Risk Factors of Fatal Outcome in Hospitalized Subjects With Coronavirus Disease 2019 From a Nationwide Analysis in China - Chen, Ruchong; Liang, Wenhua; Jiang, Mei; Guan, Weijie; Zhan, Chen; Wang, Tao; Tang, Chunli; Sang, Ling; Liu, Jiaxing; Ni, Zhengyi; Hu, Yu; Liu, Lei; et al. (Chest, 2020)
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.