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

Author:

Hu Chuanyu1,Liu Zhenqiu23ORCID,Jiang Yanfeng23,Shi Oumin4,Zhang Xin56,Xu Kelin7,Suo Chen56,Wang Qin1,Song Yujing1,Yu Kangkang8,Mao Xianhua23,Wu Xuefu56,Wu Mingshan56,Shi Tingting5,Jiang Wei23,Mu Lina9,Tully Damien C10,Xu Lei11,Jin Li23,Li Shusheng1,Tao Xuejin1,Zhang Tiejun56,Chen Xingdong23

Affiliation:

1. Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China

2. State Key Laboratory of Genetic Engineering, Human Phenome Institute, and School of Life Sciences, Fudan University, Shanghai, China

3. Fudan University Taizhou Institute of Health Sciences, Taizhou, China

4. Health Science Center, Shenzhen Second People's Hospital, TFirst Affiliated Hospital of Shenzhen University, Shenzhen, China

5. Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China

6. Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China

7. Department of Biostatistics, School of Public Health, Fudan University, Shanghai, China

8. Department of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China

9. Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo State University of New York, Buffalo, NY, USA

10. Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK

11. Emergency Medicine Department, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China

Abstract

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_scores/]. 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.

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Science and Technology Commission of Shanghai Municipality

Shanghai Municipal Science and Technology Major Project

Natural Science Foundation of Hubei

Publisher

Oxford University Press (OUP)

Subject

General Medicine,Epidemiology

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