Training and Validation Cohorts
Patients were randomly split into training (80%) and validation (20%) cohorts, ensuring equal distribution of the primary outcome in each cohort. This method of stratifying by primary outcome before randomly assigning patients to a cohort ensures an equal distribution of mortality between training and validation datasets to avoid biasing final model performance. The training cohort was used for feature selection, dimensionality reduction, and machine learning model development, keeping the validation cohort entirely separate and unseen until assessment of the final model performance.