Abstract 15572: Gender, Race, and Age Specific Baseline Predictors of All-cause Mortality in STICHES Trial Identified by Machine Learning

Abstract

Introduction: NHLBI supported STICHES trial (The Surgical Treatment for Ischemic Heart Failure Extended Study) (NCT00023595) was conducted to test whether blood flow restoration by coronary revascularization recovers chronic left ventricular dysfunction and improves survival, as compared to medical therapy alone in patients with congestive heart failure and coronary artery disease amenable to surgical revascularization. We reused publicly available individual patient-level STICHES trial data from NHLBI Data Repository (BioLINCC) to perform hypothesis-generating secondary analyses by machine learning (ML) using random survival forest (RSF) to identify gender, race and ethnicity, and age specific predictors for all-cause mortality (ACM). Methods: The population was sub-grouped by gender (male vs. female), race (white vs. Hispanic/Latinos/non-white), and age (< 55, 55-60, 61-69, and ≥70). RSF was performed on 48 baseline variables from 1212 patients to identify predictors of ACM. Top 10 RSF predictors for each subgroup were included in a multivariate analysis using a Cox proportional hazards model. Results: Top 10 predictors of ACM are shown in Table 1. While known cardiometabolic and vascular predictors were among the top predictors, RSF uniquely identified renal function related biomarkers and plasma sodium among important top predictors across the subgroups. Age was an important predictor for male and female, Hispanics/Latinos/non-whites, and patient groups ≥70 years old. Also, top predictors of ACM were current smoking status among age groups of <55 and 55-60, clinical recruitment site in age group 61-69, and female gender in age group 55-60. Conclusions: Using ML, we uncovered in an unbiased fashion, gender, age, race and ethnicity specific, unanticipated top predictors of ACM in STICHES trial. This highlights the value of ML for analyzing disease and therapeutic intervention outcomes to help implement precision medicine.