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.
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.
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.
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