Development and external validation of a short prognostic screening instrument for PTSD one year following individual civilian trauma

Background: Timely identification of individuals at risk for developing PTSD following trauma is crucial for providing targeted preventive interventions. Machine learning techniques show promise for deriving accurate prognostic screening instruments. However, accurate externally validated prognostic screening instruments for broad application in trauma-exposed civilians are not yet available. Moreover, it remains unknown whether prognostic screening instrument accuracy may be improved if developed in a sex-stratified manner. Objective: We aimed to develop an externally validated prognostic PTSD screening instrument based on self-report information obtained within 2 months post-trauma in two independent cohorts of recently trauma-exposed civilians, using machine learning techniques allowing for extraction of a short screener. We examined whether separate models for males and females improved prognostic accuracy compared to sex-combined models. Methods: Prognostic machine learning models (CART and XGBoost) were developed in a longitudinal cohort of N = 327 adults (38% females) requiring evaluation of (suspected) serious injury by an emergency department. External validation was performed in another longitudinal cohort of N = 466 adults (57% females) referred for emotional, practical or legal victim support following crime or traffic accidents. PTSD status at 1 year post-trauma was based on CAPS-IV for internal and PCL-5 for external validation. Results: During internal validation, all models achieved excellent accuracy (AUC/sensitivity/specificity > 0.90). During external validation, sufficient accuracy was only achieved for the sex-combined XGBoost model (AUC = 0.73, sensitivity = 0.69, specificity = 0.68), including 22 items of demographic and health characteristics, trauma characteristics, peri-traumatic distress or dissociation, post-traumatic cognitions, PTSD symptoms and social support. Conclusion: We developed an accurate externally validated short prognostic screening instrument for PTSD based on self-report questions that is applicable to a broad population of recently trauma-exposed civilians. This novel instrument enables timely identification of individuals at risk for PTSD following trauma, and research into early targeted interventions to prevent long-term PTSD for civilians following trauma. We developed an accurate externally validated short screening instrument for PTSD risk 1 year post-trauma. It includes 22 self-report questions obtained within 2 months post-trauma and is applicable to a broad population of recently trauma-exposed civilians. This novel instrument enables timely identification of individuals at risk for PTSD following trauma. We developed an accurate externally validated short screening instrument for PTSD risk 1 year post-trauma. It includes 22 self-report questions obtained within 2 months post-trauma and is applicable to a broad population of recently trauma-exposed civilians. This novel instrument enables timely identification of individuals at risk for PTSD following trauma.背景：及时识别创伤后罹患创伤后应激障碍（Post-Traumatic Stress Disorder, PTSD）风险人群，对于开展针对性预防干预至关重要。机器学习技术在构建精准预后筛查工具方面展现出应用潜力。然而，目前尚无适用于创伤暴露平民大范围应用的、经过外部验证的精准预后筛查工具。此外，若采用性别分层方式构建筛查工具，能否提升其预后预测准确性，仍有待探明。 研究目的：本研究基于两个独立队列中近期创伤暴露平民于创伤后2个月内收集的自我报告信息，结合可提取精简筛查条目的机器学习技术，开发经过外部验证的创伤后应激障碍预后筛查工具；并对比性别分层模型与性别合并模型的预后预测准确性，探究前者是否能提升预测性能。 研究方法：本研究在一个纳入327名成年人的纵向队列中构建预后机器学习模型（分类与回归树（Classification and Regression Tree, CART）与极限梯度提升（Extreme Gradient Boosting, XGBoost）），该队列成员均因疑似重伤前往急诊科就诊，其中女性占比38%。外部验证则在另一个纵向队列中开展，该队列包含466名成年人，女性占比57%，均为因犯罪或交通事故后需要情绪、实务或法律层面受害者援助的人群。创伤后1年的创伤后应激障碍状态评估，内部验证采用《临床医师版创伤后应激障碍诊断量表第四版（Clinician-Administered PTSD Scale for DSM-IV, CAPS-IV）》，外部验证采用《创伤后应激障碍检查表第五版（PTSD Checklist for DSM-5, PCL-5）》。 研究结果：内部验证阶段，所有模型均展现出优异的预测性能（受试者工作特征曲线下面积（Area Under the Receiver Operating Characteristic Curve, AUC）、灵敏度、特异度均大于0.90）。外部验证阶段，仅性别合并的XGBoost模型达到了足够的预测准确性（AUC=0.73，灵敏度=0.69，特异度=0.68），该模型纳入了22个条目，涵盖人口统计学与健康特征、创伤特征、创伤围期痛苦或解离症状、创伤后认知、创伤后应激障碍症状以及社会支持情况。 研究结论：本研究基于自我报告条目开发了一款经过外部验证的精准精简型创伤后应激障碍预后筛查工具，可适用于大范围近期创伤暴露平民群体。该新型工具可及时识别创伤后罹患创伤后应激障碍的风险人群，为开展早期针对性干预以预防平民创伤后长期创伤后应激障碍提供了支撑。 本研究开发了一款经过外部验证的精准精简型工具，可用于预测创伤后1年的创伤后应激障碍风险。该工具包含22个于创伤后2个月内收集的自我报告条目，适用于大范围近期创伤暴露平民群体，可及时识别创伤后罹患创伤后应激障碍的风险人群。 本研究开发了一款经过外部验证的精准精简型工具，可用于预测创伤后1年的创伤后应激障碍风险。该工具包含22个于创伤后2个月内收集的自我报告条目，适用于大范围近期创伤暴露平民群体，可及时识别创伤后罹患创伤后应激障碍的风险人群。