@article{ref1,
title="Predicting posttraumatic stress disorder among survivors of recent interpersonal  violence",
journal="Journal of interpersonal violence",
year="2020",
author="Morris, Matthew C. and Sanchez-Sáez, Francisco and Bailey, Brooklynn and Hellman, Natalie and Williams, Amber and Schumacher, Julie A. and Rao, Uma",
volume="ePub",
number="ePub",
pages="ePub-ePub",
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.<p /> <p>Language: en</p>",
language="en",
issn="0886-2605",
doi="10.1177/0886260520978195",
url="http://dx.doi.org/10.1177/0886260520978195"
}