Why translation from basic discoveries to clinical applications is so difficult for atrial fibrillation and possible approaches to improving it
Bibliographic record
Abstract
Atrial fibrillation (AF) is the most common sustained clinical arrhythmia, with a lifetime incidence of up to 37%, and is a major contributor to population morbidity and mortality. Important components of AF management include control of cardiac rhythm, rate, and thromboembolic risk. In this narrative review article, we focus on rhythm-control therapy. The available therapies for cardiac rhythm control include antiarrhythmic drugs and catheter-based ablation procedures; both of these are presently neither optimally effective nor safe. In order to develop improved treatment options, it is necessary to use preclinical models, both to identify novel mechanism-based therapeutic targets and to test the effects of putative therapies before initiating clinical trials. Extensive research over the past 30 years has provided many insights into AF mechanisms that can be used to design new rhythm-maintenance approaches. However, it has proven very difficult to translate these mechanistic discoveries into clinically applicable safe and effective new therapies. The aim of this article is to explore the challenges that underlie this phenomenon. We begin by considering the basic problem of AF, including its clinical importance, the current therapeutic landscape, the drug development pipeline, and the notion of upstream therapy. We then discuss the currently available preclinical models of AF and their limitations, and move on to regulatory hurdles and considerations and then review industry concerns and strategies. Finally, we evaluate potential paths forward, attempting to derive insights from the developmental history of currently used approaches and suggesting possible paths for the future. While the introduction of successful conceptually innovative new treatments for AF control is proving extremely difficult, one significant breakthrough is likely to revolutionize both AF management and the therapeutic development landscape.
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".