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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
- Source :
- Annals of Medicine, Vol 53, Iss 1, Pp 257-266 (2021)
- Publication Year :
- 2021
- Publisher :
- Taylor & Francis Group, 2021.
-
Abstract
- AbstractObjectives To appraise effective predictors for COVID-19 mortality in a retrospective cohort study.Methods 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.Results 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.Conclusion 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 MESSAGESA 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.
- Subjects :
- COVID-19
machine learning
fatal risk
extreme gradient boosting
Medicine
Subjects
Details
- Language :
- English
- ISSN :
- 07853890, 13652060, and 54241677
- Volume :
- 53
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- Annals of Medicine
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.18b5424167794314879fb94f09136bf2
- Document Type :
- article
- Full Text :
- https://doi.org/10.1080/07853890.2020.1868564