National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.
Wuhan Jin-yin tan Hospital, Wuhan, Hubei, China.
Chest. 2020 Jul;158(1):97-105. doi: 10.1016/j.chest.2020.04.010. Epub 2020 Apr 15.
The novel coronavirus disease 2019 (COVID-19) has become a global health emergency. The cumulative number of new confirmed cases and deaths are still increasing out of China. Independent predicted factors associated with fatal outcomes remain uncertain.
The goal of the current study was to investigate the potential risk factors associated with fatal outcomes from COVID-19 through a multivariate Cox regression analysis and a nomogram model.
A retrospective cohort of 1,590 hospitalized patients with COVID-19 throughout China was established. The prognostic effects of variables, including clinical features and laboratory findings, were analyzed by using Kaplan-Meier methods and a Cox proportional hazards model. A prognostic nomogram was formulated to predict the survival of patients with COVID-19.
In this nationwide cohort, nonsurvivors included a higher incidence of elderly people and subjects with coexisting chronic illness, dyspnea, and laboratory abnormalities on admission compared with survivors. Multivariate Cox regression analysis showed that age ≥ 75 years (hazard ratio [HR], 7.86; 95% CI, 2.44-25.35), age between 65 and 74 years (HR, 3.43; 95% CI, 1.24-9.5), coronary heart disease (HR, 4.28; 95% CI, 1.14-16.13), cerebrovascular disease (HR, 3.1; 95% CI, 1.07-8.94), dyspnea (HR, 3.96; 95% CI, 1.42-11), procalcitonin level > 0.5 ng/mL (HR, 8.72; 95% CI, 3.42-22.28), and aspartate aminotransferase level > 40 U/L (HR, 2.2; 95% CI, 1.1-6.73) were independent risk factors associated with fatal outcome. A nomogram was established based on the results of multivariate analysis. The internal bootstrap resampling approach suggested the nomogram has sufficient discriminatory power with a C-index of 0.91 (95% CI, 0.85-0.97). The calibration plots also showed good consistency between the prediction and the observation.
The proposed nomogram accurately predicted clinical outcomes of patients with COVID-19 based on individual characteristics. Earlier identification, more intensive surveillance, and appropriate therapy should be considered in patients at high risk.
新型冠状病毒病 2019（COVID-19）已成为全球卫生紧急事件。中国境外的新确诊病例和死亡人数仍在不断增加。与致命结局相关的独立预测因素仍不确定。
本研究的目的是通过多变量 Cox 回归分析和列线图模型探讨与 COVID-19 致命结局相关的潜在危险因素。
建立了中国 1590 例住院 COVID-19 患者的回顾性队列。采用 Kaplan-Meier 方法和 Cox 比例风险模型分析了包括临床特征和实验室检查结果在内的变量的预后作用。制定了一个预测 COVID-19 患者生存的列线图。
在这个全国性队列中，与幸存者相比，非幸存者中老年人和合并慢性疾病、呼吸困难以及入院时实验室异常的比例更高。多变量 Cox 回归分析显示，年龄≥75 岁（危险比[HR]，7.86；95%CI，2.44-25.35）、年龄 65-74 岁（HR，3.43；95%CI，1.24-9.5）、冠心病（HR，4.28；95%CI，1.14-16.13）、脑血管病（HR，3.1；95%CI，1.07-8.94）、呼吸困难（HR，3.96；95%CI，1.42-11）、降钙素原水平>0.5ng/mL（HR，8.72；95%CI，3.42-22.28）和天门冬氨酸氨基转移酶水平>40U/L（HR，2.2；95%CI，1.1-6.73）是与死亡结局相关的独立危险因素。基于多变量分析的结果建立了一个列线图。内部 bootstrap 重采样方法表明，该列线图具有足够的判别能力，C 指数为 0.91（95%CI，0.85-0.97）。校准图也显示了预测和观察之间的良好一致性。
该列线图基于个体特征准确预测 COVID-19 患者的临床结局。应考虑对高危患者进行早期识别、更密切的监测和适当的治疗。
