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

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.

Keywords: COVID-19; extreme gradient boosting; fatal risk; machine learning.

Figure 1.

A decision rule using 6 key clinical features and their thresholds in absolute values among COVID-19 patients admitted in the Sino French New City Branch of Wuhan Tongji Hospital (n = 787). Num: the number of patients in a class; T: the number of correctly classified; F: the number of misclassified patients.

Figure 2.

ROC curves for COVID-19 patients in the training (A) and internal validation (B) sets from Sino French New City Branch of Wuhan Tongji hospital (n = 787) and in the external validation set (C) from Optical Valley Branch of Wuhan Tongji Hospital (n = 286). Since 197 subjects with missing detection for at least one of the 6 features, the remaining 787 cases were randomly split into training (n = 554) and internal validation sets (n = 233) in the ratio 7:3.