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目的:分析重型和危重型新型冠状病毒肺炎（简称新冠肺炎）患者死亡的危险因素及其预测价值。方法:利用武汉市长江航运总医院临床和流行病学数据库，收集2020年1月至3月收治的105例重型及危重型新冠肺炎患者的临床和流行病学等资料。采用多因素非条件Logistic回归法分析患者住院期间死亡危险因素，并根据分析结果构建死亡预测模型；绘制受试者工作特征曲线（ROC），分析该死亡预测模型的预测价值。结果:105例重型和危重型新冠肺炎患者中，男性66例（占62.9%），女性39例（占37.1%）；年龄（58.2±14.4）岁；住院期间存活63例，死亡42例；死亡患者中以男性为主，占69.0%（29/42），且年龄≥60岁者占78.6%（33/42）。与存活患者相比，死亡患者年龄更大（岁：59.2±12.5比51.2±11.4），合并冠心病、高血压及并发心肌受损和血小板减少的患者更多（冠心病：33.3%比11.1%，高血压：28.6%比9.5%，心肌受损：73.8%比11.1%，血小板减少：61.9%比14.3%），机械通气比例更高（92.9%比44.4%），差异均有统计学意义（均n P<0.01）。将患者的性别、年龄、基础疾病、机械通气及并发症等变量纳入非条件Logistic回归分析，结果显示，性别 〔优势比（n OR）＝2.852，95%可信区间（95%n CI）为0.122～66.694〕、年龄（n OR＝3.257，95%n CI为0.466～18.584）、合并冠心病（n OR＝7.337，95%n CI为0.227～87.021）和高血压（n OR＝5.517，95%n CI为0.258～65.024）、并发心肌受损（n OR＝7.322，95%n CI为0.278～95.020）和血小板减少（n OR＝3.968，95%n CI为0.325～35.549）是重型及危重型新冠肺炎患者住院期间死亡的独立危险因素。根据危险因素构建死亡预测模型，并进行ROC曲线分析，结果显示该模型预测重型和危重型新冠肺炎患者住院期间死亡的ROC曲线下面积（AUC）为0.804，敏感度为83.8%，特异度为82.3%。n 结论:重型和危重型新冠肺炎患者的死亡结局与性别、年龄、合并症及并发症等多种因素有关；用性别、年龄、合并冠心病和高血压、并发心肌受损和血小板减少构建的死亡预测模型对重型及危重型新冠肺炎患者死亡有一定预测价值。“,”Objective:To analyze the risk factors of death in patients with severe and critical coronavirus disease 2019 (COVID-19) and their predictive value.Methods:Using the clinical and epidemiological database of Yangtze River Shipping General Hospital in Wuhan, the clinical and epidemiological data of 105 patients with severe and critical COVID-19 from January to March in 2020 were collected. Multivariate unconditional Logistic regression method was used to analyze the death risk factors of patients during hospitalization. The receiver operating characteristic (ROC) curve was drawn according to the multivariate analysis results to construct a death prediction model; the prediction value of the model was analyzed.Results:The 105 patients with severe and critical COVID-19 were enrolled with 66 males (62.9%) and 39 females (37.1%). The age was (58.2±14.4) years old. Forty-two patients died in hospital and 63 survived. Among the dead patients, 69.0% (29/42) were male, and 78.6% (33/42) were over 60 years old. Compared with survival patients, the non-survival patients were older (years old: 59.2±12.5 vs. 51.2±11.4), and had more comorbidities, including coronary heart disease, hypertension, myocardial damage and thrombocytopenia (coronary heart disease: 33.3% vs. 11.1%, hypertension: 28.6% vs. 9.5%, myocardial damage: 73.8% vs. 11.1%, thrombocytopenia: 61.9% vs. 14.3%), and received more mechanical ventilation (92.9% vs. 44.4%), with significant differences (all n P < 0.01). The variables of gender, age, basic diseases, mechanical ventilation and complications were included in the unconditional Logistic regression analysis, which showed that gender [odds ratio ( n OR) = 2.852, 95% confidence interval (95%n CI) was 0.122-66.694], age (n OR = 3.257, 95%n CI was 0.466-18.584), coronary heart disease (n OR = 7.337, 95%n CI was 0.227-87.021), hypertension (n OR = 5.517, 95%n CI was 0.258-65.024) and concurrent myocardial damage (n OR = 7.322, 95%n CI was 0.278-95.020) and thrombocytopenia (n OR = 3.968, 95%n CI was 0.325-35.549) were independent risk factors for death in patients with severe and critical COVID-19 during hospitalization. According to the risk factors, the death prediction model was constructed and ROC curve was analyzed, which showed that the area under ROC curve (AUC) of death prediction model for predicting the mortality of patients with severe and critical COVID-19 during hospitalization was 0.804, the sensitivity was 83.8%, and the specificity was 82.3%.n Conclusions:Various risk factors are associated with the death of severe or critical COVID-19 patients, such as gender, age, basic diseases and complications. The death prediction model is constructed by gender, age, basic diseases with coronary heart disease and hypertension, concurrent myocardial damage and thrombocytopenia, which has certain predictive value for the death of patients with severe or critical COVID-19.