Abstract 15971: Gender, Race, and Age Specific Baseline Predictors of All-Cause Mortality in BARI2D Trial Identified by Machine Learning

Introduction: NHLBI supported Bypass Angioplasty Revascularization Investigation in Type 2 Diabetes trial (BARI2D) (NCT00006305) evaluated patients with type 2 diabetes and coronary artery disease. Primary trial analysis found no significant differences in rates of all-cause mortality (ACM) among patients who underwent 1) prompt revascularization with medical therapy versus aggressive medical therapy alone and 2) insulin-sensitization medical strategies versus insulin-provision. We reused publicly available individual patient-level data from NHLBI Data Repository (BioLINCC) to perform hypothesis-generating secondary analysis by machine learning (ML), using random survival forest (RSF) to identify gender, race, and age specific baseline predictors for ACM. Methods: The total 2368 trial participants was separated into several subgroups based on gender (female and male), age (40-49, 50-59, 60-69, 70-80), and race (Non-Hispanic White, Hispanic White, Non-Hispanic Non-White, and Hispanic Non-White). RSF was performed on 84 baseline variables to identify predictors of the primary outcome, ACM. The top 10 predictors for each subgroup were tested in a Cox proportional hazards model Results: Top 10 predictors of ACM are shown in Table 1. Although anticipated cardiovascular (CV) and diabetic predictors appeared among the top predictors, at the same time, renal function biomarkers like serum creatinine, urine albumin/creatinine ratio, and serum potassium uniquely showed among the top 5 predictors across the gender, age, and race specific subgroups. Conclusions: Using ML, we uncovered in an unbiased fashion, gender, race and age groups specific unanticipated top baseline predictors of ACM in BARI2D trial. This highlights the value of gender, race and age groups specific predictors of outcomes for determining the efficacy of therapeutic interventions and help advance precision medicine.