AF‐FLOW Global Registry Confirms Validity of Electrographic Flow Mapping as a Phenotyping Tool for Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND: Electrographic flow (EGF) mapping allows for the visualization of global atrial wavefront propagations. One mechanism of initiation and maintenance of atrial fibrillation (AF) is stimulation from EGF-identified focal sources that serve as driver sites of fibrillatory conduction. Electrographic flow consistency (EGFC) further quantifies the concordance of observed wavefront patterns, indicating that a healthier substrate shows more organized wavefront propagation and higher EGFC. Freedom from AF (FFAF) recurrence has accordingly been shown to be higher in patients with ablated vs. unablated sources and with high vs. low EGFC. OBJECTIVES: (1) Measure FFAF across EGF-derived phenotypes in patients enrolled in the AF-FLOW Global Registry; (2) determine if a relationship exists between EGFC and percentage of healthy voltage as measured from bipolar voltage maps. METHODS: The AF-FLOW Global Registry is a multicenter, prospective study of 25 all-comer AF patients who underwent concomitant high-density bipolar voltage mapping with a 16-electrode grid mapping catheter and EGF mapping with a 64-pole basket catheter. The EGF algorithm detects extra-pulmonary vein sources as origins of excitation from a singularity of divergent flow vectors and was used to localize RF ablation targets. Overall, EGFC per atrium was also computed as the average of the modulus of individual EGF vectors, where the vector length represents the consistency of flow patterns. Patients were then assigned phenotypes on the basis of source presence or absence and EGFC, and rates of FFAF at 1-year were compared across the four resulting phenotypes. Atrial EGFC was also compared to the percentage of healthy tissue determined by bipolar voltage mapping. RESULTS: Patients with paroxysmal AF had higher FFAF than persistent AF (PeAF) and long-standing PeAF patients; patients receiving de novo ablation had higher FFAF than those receiving redo ablation. Patient phenotyping revealed that those with high EGFC had higher FFAF than those with low EGFC (p = 0.015). Atrial EGFC was also correlated to the percent of high voltage tissue across all patients (r = 0.651, p < 0.0001). CONCLUSIONS: EGF mapping provides insights into the mechanistic nature of AF and the atrial health of the underlying substrate. Therefore, further studies are needed to develop phenotype-specific treatments for the disease. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT05481359.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".