Triage Modeling for Differential Diagnosis Between COVID-19 and Human Influenza A Pneumonia: Classification and Regression Tree Analysis

Anling Xiao; Huijuan Zhao; Jianbing Xia; Ling Zhang; Chao Zhang; Zhuoying Ruan; Nan Mei; Xun Li; Wuren Ma; Zhuozhu Wang; Yi He; Jimmy Lee; Weiming Zhu; Dajun Tian; Kunkun Zhang; Weiwei Zheng; Bo Yin

doi:10.3389/fmed.2021.673253

Triage Modeling for Differential Diagnosis Between COVID-19 and Human Influenza A Pneumonia: Classification and Regression Tree Analysis

Front Med (Lausanne). 2021 Aug 10:8:673253. doi: 10.3389/fmed.2021.673253. eCollection 2021.

Authors

Anling Xiao¹, Huijuan Zhao^{2

3}, Jianbing Xia⁴, Ling Zhang⁴, Chao Zhang^{2

3}, Zhuoying Ruan⁵, Nan Mei⁶, Xun Li^{2

3}, Wuren Ma^{2

3}, Zhuozhu Wang⁷, Yi He⁸, Jimmy Lee⁹, Weiming Zhu¹⁰, Dajun Tian¹¹, Kunkun Zhang¹², Weiwei Zheng^{2

3}, Bo Yin⁶

Affiliations

¹ Department of Radiology, Fu Yang No.2 People's Hospital, Fuyang, China.
² Key Laboratory of Public Health Safety, Ministry of Education, Department of Environmental Health, School of Public Health, Fudan University, Shanghai, China.
³ Key Laboratory of Health Technology Assessment, National Health Commission of the People's Republic of China, Fudan University, Shanghai, China.
⁴ Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, China.
⁵ Department of Radiology, Shanghai Institute of Medical Imaging, Shanghai, China.
⁶ Huashan Hospital, Fudan University, Shanghai, China.
⁷ Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States.
⁸ Curtin University of Technology, Perth, WA, Australia.
⁹ Department of Management, University of California, Los Angeles, Los Angeles, CA, United States.
¹⁰ Department of Epidemiology, University of California, Los Angeles, Los Angeles, CA, United States.
¹¹ Department of Epidemiology and Biostatistics, College for Public Health and Social Justice, Saint Louis University, St. Louis, MO, United States.
¹² Department of Finance, Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai, China.

Abstract

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.

Keywords: COVID-19; differential diagnosis; influenza A; rapid triage tools; regression tree analysis.