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 < .001) in the training group. The survival analysis indicated that the high-risk group displayed substantially lower survival probability than the low-risk group (log-rank test, P < .001). The area under the receiver operating characteristic (ROC) curve showed that the signature of COVID-19 displayed the highest predictive accuracy regarding disease prognosis, which was 0.955 in the training group and 0.945 in the test group. The ROC analysis of both groups demonstrated that the predictive ability of the signature surpassed the use of each of the 5 indicators alone.

Conclusions: The signature of COVID-19 presents a novel predictor and prognostic biomarker for closely monitoring patients and providing timely treatment for those who are severely or critically ill.

Keywords: COVID-19; coronavirus; prediction; risk model; signature.