目的 构建新型冠状病毒肺炎（COVID-19）病例危重症发生的预测模型，为临床早期快速识别进展为危重症患者提供一种新思路。方法 回顾性比较武汉市第三医院2020年1月17日~2020年2月25日诊断为COVID-19的152例普通型患者和323例重型/危重型患者的一般资料，入院首次的发热情况、血常规、肝肾功能、凝血功能、C反应蛋白（CRP）以及核酸检测结果等的差异，将差异具有统计学意义的指标纳入多因素Logistic回归分析，得到影响COVID-19患者发生危重症的独立相关因素，并构建COVID-19临床确诊病例危重症预测模型，作受试者工作特征（ROC）曲线评价该模型预测的准确性。结果 年龄、是否发热、中性粒细胞比值、淋巴细胞比值、血肌酐及联合诊断的灵敏度分别为0.664、0.671、0.607、0.669、0.302、0.710；特异度分别为0.669、0.585、0.795、0.685、0.895、0.802；曲线下面积（AUC）分别为0.725、0.628、0.721、0.681、0.590、0.795；联合诊断时AUC均较单独诊断时高（P<0.05）。结论 由年龄、是否发热、中性粒细胞比值、淋巴细胞比值、血肌酐构建的Logistic回归和ROC曲线模型可以对COVID-19患者危重症的发生起到较好的预测作用，值得临床推广应用。

Objective To build a model to predict critically ill-patients with coronavirus disease 2019 (COVID-19), and provide a new idea for the rapid identification of clinical progression in the early stage of critically ill-patients. Methods A retrospective analysis of the general data of 152 general patients and 323 critically ill-patients diagnosed with COVID-19 from Jan 17th, 2020 to Feb 25th, 2020 in Wuhan Third Hospital was carried out; At the same time, the differences in fever, blood routine, liver and kidney function, coagulation function, C-reactive protein (CRP), and nucleic acid reagent testing results from the day of admission were statistically analyzed. Factors with statistical significance were included in a multivariate logistic regression analysis to obtain independent relevant factors that affect the critical ill-patients with COVID-19. Then a prediction model was built based on these factors and its accuracy was evaluated by the receiver operating characteristic (ROC) curve. Results The sensitivities of age, fever, neutrophil ratio, lymphocyte ratio, serum creatinine (Scr) and combined diagnosis were 0.664, 0.671, 0.607, 0.669, 0.302 and 0.710, respectively; The specificities were 0.669, 0.585, 0.795, 0.685, 0.895 and 0.802, respectively; The area under the curve (AUC) were 0.725, 0.628, 0.721, 0.681, 0.590 and 0.795, respectively; The AUC of combined diagnosis was higher than that of single diagnosis (P < 0.05). Conclusion The logistic regression and combined with ROC curve model based on multi-factors, including age, fever status, neutrophil ratio, lymphocyte ratio, and Scr, can play a good role in predicting the occurrence of critically ill-patients with COVID-19, which is worthy of further promotion and application.

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国家中医药管理局项目：中西医结合治疗新型冠状病毒肺炎重型/危重型患者临床疗效观察（2020ZYLCYJ03-10）