A composite risk model predicts disease progression in early stages of COVID-19: A propensity score-matched cohort study

Abstract

Background: Recently, studies on COVID-19 have focused on the epidemiology of the disease and clinical characteristics of patients, as well as on the risk factors associated with mortality during hospitalization in critical COVID-19 cases. However, few research has been performed on the prediction of disease progression in particular group of patients in the early stages of COVID-19. Methods: The study included 338 patients with COVID-19 treated at two hospitals in Wuhan, China, from December 2019 to March 2020. Predictors of the progression of COVID-19 from mild to severe stages were selected by the logistic regression analysis. Results: COVID-19 progression to severe and critical stages was confirmed in 78 (23.1%) patients. The average value of the neutrophil-to-lymphocyte ratio (NLR) was higher in patients in the disease progression group than in the improvement group. Multivariable logistic regression analysis revealed that elevated NLR, LDH and IL-10 were independent predictors of disease progression. The optimal cut-off value of NLR was 3.75. The values of the area under the curve, reflecting the accuracy of predicting COVID-19 progression by NLR was 0.739 (95%CI: 0.605–0.804). The risk model based on NLR, LDH and IL-10 had the highest area under the ROC curve. Conclusions: The performed analysis demonstrates that high concentrations of NLR, LDH and IL-10 were independent risk factors for predicting disease progression in patients at the early stage of COVID-19. The risk model combined with NLR, LDH and IL-10 improved the accuracy of the prediction of disease progression in patients in the early stages of COVID-19.