Atrial Fibrillation Pathophysiology
Bibliographic record
Abstract
Atrial fibrillation (AF), the most common sustained cardiac arrhythmia, is an important contributor to population morbidity and mortality. An arrhythmia that is particularly common in the elderly, AF is growing in prevalence with the aging of the population. Our understanding of the basic mechanisms that govern AF occurrence and persistence has been increasing rapidly. This article reviews the basic pathophysiology of AF over a broad range of levels, touching on the tissue mechanisms that maintain the arrhythmia, the relationship between clinical presentation and basic mechanisms, ion channel and transporter abnormalities that lead to ectopic impulse formation, basic models and tissue determinants of reentry, ion channel determinants of reentry, the nature and roles of electric and structural remodeling, autonomic neural components, anatomic factors, interactions between atrial and ventricular functional consequences of AF, and the basic determinants of atrial thromboembolism. We then review the potential implications of the basic pathophysiology of the arrhythmia for its management. We first discuss consequences for improved rhythm control pharmacotherapy: targeting underlying conditions, new atrium-selective drug targets, new targets for focal ectopic source suppression, and upstream therapy aiming to prevent remodeling. We then review the implications of basic mechanistic considerations for rate control therapy, AF ablation, and the prevention of thromboembolic events. We conclude with some thoughts about the future of translational research related to AF mechanisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".