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.
翻译:这项研究评估了美国前线医生(治疗COVID-19病人)与二线医生(不治疗COVID-19病人)相比创伤后应激障碍(PSTSD)的创伤后应激障碍(PTSD),并查明了与创伤后应激障碍风险较高相关因素的重要性和模式。2020年8月和9月,为18个州的18个具有最大COVID-19病例的执业医生进行了跨部门、基于网络的调查。在1,478个作出答复的医生中,1,017名医生完成了PTSD检查清单(PCL-5)。首先,PCL-5用于比较两个医生组之间的症状认可。比二线医生的更大比例是临床上相当的PCL-5症状和PCL-5分数。第二,物流回归和七种非线机学习(ML)算法,通过分析不同重要性和部分依赖性地块来查明PTSD风险的潜在预测因素。PTSD风险的预测因素包括认知/心理诊断、职业特征、工作经验、社会支持、人口和工作场所特征特征特征。 最终的ML模型森林,查明的PLS-19级健康健康健康的症状的症状和心脏内伤后期研究中,这两类医生的不测测测测测算的不力风险。