Jiang and colleagues conducted a retrospective study in cardiac surgery patients using machine learning models. Comparing logistic regression, random forest, and XGBoost, they found XGBoost excelled, with the anion gap as a crucial predictor. The study offers actionable insights for clinicians to adopt data-driven decision-making in patient care.