看不见的COVID-19危机：一线医生治疗COVID-19患者的创伤后应激障碍风险
The Invisible COVID-19 Crisis: Post-Traumatic Stress Disorder Risk Among Frontline Physicians Treating COVID-19 Patients
摘要：
这项研究评估了美国一线医生（治疗COVID-19患者）与二线医生（不治疗COVID-19患者）之间的创伤后应激障碍（PTSD），并确定了与较高PTSD风险相关的因素的重要性和模式。于2020年8月至9月期间，向COVID-19病例最多的18个州的执业医生进行了一项基于网络的横断面调查。在1,478名医生中，有1,017人完成了PTSD检查表（PCL-5）。首先，PCL-5被用来比较两个医生组之间的症状认可。一线医生对PCL-5症状和PCL-5评分具有临床意义的认可比例高于二线医生。其次，逻辑回归和七个非线性机器学习（ML）算法被用来通过分析变量重要性和部分依赖图来识别PTSD风险的潜在预测因子。PTSD风险的预测因素包括认知/心理测量、职业特征、工作经历、社会支持、人口统计学和工作场所特征。重要的是，最终的ML模型随机森林确定了一线医生中PTSD风险的破坏性和保护性预测因子的模式。关键的损害因素包括抑郁、倦怠、消极应对、对感染/传播COVID-19的恐惧、感知到的耻辱感以及治疗COVID-19患者的资源不足。保护性因素包括韧性和来自雇主/朋友/家人/重要他人的支持。这项研究强调了ML算法在一线医生中揭示PTSD保护性/破坏性风险因素之间的非线性关系的价值，这可能会更好地为干预措施提供信息，为未来的流行病/大流行病做好准备。
英文摘要：
This study evaluated post traumatic stress disorder (PTSD) among frontline US physicians (treating COVID-19 patients) in comparison with second-line physicians (not treating COVID-19 patients), and identified the significance and patterns of factors associated with higher PTSD risk. A cross-sectional, web-based survey was deployed during August and September, 2020, to practicing physicians in the 18 states with the largest COVID-19 cases. Among 1,478 responding physicians, 1,017 completed the PTSD Checklist (PCL-5). First, the PCL-5 was used to compare symptom endorsement between the two physician groups. A greater percentage of frontline than second-line physicians had clinically significant endorsement of PCL-5 symptoms and higher PCL-5 scores. Second, logistic regression and seven nonlinear machine learning (ML) algorithms were leveraged to identify potential predictors of PTSD risk by analyzing variable importance and partial dependence plots. Predictors of PTSD risk included cognitive/psychological measures, occupational characteristics, work experiences, social support, demographics, and workplace characteristics. Importantly, the final ML model random forest, identified patterns of both damaging and protective predictors of PTSD risk among frontline physicians. Key damaging factors included depression, burnout, negative coping, fears of contracting/transmitting COVID-19, perceived stigma, and insufficient resources to treat COVID-19 patients. Protective factors included resilience and support from employers/friends/family/significant others. This study underscores the value of ML algorithms to uncover nonlinear relationships among protective/damaging risk factors for PTSD in frontline physicians, which may better inform interventions to prepare healthcare systems for future epidemics/pandemics.
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