Risk Factors of Fatal Outcome in Hospitalized Subjects With Coronavirus Disease 2019 From a Nationwide Analysis in China

Ruchong Chen, Wenhua Liang, Mei Jiang, Weijie Guan, Chen Zhan, Tao Wang, Chunli Tang, Ling Sang, Jiaxing Liu, Zhengyi Ni, Yu Hu, Lei Liu, Hong Shan, Chunliang Lei, Yixiang Peng, Li Wei, Yong Liu, Yahua Hu, Peng Peng, Jianming Wang, Jiyang Liu, Zhong Chen, Gang Li, Zhijian Zheng, Shaoqin Qiu, Jie Luo, Changjiang Ye, Shaoyong Zhu, Xiaoqing Liu, Linling Cheng, Feng Ye, Jinping Zheng, Nuofu Zhang, Yimin Li, Jianxing He, Shiyue Li, Nanshan Zhong, Medical Treatment Expert Group for COVID-19, Ruchong Chen, Wenhua Liang, Mei Jiang, Weijie Guan, Chen Zhan, Tao Wang, Chunli Tang, Ling Sang, Jiaxing Liu, Zhengyi Ni, Yu Hu, Lei Liu, Hong Shan, Chunliang Lei, Yixiang Peng, Li Wei, Yong Liu, Yahua Hu, Peng Peng, Jianming Wang, Jiyang Liu, Zhong Chen, Gang Li, Zhijian Zheng, Shaoqin Qiu, Jie Luo, Changjiang Ye, Shaoyong Zhu, Xiaoqing Liu, Linling Cheng, Feng Ye, Jinping Zheng, Nuofu Zhang, Yimin Li, Jianxing He, Shiyue Li, Nanshan Zhong, Medical Treatment Expert Group for COVID-19

Abstract

Background: 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.

Research question: 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.

Study design and methods: 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.

Results: 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.

Interpretation: 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.

Keywords: COVID-19; fatal outcome; nomogram; risk factors.

Copyright © 2020 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

Figures

Figure 1
Figure 1
A-C, Clinical characteristics of 50 fatal cases with coronavirus disease 2019. The percentages of coexisting chronic illness in fatal cases (A), treatments in fatal cases (B), and complications in fatal cases (C). ARF = acute renal failure; CRRT = continuous renal replacement therapy; DIC = disseminated intravascular coagulation; ECMO = extracorporeal membrane oxygenation; IMV = invasive mechanical ventilation; NIV = noninvasive ventilation.
Figure 2
Figure 2
Risk factor of the fatal outcome in the multivariate Cox proportional hazards regression model. The figure presents the HRs and the 95% CIs associated with the end point. AST = aspartate aminotransferase; CHD = coronary heart disease; CVD = cerebrovascular disease; HR = hazard ratio; PCT = procalcitonin; TBIL = total bilirubin.
Figure 3
Figure 3
Kaplan-Meier survival plots for different prognostic factors. The figure displays the Kaplan-Meier survival plots according to (A) age, (B) CHD, (C) CVD, (D) dyspnea, (E) PCT, and (F) AST. See Figure 2 legend for expansion of abbreviations.
Figure 4
Figure 4
Prognostic nomogram for predicting the overall survival probability of patients with coronavirus disease 2019. Prognostic patient’s value is located on each variable axis, and a line is drawn upward to determine the number of point nomogram for predicting overall survival probability of patients with coronavirus disease 2019. The sum of these numbers is located on the Total Points axis, and a line is drawn downward to the survival axes to determine the likelihood of 14-day, 21-day, and 28-day survival. See Figure 2 legend for expansion of abbreviations.
Figure 5
Figure 5
Calibration curves of the nomogram predicting OS in patients with coronavirus disease 2019. Calibration curves of the nomogram predict 14-day (A), 21-day (B), and 28-day (C) OS in patients with coronavirus disease 2019. Nomogram-predicted probability of OS is plotted on the x-axis; actual OS is plotted on the y-axis. OS = overall survival.

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