Importance A substantial proportion of the 40 million people in the US who present to emergency departments (EDs) each year after traumatic events develop posttraumatic stress disorder (PTSD) or major depressive episode (MDE). Accurately identifying patients at high risk in the ED would facilitate the targeting of preventive interventions. Objectives To develop and validate a prediction tool based on ED reports after a motor vehicle collision to predict PTSD or MDE 3 months later. Design, Setting, and Participants The Advancing Understanding of Recovery After Trauma (AURORA) study is a longitudinal study that examined adverse posttraumatic neuropsychiatric sequalae among patients who presented to 28 US urban EDs in the immediate aftermath of a traumatic experience. Enrollment began on September 25, 2017. The 1003 patients considered in this diagnostic/prognostic report completed 3-month assessments by January 31, 2020. Each patient received a baseline ED assessment along with follow-up self-report surveys 2 weeks, 8 weeks, and 3 months later. An ensemble machine learning method was used to predict 3-month PTSD or MDE from baseline information. Data analysis was performed from November 1, 2020, to May 31, 2021. Main Outcomes and Measures The PTSD Checklist for DSM-5 was used to assess PTSD and the Patient Reported Outcomes Measurement Information System Depression Short-Form 8b to assess MDE. Results A total of 1003 patients (median [interquartile range] age, 34.5 [24-43] years; 715 [weighted 67.9%] female; 100 [weighted 10.7%] Hispanic, 537 [weighted 52.7%] non-Hispanic Black, 324 [weighted 32.2%] non-Hispanic White, and 42 [weighted 4.4%] of non-Hispanic other race or ethnicity were included in this study. A total of 274 patients (weighted 26.6%) met criteria for 3-month PTSD or MDE. An ensemble machine learning model restricted to 30 predictors estimated in a training sample (patients from the Northeast or Midwest) had good prediction accuracy (mean [SE] area under the curve [AUC], 0.815 [0.031]) and calibration (mean [SE] integrated calibration index, 0.040 [0.002]; mean [SE] expected calibration error, 0.039 [0.002]) in an independent test sample (patients from the South). Patients in the top 30% of predicted risk accounted for 65% of all 3-month PTSD or MDE, with a mean (SE) positive predictive value of 58.2% (6.4%) among these patients at high risk. The model had good consistency across regions of the country in terms of both AUC (mean [SE], 0.789 [0.025] using the Northeast as the test sample and 0.809 [0.023] using the Midwest as the test sample) and calibration (mean [SE] integrated calibration index, 0.048 [0.003] using the Northeast as the test sample and 0.024 [0.001] using the Midwest as the test sample; mean [SE] expected calibration error, 0.034 [0.003] using the Northeast as the test sample and 0.025 [0.001] using the Midwest as the test sample). The most important predictors in terms of Shapley Additive Explanations values were symptoms of anxiety sensitivity and depressive disposition, psychological distress in the 30 days before motor vehicle collision, and peritraumatic psychosomatic symptoms. Conclusions and Relevance The results of this study suggest that a short set of questions feasible to administer in an ED can predict 3-month PTSD or MDE with good AUC, calibration, and geographic consistency. Patients at high risk can be identified in the ED for targeting if cost-effective preventive interventions are developed.

中文翻译：

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开发和验证预测机动车碰撞后创伤后应激障碍和严重抑郁症的模型。

重要性 在美国，每年有 4000 万人在创伤事件后到急诊室 (ED) 就诊，其中很大一部分人会发展为创伤后应激障碍 (PTSD) 或重度抑郁发作 (MDE)。在急诊室准确识别高危患者将有助于预防性干预的针对性。目的 开发和验证基于机动车碰撞后 ED 报告的预测工具，以预测 3 个月后的 PTSD 或 MDE。设计、设置和参与者 创伤后恢复的推进理解 (AURORA) 研究是一项纵向研究，检查了在创伤经历后立即就诊于 28 家美国城市急诊室的患者的不良创伤后神经精神后遗症。报名于 2017 年 9 月 25 日开始。本诊断/预后报告中考虑的 1003 名患者在 2020 年 1 月 31 日之前完成了为期 3 个月的评估。每位患者都接受了基线 ED 评估以及 2 周、8 周和 3 个月后的后续自我报告调查。集成机器学习方法用于根据基线信息预测 3 个月的 PTSD 或 MDE。数据分析于 2020 年 11 月 1 日至 2021 年 5 月 31 日进行。主要结果和测量 DSM-5 的 PTSD 检查表用于评估 PTSD，患者报告结果测量信息系统抑郁简表 8b 用于评估 MDE。结果 共有 1003 名患者（中位 [四分位间距] 年龄，34.5 [24-43] 岁；715 名 [加权 67.9%] 女性；100 名 [加权 10.7%] 西班牙裔，537 名 [加权 52.7%] 非西班牙裔黑人，324 [加权 32.2%] 非西班牙裔白人，以及 42 [加权 4. 4%] 的非西班牙裔其他种族或民族被纳入本研究。共有 274 名患者（加权 26.6%）符合 3 个月 PTSD 或 MDE 的标准。在训练样本（来自东北或中西部的患者）中估计的仅限于 30 个预测变量的集成机器学习模型具有良好的预测精度（平均 [SE] 曲线下面积 [AUC]，0.815 [0.031]）和校准（平均 [SE] ] 综合校准指数，0.040 [0.002]；在独立测试样本（来自南方的患者）中的平均 [SE] 预期校准误差，0.039 [0.002]）。预测风险前 30% 的患者占所有 3 个月 PTSD 或 MDE 的 65%，这些高风险患者的平均 (SE) 阳性预测值为 58.2% (6.4%)。该模型在 AUC（均值 [SE]，0.789 [0.789 [0. 025]以东北为测试样本，0.809[0.023]以中西部为测试样本）和校准（平均[SE]综合校准指数，0.048[0.003]以东北为测试样本，0.024[0.001]使用中西部作为测试样本；平均 [SE] 预期校准误差，0.034 [0.003] 使用东北作为测试样本，0.025 [0.001] 使用中西部作为测试样本）。就 Shapley 加法解释值而言，最重要的预测因子是焦虑敏感性和抑郁倾向的症状、机动车碰撞前 30 天的心理困扰，以及外伤性心身症状。结论和相关性 本研究的结果表明，在 ED 中可行的一组简短问题可以预测 3 个月的 PTSD 或 MDE，具有良好的 AUC、校准、和地理一致性。如果开发出具有成本效益的预防干预措施，则可以在急诊室识别高危患者作为目标。