Cardiac resynchronization therapy in patients with a prior history of atrial fibrillation: Insights from four major clinical trials
Bibliographic record
Abstract
AIMS: To investigate the association of cardiac resynchronization therapy (CRT) on outcomes among participants with and without a history of atrial fibrillation (AF). METHODS: Individual-patient-data from four randomized trials investigating CRT-Defibrillators (COMPANION, MADIT-CRT, REVERSE) or CRT-Pacemakers (COMPANION, MIRACLE) were analyzed. Outcomes were time to a composite of heart failure hospitalization or all-cause mortality or to all-cause mortality alone. The association of CRT on outcomes for patients with and without a history of AF was assessed using a Bayesian-Weibull survival regression model adjusting for baseline characteristics. RESULTS: Of 3964 patients included, 586 (14.8%) had a history of AF; 2245 (66%) were randomized to CRT. Overall, CRT reduced the risk of the primary composite endpoint (hazard ratio [HR]: 0.69, 95% credible interval [CI]: 0.56-0.81). The effect was similar (posterior probability of no interaction = 0.26) in patients with (HR: 0.78, 95% CI: 0.55-1.10) and without a history of AF (HR: 0.67, 95% CI: 0.55-0.80). In these four trials, CRT did not reduce mortality overall (HR: 0.82, 95% CI: 0.66-1.01) without evidence of interaction (posterior probability of no interaction = 0.14) for patients with (HR: 1.09, 95% CI: 0.70-1.74) or without a history of AF (HR: 0.70, 95% CI: 0.60-0.97). CONCLUSION: The association of CRT on the composite endpoint or mortality was not statistically different for patients with or without a history of AF, but this could reflect inadequate power. Our results call for trials to confirm the benefit of CRT recipients with a history of AF.
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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 | 0.035 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".