Development of a model to predict psychotherapy response for depression among Veterans.

BACKGROUND: Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan. METHODS: This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018-2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample. RESULTS: 32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables. CONCLUSIONS: Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.

Development of a model to predict antidepressant treatment response for depression among Veterans.

BACKGROUND: Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA). METHODS: A 2018-2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample. RESULTS: In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors. CONCLUSIONS: Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

Antidepressant use in low- middle- and high-income countries: a World Mental Health Surveys report.

BACKGROUND: The most common treatment for major depressive disorder (MDD) is antidepressant medication (ADM). Results are reported on frequency of ADM use, reasons for use, and perceived effectiveness of use in general population surveys across 20 countries. METHODS: Face-to-face interviews with community samples totaling n = 49 919 respondents in the World Health Organization (WHO) World Mental Health (WMH) Surveys asked about ADM use anytime in the prior 12 months in conjunction with validated fully structured diagnostic interviews. Treatment questions were administered independently of diagnoses and asked of all respondents. RESULTS: 3.1% of respondents reported ADM use within the past 12 months. In high-income countries (HICs), depression (49.2%) and anxiety (36.4%) were the most common reasons for use. In low- and middle-income countries (LMICs), depression (38.4%) and sleep problems (31.9%) were the most common reasons for use. Prevalence of use was 2-4 times as high in HICs as LMICs across all examined diagnoses. Newer ADMs were proportionally used more often in HICs than LMICs. Across all conditions, ADMs were reported as very effective by 58.8% of users and somewhat effective by an additional 28.3% of users, with both proportions higher in LMICs than HICs. Neither ADM class nor reason for use was a significant predictor of perceived effectiveness. CONCLUSION: ADMs are in widespread use and for a variety of conditions including but going beyond depression and anxiety. In a general population sample from multiple LMICs and HICs, ADMs were widely perceived to be either very or somewhat effective by the people who use them.

National Trends in Antihypertensive Treatment Among Older Adults by Race and Presence of Comorbidity, 2008 to 2017.

BACKGROUND: In 2014, hypertension guidelines for older adults endorsed increased use of fixed-dose combinations, prioritized thiazide diuretics and calcium channel blockers (CCBs) for Black patients, and no longer recommend beta-blockers as first-line therapy. OBJECTIVE: To evaluate older adults' antihypertensive use following guideline changes. DESIGN: Time series analysis. PATIENTS: Twenty percent national sample of Medicare Part D beneficiaries aged 66 years and older with hypertension. INTERVENTION: Eighth Joint National Committee (JNC8) guidelines MAIN MEASURES: Quarterly trends in prevalent and initial antihypertensive use were examined before (2008 to 2013) and after (2014 to 2017) JNC8. Analyses were conducted among all beneficiaries with hypertension, beneficiaries without chronic conditions that might influence antihypertensive selection (hypertension-only cohort), and among Black patients, given race-based guideline recommendations. KEY RESULTS: The number of beneficiaries with hypertension increased from 1,978,494 in 2008 to 2,809,680 in 2017, the proportions using antihypertensives increased from 80.3 to 81.2%, and the proportion using multiple classes and fixed-dose combinations declined (60.8 to 58.1% and 20.7 to 15.1%, respectively, all P<.01). Prior to JNC8, the use of angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, and CCBs was increasing. Use of CCBs as initial therapy increased more rapidly following JNC8 (relative change in quarterly trend 0.15% [95% CI, 0.13-0.18%), especially among Black beneficiaries (relative change 0.44% [95% CI, 0.21-0.68%]). Contrary to guidelines, the use of thiazides and combinations as initial therapy consistently decreased in the hypertension-only cohort (13.8 to 8.3% and 25.1 to 15.7% respectively). By 2017, 65.9% of Black patients in the hypertension-only cohort were initiated on recommended first-line or combination therapy compared to 80.3% of non-Black patients. CONCLUSIONS: Many older adults, particularly Black patients, continue to be initiated on antihypertensive classes not recommended as first-line, indicating opportunities to improve the effectiveness and equity of hypertension care and potentially reduce antihypertensive regimen complexity.

Are Veterans Getting Their Preferred Depression Treatment? A National Observational Study in the Veterans Health Administration.

BACKGROUND: Physician responsiveness to patient preferences for depression treatment may improve treatment adherence and clinical outcomes. OBJECTIVE: To examine associations of patient treatment preferences with types of depression treatment received and treatment adherence among Veterans initiating depression treatment. DESIGN: Patient self-report surveys at treatment initiation linked to medical records. SETTING: Veterans Health Administration (VA) clinics nationally, 2018-2020. PARTICIPANTS: A total of 2582 patients (76.7% male, mean age 48.7 years, 62.3% Non-Hispanic White) MAIN MEASURES: Patient self-reported preferences for medication and psychotherapy on 0-10 self-anchoring visual analog scales (0="completely unwilling"; 10="completely willing"). Treatment receipt and adherence (refilling medications; attending 3+ psychotherapy sessions) over 3 months. Logistic regression models controlled for socio-demographics and geographic variables. KEY RESULTS: More patients reported strong preferences (10/10) for psychotherapy than medication (51.2% versus 36.7%, McNemar χ21=175.3, p<0.001). A total of 32.1% of patients who preferred (7-10/10) medication and 21.8% who preferred psychotherapy did not receive these treatments. Patients who strongly preferred medication were substantially more likely to receive medication than those who had strong negative preferences (odds ratios [OR]=17.5; 95% confidence interval [CI]=12.5-24.5). Compared with patients who had strong negative psychotherapy preferences, those with strong psychotherapy preferences were about twice as likely to receive psychotherapy (OR=1.9; 95% CI=1.0-3.5). Patients who strongly preferred psychotherapy were more likely to adhere to psychotherapy than those with strong negative preferences (OR=3.3; 95% CI=1.4-7.4). Treatment preferences were not associated with medication or combined treatment adherence. Patients in primary care settings had lower odds of receiving (but not adhering to) psychotherapy than patients in specialty mental health settings. Depression severity was not associated with treatment receipt or adherence. CONCLUSIONS: Mismatches between treatment preferences and treatment type received were common and associated with worse treatment adherence for psychotherapy. Future research could examine ways to decrease mismatch between patient preferences and treatments received and potential effects on patient outcomes.

Childhood adversities and suicidal thoughts and behaviors among first-year college students: results from the WMH-ICS initiative.

PURPOSE: To investigate the associations of childhood adversities (CAs) with lifetime onset and transitions across suicidal thoughts and behaviors (STB) among incoming college students. METHODS: Web-based self-report surveys administered to 20,842 incoming college students from nine countries (response rate 45.6%) assessed lifetime suicidal ideation, plans and attempts along with seven CAs: parental psychopathology, three types of abuse (emotional, physical, sexual), neglect, bully victimization, and dating violence. Logistic regression estimated individual- and population-level associations using CA operationalizations for type, number, severity, and frequency. RESULTS: Associations of CAs with lifetime ideation and the transition from ideation to plan were best explained by the exact number of CA types (OR range 1.32-52.30 for exactly two to seven CAs). Associations of CAs with a transition to attempts were best explained by the frequency of specific CA types (scaled 0-4). Attempts among ideators with a plan were significantly associated with all seven CAs (OR range 1.16-1.59) and associations remained significant in adjusted analyses with the frequency of sexual abuse (OR = 1.42), dating violence (OR = 1.29), physical abuse (OR = 1.17) and bully victimization (OR = 1.17). Attempts among ideators without plan were significantly associated with frequency of emotional abuse (OR = 1.29) and bully victimization (OR = 1.36), in both unadjusted and adjusted analyses. Population attributable risk simulations found 63% of ideation and 30-47% of STB transitions associated with CAs. CONCLUSION: Early-life adversities represent a potentially important driver in explaining lifetime STB among incoming college students. Comprehensive intervention strategies that prevent or reduce the negative effects of CAs may reduce subsequent onset of STB.

Association of Medicare Advantage vs Traditional Medicare With 30-Day Mortality Among Patients With Acute Myocardial Infarction.

Importance: Medicare Advantage health plans covered 37% of beneficiaries in 2018, and coverage increased to 48% in 2022. Whether Medicare Advantage plans provide similar care for patients presenting with specific clinical conditions is unknown. Objective: To compare 30-day mortality and treatment for Medicare Advantage and traditional Medicare patients presenting with acute myocardial infarction (MI) from 2009 to 2018. Design, Setting, and Participants: Retrospective cohort study that included 557 309 participants with ST-segment elevation [acute] MI (STEMI) and 1 670 193 with non-ST-segment elevation [acute] MI (NSTEMI) presenting to US hospitals from 2009-2018 (date of final follow up, December 31, 2019). Exposures: Enrollment in Medicare Advantage vs traditional Medicare. Main Outcomes and Measures: The primary outcome was adjusted 30-day mortality. Secondary outcomes included age- and sex-adjusted rates of procedure use (catheterization, revascularization), postdischarge medication prescriptions and adherence, and measures of health system performance (intensive care unit [ICU] admission and 30-day readmissions). Results: The study included a total of 2 227 502 participants, and the mean age in 2018 ranged from 76.9 years (Medicare Advantage STEMI) to 79.3 years (traditional Medicare NSTEMI), with similar proportions of female patients in Medicare Advantage and traditional Medicare (41.4% vs 41.9% for STEMI in 2018). Enrollment in Medicare Advantage vs traditional Medicare was associated with significantly lower adjusted 30-day mortality rates in 2009 (19.1% vs 20.6% for STEMI; difference, -1.5 percentage points [95% CI, -2.2 to -0.7] and 12.0% vs 12.5% for NSTEMI; difference, -0.5 percentage points [95% CI, -0.9% to -0.1%]). By 2018, mortality had declined in all groups, and there were no longer statically significant differences between Medicare Advantage (17.7%) and traditional Medicare (17.8%) for STEMI (difference, 0.0 percentage points [95% CI, -0.7 to 0.6]) or between Medicare Advantage (10.9%) and traditional Medicare (11.1%) for NSTEMI (difference, -0.2 percentage points [95% CI, -0.4 to 0.1]). By 2018, there was no statistically significant difference in standardized 90-day revascularization rates between Medicare Advantage and traditional Medicare. Rates of guideline-recommended medication prescriptions were significantly higher in Medicare Advantage (91.7%) vs traditional Medicare patients (89.0%) who received a statin prescription (difference, 2.7 percentage points [95% CI, 1.2 to 4.2] for 2018 STEMI). Medicare Advantage patients were significantly less likely to be admitted to an ICU than traditional Medicare patients (for 2018 STEMI, 50.3% vs 51.2%; difference, -0.9 percentage points [95% CI, -1.8 to 0.0]) and significantly more likely to be discharged to home rather than to a postacute facility (for 2018 STEMI, 71.5% vs 70.2%; difference, 1.3 percentage points [95% CI, 0.5 to 2.1]). Adjusted 30-day readmission rates were consistently lower in Medicare Advantage than in traditional Medicare (for 2009 STEMI, 13.8% vs 15.2%; difference, -1.3 percentage points [95% CI, -2.0 to -0.6]; and for 2018 STEMI, 11.2% vs 11.9%; difference, 0.6 percentage points [95% CI, -1.5 to 0.0]). Conclusions and Relevance: Among Medicare beneficiaries with acute MI, enrollment in Medicare Advantage, compared with traditional Medicare, was significantly associated with modestly lower rates of 30-day mortality in 2009, and the difference was no longer statistically significant by 2018. These findings, considered with other outcomes, may provide insight into differences in treatment and outcomes by Medicare insurance type.

Suicide prediction models: a critical review of recent research with recommendations for the way forward.

Suicide is a leading cause of death. A substantial proportion of the people who die by suicide come into contact with the health care system in the year before their death. This observation has resulted in the development of numerous suicide prediction tools to help target patients for preventive interventions. However, low sensitivity and low positive predictive value have led critics to argue that these tools have no clinical value. We review these tools and critiques here. We conclude that existing tools are suboptimal and that improvements, if they can be made, will require developers to work with more comprehensive predictor sets, staged screening designs, and advanced statistical analysis methods. We also conclude that although existing suicide prediction tools currently have little clinical value, and in some cases might do more harm than good, an even-handed assessment of the potential value of refined tools of this sort cannot currently be made because such an assessment would depend on evidence that currently does not exist about the effectiveness of preventive interventions. We argue that the only way to resolve this uncertainty is to link future efforts to develop or evaluate suicide prediction tools with concrete questions about specific clinical decisions aimed at reducing suicides and to evaluate the clinical value of these tools in terms of net benefit rather than sensitivity or positive predictive value. We also argue for a focus on the development of individualized treatment rules to help select the right suicide-focused treatments for the right patients at the right times. Challenges will exist in doing this because of the rarity of suicide even among patients considered high-risk, but we offer practical suggestions for how these challenges can be addressed.

Self-injurious thoughts and behaviors that differentiate soldiers who attempt suicide from those with recent suicide ideation.

BACKGROUND: Risk for suicide attempt (SA) versus suicide ideation (SI) is clinically important and difficult to differentiate. We examined whether a history of self-injurious thoughts and behaviors (SITBs) differentiates soldiers with a recent SA from nonattempting soldiers with current/recent SI. METHODS: Using a unique case-control design, we administered the same questionnaire (assessing the history of SITBs and psychosocial variables) to representative U.S. Army soldiers recently hospitalized for SA (n = 132) and soldiers from the same Army installations who reported 30-day SI but did not make an attempt (n = 125). Logistic regression analyses examined whether SITBs differentiated attempters and ideators after controlling for previously identified covariates. RESULTS: In separate models that weighted for systematic nonresponse and controlled for gender, education, posttraumatic stress disorder, and intermittent explosive disorder, SA was positively and significantly associated with the history of suicide plan and/or intention to act (odds ratio [OR] = 12.1 [95% confidence interval {CI} = 3.6-40.4]), difficulty controlling suicidal thoughts during the worst week of ideation (OR = 3.5 [95% CI = 1.1-11.3]), and nonsuicidal self-injury (NSSI) (OR = 4.9 [95% CI = 1.3-18.0]). Area under the curve was 0.87 in a full model that combined these SITBs and covariates. The top ventile based on predicted risk had a sensitivity of 24.7%, specificity of 99.8%, and positive predictive value of 97.5%. CONCLUSIONS: History of suicide plan/intention, difficult to control ideation, and NSSI differentiate soldiers with recent SA from those with current/recent SI independent of sociodemographic characteristics and mental disorders. Longitudinal research is needed to determine whether these factors are prospectively associated with the short-term transition from SI to SA.

Validating Reports of Chronic Conditions in the Medicare CAHPS Survey.

BACKGROUND: The Medicare Consumer Assessment of Healthcare Providers and Systems (CAHPS) survey includes items about chronic conditions, health history, and self-rated health. Questions remain about the concordance between patient reports and administrative sources on questions related to health history. OBJECTIVE: To validate CAHPS measures of chronic conditions against claims-based measures from the Medicare Chronic Conditions Warehouse (CCW). METHODS: We linked CAHPS responses from 301,050 fee-for-service Medicare beneficiaries in 2010-2012 with summaries of their claims in the CCW and identified nearest equivalent measures of conditions across sources. We calculated sensitivities and specificities for conditions and estimated regression models to assess the effects of patient characteristics on the sensitivity. RESULTS: The sensitivity of CAHPS measures differed across conditions, ranging from 0.513 for history of stroke to 0.773 for history of cancer. Sensitivity was generally lower for older beneficiaries, those reporting good self-rated health, and those with fair or poor mental health. Specificity was 0.904 or greater for all conditions, up to 0.961 for stroke. CONCLUSIONS: Despite difference in timeframes and definitions of conditions, the measured sensitivities demonstrated reasonable validity. Variation in sensitivity is consistent with covariates that either directly measure health severity within a diagnosis or can be construed as a proxy for severity of illness.