Abstract Objectives 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 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.
【저자키워드】 COVID-19, machine learning, Extreme gradient boosting, fatal risk, 【초록키워드】 Risk factors, severity, disease severity, hospital, LDH, risk, ferritin, lactate dehydrogenase, sensitivity, Features, Accuracy, Wuhan, death, age, predictor, IL-10, performance, predict, COVID-19 patients, COVID-19 mortality, COVID-19 cases, external validation, AUC, retrospective cohort study, High-sensitivity C-reactive protein, Precision, Serum level, city, help, French, branch, objective, risk of COVID-19, feature, effective, Randomly, extreme, Result, selected, identify, was used, the disease, evaluated, conducted, screened, New, build, laboratory feature, Valley, 【제목키워드】 risk, clinical, Inflammatory, retrospective cohort study, hospitalized COVID-19, feature,