A Tool for Early Prediction of Severe Coronavirus Disease 2019 (COVID-19): A Multicenter Study Using the Risk Nomogram in Wuhan and Guangdong, China

Background. Because there is no reliable risk stratification tool for severe coronavirus disease 2019 (COVID-19) patients at admission, we aimed to construct an effective model for early identification of cases at high risk of progression to severe COVID-19. Methods. In this retrospective multicenter study, 372 hospitalized patients with nonsevere COVID-19 were followed for > 15 days after admission. Patients who deteriorated to severe or critical COVID-19 and those who maintained a nonsevere state were assigned to the severe and nonsevere groups, respectively. Based on baseline data of the 2 groups, we constructed a risk prediction nomogram for severe COVID-19 and evaluated its performance. Results. The training cohort consisted of 189 patients, and the 2 independent validation cohorts consisted of 165 and 18 patients. Among all cases, 72 (19.4%) patients developed severe COVID-19. Older age; higher serum lactate dehydrogenase, C-reactive protein, coefficient of variation of red blood cell distribution width, blood urea nitrogen, and direct bilirubin; and lower albumin were associated with severe COVID-19. We generated the nomogram for early identifying severe COVID-19 in the training cohort (area under the curve [AUC], 0.912 [95% confidence interval {CI}, .846-.978]; sensitivity 85.7%, specificity 87.6%) and the validation cohort (AUC, 0.853 [95% CI, .790-.916]; sensitivity 77.5%, specificity 78.4%). The calibration curve for probability of severe COVID19 showed optimal agreement between prediction by nomogram and actual observation. Decision curve and clinical impact curve analyses indicated that nomogram conferred high clinical net benefit. Conclusions. Our nomogram could help clinicians with early identification of patients who will progress to severe COVID-19, which will enable better centralized management and early treatment of severe disease.