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Early prediction of mortality risk among patients with severe COVID-19, using machine learning.

Early prediction of mortality risk among patients with severe COVID-19, using machine learning.

Authors :
Hu, Chuanyu
Liu, Zhenqiu
Jiang, Yanfeng
Shi, Oumin
Zhang, Xin
Xu, Kelin
Suo, Chen
Wang, Qin
Song, Yujing
Yu, Kangkang
Mao, Xianhua
Wu, Xuefu
Wu, Mingshan
Shi, Tingting
Jiang, Wei
Mu, Lina
Tully, Damien C
Xu, Lei
Jin, Li
Li, Shusheng
Source :
International Journal of Epidemiology; Dec2020, Vol. 49 Issue 6, p1918-1929, 12p
Publication Year :
2020

Abstract

Background Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 infection, has been spreading globally. We aimed to develop a clinical model to predict the outcome of patients with severe COVID-19 infection early. Methods Demographic, clinical and first laboratory findings after admission of 183 patients with severe COVID-19 infection (115 survivors and 68 non-survivors from the Sino-French New City Branch of Tongji Hospital, Wuhan) were used to develop the predictive models. Machine learning approaches were used to select the features and predict the patients' outcomes. The area under the receiver operating characteristic curve (AUROC) was applied to compare the models' performance. A total of 64 with severe COVID-19 infection from the Optical Valley Branch of Tongji Hospital, Wuhan, were used to externally validate the final predictive model. Results The baseline characteristics and laboratory tests were significantly different between the survivors and non-survivors. Four variables (age, high-sensitivity C-reactive protein level, lymphocyte count and d-dimer level) were selected by all five models. Given the similar performance among the models, the logistic regression model was selected as the final predictive model because of its simplicity and interpretability. The AUROCs of the external validation sets were 0.881. The sensitivity and specificity were 0.839 and 0.794 for the validation set, when using a probability of death of 50% as the cutoff. Risk score based on the selected variables can be used to assess the mortality risk. The predictive model is available at [ https://phenomics.fudan.edu.cn/risk%5fscores/]. Conclusions Age, high-sensitivity C-reactive protein level, lymphocyte count and d-dimer level of COVID-19 patients at admission are informative for the patients' outcomes. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
03005771
Volume :
49
Issue :
6
Database :
Complementary Index
Journal :
International Journal of Epidemiology
Publication Type :
Academic Journal
Accession number :
148569651
Full Text :
https://doi.org/10.1093/ije/dyaa171