Development and validation of predictive models for mortality of cases with COVID-19 (Omicron BA.5.2.48 and B.7.14): A retrospective study

Abstract

Background With the emergence of new COVID-19 variants (Omicron BA.5.2.48 and B.7.14), predicting the mortality of infected patients has become increasingly challenging due to the continuous mutation of the virus. Existing models have shown poor performance and limited clinical utility. This study aims to identify the independent risk factors and develop a practical predictive model for mortality among patients infected with new COVID-19 variants.Methods Demographic, clinical, and laboratory data of COVID-19 patients were retrospectively collected at our hospital between December 22, 2022, and February 15, 2023. Logistic regression (LR), decision tree (DT), and Extreme Gradient Boosting (XGBoost) models were developed to predict mortality. Those models were separately visualized via nomogram, decision trees, and Shapley Additive Explanations (SHAP). To evaluate those models, accuracy, sensitivity, specificity, precision, Youden’s index, and area under curve (AUC, 95% CI) were calculated.Results A total of 987 cases with new COVID-19 variants (Omicron BA.5.2.48 and B.7.14) were eventually included, among them, 153 (15.5%) died. Noninvasive ventilation, intubation, myoglobin, INR, age, number of diagnoses, respiratory, pulse, neutrophil, and albumin were the most important predictors of mortality among new COVID-19 variants. The AUC of LR, DT, and XGBoost models were 0.959, 0.878, and 0.961, respectively. The diagnostic accuracy was 0.926 for LR, 0.913 for DT, and 0.977 for XGBoost. XGBoost model had the highest sensitivity (0.983) and specificity (0.940).Conclusion Our study developed and validated three practical models for predicting mortality in patients with new COVID-19 variants. All models performed well, and XGBoost was the best-performing model.