Table 2_Interpretable admission-stage machine learning for early Sepsis-3 identification in the emergency department.docx

Penalized logistic regression (LR), Explainable Boosting Machine (EBM), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) models were developed using five-fold cross-validation. Performance was evaluated by discrimination, classification, calibration, decision curve analysis, and explainability.</p>Results<p>Among 1,572 adult patients, 560 met the Sepsis-3 criteria. In the internal holdout test set, AUROC values were 0.753 (LR), 0.763 (XGBoost), 0.767 (LightGBM), and 0.766 (EBM).

Conventional biomarkers and SIRS showed lower discrimination (AUROC 0.599–0.693), whereas a biomarker-only logistic regression model achieved an AUROC of 0.710. No significant AUROC differences were observed among the machine learning models. LightGBM achieved the numerically highest AUROC and accuracy, whereas EBM achieved the highest sensitivity (0.845).

Age, procalcitonin, oxygen saturation, respiratory rate, and neutrophil-to-lymphocyte ratio were the most influential predictors.</p>Conclusions<p>Interpretable machine learning models based on routinely available emergency department admission variables provided moderate discrimination, clinically relevant sensitivity, and transparent predictor effects for identifying Sepsis-3-defined sepsis. Compared with conventional biomarkers, SIRS, and a biomarker-only logistic regression model, the machine learning models generally showed higher discrimination, although the magnitude and statistical significance of these differences varied across comparators.

External validation is required before clinical implementation.</p>