Development and Validation of a New Prognostic Scoring System for COVID-19

Ma, Ke; Xia, Yan; Hu, Boqi; Zhang, Yingli; Xu, Xiaoming; Zhang, Nan; Xu, Hong

This study aimed to develop and validate a bedside risk analysis system for predicting the clinical severity and prognosis of patients with coronavirus disease 2019 (COVID-19). In total, 444 COVID-19 patients were included and randomly assigned in a 2:1 ratio to 2 groups: derivation group and validation group. The new scoring system comprised of the following 8 variables: history of malignant diseases, history of diabetes mellitus, dyspnea, respiratory rate >24 breaths/min, C-reactive protein level >14 mg/L, white blood cell count >8×109/L, platelets count <180 × 1012/L, and lymphocyte count <1 × 109/L. The sensitivity analysis revealed that this new scoring system was more efficient than the sequential organ failure assessment scoring system on the first day of admission. The receiver characteristic curve analysis revealed that the new risk scoring predicted the severe cases of COVID-19 infection with an area under the curve of 0.831 (95% confidence interval [CI]: 0.783-0.879) and 0.798 (95% CI: 0.727-0.869) in the derivation and validation groups, respectively. This proposed risk score system is a fairly reliable and robust tool for evaluating the severity and prognosis of patients with COVID-19. This may help in the early identification of severe COVID-19 patients with poor prognosis, requiring more intense interventions.