Logistic regression combined with ROC curve model to predict risk of critically ill-patients with COVID-19

Abstract

Objective: To build a model to predict critically ill-patients with coronavirus disease 2019 (COVID-19), and provide a new idea for the rapid identification of clinical progression in the early stage of critically ill-patients Methods: A retrospective analysis of the general data of 152 general patients and 323 critically ill-patients diagnosed with COVID-19 from Jan 17th, 2020 to Feb 25th, 2020 in Wuhan Third Hospital was carried out;At the same time, the differences in fever, blood routine, liver and kidney function, coagulation function, C-reactive protein (CRP), and nucleic acid reagent testing results from the day of admission were statistically analyzed Factors with statistical significance were included in a multivariate logistic regression analysis to obtain independent relevant factors that affect the critical ill-patients with COVID-19 Then a prediction model was built based on these factors and its accuracy was evaluated by the receiver operating characteristic (ROC) curve Results: The sensitivities of age, fever, neutrophil ratio, lymphocyte ratio, serum creatinine (Scr) and combined diagnosis were 0 664, 0 671, 0 607, 0 669, 0 302 and 0 710, respectively;The specificities were 0 669, 0 585, 0 795, 0 685, 0 895 and 0 802, respectively;The area under the curve (AUC) were 0 725, 0 628, 0 721, 0 681, 0 590 and 0 795, respectively;The AUC of combined diagnosis was higher than that of single diagnosis (P < 0 05) Conclusion: The logistic regression and combined with ROC curve model based on multi-factors, including age, fever status, neutrophil ratio, lymphocyte ratio, and Scr, can play a good role in predicting the occurrence of critically ill-patients with COVID-19, which is worthy of further promotion and application