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

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.

Figure 1

The importance of variables in the random forest algorithm. (A) The importance of variables was calculated by all patients. (B) The importance of variables was calculated by training set. A multi-indicator model was constructed by combining 61 variables (P < 0.05 in logistic regression analysis). Only Top 20 was shown. EO#, eosinophil count; LDL-c, Low-density lipoprotein cholesterol; HCT, Hematocrit; MONO%, monocyte ratio; CK, creatine kinase; T, Body temperature of the admission day; T.d1.max, Highest temperature on the first day of admission; T.d2.max, Highest temperature on the second day of admission; T.d1-3.max, Highest body temperature during the first 3 days of admission; RES., The number of respiratory symptoms; SP.P, Sputum production; CRP, C-reactive protein; EO%, Eosinophil ratio; MONO#, Monocyte count; MCHC#, Mean corpuscular hemoglobin concentration; NEUT%, Neutrophil ratio; LYMPH#, Lymphocyte count; Cys C, Cystatin C; UA, Uric acid; PALB, Prealbumin; TP, Total protein.

Figure 2

Classification and regression tree analysis of variables that most distinguish COVID-19 from Influenza A in clinical signs and symptoms and in serum biochemistry (model A, optimal model). (A) 0, Influenza A; 1, COVID-19; N, the total number of patients; RES., The number of respiratory symptoms; T.day1.max, Highest temperature on the first day of admission; LDL-c, Low-density lipoprotein cholesterol; CK, creatine kinase. All factors are compared with the limit of the range of medical reference value. (B) Performance characteristics of the model validated by the testing set.

Figure 3

Classification and regression tree analysis of variables that most distinguish COVID-19 from Influenza A in clinical signs and symptoms and in routine blood (model B, suboptimal model). (A) 0, Influenza A; 1, COVID-19; N, the total number of patients; RES., The number of respiratory symptoms; T.day1.max, Highest temperature on the first day of admission; EO#, eosinophil count; MONO%, monocyte ratio; HCT, Hematocrit. All factors are compared with the limit of the range of medical reference value. (B) Performance characteristics of the model validated by the testing set.