Dynamic risk assessment to improve quality of care in patients with atrial fibrillation: the 7th AFNET/EHRA Consensus Conference
Bibliographic record
Abstract
AIMS: The risk of developing atrial fibrillation (AF) and its complications continues to increase, despite good progress in preventing AF-related strokes. METHODS AND RESULTS: This article summarizes the outcomes of the 7th Consensus Conference of the Atrial Fibrillation NETwork (AFNET) and the European Heart Rhythm Association (EHRA) held in Lisbon in March 2019. Sixty-five international AF specialists met to present new data and find consensus on pressing issues in AF prevention, management and future research to improve care for patients with AF and prevent AF-related complications. This article is the main outcome of an interactive, iterative discussion between breakout specialist groups and the meeting plenary. AF patients have dynamic risk profiles requiring repeated assessment and risk-based therapy stratification to optimize quality of care. Interrogation of deeply phenotyped datasets with outcomes will lead to a better understanding of the cardiac and systemic effects of AF, interacting with comorbidities and predisposing factors, enabling stratified therapy. New proposals include an algorithm for the acute management of patients with AF and heart failure, a call for a refined, data-driven assessment of stroke risk, suggestions for anticoagulation use in special populations, and a call for rhythm control therapy selection based on risk of AF recurrence. CONCLUSION: The remaining morbidity and mortality in patients with AF needs better characterization. Likely drivers of the remaining AF-related problems are AF burden, potentially treatable by rhythm control therapy, and concomitant conditions, potentially treatable by treating these conditions. Identifying the drivers of AF-related complications holds promise for stratified therapy.
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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 | 0.213 | 0.129 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.010 | 0.017 |
| Research integrity | 0.014 | 0.025 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".