4.7 Article

Derivation and validation of risk prediction for posttraumatic stress symptoms following trauma exposure

期刊

PSYCHOLOGICAL MEDICINE
卷 53, 期 11, 页码 4952-4961

出版社

CAMBRIDGE UNIV PRESS
DOI: 10.1017/S003329172200191X

关键词

Machine learning; prediction; PTSD; trauma; risk factors

向作者/读者索取更多资源

This study utilized baseline survey data from prospective cohort studies to identify influential predictors of posttraumatic stress symptoms (PTSS) following traumatic stress exposure. The findings revealed that factors such as acute pain severity, recovery expectations, socioeconomic status, self-reported race, and psychological symptoms significantly predicted the occurrence of PTSS.
Background Posttraumatic stress symptoms (PTSS) are common following traumatic stress exposure (TSE). Identification of individuals with PTSS risk in the early aftermath of TSE is important to enable targeted administration of preventive interventions. In this study, we used baseline survey data from two prospective cohort studies to identify the most influential predictors of substantial PTSS. Methods Self-identifying black and white American women and men (n = 1546) presenting to one of 16 emergency departments (EDs) within 24 h of motor vehicle collision (MVC) TSE were enrolled. Individuals with substantial PTSS (> 33, Impact of Events Scale - Revised) 6 months after MVC were identified via follow-up questionnaire. Sociodemographic, pain, general health, event, and psychological/cognitive characteristics were collected in the ED and used in prediction modeling. Ensemble learning methods and Monte Carlo cross-validation were used for feature selection and to determine prediction accuracy. External validation was performed on a hold-out sample (30% of total sample). Results Twenty-five percent (n = 394) of individuals reported PTSS 6 months following MVC. Regularized linear regression was the top performing learning method. The top 30 factors together showed good reliability in predicting PTSS in the external sample (Area under the curve = 0.79 +/- 0.002). Top predictors included acute pain severity, recovery expectations, socioeconomic status, self-reported race, and psychological symptoms. Conclusions These analyses add to a growing literature indicating that influential predictors of PTSS can be identified and risk for future PTSS estimated from characteristics easily available/assessable at the time of ED presentation following TSE.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据