Transforming atrial fibrillation management by targeting comorbidities and reducing atrial fibrillation burden: the 10th AFNET/EHRA consensus conference
Bibliographic record
Abstract
Atrial fibrillation (AF) is a growing unmet medical need. To reduce its impact on patients' lives, improvements in stroke prevention therapy, treatment of concomitant conditions, and rhythm control therapy are actively developed: Innovations in anti-thrombotic agents, new anti-arrhythmic drugs (AADs), and novel interventional rhythm control therapies emerge alongside AF-reducing effects of general cardiometabolic therapies. Simple risk scores are slowly replaced by personalized AF risk estimation using quantifiable features. These developments were discussed by over 80 experts from academia and industry during the 10th Atrial Fibrillation NETwork /European Heart Rhythm Association consensus conference from 5 to 7 May 2025. The emerging consensus, described here, is multi-domain therapy combining stroke prevention, rhythm control, and therapy of concomitant cardiovascular conditions. This combines anti-coagulants, AADs, and AF ablation with old and new cardiometabolic drugs that can reduce AF risk, AF burden, and AF-related complications at scale. The paper furthermore describes quantitative traits that may enable a shift towards risk-driven therapy based on AF phenotypes. These can enable adjusted therapy strategies that are safe, accessible, and patient-centred. Applying modern data science and artificial intelligence methods to quantitative phenotypic and genetic features can further improve risk estimation and personalized therapy selection. At the same time, translational and clinical research into reversing the drivers of AF and into improved stroke prevention through new drugs and through combination therapies is needed. Together, these efforts offer pathways towards personalized, patient-centred, multi-modal, and accessible AF management that integrates rhythm control, stroke prevention, and therapy of concomitant conditions to bridge today's practical needs with tomorrow's therapeutic innovation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".