Abstract 12202: New York Heart Association Class and the Mortality Benefit From Primary Prevention Implantable Cardioverter Defibrillator Use: A Pooled Analysis of 4 Randomized Control Trials
Bibliographic record
Abstract
Introduction: Primary prevention implantable cardioverter defibrillators (ICDs) reduce all-cause mortality by reducing sudden cardiac death. There are conflicting data regarding whether patients with more advanced heart failure (HF) derive ICD benefit owing to the competing risk of death from pump failure. Methods: We performed a patient level data meta-analysis of 4 primary prevention ICD trials (MADIT-I, MADIT-II, DEFINITE, SCD-HeFT) to determine the effect of ICD use on all-cause mortality by NYHA class. We included ICD and control patients with EF ≤35%, NYHA II or III HF, and myocardial infarction ≥40 days prior to randomization. Bayesian-Weibull survival regression models were employed to assess the impact of the ICD on mortality among the study population and by NYHA class. Results: Of the 2,763 patients who met study criteria, 68% (n=1,867) were NYHA II and 52% (n=1,435) were randomized to an ICD. In a multivariable model including all study patients, the ICD reduced mortality [HR 0.65, 95% posterior credibility interval (PCI) 0.40-0.99]. The interaction between NYHA class and ICD use on mortality was significant (posterior probability of no interaction = 0.036). The unadjusted effect of the ICD on mortality by NYHA class is depicted in Figures A&B. In stratified multivariable models including an interaction term for the NYHA class and ICD therapy, the ICD reduced mortality among NYHA II (HR 0.55, 95% PCI 0.35-0.85) and there was a trend toward reduced mortality in NYHA III patients (HR 0.76, 95% PCI 0.48-1.24). Between trial differences in mortality rates were observed across the NYHA III control patients ( Figure C) suggesting heterogeneous risk profiles. Conclusions: Primary prevention ICDs reduce mortality in NYHA II patients and trend towards reducing mortality in NYHA III patients. An improved understanding of the heterogeneous natural history of NYHA III patients may help improve patient selection for primary prevention ICDs in this important subgroup.
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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 | 0.026 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.033 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".