Risk factors of fatal outcome in hospitalized subjects with coronavirus disease 2019 from a nationwide analysis in China.

Link to article at PubMed

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Risk factors of fatal outcome in hospitalized subjects with coronavirus disease 2019 from a nationwide analysis in China.

Chest. 2020 Apr 15;:

Authors: Chen R, Liang W, Jiang M, Guan W, Zhan C, Wang T, Tang C, Sang L, Liu J, Ni Z, Hu Y, Liu L, Shan H, Lei C, Peng Y, Wei L, Liu Y, Hu Y, Peng P, Wang J, Liu J, Chen Z, Li G, Zheng Z, Qiu S, Luo J, Ye C, Zhu S, Liu X, Cheng L, Ye F, Zheng J, Zhang N, Li Y, He J, Li S, Zhong N, Medical Treatment Expert Group for COVID-19

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) has become a global health emergency. Cumulative number of new confirmed case and death are still increasing out of China. However, the independent predicted factors associated with the fatal outcome remain uncertain.
METHODS: A retrospective cohort of 1590 hospitalized subjects with COVID-19 throughout China was established. The prognostic effects of variables, including clinical features and laboratory findings, were analyzed using Kapla-Meier method and Cox proportional hazard model. A prognostic nomogram was formulated to predict the survival of patient with COVID-19.
RESULTS: In this nationwide cohort, non-survivors showed higher incidence of elderly people, subjects with co-existing chronic illness, dyspnea and laboratory abnormalities on admission, compared with survivors. Multivariate Cox regression analysis showed that age≥75 (HR: 7.86, 95% CI: 2.44-25.35), age between 65-74 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>0.5ng/ml(HR:8.72, 95%CI:3.42-22.28), aspartate aminotransferase>40U/liter (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 the C-index of 0.91 (95%CI 0.85-0.97). The calibration plots also demonstrated good consistence between the prediction and the observation.
CONCLUSIONS: The proposed nomogram accurately predict clinical outcomes of patients with COVID-19 based on individual characteristics. Earlier identification, more intensive surveillance and appropriate therapy should be considered in patients with high risk.

PMID: 32304772 [PubMed - as supplied by publisher]

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