Predicting U.S. veteran dropout from trauma-focused eye movement desensitization and reprocessing therapy using machine learning.

Authors

Elizabeth A Penix-Smith, Stephanie C Glitsos, Phillip J Quartana

Published in

Psychological services. Nov 12, 2026. Epub Nov 12, 2026.

Abstract

Eye movement desensitization and reprocessing therapy (EMDR) is a first-line posttraumatic stress disorder treatment for veterans (U.S. Department of Veterans Affairs & U.S. Department of Defense, 2023). Yet, little is known about EMDR dropout in veterans. This study explored the utility of machine learning for (a) predicting EMDR dropout in veterans (N = 279; Fairbanks et al., 2026) and (b) identifying variables linked with dropout. Clients were randomized to training (n = 224; 80%) and testing (n = 55; 20%) data sets. At termination, veterans who dropped out (n = 114; 40.9%) reported worse posttraumatic stress disorder and depression symptoms than veterans who completed EMDR. Optimal performance for predicting dropout varied by algorithm and metric, including the final ensemble (precision-recall area under the receiver operating characteristic curve [AUC] = .83, Brier score = .13), random forest (precision-recall AUC = .83, AUC = .89, accuracy = .85), and logistic regression models (precision-recall AUC = .83, specificity = .94). Variables yielding the highest feature importance values that were linked with higher dropout included younger age, more trauma types, worsening posttraumatic stress disorder symptoms, improving depression symptoms, and missing symptom measures. Together, results underscore the importance of better understanding EMDR dropout among veterans. Findings suggest that dropout risk factors may operate differently across veteran subgroups and that machine learning may be useful for characterizing this heterogeneity. Therapists, researchers, and clinics may consider using research-based dropout prevention strategies for veterans at risk of dropping out. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

PMID:
43019483
Bibliographic data and abstract were imported from PubMed on 13 Nov 2026.

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