Comparing Rapid Scoring Systems in Mortality Prediction of Critically Ill Patients With Novel Coronavirus Disease

Abstract

Objectives Rapid and early severity-of-illness assessment appears to be important for critically ill patients with novel coronavirus disease (COVID-19). This study aimed to evaluate the performance of the rapid scoring system on admission of these patients. Methods A total of 138 medical records of critically ill patients with COVID-19 were included in the study. Demographic and clinical characteristics on admission used for calculating Modified Early Warning Score (MEWS) and Rapid Emergency Medicine Score (REMS) and outcomes (survival or death) were collected for each case and extracted for analysis. All patients were divided into two age subgroups (= 65 years). The receiver operating characteristic (ROC) curve analyses were performed for overall patients and both subgroups. Results The median [25th quartile, 75th quartile] of MEWS of survivors versus nonsurvivors were 1 [1, 2] and 2 [1, 3] and those of REMS were 5 [2, 6] and 7 [6, 10], respectively. In overall analysis, the area under the ROC curve for the REMS in predicting mortality was 0.833 (95% confidence interval [CI] = 0.737 to 0.928), higher than that of MEWS (0.677, 95% CI = 0.541 to 0.813). An optimal cutoff of REMS (>= 6) had a sensitivity of 89.5%, a specificity of 69.8%, a positive predictive value of 39.5%, and a negative predictive value of 96.8%. In the analysis of subgroup of patients aged <65 years, the area under the ROC curve for the REMS in predicting mortality was 0.863 (95% CI = 0.743 to 0.941), higher than that of MEWS (0.603, 95% CI = 0.462 to 0.732). Conclusion To our knowledge, this study was the first exploration on rapid scoring systems for critically ill patients with COVID-19. The REMS could provide emergency clinicians with an effective adjunct risk stratification tool for critically ill patients with COVID-19, especially for the patients aged <65 years. The effectiveness of REMS for screening these patients is attributed to its high negative predictive value.