Identification and Validation of a Novel Clinical Signature to Predict the Prognosis in Confirmed Coronavirus Disease 2019 Patients

Abstract

Background: Our aim in this study was to identify a prognostic biomarker to predict the disease prognosis and reduce the mortality rate of coronavirus disease 2019 (COVID-19), which has caused a worldwide pandemic. Methods: COVID-19 patients were randomly divided into training and test groups. Univariate and multivariate Cox regression analyses were performed to identify the disease prognosis signature, which was selected to establish a risk model in the training group. The disease prognosis signature of COVID-19 was validated in the test group. Results: The signature of COVID-19 was combined with the following 5 indicators: neutrophil count, lymphocyte count, procalcitonin, age, and C-reactive protein. The signature stratified patients into high-and low-risk groups with significantly relevant disease prognosis (log-rank test, P