Predictive models of miscarriage on the basis of data from a preconception cohort study.

OBJECTIVE: To use self-reported preconception data to derive models that predict the risk of miscarriage. DESIGN: Prospective preconception cohort study. SETTING: Not applicable. PATIENTS: Study participants were female, aged 21-45 years, residents of the United States or Canada, and attempting spontaneous pregnancy at enrollment during 2013-2022. Participants were followed for up to 12 months of pregnancy attempts; those who conceived were followed through pregnancy and postpartum. We restricted analyses to participants who conceived during the study period. EXPOSURE: On baseline and follow-up questionnaires completed every 8 weeks until pregnancy, we collected self-reported data on sociodemographic factors, reproductive history, lifestyle, anthropometrics, diet, medical history, and male partner characteristics. We included 160 potential predictor variables in our models. MAIN OUTCOME MEASURES: The primary outcome was a miscarriage, defined as pregnancy loss before 20 weeks of gestation. We followed participants from their first positive pregnancy test until miscarriage or a censoring event (induced abortion, ectopic pregnancy, loss of follow-up, or 20 weeks of gestation), whichever occurred first. We fit both survival and static models using Cox proportional hazards models, logistic regression, support vector machines, gradient-boosted trees, and random forest algorithms. We evaluated model performance using the concordance index (survival models) and the weighted F1 score (static models). RESULTS: Among the 8,720 participants who conceived, 20.4% reported miscarriage. In multivariable models, the strongest predictors of miscarriage were female age, history of miscarriage, and male partner age. The weighted F1 score ranged from 73%-89% for static models and the concordance index ranged from 53%-56% for survival models, indicating better discrimination for the static models compared with the survival models (i.e., the ability of the model to discriminate between individuals with and without miscarriage). No appreciable differences were observed across strata of miscarriage history or among models restricted to ≥8 weeks of gestation. CONCLUSION: Our findings suggest that miscarriage is not easily predicted on the basis of preconception lifestyle characteristics and that advancing age and a history of miscarriage are the most important predictors of incident miscarriage.

Prediction of Suicide Attempts Among Persons with Depression: A Population-Based Case Cohort Study.

Studies have highlighted the potential importance of modeling interactions for suicide attempt prediction. This case-cohort study identified risk factors for suicide attempts among persons with depression in Denmark using statistical approaches that do (random forests) or do not model interactions (least absolute shrinkage and selection operator regression [LASSO]). Cases made a non-fatal suicide attempt (n = 6,032) between 1995 and 2015. The comparison subcohort was a 5% random sample of all persons in Denmark on January 1, 1995 (n = 11,963). We used random forests and LASSO for sex-stratified prediction of suicide attempts from demographic variables, psychiatric and somatic diagnoses, and treatments. Poisonings, psychiatric disorders, and medications were important predictors for both sexes. Area under the receiver operating characteristic curve (AUC) values were higher in LASSO models (0.85 [95% CI = 0.84, 0.86] in men; 0.89 [95% CI = 0.88, 0.90] in women) than random forests (0.76 [95% CI = 0.74, 0.78] in men; 0.79 [95% CI = 0.78, 0.81] in women). Automatic detection of interactions via random forests did not result in better model performance than LASSO models that did not model interactions. Due to the complex nature of psychiatric comorbidity and suicide, modeling interactions may not always be the optimal statistical approach to enhancing suicide attempt prediction in high-risk samples.

Introduction to the special section on causal inference and agent-based modeling.

This is an introduction to the special section "Causal Inference and Agent-Based Modeling" in trauma research. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

An introduction to directed acyclic graphs in trauma research.

OBJECTIVE: Directed acyclic graphs (DAGs) are visual representations of the presumed causal structure of an empirical research data set. They are important tools for researchers but have been used rarely in the psychological trauma literature. The purpose of this article is to explain what DAGs are and why (and how) they are useful for trauma researchers. METHOD: We first describe the utility of DAGs for making causal assumptions explicit, identifying causal effects, and preventing bias. Basic definitions and rules governing the use of DAGs are presented using a hypothetical DAG. We explain why conditioning on a variable, for example, by controlling for it in a multivariable model, can in some circumstances actually introduce bias and not prevent it. We also provide references for topics related to DAGs that are beyond the scope of this introductory article. RESULTS: DAGs are illustrated using the example of the effect of posttraumatic stress disorder (PTSD) on Parkinson's disease. We demonstrate that a multivariable model controlling for all covariates that are being considered introduces bias and would make it impossible to identify the causal effect of PTSD on Parkinson's disease. CONCLUSIONS: DAGs can help trauma researchers to understand when they can and when they cannot draw causal conclusions based on research data. This introduction to DAGs should help readers understand their use in the articles on marginal structural models, causal mediation analysis, and instrumental variable methods in this special section, Causal inference and agent-based modeling in trauma research. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A Critical Appraisal of Matching-Adjusted Indirect Comparisons in Spinal Muscular Atrophy.

In the absence of head-to-head trials, indirect treatment comparisons (ITCs) are often used to compare the efficacy of different therapies to support decision-making. Matching-adjusted indirect comparison (MAIC), a type of ITC, is increasingly used to compare treatment efficacy when individual patient data are available from one trial and only aggregate data are available from the other trial. This paper examines the conduct and reporting of MAICs to compare treatments for spinal muscular atrophy (SMA), a rare neuromuscular disease. A literature search identified three studies comparing approved treatments for SMA including nusinersen, risdiplam, and onasemnogene abeparvovec. The quality of the MAICs was assessed on the basis of the following principles consolidated from published MAIC best practices: (1) justification for the use of MAIC is clearly stated, (2) the included trials with respect to study population and design are comparable, (3) all known confounders and effect modifiers are identified a priori and accounted for in the analysis, (4) outcomes should be similar in definition and assessment, (5) baseline characteristics are reported before and after adjustment, along with weights, and (6) key details of a MAIC are reported. In the three MAIC publications in SMA to date, the quality of analysis and reporting varied greatly. Various sources of bias in the MAICs were identified, including lack of control for key confounders and effect modifiers, inconsistency in outcome definitions across trials, imbalances in important baseline characteristics after weighting, and lack of reporting key elements. These findings highlight the importance of evaluating MAICs according to best practices when assessing the conduct and reporting of MAICs.

Matching-Adjusted Indirect Comparisons of Diroximel Fumarate, Ponesimod, and Teriflunomide for Relapsing Multiple Sclerosis.

INTRODUCTION: Diroximel fumarate (DRF), ponesimod (PON), and teriflunomide (TERI) are oral disease-modifying therapies approved for the treatment of relapsing multiple sclerosis. No randomized trials have compared DRF versus PON or TERI. OBJECTIVES: The objectives of this analysis were to compare DRF versus PON and DRF versus TERI for clinical and radiological outcomes. METHODS: We used individual patient data from EVOLVE-MS-1, a 2-year, open-label, single-arm, phase III trial of DRF (n = 1057), and aggregated data from OPTIMUM, a 2-year, double-blind, phase III trial comparing PON (n = 567) and TERI (n = 566). To account for cross-trial differences, EVOLVE-MS-1 data were weighted to match OPTIMUM's average baseline characteristics using an unanchored matching-adjusted indirect comparison. We examined the outcomes of annualized relapse rate (ARR), 12-week confirmed disability progression (CDP), 24-week CDP, absence of gadolinium-enhancing (Gd+) T1 lesions, and absence of new/newly enlarging T2 lesions. RESULTS: After weighting, we did not observe strong evidence of differences between DRF and PON for ARR [DRF versus PON incidence rate difference (IRD) -0.02; 95% confidence interval (CI) -0.08, 0.04; incidence rate ratio (IRR) 0.92; 95% CI 0.61, 1.2], 12-week CDP [risk difference (RD) -2.5%; 95% CI -6.3, 1.2; risk ratio (RR) 0.76; 95% CI 0.38, 1.1], 24-week CDP (RD -2.7%; 95% CI -6.0, 0.63; RR 0.68; 95% CI 0.28, 1.0), and absence of new/newly enlarging T2 lesions (RD -2.5%; 95% CI -13, 7.4; RR 0.94; 95% CI 0.70, 1.2). However, a higher proportion of DRF-treated patients were free of Gd+ T1 lesions than PON-treated patients (RD 11%; 95% CI 6.0, 16; RR 1.1; 95% CI 1.06, 1.2). Compared with TERI, DRF showed improved ARR (IRD -0.08; 95% CI -0.15, -0.01; IRR 0.74; 95% CI 0.50, 0.94), 12-week CDP (RD -4.2%; 95% CI -7.9, -0.48; RR 0.67; 95% CI 0.38, 0.90), 24-week CDP (RD -4.3%; 95% CI -7.7, -1.1; RR 0.57; 95% CI 0.26, 0.81), and absence of Gd+ T1 lesions (RD 25%; 95% CI 19, 30; RR 1.4; 95% CI 1.3, 1.5). However, DRF and TERI did not appear to differ significantly with respect to absence of new/newly enlarging T2 lesions when based on comparisons using the overall EVOLVE-MS-1 sample (RD 8.5%; 95% CI -0.93, 18; RR 1.3; 95% CI 0.94, 1.6), or in a sensitivity analysis restricted to newly enrolled EVOLVE-MS-1 patients (RD 2.7%; 95% CI -9.1, 14; RR 1.1; 95% CI 0.68, 1.5). CONCLUSIONS: We did not observe differences between DRF and PON for ARR, CDP, and absence of new/newly enlarging T2 lesions, but there was a higher proportion of patients free of Gd+ T1 lesions among DRF-treated patients than PON-treated patients. DRF had improved efficacy versus TERI for all clinical and radiological outcomes, except for absence of new/newly enlarging T2 lesions. CLINICAL TRIALS REGISTRATION: EVOLVE-MS-1 (ClinicalTrials.gov identifier: NCT02634307); OPTIMUM (ClinicalTrials.gov identifier: NCT02425644).

Risk factors for suicide one year after discharge from hospitalization for physical illness in Denmark.

While suicide risk following psychiatric hospitalization has been studied extensively, risk following hospitalization for physical illness is less well understood. We used random forests to examine risk factors for suicide in the year following physical illness hospitalization in Denmark. In this case-cohort study, suicide cases were all individuals who died by suicide within one year of a hospitalization for a physical illness (n = 4563) and the comparison subcohort was a 5% random sample of individuals living in Denmark on January 1, 1995 who had a hospitalization for a physical illness between January 1, 1995 and December 31, 2015 (n = 177,664). We used random forests to examine identify the most important predictors of suicide stratified by sex. For women, the top 10 most important variables for random forest prediction were all related to psychiatric diagnoses. For men, many physical health conditions also appeared important to suicide prediction. Among the top 10 variables in the variable importance plot for men were influenza, injuries to the head, nervous system surgeries, and cerebrovascular diseases. Suicide prediction after a physical illness hospitalization requires comprehensive consideration of different and multiple factors for each sex.

Changes in Alcohol Consumption following Direct-Acting Antiviral Treatment for Hepatitis C in VA Patients with Comorbid Alcohol Use Disorder and PTSD.

OBJECTIVE: To investigate whether direct-acting antivirals (DAA) for hepatitis C viral infection (HCV): glecaprevir/pibrentasvir (GLE/PIB), ledipasvir/sofosbuvir (LDV/SOF), and sofosbuvir/velpatasvir (SOF/VEL) are associated with reduced alcohol consumption among veterans with alcohol use disorder (AUD) and co-occurring post-traumatic stress disorder (PTSD). METHODS: We measured change in Alcohol Use Disorder Identification Test-Consumption Module (AUDIT-C) scores in a retrospective cohort of veterans with PTSD and AUD receiving DAAs for HCV. RESULTS: One thousand two hundred and eleven patients were included (GLE/PIB n = 174, LDV/SOF n = 808, SOF/VEL n = 229). Adjusted frequencies of clinically meaningful improvement were 30.5% for GLE/PIB, 45.5% for LDV/SOF, and 40.5% for SOF/VEL. The frequency was lower for GLE/PIB than for LDV/SOF (OR = 0.59; 95% CI [0.40, 0.87]) or SOF/VEL (OR = 0.66; 95% CI [0.42, 1.04]). CONCLUSIONS: DAA treatment for HCV was associated with a substantial reduction in alcohol use in patients with AUD and co-occurring PTSD. Further exploration of the role of DAAs in AUD treatment is warranted.

Association between post-traumatic stress disorder severity and death by suicide in US military veterans: retrospective cohort study.

BACKGROUND: There is mixed evidence regarding the direction of a potential association between post-traumatic stress disorder (PTSD) and suicide mortality. AIMS: This is the first population-based study to account for both PTSD diagnosis and PTSD symptom severity simultaneously in the examination of suicide mortality. METHOD: Retrospective study that included all US Department of Veterans Affairs (VA) patients with a PTSD diagnosis and at least one symptom severity assessment using the PTSD Checklist (PCL) between 1 October 1999 and 31 December 2018 (n = 754 197). We performed multivariable proportional hazards regression models using exposure groups defined by level of PTSD symptom severity to estimate suicide mortality rates. For patients with multiple PCL scores, we performed additional models using exposure groups defined by level of change in PTSD symptom severity. We assessed suicide mortality using the VA/Department of Defense Mortality Data Repository. RESULTS: Any level of PTSD symptoms above the minimum threshold for symptomatic remission (i.e. PCL score >18) was associated with double the suicide mortality rate at 1 month after assessment. This relationship decreased over time but patients with moderate to high symptoms continued to have elevated suicide rates. Worsening PTSD symptoms were associated with a 25% higher long-term suicide mortality rate. Among patients with improved PTSD symptoms, those with symptomatic remission had a substantial and sustained reduction in the suicide rate compared with those without symptomatic remission (HR = 0.56; 95% CI 0.37-0.88). CONCLUSIONS: Ameliorating PTSD can reduce risk of suicide mortality, but patients must achieve symptomatic remission to attain this benefit.

Comparative Effectiveness of Direct-Acting Antivirals for Posttraumatic Stress Disorder in Veterans Affairs Patients With Hepatitis C Virus Infection.

We recently conducted an exploratory study that indicated that several direct-acting antivirals (DAAs), highly effective medications for hepatitis C virus (HCV) infection, were also associated with improvement in posttraumatic stress disorder (PTSD) among a national cohort of US Department of Veterans Affairs (VA) patients treated between October 1, 1999, and September 30, 2019. Limiting the same cohort to patients with PTSD and HCV, we compared the associations of individual DAAs with PTSD symptom improvement using propensity score weighting. After identifying patients who had available baseline and endpoint PTSD symptom data as measured with the PTSD Checklist (PCL), we compared changes over the 8-12 weeks of DAA treatment. The DAAs most prescribed in conjunction with PCL measurement were glecaprevir/pibrentasvir (GLE/PIB; n = 54), sofosbuvir/velpatasvir (SOF/VEL; n = 54), and ledipasvir/sofosbuvir (LDV/SOF; n = 145). GLE/PIB was superior to LDV/SOF, with a mean difference in improvement of 7.3 points on the PCL (95% confidence interval (CI): 1.1, 13.6). The mean differences in improvement on the PCL were smaller between GLE/PIB and SOF/VEL (3.0, 95% CI: -6.3, 12.2) and between SOF/VEL and LDV/SOF (4.4, 95% CI: -2.4, 11.2). While almost all patients were cured of HCV (92.5%) regardless of the agent received, PTSD outcomes were superior for those receiving GLE/PIB compared with those receiving LDV/SOF, indicating that GLE/PIB may merit further investigation as a potential PTSD treatment.

Predictors of nonfatal suicide attempts within 30 days of discharge from psychiatric hospitalization: Sex-specific models developed using population-based registries.

BACKGROUND: Risk for nonfatal suicide attempts is heightened in the month after psychiatric hospitalization discharge. Investigations of factors associated with such attempts are limited. METHODS: We conducted a case-subcohort study using data from Danish medical, administrative, and social registries to develop sex-specific risk models using two machine learning methods: classification trees and random forests. Cases included individuals who received a diagnostic code for a nonfatal suicide attempt within 30 days of discharge following a psychiatric hospitalization between January 1, 1995 and December 31, 2015 (n = 3166, 56.5% female). The comparison subcohort consisted of a 5% random sample of individuals living in Denmark (n = 24,559, 51.3% female) on January 1, 1995 who had a psychiatric hospitalization during the study period. RESULTS: Histories of self-poisoning, substance-related disorders, and eating disorders were important predictors of nonfatal suicide attempt among women, with notable interactions observed between age, self-poisoning history, and other characteristics (e.g., medication use). Self-poisoning, substance-related disorders, and severe stress reactions were among the most important variables for men, with key interactions noted between self-poisoning history, age, major depressive disorder diagnosis, and prescription classes. LIMITATIONS: Findings are based on Danish administrative data, which may be subject to inaccuracies, missingness, etc. It is unclear whether results would generalize to other populations. CONCLUSIONS: Markers of behavioral dysregulation were important predictors of nonfatal suicide attempts in the 30 days after psychiatric hospitalization discharge for both sexes. Examining risk markers for nonfatal suicide attempt following discharge is important to enhance support for this vulnerable population.

Predictive models of pregnancy based on data from a preconception cohort study.

STUDY QUESTION: Can we derive adequate models to predict the probability of conception among couples actively trying to conceive? SUMMARY ANSWER: Leveraging data collected from female participants in a North American preconception cohort study, we developed models to predict pregnancy with performance of ∼70% in the area under the receiver operating characteristic curve (AUC). WHAT IS KNOWN ALREADY: Earlier work has focused primarily on identifying individual risk factors for infertility. Several predictive models have been developed in subfertile populations, with relatively low discrimination (AUC: 59-64%). STUDY DESIGN, SIZE, DURATION: Study participants were female, aged 21-45 years, residents of the USA or Canada, not using fertility treatment, and actively trying to conceive at enrollment (2013-2019). Participants completed a baseline questionnaire at enrollment and follow-up questionnaires every 2 months for up to 12 months or until conception. We used data from 4133 participants with no more than one menstrual cycle of pregnancy attempt at study entry. PARTICIPANTS/MATERIALS, SETTING, METHODS: On the baseline questionnaire, participants reported data on sociodemographic factors, lifestyle and behavioral factors, diet quality, medical history and selected male partner characteristics. A total of 163 predictors were considered in this study. We implemented regularized logistic regression, support vector machines, neural networks and gradient boosted decision trees to derive models predicting the probability of pregnancy: (i) within fewer than 12 menstrual cycles of pregnancy attempt time (Model I), and (ii) within 6 menstrual cycles of pregnancy attempt time (Model II). Cox models were used to predict the probability of pregnancy within each menstrual cycle for up to 12 cycles of follow-up (Model III). We assessed model performance using the AUC and the weighted-F1 score for Models I and II, and the concordance index for Model III. MAIN RESULTS AND THE ROLE OF CHANCE: Model I and II AUCs were 70% and 66%, respectively, in parsimonious models, and the concordance index for Model III was 63%. The predictors that were positively associated with pregnancy in all models were: having previously breastfed an infant and using multivitamins or folic acid supplements. The predictors that were inversely associated with pregnancy in all models were: female age, female BMI and history of infertility. Among nulligravid women with no history of infertility, the most important predictors were: female age, female BMI, male BMI, use of a fertility app, attempt time at study entry and perceived stress. LIMITATIONS, REASONS FOR CAUTION: Reliance on self-reported predictor data could have introduced misclassification, which would likely be non-differential with respect to the pregnancy outcome given the prospective design. In addition, we cannot be certain that all relevant predictor variables were considered. Finally, though we validated the models using split-sample replication techniques, we did not conduct an external validation study. WIDER IMPLICATIONS OF THE FINDINGS: Given a wide range of predictor data, machine learning algorithms can be leveraged to analyze epidemiologic data and predict the probability of conception with discrimination that exceeds earlier work. STUDY FUNDING/COMPETING INTEREST(S): The research was partially supported by the U.S. National Science Foundation (under grants DMS-1664644, CNS-1645681 and IIS-1914792) and the National Institutes for Health (under grants R01 GM135930 and UL54 TR004130). In the last 3 years, L.A.W. has received in-kind donations for primary data collection in PRESTO from FertilityFriend.com, Kindara.com, Sandstone Diagnostics and Swiss Precision Diagnostics. L.A.W. also serves as a fibroid consultant to AbbVie, Inc. The other authors declare no competing interests. TRIAL REGISTRATION NUMBER: N/A.

The Joint Effects of Depression and Comorbid Psychiatric Disorders on Suicide Deaths: Competing Antagonism as an Explanation for Subadditivity.

BACKGROUND: Previous studies of the effect of interaction between psychiatric disorders on suicide have reported mixed results. We investigated the joint effect of depression and various comorbid psychiatric disorders on suicide. METHODS: We conducted a population-based case-cohort study with all suicide deaths occurring between 1 January 1995 and 31 December 2015 in Denmark (n = 14,103) and a comparison subcohort comprised of a 5% random sample of the source population at baseline (n = 265,183). We quantified the joint effect of pairwise combinations of depression and major psychiatric disorders (e.g., organic disorders, substance use disorders, schizophrenia, bipolar disorder, neurotic disorders, eating disorders, personality disorders, intellectual disabilities, developmental disorders, and behavioral disorders) on suicide using marginal structural models and calculated the relative excess risk due to interaction. We assessed for the presence of competing antagonism for negative relative excess risk due to interactions. RESULTS: All combinations of depression and comorbid psychiatric disorders were associated with increased suicide risk. For example, the rate of suicide among men with depression and neurotic disorders was 20 times (95% CI = 15, 26) the rate in men with neither disorder. Most disorder combinations were associated with subadditive suicide risk, and there was evidence of competing antagonism in most of these cases. CONCLUSIONS: Subadditivity may be explained by competing antagonism. When both depression and a comorbid psychiatric disorder are present, they may compete to cause the outcome such that having 2 disorders may be no worse than having a single disorder with respect to suicide risk.

Influence of contextual factors on death by suicide in rural and urban settings.

PURPOSE: To examine the association between contextual factors, represented by geographic and community health variables, and suicide among rural and urban Department of Veterans Affairs health care users (VA users). METHODS: We performed a retrospective cohort study of 12,700,847 VA users between 2003 and 2017. We assigned contextual factors based on individuals' home address at the ZIP Code (area deprivation), county (sunlight exposure, altitude, and community health), and state level (firearm ownership), using publicly available data sources. We grouped contextual factors by quintiles or prespecified thresholds, depending on the nature of each variable. We obtained mortality data from the National Death Index. We measured the effect of living in a place with the highest versus lowest level of each contextual factor on odds of suicide using logistic regression, adjusting for individual compositional factors abstracted from VA electronic medical records data. We used random forest modeling to build prediction models for suicide based on contextual factors among rural and urban veterans. FINDINGS: Almost all contextual factors we examined were significantly associated with suicide among rural and urban VA users, even after adjusting for individual compositional factors. However, no contextual variables were strong protective or risk factors (0.52.0), and prediction models leveraging these contextual factors had poor accuracy among both rural (0.51, 95% CI: 0.48-0.54) and urban (0.53, 95% CI: 0.51-0.55) VA users. CONCLUSIONS: A wide variety of contextual factors is significantly associated with suicide among rural and urban VA users. However, the factors we measured contributed very little to individual-level suicide risk.

The Recent Rise of Suicide Mortality in the United States.

Suicide is a major public health concern in the United States. Between 2000 and 2018, US suicide rates increased by 35%, contributing to the stagnation and subsequent decrease in US life expectancy. During 2019, suicide declined modestly, mostly owing to slight reductions in suicides among Whites. Suicide rates, however, continued to increase or remained stable among all other racial/ethnic groups, and little is known about recent suicide trends among other vulnerable groups. This article (a) summarizes US suicide mortality trends over the twentieth and early twenty-first centuries, (b) reviews potential group-level causes of increased suicide risk among subpopulations characterized by markers of vulnerability to suicide, and (c) advocates for combining recent advances in population-based suicide prevention with a socially conscious perspective that captures the social, economic, and political contexts in which suicide risk unfolds over the life course of vulnerable individuals.

Mining Clinical Data for Novel Posttraumatic Stress Disorder Medications.

BACKGROUND: Despite the prevalence and negative impact of posttraumatic stress disorder (PTSD), there are few medications approved by the U.S. Food and Drug Administration for treatment, and approved medications do not work well enough. We leveraged large-scale electronic health record data to identify existing medications that may be repurposed as PTSD treatments. METHODS: We constructed a mechanistic tree of all Food and Drug Administration-approved medications and used the tree-based scan statistic to identify medications associated with greater than expected levels of clinically meaningful improvement in PTSD symptoms using electronic health record data from the U.S. Department of Veterans Affairs. Our cohort included patients with a diagnosis of PTSD who had repeated symptom measurements using the PTSD Checklist over a 20-year period (N = 168,941). We calculated observed numbers based on patients taking each drug or mechanistically related class of drugs and the expected numbers based on the tree as a whole. RESULTS: Medications typically used to treat PTSD, such as the Food and Drug Administration-approved agent sertraline, were associated with improvement in PTSD symptoms, but the effects were small. Several, but not all, direct-acting antivirals used in the treatment of hepatitis C virus demonstrated a strong association with PTSD improvement. The finding was robust to a sensitivity analysis excluding patients who received established PTSD treatments, including trauma-focused psychotherapy, concurrent with hepatitis treatment. CONCLUSIONS: Our exploratory approach both demonstrated findings that are consistent with what is known about pharmacotherapy for PTSD and uncovered a novel class of medications that may improve PTSD symptoms.

A Machine Learning Approach to Predicting New-onset Depression in a Military Population.

OBJECTIVE: Depression is one of the most common mental disorders in the United States in both civilian and military populations, but few prospective studies assess a wide range of predictors across multiple domains for new-onset (incident) depression in adulthood. Supervised machine learning methods can identify predictors of incident depression out of many different candidate variables, without some of the assumptions and constraints that underlie traditional regression analyses. The objectives of this study were to identify predictors of incident depression across 5 years of follow-up using machine learning, and to assess prediction accuracy of the algorithms. METHODS: Data were from a cohort of Army National Guard members free of history of depression at baseline (n = 1951 men and 298 women), interviewed once per year for probable depression. Classification trees and random forests were constructed and cross-validated, using 84 candidate predictors from the baseline interviews. RESULTS: Stressors and traumas such as emotional mistreatment and adverse childhood experiences, demographics such as being a parent or student, and military characteristics including paygrade and deployment location were predictive of probable depression. Cross-validated random forest algorithms were moderately accurate (68% for women and 73% for men). CONCLUSIONS: Events and characteristics throughout the life course, both in and outside of deployment, predict incident depression in adulthood among military personnel. Although replication studies are needed, these results may help inform potential intervention targets to reduce depression incidence among military personnel. Future research should further refine and explore interactions between identified variables.

A comorbid mental disorder paradox: Using causal diagrams to understand associations between posttraumatic stress disorder and suicide.

OBJECTIVE: Although some studies document that posttraumatic stress disorder (PTSD) increases suicide risk, other studies have produced the paradoxical finding that PTSD decreases suicide risk. We sought to understand methodologic biases that may explain these paradoxical findings through the use of directed acyclic graphs (DAGs). METHOD: DAGs are causal diagrams that visually encode a researcher's assumptions about data generating mechanisms and assumed causal relations among variables. DAGs can connect theories to data and guide statistical choices made in study design and analysis. In this article, we describe DAGs and explain how they can be used to identify biases that may arise from inappropriate analytic decisions and data limitations. RESULTS: We define a particular form of bias, collider bias, that is a likely explanation for why studies have found a supposedly protective association of PTSD with suicide. This protective association is interpreted by some researchers as evidence that PTSD reduces the risk of suicide. Collider bias may occur through inappropriate adjustment for a psychiatric comorbidity, such as adjustment for variables that are affected by PTSD and share common causes with suicide. CONCLUSIONS: We recommend that researchers collect longitudinal measurements of psychiatric comorbidities, which would help establish the temporal ordering of variables and avoid the biases discussed in this article. Furthermore, researchers could use DAGs to explore how results may be impacted by design and analytic decisions prior to execution. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Suicide prediction among men and women with depression: A population-based study.

BACKGROUND: Accurate identification of persons at risk of suicide is challenging because suicide is a rare outcome with a multifactorial origin. The purpose of this study was to predict suicide among persons with depression using machine learning methods. METHODS: A case-cohort study was conducted in Denmark between January 1, 1995 and December 31, 2015. Cases were all persons who died by suicide and had an incident depression diagnosis in Denmark (n = 2,774). The comparison subcohort was a 5% random sample of all individuals in Denmark at baseline, restricted to persons with an incident depression diagnosis during the study period (n = 11,963). Classification trees and random forests were used to predict suicide. RESULTS: In men with depression, there was a high risk of suicide among those who were prescribed other analgesics and antipyretics (i.e., non-opioid analgesics such as acetaminophen), prescribed hypnotics and sedatives, and diagnosed with a poisoning (n = 96; risk = 81%). In women with depression, there was an elevated risk of suicide among those who were prescribed other analgesics and antipyretics, anxiolytics, and hypnotics and sedatives, but were not diagnosed with poisoning nor cerebrovascular diseases (n = 338; risk = 58%). DISCUSSION: Psychiatric disorders and their associated medications were strongly indicative of suicide risk. Notably, anti-inflammatory medications (e.g., acetaminophen) prescriptions, which are used to treat chronic pain and illnesses, were associated with suicide risk in persons with depression. Machine learning may advance our ability to predict suicide deaths.