Outcomes of Cardiac Resynchronization Therapy by New York Heart Association Class: A Patient-Level Meta-Analysis
Bibliographic record
Abstract
Data on the benefits of cardiac resynchronization therapy (CRT) in patients with severe heart failure (HF) symptoms are limited. We investigated the relative effects of CRT in patients with ambulatory NYHA IV vs. III functional class at the time of device implantation. In this meta-analysis, we pooled patient-level data from the MIRACLE, MIRACLE-ICD, and COMPANION trials. Outcomes evaluated were time to the composite endpoint of first HF hospitalization (HFH) or all-cause mortality and time to all-cause mortality alone. The association between CRT and outcomes was evaluated using a Bayesian Hierarchical Weibull survival regression model. We assessed if this association differs between NYHA III and IV groups by adding an interaction term between CRT and NYHA class as a random effect. A sensitivity analysis was performed by including data from the RAFT trial. Our pooled analysis included 2309 patients. Overall, CRT was associated with a longer time to HFH or all-cause mortality (adjusted hazard ratio [aHR] 0.79, 95%CI 0.64 - 0.99, p = 0.044), with a similar association with time to all-cause mortality (aHR 0.78, 95% CI 0.59 - 1.03, p = 0.083). Associations of CRT with outcomes were not significantly different for those in NYHA III and IV classes (ratio of aHR 0.72, 95% CI 0.30 - 1.27, p = 0.23 for HFH/mortality; ratio of aHR 0.70, 95% CI 0.35 - 1.34, p = 0.27 for all-cause mortality alone). The sensitivity analysis, including RAFT data, did not show a significant relative CRT benefit between NYHA III and IV classes. Overall, there was no significant difference in the association of CRT with either outcome for patients in NYHA functional class III compared with functional class IV.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.057 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".