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

Guan, Xin; Zhang, Bo; Fu, Ming; Li, Mengying; Yuan, Xu; Zhu, Yaowu; Peng, Jing; Guo, Huan; Lu, Yanjun

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: KEY MESSAGES: 1. A machine learning method is used to build death risk model for COVID-19 patients. 2. Disease severity, age, hs-CRP, LDH, ferritin, and IL-10 are death risk factors. 3. These findings may help to identify the high-risk COVID-19 cases.