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Risk of U.S. Army soldier suicide-related behaviors increases substantially after separation from service. As universal prevention programs have been unable to resolve this problem, a previously reported machine learning model was developed using pre-separation predictors to target high-risk transitioning service members (TSMs) for more intensive interventions. This model is currently being used in a demonstration project. The model is limited, though, in two ways. First, the model was developed and trained in a relatively small cross-validation sample (n = 4044) and would likely be improved if a larger sample was available. Second, the model provides no guidance on subtyping high-risk TSMs. This report presents results of an attempt to refine the model to address these limitations by re-estimating the model in a larger sample (n = 5909) and attempting to develop embedded models for differential risk of post-separation stressful life events (SLEs) known to mediate the association of model predictions with post-separation nonfatal suicide attempts (SAs; n = 4957). Analysis used data from the Army STARRS Longitudinal Surveys. The revised model improved prediction of post-separation SAs in the first year (AUC = 0.85) and second-third years (AUC = 0.77) after separation, but embedded models could not predict post-separation SLEs with enough accuracy to support intervention targeting., Competing Interests: Ethics approval: The Human Subjects Committees of the University of Michigan, the Uniformed Services University of the Health Sciences, and the Army Medical Research and Materiel Command approved all recruitment, consent, and field procedures. A total of n = 72,387 respondents across these three STARRS surveys consented to link their deidentified survey data with Army administrative data. There are no identifiable images from human research participants involved in the current study. The present study was conducted in accordance with the Declaration of Helsinki. Informed consent: All participants provided written informed consent. Competing interests: In the past 3 years, Dr. Kessler was a consultant for Cambridge Health Alliance, Canandaigua VA Medical Center, Child Mind Institute, Holmusk, Massachusetts General Hospital, Partners Healthcare, Inc., RallyPoint Networks, Inc., Sage Therapeutics and University of North Carolina. He has stock options in Cerebral Inc., Mirah, PYM (Prepare Your Mind), Roga Sciences and Verisense Health. In the past 3 years, Dr. Stein received consulting income from Actelion, Acadia Pharmaceuticals, Aptinyx, atai Life Sciences, Boehringer Ingelheim, Bionomics, BioXcel Therapeutics, Clexio, EmpowerPharm, Engrail Therapeutics, GW Pharmaceuticals, Janssen, Jazz Pharmaceuticals, and Roche/Genentech. He has stock options in Oxeia Biopharmaceuticals and EpiVario. He is paid for his editorial work on Biological Psychiatry (Deputy Editor) and UpToDate (Co-Editor-in-Chief for Psychiatry). The remaining authors declare no competing interests., (© 2025. The Author(s).)

Importance: The suicide rate of military servicemembers increases sharply after returning to civilian life. Identifying high-risk servicemembers before they leave service could help target preventive interventions., Objective: To develop a model based on administrative data for regular US Army soldiers that can predict suicides 1 to 120 months after leaving active service., Design, Setting, and Participants: In this prognostic study, a consolidated administrative database was created for all regular US Army soldiers who left service from 2010 through 2019. Machine learning models were trained to predict suicides over the next 1 to 120 months in a random 70% training sample. Validation was implemented in the remaining 30%. Data were analyzed from March 2023 through March 2024., Main Outcome and Measures: The outcome was suicide in the National Death Index. Predictors came from administrative records available before leaving service on sociodemographics, Army career characteristics, psychopathologic risk factors, indicators of physical health, social networks and supports, and stressors., Results: Of the 800 579 soldiers in the cohort (84.9% male; median [IQR] age at discharge, 26 [23-33] years), 2084 suicides had occurred as of December 31, 2019 (51.6 per 100 000 person-years). A lasso model assuming consistent slopes over time discriminated as well over all but the shortest risk horizons as more complex stacked generalization ensemble machine learning models. Test sample area under the receiver operating characteristic curve ranged from 0.87 (SE = 0.06) for suicides in the first month after leaving service to 0.72 (SE = 0.003) for suicides over 120 months. The 10% of soldiers with highest predicted risk accounted for between 30.7% (SE = 1.8) and 46.6% (SE = 6.6) of all suicides across horizons. Calibration was for the most part better for the lasso model than the super learner model (both estimated over 120-month horizons.) Net benefit of a model-informed prevention strategy was positive compared with intervene-with-all or intervene-with-none strategies over a range of plausible intervention thresholds. Sociodemographics, Army career characteristics, and psychopathologic risk factors were the most important classes of predictors., Conclusions and Relevance: These results demonstrated that a model based on administrative variables available at the time of leaving active Army service can predict suicides with meaningful accuracy over the subsequent decade. However, final determination of cost-effectiveness would require information beyond the scope of this report about intervention content, costs, and effects over relevant horizons in relation to the monetary value placed on preventing suicides.

Background: The period after psychiatric hospital discharge is one of elevated risk for suicide-related behaviors (SRBs). Post-discharge clinical outreach, although potentially effective in preventing SRBs, would be more cost-effective if targeted at high-risk patients. To this end, a machine learning model was developed to predict post-discharge suicides among Veterans Health Administration (VHA) psychiatric inpatients and target a high-risk preventive intervention., Methods: The Veterans Coordinated Community Care (3C) Study is a multicenter randomized controlled trial using this model to identify high-risk VHA psychiatric inpatients (n = 850) randomized with equal allocation to either the Coping Long Term with Active Suicide Program (CLASP) post-discharge clinical outreach intervention or treatment-as-usual (TAU). The primary outcome is SRBs over a 6-month follow-up. We will estimate average treatment effects adjusted for loss to follow-up and investigate the possibility of heterogeneity of treatment effects., Results: Recruitment is underway and will end September 2024. Six-month follow-up will end and analysis will begin in Summer 2025., Conclusion: Results will provide information about the effectiveness of CLASP versus TAU in reducing post-discharge SRBs and provide guidance to VHA clinicians and policymakers about the implications of targeted use of CLASP among high-risk psychiatric inpatients in the months after hospital discharge., Clinical Trials Registration: ClinicalTrials.Gov identifier: NCT05272176 (https://www., Clinicaltrials: gov/ct2/show/NCT05272176)., (© 2024 The Author(s). International Journal of Methods in Psychiatric Research published by John Wiley & Sons Ltd.)

Background: Heterogeneity of treatment effects (HTEs) can occur because of either differential treatment compliance or differential treatment effectiveness. This distinction is important, as it has action implications, but it is unclear how to distinguish these two possibilities statistically in precision treatment analysis given that compliance is not observed until after randomization. We review available statistical methods and illustrate a recommended method in secondary analysis in a trial focused on HTE., Methods: The trial randomized n = 880 anxious and/or depressed university students to guided internet-delivered cognitive behavioral therapy (i-CBT) or treatment-as-usual (TAU) and evaluated joint remission. Previously reported analyses documented superiority of i-CBT but significant HTE. In the reanalysis reported here, we used baseline (i.e., pre-randomization) covariates to predict compliance among participants randomized to guided i-CBT, generated a cross-validated within-person expected compliance score based on this model in both intervention groups, and then used this expected composite score as a predictor in an expanded HTE analysis., Results: The significant intervention effect was limited to participants with high expected compliance. Residual HTE was nonsignificant., Conclusions: Future psychotherapy HTE trials should routinely develop and include expected compliance composite scores to distinguish the effects of differential treatment compliance from the effects of differential treatment effectiveness., (© 2025 The Author(s). International Journal of Methods in Psychiatric Research published by John Wiley & Sons Ltd.)

Efforts to develop an individualized treatment rule (ITR) to optimize major depressive disorder (MDD) treatment with antidepressant medication (ADM), psychotherapy, or combined ADM-psychotherapy have been hampered by small samples, small predictor sets, and suboptimal analysis methods. Analyses of large administrative databases designed to approximate experiments followed iteratively by pragmatic trials hold promise for resolving these problems. The current report presents a proof-of-concept study using electronic health records (EHR) of n = 43,470 outpatients beginning MDD treatment in Veterans Health Administration Primary Care Mental Health Integration (PC-MHI) clinics, which offer access not only to ADMs but also psychotherapy and combined ADM-psychotherapy. EHR and geospatial databases were used to generate an extensive baseline predictor set (5,865 variables). The outcome was a composite measure of at least one serious negative event (suicide attempt, psychiatric emergency department visit, psychiatric hospitalization, suicide death) over the next 12 months. Best-practices methods were used to adjust for nonrandom treatment assignment and to estimate a preliminary ITR in a 70% training sample and to evaluate the ITR in the 30% test sample. Statistically significant aggregate variation was found in overall probability of the outcome related to baseline predictors (AU-ROC = 0.68, S.E. = 0.01), with test sample outcome prevalence of 32.6% among the 5% of patients having highest predicted risk compared to 7.1% in the remainder of the test sample. The ITR found that psychotherapy-only was the optimal treatment for 56.0% of patients (roughly 20% lower risk of the outcome than if receiving one of the other treatments) and that treatment type was unrelated to outcome risk among other patients. Change in aggregate treatment costs of implementing this ITR would be negligible, as 16.1% fewer patients would be prescribed ADMs and 2.9% more would receive psychotherapy. A pragmatic trial would be needed to confirm the accuracy of the ITR., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Introduction: This study develops a practical method to triage Army transitioning service members (TSMs) at highest risk of homelessness to target a preventive intervention., Methods: The sample included 4,790 soldiers from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS) who participated in 1 of 3 Army STARRS 2011-2014 baseline surveys followed by the third wave of the STARRS-LS online panel surveys (2020-2022). Two machine learning models were trained: a Stage-1 model that used administrative predictors and geospatial data available for all TSMs at discharge to identify high-risk TSMs for initial outreach; and a Stage-2 model estimated in the high-risk subsample that used self-reported survey data to help determine highest risk based on additional information collected from high-risk TSMs once they are contacted. The outcome in both models was homelessness within 12 months after leaving active service., Results: Twelve-month prevalence of post-transition homelessness was 5.0% (SE=0.5). The Stage-1 model identified 30% of high-risk TSMs who accounted for 52% of homelessness. The Stage-2 model identified 10% of all TSMs (i.e., 33% of high-risk TSMs) who accounted for 35% of all homelessness (i.e., 63% of the homeless among high-risk TSMs)., Conclusions: Machine learning can help target outreach and assessment of TSMs for homeless prevention interventions., (Published by Elsevier Inc.)

Study Objectives: The standard of care for military personnel with insomnia is cognitive behavioral therapy for insomnia (CBT-I). However, only a minority seeking insomnia treatment receive CBT-I, and little reliable guidance exists to identify those most likely to respond. As a step toward personalized care, we present results of a machine learning (ML) model to predict CBT-I response., Methods: Administrative data were examined for n = 1,449 nondeployed US Army soldiers treated for insomnia with CBT-I who had moderate-severe baseline Insomnia Severity Index (ISI) scores and completed 1 or more follow-up ISIs 6-12 weeks after baseline. An ensemble ML model was developed in a 70% training sample to predict clinically significant ISI improvement (reduction of at least 2 standard deviations on the baseline ISI distribution). Predictors included a wide range of military administrative and baseline clinical variables. Model accuracy was evaluated in the remaining 30% test sample., Results: 19.8% of patients had clinically significant ISI improvement. Model area under the receiver operating characteristic curve (standard error) was 0.60 (0.03). The 20% of test-sample patients with the highest probabilities of improvement were twice as likely to have clinically significant improvement compared with the remaining 80% (36.5% vs 15.7%; χ 2 1 = 9.2, P = .002). Nearly 85% of prediction accuracy was due to 10 variables, the most important of which were baseline insomnia severity and baseline suicidal ideation., Conclusions: Pending replication, the model could be used as part of a patient-centered decision-making process for insomnia treatment. Parallel models will be needed for alternative treatments before such a system is of optimal value., Citation: Gabbay FH, Wynn GH, Georg MW, et al. Toward personalized care for insomnia in the US Army: a machine learning model to predict response to cognitive behavioral therapy for insomnia. J Clin Sleep Med . 2024;20(6):921-931., (© 2024 American Academy of Sleep Medicine.)

Importance: Psychiatric hospitalization is the standard of care for patients presenting to an emergency department (ED) or urgent care (UC) with high suicide risk. However, the effect of hospitalization in reducing subsequent suicidal behaviors is poorly understood and likely heterogeneous., Objectives: To estimate the association of psychiatric hospitalization with subsequent suicidal behaviors using observational data and develop a preliminary predictive analytics individualized treatment rule accounting for heterogeneity in this association across patients., Design, Setting, and Participants: A machine learning analysis of retrospective data was conducted. All veterans presenting with suicidal ideation (SI) or suicide attempt (SA) from January 1, 2010, to December 31, 2015, were included. Data were analyzed from September 1, 2022, to March 10, 2023. Subgroups were defined by primary psychiatric diagnosis (nonaffective psychosis, bipolar disorder, major depressive disorder, and other) and suicidality (SI only, SA in past 2-7 days, and SA in past day). Models were trained in 70.0% of the training samples and tested in the remaining 30.0%., Exposures: Psychiatric hospitalization vs nonhospitalization., Main Outcomes and Measures: Fatal and nonfatal SAs within 12 months of ED/UC visits were identified in administrative records and the National Death Index. Baseline covariates were drawn from electronic health records and geospatial databases., Results: Of 196 610 visits (90.3% men; median [IQR] age, 53 [41-59] years), 71.5% resulted in hospitalization. The 12-month SA risk was 11.9% with hospitalization and 12.0% with nonhospitalization (difference, -0.1%; 95% CI, -0.4% to 0.2%). In patients with SI only or SA in the past 2 to 7 days, most hospitalization was not associated with subsequent SAs. For patients with SA in the past day, hospitalization was associated with risk reductions ranging from -6.9% to -9.6% across diagnoses. Accounting for heterogeneity, hospitalization was associated with reduced risk of subsequent SAs in 28.1% of the patients and increased risk in 24.0%. An individualized treatment rule based on these associations may reduce SAs by 16.0% and hospitalizations by 13.0% compared with current rates., Conclusions and Relevance: The findings of this study suggest that psychiatric hospitalization is associated with reduced average SA risk in the immediate aftermath of an SA but not after other recent SAs or SI only. Substantial heterogeneity exists in these associations across patients. An individualized treatment rule accounting for this heterogeneity could both reduce SAs and avert hospitalizations.

Objective: Untreated mental disorders are important among low- and middle-income country (LMIC) university students in Latin America, where barriers to treatment are high. Scalable interventions are needed. This study compared transdiagnostic self-guided and guided internet-delivered cognitive behavioral therapy (i-CBT) with treatment as usual (TAU) for clinically significant anxiety and depression among undergraduates in Colombia and Mexico., Method: 1,319 anxious, as determined by the Generalized Anxiety Disorder-7 (GAD-7) = 10+ and/or depressed, as determined by the Patient Health Questionnaire-9 (PHQ-9) = 10+, undergraduates (mean [SD] age = 21.4 [3.2]); 78.7% female; 55.9% first-generation university student) from seven universities in Colombia and Mexico were randomized to culturally adapted versions of self-guided i-CBT (n = 439), guided i-CBT (n = 445), or treatment as usual (TAU; n = 435). All randomized participants were reassessed 3 months after randomization. The primary outcome was remission of both anxiety (GAD-7 = 0-4) and depression (PHQ-9 = 0-4). We hypothesized that remission would be higher with guided i-CBT than with the other interventions., Results: Intent-to-treat analysis found significantly higher adjusted (for university and loss to follow-up) remission rates (ARD) among participants randomized to guided i-CBT than either self-guided i-CBT (ARD = 13.1%, χ12 = 10.4, p = .001) or TAU (ARD = 11.2%, χ12 = 8.4, p = .004), but no significant difference between self-guided i-CBT and TAU (ARD = -1.9%, χ12 = 0.2, p = .63). Per-protocol sensitivity analyses and analyses of dimensional outcomes yielded similar results., Conclusions: Significant reductions in anxiety and depression among LMIC university students could be achieved with guided i-CBT, although further research is needed to determine which students would most likely benefit from this intervention. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Background: Risk of suicide-related behaviors is elevated among military personnel transitioning to civilian life. An earlier report showed that high-risk U.S. Army soldiers could be identified shortly before this transition with a machine learning model that included predictors from administrative systems, self-report surveys, and geospatial data. Based on this result, a Veterans Affairs and Army initiative was launched to evaluate a suicide-prevention intervention for high-risk transitioning soldiers. To make targeting practical, though, a streamlined model and risk calculator were needed that used only a short series of self-report survey questions., Methods: We revised the original model in a sample of n = 8335 observations from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS) who participated in one of three Army STARRS 2011-2014 baseline surveys while in service and in one or more subsequent panel surveys (LS1: 2016-2018, LS2: 2018-2019) after leaving service. We trained ensemble machine learning models with constrained numbers of item-level survey predictors in a 70% training sample. The outcome was self-reported post-transition suicide attempts (SA). The models were validated in the 30% test sample., Results: Twelve-month post-transition SA prevalence was 1.0% (s.e. = 0.1). The best constrained model, with only 17 predictors, had a test sample ROC-AUC of 0.85 (s.e. = 0.03). The 10-30% of respondents with the highest predicted risk included 44.9-92.5% of 12-month SAs., Conclusions: An accurate SA risk calculator based on a short self-report survey can target transitioning soldiers shortly before leaving service for intervention to prevent post-transition SA.

Study Objectives: Although many military personnel with insomnia are treated with prescription medication, little reliable guidance exists to identify patients most likely to respond. As a first step toward personalized care for insomnia, we present results of a machine-learning model to predict response to insomnia medication., Methods: The sample comprised n = 4,738 nondeployed US Army soldiers treated with insomnia medication and followed 6-12 weeks after initiating treatment. All patients had moderate-severe baseline scores on the Insomnia Severity Index (ISI) and completed 1 or more follow-up ISIs 6-12 weeks after baseline. An ensemble machine-learning model was developed in a 70% training sample to predict clinically significant ISI improvement, defined as reduction of at least 2 standard deviations on the baseline ISI distribution. Predictors included a wide range of military administrative and baseline clinical variables. Model accuracy was evaluated in the remaining 30% test sample., Results: 21.3% of patients had clinically significant ISI improvement. Model test sample area under the receiver operating characteristic curve (standard error) was 0.63 (0.02). Among the 30% of patients with the highest predicted probabilities of improvement, 32.5.% had clinically significant symptom improvement vs 16.6% in the 70% sample predicted to be least likely to improve (χ 2 1 = 37.1, P < .001). More than 75% of prediction accuracy was due to 10 variables, the most important of which was baseline insomnia severity., Conclusions: Pending replication, the model could be used as part of a patient-centered decision-making process for insomnia treatment, but parallel models will be needed for alternative treatments before such a system is of optimal value., Citation: Gabbay FH, Wynn GH, Georg MW, et al. Toward personalized care for insomnia in the US Army: development of a machine-learning model to predict response to pharmacotherapy. J Clin Sleep Med . 2023;19(8):1399-1410., (© 2023 American Academy of Sleep Medicine.)

Importance: Guided internet-delivered cognitive behavioral therapy (i-CBT) is a low-cost way to address high unmet need for anxiety and depression treatment. Scalability could be increased if some patients were helped as much by self-guided i-CBT as guided i-CBT., Objective: To develop an individualized treatment rule using machine learning methods for guided i-CBT vs self-guided i-CBT based on a rich set of baseline predictors., Design, Setting, and Participants: This prespecified secondary analysis of an assessor-blinded, multisite randomized clinical trial of guided i-CBT, self-guided i-CBT, and treatment as usual included students in Colombia and Mexico who were seeking treatment for anxiety (defined as a 7-item Generalized Anxiety Disorder [GAD-7] score of ≥10) and/or depression (defined as a 9-item Patient Health Questionnaire [PHQ-9] score of ≥10). Study recruitment was from March 1 to October 26, 2021. Initial data analysis was conducted from May 23 to October 26, 2022., Interventions: Participants were randomized to a culturally adapted transdiagnostic i-CBT that was guided (n = 445), self-guided (n = 439), or treatment as usual (n = 435)., Main Outcomes and Measures: Remission of anxiety (GAD-7 scores of ≤4) and depression (PHQ-9 scores of ≤4) 3 months after baseline., Results: The study included 1319 participants (mean [SD] age, 21.4 [3.2] years; 1038 women [78.7%]; 725 participants [55.0%] came from Mexico). A total of 1210 participants (91.7%) had significantly higher mean (SE) probabilities of joint remission of anxiety and depression with guided i-CBT (51.8% [3.0%]) than with self-guided i-CBT (37.8% [3.0%]; P = .003) or treatment as usual (40.0% [2.7%]; P = .001). The remaining 109 participants (8.3%) had low mean (SE) probabilities of joint remission of anxiety and depression across all groups (guided i-CBT: 24.5% [9.1%]; P = .007; self-guided i-CBT: 25.4% [8.8%]; P = .004; treatment as usual: 31.0% [9.4%]; P = .001). All participants with baseline anxiety had nonsignificantly higher mean (SE) probabilities of anxiety remission with guided i-CBT (62.7% [5.9%]) than the other 2 groups (self-guided i-CBT: 50.2% [6.2%]; P = .14; treatment as usual: 53.0% [6.0%]; P = .25). A total of 841 of 1177 participants (71.5%) with baseline depression had significantly higher mean (SE) probabilities of depression remission with guided i-CBT (61.5% [3.6%]) than the other 2 groups (self-guided i-CBT: 44.3% [3.7%]; P = .001; treatment as usual: 41.8% [3.2%]; P < .001). The other 336 participants (28.5%) with baseline depression had nonsignificantly higher mean (SE) probabilities of depression remission with self-guided i-CBT (54.4% [6.0%]) than guided i-CBT (39.8% [5.4%]; P = .07)., Conclusions and Relevance: Guided i-CBT yielded the highest probabilities of remission of anxiety and depression for most participants; however, these differences were nonsignificant for anxiety. Some participants had the highest probabilities of remission of depression with self-guided i-CBT. Information about this variation could be used to optimize allocation of guided and self-guided i-CBT in resource-constrained settings., Trial Registration: ClinicalTrials.gov Identifier: NCT04780542.

Background: The transition from military service to civilian life is a high-risk period for suicide attempts (SAs). Although stressful life events (SLEs) faced by transitioning soldiers are thought to be implicated, systematic prospective evidence is lacking., Methods: Participants in the Army Study to Assess Risk and Resilience in Servicemembers (STARRS) completed baseline self-report surveys while on active duty in 2011-2014. Two self-report follow-up Longitudinal Surveys (LS1: 2016-2018; LS2: 2018-2019) were subsequently administered to probability subsamples of these baseline respondents. As detailed in a previous report, a SA risk index based on survey, administrative, and geospatial data collected before separation/deactivation identified 15% of the LS respondents who had separated/deactivated as being high-risk for self-reported post-separation/deactivation SAs. The current report presents an investigation of the extent to which self-reported SLEs occurring in the 12 months before each LS survey might have mediated/modified the association between this SA risk index and post-separation/deactivation SAs., Results: The 15% of respondents identified as high-risk had a significantly elevated prevalence of some post-separation/deactivation SLEs. In addition, the associations of some SLEs with SAs were significantly stronger among predicted high-risk than lower-risk respondents. Demographic rate decomposition showed that 59.5% (s.e. = 10.2) of the overall association between the predicted high-risk index and subsequent SAs was linked to these SLEs., Conclusions: It might be possible to prevent a substantial proportion of post-separation/deactivation SAs by providing high-risk soldiers with targeted preventive interventions for exposure/vulnerability to commonly occurring SLEs.

Importance: The months after psychiatric hospital discharge are a time of high risk for suicide. Intensive postdischarge case management, although potentially effective in suicide prevention, is likely to be cost-effective only if targeted at high-risk patients. A previously developed machine learning (ML) model showed that postdischarge suicides can be predicted from electronic health records and geospatial data, but it is unknown if prediction could be improved by adding additional information., Objective: To determine whether model prediction could be improved by adding information extracted from clinical notes and public records., Design, Setting, and Participants: Models were trained to predict suicides in the 12 months after Veterans Health Administration (VHA) short-term (less than 365 days) psychiatric hospitalizations between the beginning of 2010 and September 1, 2012 (299 050 hospitalizations, with 916 hospitalizations followed within 12 months by suicides) and tested in the hospitalizations from September 2, 2012, to December 31, 2013 (149 738 hospitalizations, with 393 hospitalizations followed within 12 months by suicides). Validation focused on net benefit across a range of plausible decision thresholds. Predictor importance was assessed with Shapley additive explanations (SHAP) values. Data were analyzed from January to August 2022., Main Outcomes and Measures: Suicides were defined by the National Death Index. Base model predictors included VHA electronic health records and patient residential data. The expanded predictors came from natural language processing (NLP) of clinical notes and a social determinants of health (SDOH) public records database., Results: The model included 448 788 unique hospitalizations. Net benefit over risk horizons between 3 and 12 months was generally highest for the model that included both NLP and SDOH predictors (area under the receiver operating characteristic curve range, 0.747-0.780; area under the precision recall curve relative to the suicide rate range, 3.87-5.75). NLP and SDOH predictors also had the highest predictor class-level SHAP values (proportional SHAP = 64.0% and 49.3%, respectively), although the single highest positive variable-level SHAP value was for a count of medications classified by the US Food and Drug Administration as increasing suicide risk prescribed the year before hospitalization (proportional SHAP = 15.0%)., Conclusions and Relevance: In this study, clinical notes and public records were found to improve ML model prediction of suicide after psychiatric hospitalization. The model had positive net benefit over 3-month to 12-month risk horizons for plausible decision thresholds. Although caution is needed in inferring causality based on predictor importance, several key predictors have potential intervention implications that should be investigated in future studies.

Background: The World Health Organization World Mental Health International College Student (WMH-ICS) initiative aims to screen for mental health and substance use problems among postsecondary students on a global scale as well as to develop and evaluate evidence-based preventive and ameliorative interventions for this population. This protocol paper presents the Canadian version of the WMH-ICS survey, detailing the adapted survey instrument, the unique weekly cross-sectional administration, the multitiered recruitment strategy, and the associated risk mitigation protocols., Objective: This paper aims to provide a methodological resource for researchers conducting cross-national comparisons of WMH-ICS data, as well as to serve as a useful guide for those interested in replicating the outlined cross-sectional methodology to better understand how mental health and substance use vary over time among university students., Methods: The online survey is based on the WMH-ICS survey instrument, modified to the Canadian context by the addition of questions pertaining to Canadian-based guidelines and the translation of the survey to Canadian French. The survey is administered through the Qualtrics survey platform and is sent to an independent stratified random sample of 350 students per site weekly, followed by two reminder emails. Upon survey closure every week, a random subsample of 70 nonresponders are followed up with via phone or through a personal email in an effort to decrease nonresponder bias. The survey is accompanied by an extensive risk mitigation protocol that stratifies respondents by the level of need and provides tailored service recommendations, including a facilitated expedited appointment to student counseling services for those at increased risk of suicide. The anticipated sample size is approximately 5500 students per site per year., Results: In February 2020, the Canadian survey was deployed at the University of British Columbia. This was followed by deployment at Simon Fraser University (November 2020), McMaster University (January 2021), and the University of Toronto (January 2022). Data collection at all 4 sites is ongoing. As of May 6, 2022, 29,503 responses have been collected., Conclusions: Based on international collaboration, the Canadian version of the WMH-ICS survey incorporates a novel methodological approach centered on the weekly administration of a comprehensive cross-sectional survey to independent stratified random samples of university students. After 27 months of consecutive survey administration, we have developed and refined a survey protocol that has proven effective in engaging students at four Canadian institutions, allowing us to track how mental health and substance use vary over time using an internationally developed university student survey based on the criteria from the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition)., International Registered Report Identifier (irrid): RR1-10.2196/35168., (©Laura B Jones, Carolina Judkowicz, Kristen L Hudec, Richard J Munthali, Ana Paula Prescivalli, Angel Y Wang, Lonna Munro, Hui Xie, Krishna Pendakur, Brian Rush, James Gillett, Marisa Young, Diana Singh, Antoaneta A Todorova, Randy P Auerbach, Ronny Bruffaerts, Sarah M Gildea, Irene McKechnie, Anne Gadermann, Chris G Richardson, Nancy A Sampson, Ronald C Kessler, Daniel V Vigo. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 29.07.2022.)

Introduction: The ability to predict and prevent homelessness has been an elusive goal. The purpose of this study was to develop a prediction model that identified U.S. Army soldiers at high risk of becoming homeless after transitioning to civilian life based on information available before the time of this transition., Methods: The prospective cohort study consisted of observations from 16,589 soldiers who were separated or deactivated from service and who had previously participated in 1 of 3 baseline surveys of the Army Study to Assess Risk and Resilience in Servicemembers in 2011-2014. A machine learning model was developed in a 70% training sample and evaluated in the remaining 30% test sample to predict self-reported homelessness in 1 of 2 Longitudinal Study surveys administered in 2016-2018 and 2018-2019. Predictors included survey, administrative, and geospatial variables available before separation/deactivation. Analysis was conducted in November 2020-May 2021., Results: The 12-month prevalence of homelessness was 2.9% (SE=0.2%) in the total Longitudinal Study sample. The area under the receiver operating characteristic curve in the test sample was 0.78 (SE=0.02) for homelessness. The 4 highest ventiles (top 20%) of predicted risk included 61% of respondents with homelessness. Self-reported lifetime histories of depression, trauma of having a loved one murdered, and post-traumatic stress disorder were the 3 strongest predictors of homelessness., Conclusions: A prediction model for homelessness can accurately target soldiers for preventive intervention before transition to civilian life., (Copyright © 2022 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.)

Background: Major depressive disorder (MDD) is a leading cause of disease morbidity. Combined treatment with antidepressant medication (ADM) plus psychotherapy yields a much higher MDD remission rate than ADM only. But 77% of US MDD patients are nonetheless treated with ADM only despite strong patient preferences for psychotherapy. This mismatch is due at least in part to a combination of cost considerations and limited availability of psychotherapists, although stigma and reluctance of PCPs to refer patients for psychotherapy are also involved. Internet-based cognitive behaviorial therapy (i-CBT) addresses all of these problems., Methods: Enrolled patients (n = 3360) will be those who are beginning ADM-only treatment of MDD in primary care facilities throughout West Virginia, one of the poorest and most rural states in the country. Participating treatment providers and study staff at West Virginia University School of Medicine (WVU) will recruit patients and, after obtaining informed consent, administer a baseline self-report questionnaire (SRQ) and then randomize patients to 1 of 3 treatment arms with equal allocation: ADM only, ADM + self-guided i-CBT, and ADM + guided i-CBT. Follow-up SRQs will be administered 2, 4, 8, 13, 16, 26, 39, and 52 weeks after randomization. The trial has two primary objectives: to evaluate aggregate comparative treatment effects across the 3 arms and to estimate heterogeneity of treatment effects (HTE). The primary outcome will be episode remission based on a modified version of the patient-centered Remission from Depression Questionnaire (RDQ). The sample was powered to detect predictors of HTE that would increase the proportional remission rate by 20% by optimally assigning individuals as opposed to randomly assigning them into three treatment groups of equal size. Aggregate comparative treatment effects will be estimated using intent-to-treat analysis methods. Cumulative inverse probability weights will be used to deal with loss to follow-up. A wide range of self-report predictors of MDD heterogeneity of treatment effects based on previous studies will be included in the baseline SRQ. A state-of-the-art ensemble machine learning method will be used to estimate HTE., Discussion: The study is innovative in using a rich baseline assessment and in having a sample large enough to carry out a well-powered analysis of heterogeneity of treatment effects. We anticipate finding that self-guided and guided i-CBT will both improve outcomes compared to ADM only. We also anticipate finding that the comparative advantages of adding i-CBT to ADM will vary significantly across patients. We hope to develop a stable individualized treatment rule that will allow patients and treatment providers to improve aggregate treatment outcomes by deciding collaboratively when ADM treatment should be augmented with i-CBT., Trial Registration: ClinicalTrials.gov NCT04120285 . Registered on October 19, 2019., (© 2022. The Author(s).)

Background: Major depressive disorder (MDD) and generalized anxiety disorder (GAD) are highly prevalent among university students and predict impaired college performance and later life role functioning. Yet most students do not receive treatment, especially in low-middle-income countries (LMICs). We aim to evaluate the effects of expanding treatment using scalable and inexpensive Internet-delivered transdiagnostic cognitive behavioral therapy (iCBT) among college students with symptoms of MDD and/or GAD in two LMICs in Latin America (Colombia and Mexico) and to investigate the feasibility of creating a precision treatment rule (PTR) to predict for whom iCBT is most effective., Methods: We will first carry out a multi-site randomized pragmatic clinical trial (N = 1500) of students seeking treatment at student mental health clinics in participating universities or responding to an email offering services. Students on wait lists for clinic services will be randomized to unguided iCBT (33%), guided iCBT (33%), and treatment as usual (TAU) (33%). iCBT will be provided immediately whereas TAU will be whenever a clinic appointment is available. Short-term aggregate effects will be assessed at 90 days and longer-term effects 12 months after randomization. We will use ensemble machine learning to predict heterogeneity of treatment effects of unguided versus guided iCBT versus TAU and develop a precision treatment rule (PTR) to optimize individual student outcome. We will then conduct a second and third trial with separate samples (n = 500 per arm), but with unequal allocation across two arms: 25% will be assigned to the treatment determined to yield optimal outcomes based on the PTR developed in the first trial (PTR for optimal short-term outcomes for Trial 2 and 12-month outcomes for Trial 3), whereas the remaining 75% will be assigned with equal allocation across all three treatment arms., Discussion: By collecting comprehensive baseline characteristics to evaluate heterogeneity of treatment effects, we will provide valuable and innovative information to optimize treatment effects and guide university mental health treatment planning. Such an effort could have enormous public-health implications for the region by increasing the reach of treatment, decreasing unmet need and clinic wait times, and serving as a model of evidence-based intervention planning and implementation., Trial Status: IRB Approval of Protocol Version 1.0; June 3, 2020. Recruitment began on March 1, 2021. Recruitment is tentatively scheduled to be completed on May 30, 2024., Trial Registration: ClinicalTrials.gov NCT04780542 . First submission date: February 28, 2021., (© 2022. The Author(s).)

Suicide risk is elevated among military service members who recently transitioned to civilian life. Identifying high-risk service members before this transition could facilitate provision of targeted preventive interventions. We investigated the feasibility of doing this by attempting to develop a prediction model for self-reported suicide attempts (SAs) after leaving or being released from active duty in the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS). This study included two self-report panel surveys (LS1: 2016-2018, LS2: 2018-2019) administered to respondents who previously participated while on active duty in one of three Army STARRS 2011-2014 baseline self-report surveys. We focus on respondents who left active duty >12 months before their LS survey (n = 8899). An ensemble machine learning model using predictors available prior to leaving active duty was developed in a 70% training sample and validated in a 30% test sample. The 12-month self-reported SA prevalence (SE) was 1.0% (0.1). Test sample AUC (SE) was 0.74 (0.06). The 15% of respondents with highest predicted risk included nearly two-thirds of 12-month SAs and over 80% of medically serious 12-month SAs. These results show that it is possible to identify soldiers at high post-transition self-report SA risk before the transition. Future model development is needed to examine prediction of SAs assessed by administrative data and using surveys administered closer to the time of leaving active duty., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

Background: A better understanding of the extent to which prior occurrences of posttraumatic stress disorder (PTSD) and major depressive episode (MDE) predict psychopathological reactions to subsequent traumas might be useful in targeting posttraumatic preventive interventions., Methods: Data come from 1306 patients presenting to 29 U.S. emergency departments (EDs) after a motor vehicle collision (MVC) in the advancing understanding of recovery after trauma study. Patients completed self-reports in the ED and 2-weeks, 8-weeks, and 3-months post-MVC. Associations of pre-MVC probable PTSD and probable MDE histories with subsequent 3-months post-MVC probable PTSD and probable MDE were examined along with mediation through intervening peritraumatic, 2-, and 8-week disorders., Results: 27.6% of patients had 3-month post-MVC probable PTSD and/or MDE. Pre-MVC lifetime histories of these disorders were not only significant (relative risk = 2.6-7.4) but were dominant (63.1% population attributable risk proportion [PARP]) predictors of this 3-month outcome, with 46.6% prevalence of the outcome among patients with pre-MVC disorder histories versus 9.9% among those without such histories. The associations of pre-MVC lifetime disorders with the 3-month outcome were mediated largely by 2- and 8-week probable PTSD and MDE (PARP decreasing to 22.8% with controls for these intervening disorders). Decomposition showed that pre-MVC lifetime histories predicted both onset and persistence of these intervening disorders as well as the higher conditional prevalence of the 3-month outcome in the presence of these intervening disorders., Conclusions: Assessments of pre-MVC PTSD and MDE histories and follow-ups at 2 and 8 weeks could help target early interventions for psychopathological reactions to MVCs., (© 2021 Wiley Periodicals LLC.)

There is a very high suicide rate in the year after psychiatric hospital discharge. Intensive postdischarge case management programs can address this problem but are not cost-effective for all patients. This issue can be addressed by developing a risk model to predict which inpatients might need such a program. We developed such a model for the 391,018 short-term psychiatric hospital admissions of US veterans in Veterans Health Administration (VHA) hospitals 2010-2013. Records were linked with the National Death Index to determine suicide within 12 months of hospital discharge (n=771). The Super Learner ensemble machine learning method was used to predict these suicides for time horizon between 1 week and 12 months after discharge in a 70% training sample. Accuracy was validated in the remaining 30% holdout sample. Predictors included VHA administrative variables and small area geocode data linked to patient home addresses. The models had AUC=.79-.82 for time horizons between 1 week and 6 months and AUC=.74 for 12 months. An analysis of operating characteristics showed that 22.4%-32.2% of patients who died by suicide would have been reached if intensive case management was provided to the 5% of patients with highest predicted suicide risk. Positive predictive value (PPV) at this higher threshold ranged from 1.2% over 12 months to 3.8% per case manager year over 1 week. Focusing on the low end of the risk spectrum, the 40% of patients classified as having lowest risk account for 0%-9.7% of suicides across time horizons. Variable importance analysis shows that 51.1% of model performance is due to psychopathological risk factors accounted, 26.2% to social determinants of health, 14.8% to prior history of suicidal behaviors, and 6.6% to physical disorders. The paper closes with a discussion of next steps in refining the model and prospects for developing a parallel precision treatment model., (Copyright © 2020 Kessler, Bauer, Bishop, Demler, Dobscha, Gildea, Goulet, Karras, Kreyenbuhl, Landes, Liu, Luedtke, Mair, McAuliffe, Nock, Petukhova, Pigeon, Sampson, Smoller, Weinstock and Bossarte.)