The World Heart Federation Roadmap for Nonvalvular Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND: The World Heart Federation has undertaken an initiative to develop a series of Roadmaps to promote development of national policies and health systems approaches, and to identify potential roadblocks on the road to effective prevention, detection, and management of cardiovascular disease in low-and middle-income countries (LMICs) and develop strategies for overcoming these. This Roadmap focuses on atrial fibrillation (AF). AF is the most common, clinically significant arrhythmia and, among other clinical outcomes, is associated with increased risk of stroke. METHODS: Development of this Roadmap included a review of published guidelines and research papers, and consultation with an expert committee comprising experts in clinical management of AF and health systems research in LMICs. The Roadmap identifies 1) key interventions for detection, diagnosis, and management of AF; 2) gaps in implementation of these interventions (knowledge-practice gaps); 3) health system roadblocks to implementation of AF interventions in LMICs; and 4) potential strategies for overcoming these. RESULTS: More research is needed on determinants and primary prevention of AF. Knowledge-practice gaps for detection, diagnosis, and management of AF are present worldwide, but may be more prominent in LMICs. Potential barriers to implementation of AF interventions include long distances to health facilities, shortage of health care professionals with training in AF, including interpretation of ECG, unaffordability of oral anticoagulants for patient households, reluctance on the part of physicians to initiate oral anticoagulant (OAC) therapy, and lack of awareness of the importance of persistent adherence to OAC therapy. Potential solutions include training of nonphysician health workers and pharmacists in pulse-taking, use of telemedicine technologies to transmit electrocardiogram results, engagement of nonphysician health workers in OAC therapy adherence support, and country-specific support and education programs for noncardiologist health care professionals. CONCLUSIONS: AF affects millions of people worldwide and, left untreated, increases the risk and severity of stroke and heart failure. Although guidelines for the detection, diagnosis, and management of AF exist, there are gaps in implementation of these guidelines globally, and in particular in LMICs. This Roadmap identifies some potential solutions that may improve AF outcomes in LMICs but require further evaluation in these settings.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.053 | 0.026 |
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".