Triage Modeling for Differential Diagnosis Between COVID-19 and Human Influenza A Pneumonia: Classification and Regression Tree Analysis - Xiao, Anling; Zhao, Huijuan; Xia, Jianbing; Zhang, Ling; Zhang, Chao; Ruan, Zhuoying; Mei, Nan; Li, Xun; Ma, Wuren; Wang, Zhuozhu; He, Yi; Lee, Jimmy; et al. (Frontiers in Medicine, 2021)
Background The coronavirus disease 2019 (COVID-19) pandemic has lasted much longer than an influenza season, but the main signs, symptoms, and some imaging findings are similar in COVID-19 and influenza patients. The aim of the current study was to construct an accurate and robust model for initial screening and differential diagnosis of COVID-19 and influenza A. Methods All patients in the study were diagnosed at Fuyang No. 2 People’s Hospital, and they included 151 with COVID-19 and 155 with influenza A. The patients were randomly assigned to training set or a testing set at a 4:1 ratio. Predictor variables were selected based on importance, assessed by random forest algorithms, and analyzed to develop classification and regression tree models. Results In the optimal model A, the best single predictor of COVID-19 patients was a normal or high level of low-density lipoprotein cholesterol, followed by low level of creatine kinase, then the presence of less than 3 respiratory symptoms, then a highest temperature on the first day of admission less than 38℃. In the suboptimal model B, the best single predictor of COVID-19 was a low eosinophil count, then a normal monocyte ratio, then a normal hematocrit value, then a highest temperature on the first day of admission of less than 37℃, then a complete lack of respiratory symptoms. Conclusions The two models provide clinicians with a rapid triage tool. The optimal model can be used to developed countries/regions and major hospitals, and the suboptimal model can be used in underdeveloped regions and small hospitals.