BMI-based prediction models for short-term and long-term mortality following coronary artery bypass grafting using...

This study aims to evaluate the relationship between obesity (measured by Body Mass Index (BMI)) and postoperative mortality in patients undergoing coronary artery bypass grafting (CABG) and to use machine learning algorithms to assess key factors in order to explore the 'obesity paradox' phenomenon.

Data were obtained from Medical Information Mart for Intensive Care IV (MIMIC-IV) V.3.0. We included adult patients who underwent CABG, excluding those with an Intensive Care Unit (ICU) stay of <24 hours or missing BMI data. Primary outcomes were 7-day, 14-day,28-day and 365-day all-cause mortality. Patients were categorised by BMI into six groups. Logistic regression, Kaplan-Meier and restricted cubic spline analyses were performed with subgroup analyses. The random forest and Boruta algorithm were used for key factor identification. Multiple machine learning models were built and assessed using area under the curve (AUC) and decision curve analysis.

Among 5790 patients who underwent CABG, being overweight (BMI 25-30) predicted the lowest 365-day mortality (adjusted OR<1). BMI showed a U-shaped association with mortality, with the nadir of risk observed between 25-35 kg/m². The protective effect persisted in patients aged ≥65 years. Key mortality drivers differed by BMI: acute physiology, comorbidity burden, metabolic stability and metabolic liver dysfunction. Extreme gradient boosting achieved the highest 365-day mortality prediction (AUC=0.70) with favourable clinical utility.

The obesity paradox is observed among patients following CABG, with distinct BMI-specific predictive factors of mortality risk identified across BMI categories. Consequently, risk-stratified monitoring strategies-tailored to BMI-defined subgroups-are warranted.