Pre-deployment risk factors for PTSD in active-duty personnel deployed to Afghanistan: a machine-learning approach for analyzing multivariate predictors

• Authors: Katharina Schultebraucks, NYU Grossman School of Medicine; Meng Qian, NYU Grossman School of Medicine; Duna Abu-Amara, NYU Grossman School of Medicine; Kelsey Dean, Harvard John A. Paulson School of Engineering and Applied Sciences; Eugene Laska, NYU Grossman School of Medicine; Carole Siegel, NYU Grossman School of Medicine; Aarti Gautam, Fort Detrick; Guia Guffanti, Harvard University; Rasha Hammamieh, Fort Detrick; Burook Misganaw, Harvard John A. Paulson School of Engineering and Applied Sciences; Synthia H Mellon, University of California, San Francisco; Owen M Wolkowitz, University of California, San Francisco; Esther M Blessing, NYU Grossman School of Medicine; Amit Etkin, Stanford University; Kerry Ressler, Emory University; Francis J Doyle, Harvard John A. Paulson School of Engineering and Applied Sciences; Marti Jett, Fort Detrick; Charles R Marmar, NYU Grossman School of Medicine
• Journal Title: Molecular Psychiatry
• Volume: Volume 26, Number 9
• Publisher: Springer Nature | 2021-09-01, Pages 5011-5022
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1038/s41380-020-0789-2
• Subjects: Health Sciences, Obstetrics and Gynecology; Biology, Biostatistics; Health Sciences, Mental Health
• Keywords: Afghanistan; Cohort Studies; Humans; Machine Learning; Military Personnel; Prospective Studies; Risk Factors; Sleep Quality; Stress Disorders, Post-Traumatic

Abstract

Active-duty Army personnel can be exposed to traumatic warzone events and are at increased risk for developing post-traumatic stress disorder (PTSD) compared with the general population. PTSD is associated with high individual and societal costs, but identification of predictive markers to determine deployment readiness and risk mitigation strategies is not well understood. This prospective longitudinal naturalistic cohort study—the Fort Campbell Cohort study—examined the value of using a large multidimensional dataset collected from soldiers prior to deployment to Afghanistan for predicting post-deployment PTSD status. The dataset consisted of polygenic, epigenetic, metabolomic, endocrine, inflammatory and routine clinical lab markers, computerized neurocognitive testing, and symptom self-reports. The analysis was computed on active-duty Army personnel (N = 473) of the 101st Airborne at Fort Campbell, Kentucky. Machine-learning models predicted provisional PTSD diagnosis 90–180 days post deployment (random forest: AUC = 0.78, 95% CI = 0.67–0.89, sensitivity = 0.78, specificity = 0.71; SVM: AUC = 0.88, 95% CI = 0.78–0.98, sensitivity = 0.89, specificity = 0.79) and longitudinal PTSD symptom trajectories identified with latent growth mixture modeling (random forest: AUC = 0.85, 95% CI = 0.75–0.96, sensitivity = 0.88, specificity = 0.69; SVM: AUC = 0.87, 95% CI = 0.79–0.96, sensitivity = 0.80, specificity = 0.85). Among the highest-ranked predictive features were pre-deployment sleep quality, anxiety, depression, sustained attention, and cognitive flexibility. Blood-based biomarkers including metabolites, epigenomic, immune, inflammatory, and liver function markers complemented the most important predictors. The clinical prediction of post-deployment symptom trajectories and provisional PTSD diagnosis based on pre-deployment data achieved high discriminatory power. The predictive models may be used to determine deployment readiness and to determine novel pre-deployment interventions to mitigate the risk for deployment-related PTSD.