Precision prediction of posttraumatic stress disorder symptom surges: A pilot study integrating real-time daily data with supervised learning.

The application of machine learning algorithms to daily diary data represents a valuable tool for improving dynamic prediction of posttraumatic stress disorder (PTSD) symptom escalations. This prospective, intensive longitudinal study aimed to evaluate whether combining baseline (static) and daily diary (dynamic) predictors with machine learning can help forecast clinically significant PTSD symptom increases among veterans. Participants were 74 recently discharged U.S. veterans (Mage = 33.5 years) who completed twice-daily diary surveys for up to 87 days via a mobile app, yielding 4,307 diary days. The outcome was a binary indicator of clinically significant daily PTSD symptom increase (> 1.0 standard deviation above a participant's individual mean over the first 2 study weeks). Random forest models identified top predictors; LASSO regression estimated effect sizes among top predictors. Daily negative affect was the top predictive variable, OR = 1.33, retained in 100% of LASSO iterations. Daily depressed mood, OR = 1.35; anxious mood, OR = 1.15; and perceived stress, OR = 1.13, were also reliably retained. Variables involving alcohol, cannabis use, and baseline impulsivity were less robust but remained prominent predictors of PTSD symptom escalations. Post hoc interaction analyses showed that co-occurring high negative affect and anxiety yielded a > 55% probability of PTSD symptom escalation. The findings show that daily affective states, especially negative mood and stress, strongly predict PTSD symptom increases in veterans. Using machine learning and high-frequency tracking, advances in personalized, real-time PTSD care are possible. Findings support just-in-time interventions for when veterans need help most: in the moment.