Predicting Posttraumatic Stress Disorder Among Survivors of Recent Interpersonal Violence

Abstract

A substantial minority of women who experience interpersonal violence will develop posttraumatic stress disorder (PTSD). One critical challenge for preventing PTSD is predicting whose acute posttraumatic stress symptoms will worsen to a clinically significant degree. This 6-month longitudinal study adopted multilevel modeling and exploratory machine learning (ML) methods to predict PTSD onset in 58 young women, ages 18 to 30, who experienced an incident of physical and/or sexual assault in the three months prior to baseline assessment. Women completed baseline assessments of theory-driven cognitive and neurobiological predictors and interview-based measures of PTSD diagnostic status and symptom severity at 1-, 3-, and 6-month follow-ups. Higher levels of self-blame, generalized anxiety disorder severity, childhood trauma exposure, and impairment across multiple domains were associated with a pattern of high and stable posttraumatic stress symptom severity over time. Predictive performance for PTSD onset was similarly strong for a gradient boosting machine learning model including all predictors and a logistic regression model including only baseline posttraumatic stress symptom severity. The present findings provide directions for future work on PTSD prediction among interpersonal violence survivors that could enhance early risk detection and potentially inform targeted prevention programs.

Advances in the molecular neurobiology of posttraumatic stress disorder from global contexts: A systematic review of longitudinal studies

“…Key processes supported by these genes are related to threat processing and responding, and fear-related memory acquisition, consolidation, and extinction. Morris et al ( 2022 ) used multilevel modelling and ML to examine demographic, cognitive, clinical and biological (diurnal and laboratory stressor-elicited cortisol and alpha-amylase levels, as well as heart rate and hair cortisol) data to predict PTSD development at 1-, 3- and 6-month timepoints in young women exposed to interpersonal violence in the 3 months prior to study initiation. These factors were not associated with baseline or longitudinal PTS symptom severity using multilevel modelling but did improve ML model accuracy, suggesting that the stress response and acute sympathetic nervous system activity play an indirect role in the development of PTSD.…”

Interpersonal violent injury and subsequent risk of suicide and deliberate self-harm: a register-based national cohort study from Norway, 2010–2018

“…Violence-injured patients with a history of psychiatric treatment had the highest observed rate for suicidal behavior, which is consistent with research reports that pre-existing psychopathology predicts more severe psychological outcomes after violence victimization. 49 However, relative associations between violent injury and DSH or suicide were stronger among those without a history of prior psychiatric treatment compared to those with such history. It may be that psychiatric disorder is such a strong predictor of suicidality that for individuals with psychopathology, experiencing a violent event has less impact on suicide risk than it would have for people without psychiatric disorder.…”

Predicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach

“…Machine-learning (ML) methods can be used to identify patterns from data that enhance predictive performance [24]; these algorithms can handle large, complex data structures and are better-suited to predict the development of pain than general linear models [25]. Indeed, ML models have been previously used on data from the current study to successfully predict PTSD onset in young women who recently experienced interpersonal violence [26].…”