Treatment Failure With Rhythm and Rate Control Strategies in Patients With Atrial Fibrillation and Congestive Heart Failure: An AF‐CHF Substudy
Bibliographic record
Abstract
INTRODUCTION: Rate and rhythm control strategies for atrial fibrillation (AF) are not always effective or well tolerated in patients with congestive heart failure (CHF). We assessed reasons for treatment failure, associated characteristics, and effects on survival. METHODS AND RESULTS: A total of 1,376 patients enrolled in the AF-CHF trial were followed for 37 ± 19 months, 206 (15.0%) of whom failed initial therapy leading to crossover. Rhythm control was abandoned more frequently than rate control (21.0% vs. 9.1%, P < 0.0001). Crossovers from rhythm to rate control were driven by inefficacy, whereas worsening heart failure was the most common reason to crossover from rate to rhythm control. In multivariate analyses, failure of rhythm control was associated with female sex, higher serum creatinine, functional class III or IV symptoms, lack of digoxin, and oral anticoagulation. Factors independently associated with failure of rate control were paroxysmal (vs. persistent) AF, statin therapy, and presence of an implantable cardioverter-defibrillator. Crossovers were not associated with cardiovascular mortality (hazard ratio [HR] 1.11 from rhythm to rate control; 95% confidence interval [95% CI, 0.73-1.73]; P = 0.6069; HR 1.29 from rate to rhythm control; 95% CI, 0.73-2.25; P = 0.3793) or all-cause mortality (HR 1.16 from rhythm to rate control, 95% CI [0.79-1.72], P = 0.4444; HR 1.15 from rate to rhythm control, 95% [0.69, 1.91], P = 0.5873). CONCLUSIONS: Rhythm control is abandoned more frequently than rate control in patients with AF and CHF. The most common reasons for treatment failure are inefficacy for rhythm control and worsening heart failure for rate control. Changing strategies does not impact survival.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".