Abstract Aim To explore the factors affecting mortality in patients with COVID‐19 and to verify the predictive value of the three rapid scoring scales MEWS, RAPS and REMS. Design Cross‐sectional observational study. Methods Kaplan–Meier and Cox survival analyses were performed to identify the risk factors associated with COVID‐19‐related death. A ROC curve analysis was used to evaluate the abilities of the three scoring scales to predict the prognosis of COVID‐19 patients. Results Age, low blood oxygen saturation level and decreased lymphocyte count were the high risk factors for COVID‐19‐related mortality. The analysis of the abilities of the three scales to predict the prognosis of COVID‐19 patients: The AUC of 0.641 for the RAPS ( p = .065). The MEWS (AUC = 0.705, p = .007), compared with RAPS, the NRI was 0.371( p = .03), and the IDI = 0.092 ( p = .046); The REMS (AUC = 0.841, p < .001), compared with MEWS, the NRI was 0.227( p = .12), and the IDI=0.09( p = .047); The Combining Predictor (AUC = 0.878, p < .001), compared with REMS, the NRI was 0.25( p = .113), and the IDI=0.02( p = .598). Conclusion Patients with an old age, low blood oxygen saturation level and decreased lymphocyte count were at a high risk of COVID‐19‐related mortality. Moreover, our analysis revealed that the REMS had a better prognostic ability than the MEWS and RAPS when applied to COVID‐19 patients. Our findings suggest that the REMS can be used as a rapid scoring tool for the early assessment of COVID‐19 severity.
【저자키워드】 COVID‐19, Risk factors, Prognosis, prediction, Rapid Scoring Scales, 【초록키워드】 Mortality, severity, risk factor, COVID‐19, Lymphocyte count, survival, Patient, death, age, prognostic, predict, Analysis, ROC Curve, AUC, high risk, Predictive, Blood oxygen saturation, Factor, COVID‐19 patients, Kaplan–Meier, Result, identify, performed, was used, evaluate, applied, can be used, affecting, 【제목키워드】 coronavirus disease, Patient, predictor,