Abstract 16998: Gender and Age Specific Baseline Predictors of MACE in PEACE Trial Identified by Machine Learning
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".