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

• Published in: Annals of medicine (Helsinki) Vol. 53; no. 1; pp. 257 - 266
• Main Authors: Guan, Xin, Zhang, Bo, Fu, Ming, Li, Mengying, Yuan, Xu, Zhu, Yaowu, Peng, Jing, Guo, Huan, Lu, Yanjun
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 2021; Taylor & Francis Group
• Subjects: Adult; Aged; Aged, 80 and over; C-Reactive Protein - metabolism; Cardiovascular Diseases - epidemiology; China - epidemiology; Clinical Decision Rules; Cohort Studies; Comorbidity; COVID-19; COVID-19 - epidemiology; COVID-19 - metabolism; COVID-19 - mortality; COVID-19 - physiopathology; Diabetes Mellitus - epidemiology; extreme gradient boosting; fatal risk; Female; Ferritins - metabolism; Hospitalization; Humans; Hypertension - epidemiology; Immunology; Interleukin-10 - metabolism; L-Lactate Dehydrogenase - metabolism; Machine Learning; Male; Middle Aged; Prognosis; Reproducibility of Results; Retrospective Studies; SARS-CoV-2; Severity of Illness Index; China; COVID-19; extreme gradient boosting; fatal risk; machine learning
• ISSN: 0785-3890; 1365-2060; 1365-2060
• DOI: 10.1080/07853890.2020.1868564

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.