those with BED, but BD patients with BED reported higher levels of childhood trauma.

BD patients with ED also reported higher levels of depressive symptoms than those without ED, although history of psychosis was less frequent among BD patients with AN and BED compared with BD patients without EDs.

Overall, “after controlling for other variables, the independent factors differentiating BD patients with versus without ED were primarily younger age, female gender, abnormal BMI, increased affective lability and higher comorbidity with anxiety disorders,” the researchers wrote. In addition, presence of EDs except for AN was associated with decreased current functioning.

The findings were limited by several factors including the cross-sectional design, lack of a control group of non-BD individuals, and the consideration of ED over a lifetime, and small number of BN cases, the researchers noted.

However, the results suggest a high prevalence of ED in BD patients and highlight the need to screen BD patients for ED and provide integrated care. More research is needed to explore the evolution of the two conditions as comorbidities and to examine subtypes and of both conditions and their interactions, they concluded.

The study was supported by the FondaMental Foundation, French National Institute for Health and Medical Research, Public Hospitals of Paris, and the French National Research Agency’s Investment for the Future program. The researchers had no financial conflicts to disclose.

Eating disorders are a common comorbidity in bipolar disorder patients, especially those with type II, based on data from more than 2,000 individuals.

Previous research of bipolar disorder (BD) shows a high rate of comorbidities with other psychiatric disorders, including eating disorders (EDs), Valentin Flaudias, PhD, of Nantes (France) University and colleagues wrote.

Valentin Flaudias

“There is growing evidence that, compared with individuals with BD alone, individuals with both BD and EDs have a more severe clinical profile, including increased mood instability, alcohol use disorders, anxiety disorders, more depressive episodes, more rapid cycling, increased suicidality, and poorer response to medication,” but studies of BD type-specific ED prevalence have been inconsistent, they said.

In a study published in the Journal of Affective Disorders, the researchers reviewed data from 2,929 outpatients who underwent assessments for BD at 1 of 12 psychiatric centers in France. Of these, 1,505 met criteria for type I and 1,424 met criteria for type II. The post hoc analysis included identification of lifetime prevalence of ED. Diagnosis was based on the DSM-4-TR and the researchers considered three ED types: anorexia nervosa (AN), bulimia nervosa (BN), and binge-eating disorder (BED). Subtypes of BD were type I and type II. DSM not otherwise specified diagnoses for BD and EDs were excluded. The mean age of the participants was 40.5 years, and 61% were women.

A total of 479 individuals met criteria for comorbid EDs (16.4%). ED prevalence was significantly higher in BD type II patients than in BD type I patients (20.6 % vs. 12.4 %, P < .001). The overall breakdown according to ED subtype was 30% for AN, 13% for BN, and 56% for BED. The researchers found no significant differences in patients with AN, BN, or BED according to BD subtype.

In a multivariate analysis, BD patients with ED were more likely than those without ED to be women (77% vs. 55%), especially those with AN (95% vs. 82%).

BD patients with ED also tended to be younger than those without ED (37 years vs. 41 years) and reported more frequent suicide attempts (50% vs. 35%). Younger age and more frequent suicide attempts were further significant among BD patients with AN, compared with those with BED, but BD patients with BED reported higher levels of childhood trauma.

BD patients with ED also reported higher levels of depressive symptoms than those without ED, although history of psychosis was less frequent among BD patients with AN and BED compared with BD patients without EDs.

Overall, “after controlling for other variables, the independent factors differentiating BD patients with versus without ED were primarily younger age, female gender, abnormal BMI, increased affective lability and higher comorbidity with anxiety disorders,” the researchers wrote. In addition, presence of EDs except for AN was associated with decreased current functioning.

The findings were limited by several factors including the cross-sectional design, lack of a control group of non-BD individuals, and the consideration of ED over a lifetime, and small number of BN cases, the researchers noted.

However, the results suggest a high prevalence of ED in BD patients and highlight the need to screen BD patients for ED and provide integrated care. More research is needed to explore the evolution of the two conditions as comorbidities and to examine subtypes and of both conditions and their interactions, they concluded.

The study was supported by the FondaMental Foundation, French National Institute for Health and Medical Research, Public Hospitals of Paris, and the French National Research Agency’s Investment for the Future program. The researchers had no financial conflicts to disclose.

A large brain imaging study of adults with six different psychiatric illnesses shows that heterogeneity in regional gray matter volume deviations is a general feature of psychiatric illness, but that these regionally heterogeneous areas are often embedded within common functional circuits and networks.

The findings suggest that “targeting brain circuits, rather than specific brain regions, may be a more effective way of developing new treatments,” study investigator Ashlea Segal said in an email.

The findings also suggest that it’s “unlikely that a single cause or mechanism of a given disorder exists, and that a ‘one-size-fits-all’ approach to treatment is likely only appropriate for a small subset of individuals. In fact, one size doesn’t fit all. It probably doesn’t even fit most,” said Ms. Segal, a PhD candidate with the Turner Institute for Brain and Mental Health’s Neural Systems and Behaviour Lab at Monash University in Melbourne.

“Focusing on brain alterations at an individual level allows us to develop more personally tailored treatments,” Ms. Segal added.

Regional heterogeneity, the authors write, “thus offers a plausible explanation for the well-described clinical heterogeneity observed in psychiatric disorders, while circuit- and network-level aggregation of deviations is a putative neural substrate for phenotypic similarities between patients assigned the same diagnosis.”

The study was published online in Nature Neuroscience
 

Beyond group averages

For decades, researchers have mapped brain areas showing reduced gray matter volume (GMV) in people diagnosed with a variety of mental illnesses, but these maps have only been generated at the level of group averages, Ms. Segal explained.

“This means that we understand how the brains of people with, say, schizophrenia, differ from those without schizophrenia on average, but we can’t really say much about individual people,” Ms. Segal said.

For their study, the researchers used new statistical techniques developed by Andre Marquand, PhD, who co-led the project, to characterize the heterogeneity of GMV differences in 1,294 individuals diagnosed with one of six psychiatric conditions and 1,465 matched controls. Dr. Marquand is affiliated with the Donders Institute for Brain, Cognition, and Behavior in Nijmegen, the Netherlands.

These techniques “allow us to benchmark the size of over 1,000 different brain regions in any given person relative to what we should expect to see in the general population. In this way, we can identify, for any person, brain regions showing unusually small or large volumes, given that person’s age and sex,” Ms. Segal told this news organization.

The clinical sample included 202 individuals with autism spectrum disorder, 153 with attention-deficit/hyperactivity disorder (ADHD), 228 with bipolar disorder, 161 with major depressive disorder, 167 with obsessive-compulsive disorder, and 383 individuals with schizophrenia.

Confirming earlier findings, those with psychiatric illness showed more GMV deviations than healthy controls, the researchers found.

However, at the individual level, deviations from population expectations for regional gray matter volumes were “highly heterogeneous,” affecting the same area in less than 7% of people with the same diagnosis, they note. “This result means that it is difficult to pinpoint treatment targets or causal mechanisms by focusing on group averages alone,” Alex Fornito, PhD, of Monash University, who led the research team, said in a statement.

“It may also explain why people with the same diagnosis show wide variability in their symptom profiles and treatment outcomes,” Dr. Fornito added.

Yet, despite considerable heterogeneity at the regional level across different diagnoses, these deviations were embedded within common functional circuits and networks in up to 56% of cases. 

The salience-ventral attention network, for example, which plays a central role in cognitive control, interoceptive awareness, and switching between internally and externally focused attention, was implicated across diagnoses, with other neural networks selectively involved in depression, bipolar disorder, schizophrenia, and ADHD.

The researchers say the approach they developed opens new opportunities for mapping brain changes in mental illness.

“The framework we have developed allows us to understand the diversity of brain changes in people with mental illness at different levels, from individual regions through to more widespread brain circuits and networks, offering a deeper insight into how the brain is affected in individual people,” Dr. Fornito said in a statement.

The study had no commercial funding. Ms. Segal, Dr. Fornito, and Dr. Marquand report no relevant financial relationships.

A version of this article first appeared on Medscape.com.

A large brain imaging study of adults with six different psychiatric illnesses shows that heterogeneity in regional gray matter volume deviations is a general feature of psychiatric illness, but that these regionally heterogeneous areas are often embedded within common functional circuits and networks.

The findings suggest that “targeting brain circuits, rather than specific brain regions, may be a more effective way of developing new treatments,” study investigator Ashlea Segal said in an email.

The findings also suggest that it’s “unlikely that a single cause or mechanism of a given disorder exists, and that a ‘one-size-fits-all’ approach to treatment is likely only appropriate for a small subset of individuals. In fact, one size doesn’t fit all. It probably doesn’t even fit most,” said Ms. Segal, a PhD candidate with the Turner Institute for Brain and Mental Health’s Neural Systems and Behaviour Lab at Monash University in Melbourne.

“Focusing on brain alterations at an individual level allows us to develop more personally tailored treatments,” Ms. Segal added.

Regional heterogeneity, the authors write, “thus offers a plausible explanation for the well-described clinical heterogeneity observed in psychiatric disorders, while circuit- and network-level aggregation of deviations is a putative neural substrate for phenotypic similarities between patients assigned the same diagnosis.”

The study was published online in Nature Neuroscience
 

Beyond group averages

For decades, researchers have mapped brain areas showing reduced gray matter volume (GMV) in people diagnosed with a variety of mental illnesses, but these maps have only been generated at the level of group averages, Ms. Segal explained.

“This means that we understand how the brains of people with, say, schizophrenia, differ from those without schizophrenia on average, but we can’t really say much about individual people,” Ms. Segal said.

For their study, the researchers used new statistical techniques developed by Andre Marquand, PhD, who co-led the project, to characterize the heterogeneity of GMV differences in 1,294 individuals diagnosed with one of six psychiatric conditions and 1,465 matched controls. Dr. Marquand is affiliated with the Donders Institute for Brain, Cognition, and Behavior in Nijmegen, the Netherlands.

These techniques “allow us to benchmark the size of over 1,000 different brain regions in any given person relative to what we should expect to see in the general population. In this way, we can identify, for any person, brain regions showing unusually small or large volumes, given that person’s age and sex,” Ms. Segal told this news organization.

The clinical sample included 202 individuals with autism spectrum disorder, 153 with attention-deficit/hyperactivity disorder (ADHD), 228 with bipolar disorder, 161 with major depressive disorder, 167 with obsessive-compulsive disorder, and 383 individuals with schizophrenia.

Confirming earlier findings, those with psychiatric illness showed more GMV deviations than healthy controls, the researchers found.

However, at the individual level, deviations from population expectations for regional gray matter volumes were “highly heterogeneous,” affecting the same area in less than 7% of people with the same diagnosis, they note. “This result means that it is difficult to pinpoint treatment targets or causal mechanisms by focusing on group averages alone,” Alex Fornito, PhD, of Monash University, who led the research team, said in a statement.

“It may also explain why people with the same diagnosis show wide variability in their symptom profiles and treatment outcomes,” Dr. Fornito added.

Yet, despite considerable heterogeneity at the regional level across different diagnoses, these deviations were embedded within common functional circuits and networks in up to 56% of cases. 

The salience-ventral attention network, for example, which plays a central role in cognitive control, interoceptive awareness, and switching between internally and externally focused attention, was implicated across diagnoses, with other neural networks selectively involved in depression, bipolar disorder, schizophrenia, and ADHD.

The researchers say the approach they developed opens new opportunities for mapping brain changes in mental illness.

“The framework we have developed allows us to understand the diversity of brain changes in people with mental illness at different levels, from individual regions through to more widespread brain circuits and networks, offering a deeper insight into how the brain is affected in individual people,” Dr. Fornito said in a statement.

The study had no commercial funding. Ms. Segal, Dr. Fornito, and Dr. Marquand report no relevant financial relationships.

A version of this article first appeared on Medscape.com.

A large brain imaging study of adults with six different psychiatric illnesses shows that heterogeneity in regional gray matter volume deviations is a general feature of psychiatric illness, but that these regionally heterogeneous areas are often embedded within common functional circuits and networks.

The findings suggest that “targeting brain circuits, rather than specific brain regions, may be a more effective way of developing new treatments,” study investigator Ashlea Segal said in an email.

The findings also suggest that it’s “unlikely that a single cause or mechanism of a given disorder exists, and that a ‘one-size-fits-all’ approach to treatment is likely only appropriate for a small subset of individuals. In fact, one size doesn’t fit all. It probably doesn’t even fit most,” said Ms. Segal, a PhD candidate with the Turner Institute for Brain and Mental Health’s Neural Systems and Behaviour Lab at Monash University in Melbourne.

“Focusing on brain alterations at an individual level allows us to develop more personally tailored treatments,” Ms. Segal added.

Regional heterogeneity, the authors write, “thus offers a plausible explanation for the well-described clinical heterogeneity observed in psychiatric disorders, while circuit- and network-level aggregation of deviations is a putative neural substrate for phenotypic similarities between patients assigned the same diagnosis.”

The study was published online in Nature Neuroscience
 

Beyond group averages

For decades, researchers have mapped brain areas showing reduced gray matter volume (GMV) in people diagnosed with a variety of mental illnesses, but these maps have only been generated at the level of group averages, Ms. Segal explained.

“This means that we understand how the brains of people with, say, schizophrenia, differ from those without schizophrenia on average, but we can’t really say much about individual people,” Ms. Segal said.

For their study, the researchers used new statistical techniques developed by Andre Marquand, PhD, who co-led the project, to characterize the heterogeneity of GMV differences in 1,294 individuals diagnosed with one of six psychiatric conditions and 1,465 matched controls. Dr. Marquand is affiliated with the Donders Institute for Brain, Cognition, and Behavior in Nijmegen, the Netherlands.

These techniques “allow us to benchmark the size of over 1,000 different brain regions in any given person relative to what we should expect to see in the general population. In this way, we can identify, for any person, brain regions showing unusually small or large volumes, given that person’s age and sex,” Ms. Segal told this news organization.

The clinical sample included 202 individuals with autism spectrum disorder, 153 with attention-deficit/hyperactivity disorder (ADHD), 228 with bipolar disorder, 161 with major depressive disorder, 167 with obsessive-compulsive disorder, and 383 individuals with schizophrenia.

Confirming earlier findings, those with psychiatric illness showed more GMV deviations than healthy controls, the researchers found.

However, at the individual level, deviations from population expectations for regional gray matter volumes were “highly heterogeneous,” affecting the same area in less than 7% of people with the same diagnosis, they note. “This result means that it is difficult to pinpoint treatment targets or causal mechanisms by focusing on group averages alone,” Alex Fornito, PhD, of Monash University, who led the research team, said in a statement.

“It may also explain why people with the same diagnosis show wide variability in their symptom profiles and treatment outcomes,” Dr. Fornito added.

Yet, despite considerable heterogeneity at the regional level across different diagnoses, these deviations were embedded within common functional circuits and networks in up to 56% of cases. 

The salience-ventral attention network, for example, which plays a central role in cognitive control, interoceptive awareness, and switching between internally and externally focused attention, was implicated across diagnoses, with other neural networks selectively involved in depression, bipolar disorder, schizophrenia, and ADHD.

The researchers say the approach they developed opens new opportunities for mapping brain changes in mental illness.

“The framework we have developed allows us to understand the diversity of brain changes in people with mental illness at different levels, from individual regions through to more widespread brain circuits and networks, offering a deeper insight into how the brain is affected in individual people,” Dr. Fornito said in a statement.

The study had no commercial funding. Ms. Segal, Dr. Fornito, and Dr. Marquand report no relevant financial relationships.

A version of this article first appeared on Medscape.com.