Abstract 16998: Gender and Age Specific Baseline Predictors of MACE in PEACE Trial Identified by Machine Learning

Introduction: The NHLBI supported Prevention of Events with Angiotensin-Converting Enzyme (ACE) Therapy trial (PEACE) (NCT00000558) found that the addition of ACE inhibitor trandolapril to conventional therapy in 8290 patients with stable coronary artery disease and preserved ejection fraction provided no benefit against MACE (cardiovascular death, nonfatal myocardial infarction, or the need for coronary revascularization), the composite primary endpoint. We reused publicly available individual patient-level PEACE data from NHLBI Data Repository (BioLINCC) to perform hypothesis-generating secondary analyses by machine learning (ML) using random survival forest (RSF) to identify gender and age group specific predictors for MACE. Methods: RSF was performed on 50 baseline variables for the MACE outcome in male and female and in age group (<60, 60-69, >69) cohorts. The top ten predictors identified in each cohort were included in a multivariate analysis using a Cox proportional hazards model with a multiple regression approach. Results: The top 10 predictors for the MACE selected by RSF are shown in Figure 1. Expected cardiovascular (CV) risk predictors like blood pressure, Canadian CV Society angina classification (CCS), age, and a history of various CV procedures consistently emerge amongst the top ten predictors of the primary MACE outcome across all gender and age specific subgroups. Interestingly, RSF also identified renal function biomarkers like serum potassium and glomerular filtration rate as common top ten predictors. Conclusion: Using ML, we uncovered in an unbiased fashion, gender and age groups specific unanticipated top predictors for MACE in PEACE trial. This underscores the value of gender and age specific predictors to examine the efficacy and outcomes of therapeutic interventions in advancing precision and personalized medicine.