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Clinical and inflammatory features based machine learning model for fatal risk prediction of hospitalized COVID-19 patients: results from a retrospective cohort study.

Authors
  • Guan, Xin1, 2
  • Zhang, Bo1
  • Fu, Ming2
  • Li, Mengying2
  • Yuan, Xu1
  • Zhu, Yaowu1
  • Peng, Jing1
  • Guo, Huan2
  • Lu, Yanjun1
  • 1 Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. , (China)
  • 2 Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. , (China)
Type
Published Article
Journal
Annals of medicine
Publication Date
Dec 01, 2021
Volume
53
Issue
1
Pages
257–266
Identifiers
DOI: 10.1080/07853890.2020.1868564
PMID: 33410720
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

To appraise effective predictors for COVID-19 mortality in a retrospective cohort study. A total of 1270 COVID-19 patients, including 984 admitted in Sino French New City Branch (training and internal validation sets randomly split at 7:3 ratio) and 286 admitted in Optical Valley Branch (external validation set) of Wuhan Tongji hospital, were included in this study. Forty-eight clinical and laboratory features were screened with LASSO method. Further multi-tree extreme gradient boosting (XGBoost) machine learning-based model was used to rank importance of features selected from LASSO and subsequently constructed death risk prediction model with simple-tree XGBoost model. Performances of models were evaluated by AUC, prediction accuracy, precision, and F1 scores. Six features, including disease severity, age, levels of high-sensitivity C-reactive protein (hs-CRP), lactate dehydrogenase (LDH), ferritin, and interleukin-10 (IL-10), were selected as predictors for COVID-19 mortality. Simple-tree XGBoost model conducted by these features can predict death risk accurately with >90% precision and >85% sensitivity, as well as F1 scores >0.90 in training and validation sets. We proposed the disease severity, age, serum levels of hs-CRP, LDH, ferritin, and IL-10 as significant predictors for death risk of COVID-19, which may help to identify the high-risk COVID-19 cases. KEY MESSAGES A machine learning method is used to build death risk model for COVID-19 patients. Disease severity, age, hs-CRP, LDH, ferritin, and IL-10 are death risk factors. These findings may help to identify the high-risk COVID-19 cases.

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