Electrographic Flow Mapping Provides Prognosis for AF Ablation Outcomes Across Two Independent Prospective Patient Cohorts
Bibliographic record
Abstract
Background/Objectives: Electrographic flow (EGF) mapping allows for the visualization and quantification of atrial fibrillation (AF) wavefront propagation patterns. EGF-identified sources were shown in the randomized controlled FLOW-AF trial to significantly increase the likelihood of AF recurrence within 1 year if left unablated. Electrographic flow consistency (EGFC) additionally measures the stability of observed wavefront patterns, such that patients with more organization have a healthier substrate and lower recurrence. Source presence and EGFC can be used collectively to assign mechanistic phenotypes to AF patients. Methods: The patient phenotypes, treatment modalities, and outcomes in FLOW-AF were compared with those of patients in the ensuing AF-FLOW Global Registry, which was conducted by separate physicians at discrete clinical centers. Results: Patients with low EGFC (≤0.62) had a 12-month freedom from AF (FFAF) of 46%, while those with a high mean EGFC (>0.62) had a FFAF of 81%. Right atrial EGFC was correlated with left atrial EGFC, and the highest recurrence occurred in those with biatrial low EGFC. Source presence also affected the recurrence rates in both trials, such that the presence of EGF-identified sources in PVI-only patients lowered the FFAF from 65% to 36%, but the elimination of sources produced a 30% absolute increase in FFAF from 36% to 66%. Conclusions: Patient outcomes by EGF-based AF phenotype were consistent across two cohorts of patients from separate clinical trials at distinct centers. Patients with a high EGFC and no sources post-procedure had the best outcomes. EGF mapping provides insights into underlying disease pathophysiology and may be employed prospectively to predict recurrence.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".