Research Priorities in the Secondary Prevention of Atrial Fibrillation: A National Heart, Lung, and Blood Institute Virtual Workshop Report
Bibliographic record
Abstract
There has been sustained focus on the secondary prevention of coronary heart disease and heart failure; yet, apart from stroke prevention, the evidence base for the secondary prevention of atrial fibrillation (AF) recurrence, AF progression, and AF-related complications is modest. Although there are multiple observational studies, there are few large, robust, randomized trials providing definitive effective approaches for the secondary prevention of AF. Given the increasing incidence and prevalence of AF nationally and internationally, the AF field needs transformative research and a commitment to evidenced-based secondary prevention strategies. We report on a National Heart, Lung, and Blood Institute virtual workshop directed at identifying knowledge gaps and research opportunities in the secondary prevention of AF. Once AF has been detected, lifestyle changes and novel models of care delivery may contribute to the prevention of AF recurrence, AF progression, and AF-related complications. Although benefits seen in small subgroups, cohort studies, and selected randomized trials are impressive, the widespread effectiveness of AF secondary prevention strategies remains unknown, calling for development of scalable interventions suitable for diverse populations and for identification of subpopulations who may particularly benefit from intensive management. We identified critical research questions for 6 topics relevant to the secondary prevention of AF: (1) weight loss; (2) alcohol intake, smoking cessation, and diet; (3) cardiac rehabilitation; (4) approaches to sleep disorders; (5) integrated, team-based care; and (6) nonanticoagulant pharmacotherapy. Our goal is to stimulate innovative research that will accelerate the generation of the evidence to effectively pursue the secondary prevention 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 | 0.041 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".