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 <3 respiratory symptoms, then a highest temperature on the first day of admission <38°C. 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 <37°C, 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.
【저자키워드】 COVID-19, influenza A, differential diagnosis, rapid triage tools, regression tree analysis, 【초록키워드】 coronavirus disease, pandemic, Influenza, Symptoms, eosinophil, hospitals, monocyte, Region, Patient, temperature, Admission, patients, COVID-19 patient, respiratory symptoms, Low-Density Lipoprotein cholesterol, Algorithms, clinician, initial screening, random, variable, Complete, Randomly, robust, selected, highest, analyzed, lack, develop, diagnosed, can be used, assigned, hematocrit value, with COVID-19, 【제목키워드】 Human, Triage, classification, modeling, Regression, tree,