Uncertainty‑aware sepsis survival prediction using conformal XGBoost on minimal clinical features under Sepsis‑3 criteria.

BACKGROUND AND

OBJECTIVE

Sepsis remains a major cause of in-hospital mortality, but many prognostic models require extensive clinical data. This study evaluated whether a minimal-feature XGBoost model combined with inductive conformal prediction could support sepsis survival prediction and uncertainty quantification under Sepsis-3 criteria.

METHODS

We performed a retrospective secondary analysis of 19,051 Norwegian Sepsis-3 admissions and externally validated the model in 137 South Korean cases. Predictors were age, sex, and septic episode number. The Norwegian cohort was split into proper-training, calibration, and internal test sets. Class imbalance in the proper-training split was handled using Random Over-Sampling Examples (ROSE), and XGBoost hyperparameters were tuned by grid search with 5-fold stratified cross-validation. Model evaluation included confusion matrices, ROC AUC, PR AUC, calibration analysis, and inductive conformal prediction at 80 %, 90 %, and 95 % nominal coverage. Ninety-five percent confidence intervals were estimated by stratified bootstrap resampling.

RESULTS

All required variables were complete in both cohorts. At the default threshold, the model classified all cases as survival in both cohorts, yielding accuracies of 0.811 and 0.825 but specificity, Cohen's kappa, and Matthews correlation coefficient of 0.000. ROC AUC was 0.578 (95 % CI 0.556-0.601) internally and 0.570 (95 % CI 0.442-0.690) externally. At 90 % nominal coverage, conformal prediction achieved empirical coverages of 0.901 (95 % CI 0.894-0.908) internally and 0.876 (95 % CI 0.847-0.912) externally, with mean prediction-set sizes of 1.411 and 1.146.

CONCLUSIONS

The conformal wrapper maintained near-target empirical coverage, but the underlying three-variable classifier showed limited discrimination. Additional predictors and broader validation are needed before clinical use.