Wu Ran, Ai Siqi, Cai Jing, Zhang Shiyu, Qian Zhengmin Min, Zhang Yunquan, Wu Yinglin, Chen Lan, Tian Fei, Li Huan, Li Mingyan, Lin Hualiang
Institute of Preventive Medicine Information, Hubei Provincial Center for Disease Control and Prevention, 6 Zhuodaoquan North Road, Wuhan, Hubei 430079, China.
Department of Epidemiology, School of Public Health, Sun Yat-sen University, 74 Zhongshan 2nd Road, Guangzhou, Guangdong 510080, China.
Innovation (Camb). 2020 Aug 28;1(2):100022. doi: 10.1016/j.xinn.2020.100022. Epub 2020 Aug 3.
An increasing number of patients are being killed by coronavirus disease 2019 (COVID-19), however, risk factors for the fatality of COVID-19 remain unclear. A total of 21,392 COVID-19 cases were recruited in the Hubei Province of China between December 2019 and February 2020, and followed up until March 18, 2020. We adopted Cox regression models to investigate the risk factors for case fatality and predicted the death probability under specific combinations of key predictors. Among the 21,392 patients, 1,020 (4.77%) died of COVID-19. Multivariable analyses showed that factors, including age (≥60 versus <45 years, hazard ratio [HR] = 7.32; 95% confidence interval [CI], 5.42, 9.89), sex (male versus female, HR = 1.31; 95% CI, 1.15, 1.50), severity of the disease (critical versus mild, HR = 39.98; 95% CI, 29.52, 48.86), comorbidity (HR = 1.40; 95% CI, 1.23, 1.60), highest body temperature (>39°C versus <39°C, HR = 1.28; 95% CI, 1.09, 1.49), white blood cell counts (>10 × 10/L versus (4-10) × 10/L, HR = 1.69; 95% CI, 1.35, 2.13), and lymphocyte counts (<0.8 × 10/L versus (0.8-4) × 10/L, HR = 1.26; 95% CI, 1.06, 1.50) were significantly associated with case fatality of COVID-19 patients. Individuals of an older age, who were male, with comorbidities, and had a critical illness had the highest death probability, with 21%, 36%, 46%, and 54% within 1-4 weeks after the symptom onset. Risk factors, including demographic characteristics, clinical symptoms, and laboratory factors were confirmed to be important determinants of fatality of COVID-19. Our predictive model can provide scientific evidence for a more rational, evidence-driven allocation of scarce medical resources to reduce the fatality of COVID-19.
越来越多的患者死于2019冠状病毒病（COVID-19），然而，COVID-19致死的危险因素仍不清楚。2019年12月至2020年2月期间，在中国湖北省共招募了21392例COVID-19病例，并随访至2020年3月18日。我们采用Cox回归模型研究病例死亡的危险因素，并预测关键预测因素特定组合下的死亡概率。在这21392例患者中，1020例（4.77%）死于COVID-19。多变量分析显示，年龄（≥60岁与<45岁，风险比[HR]=7.32；95%置信区间[CI]，5.42，9.89）、性别（男性与女性，HR=1.31；95%CI，1.15，1.50）、疾病严重程度（危重症与轻症，HR=39.98；95%CI，29.52，48.86）、合并症（HR=1.40；95%CI，1.23，1.60）、最高体温（>39℃与<39℃，HR=1.28；95%CI，1.09，1.49）、白细胞计数（>10×10⁹/L与（4-10）×10⁹/L，HR=1.69；95%CI，1.35，2.13）和淋巴细胞计数（<0.8×10⁹/L与（0.8-4）×10⁹/L，HR=1.26；95%CI，1.06，1.50）等因素与COVID-19患者的病例死亡显著相关。年龄较大、男性、有合并症且患有危重症的个体死亡概率最高，症状出现后1-4周内分别为21%、36%、46%和54%。包括人口统计学特征、临床症状和实验室因素在内的危险因素被证实是COVID-19致死的重要决定因素。我们的预测模型可为更合理、基于证据地分配稀缺医疗资源以降低COVID-19致死率提供科学依据。
