Inverse Solution Mapping of Epicardial Potentials
Bibliographic record
Abstract
BACKGROUND: Catheter ablation of ventricular tachycardia (VT) is still one of the most challenging procedures in cardiac electrophysiology, limited, in part, by unmappable arrhythmias that are nonsustained or poorly tolerated. Calculation of the inverse solution from body surface potential mapping (sometimes known as ECG imaging) has shown tremendous promise and can rapidly map these arrhythmias, but we lack quantitative assessment of its accuracy in humans. We compared inverse solution mapping with computed tomography-registered electroanatomic epicardial contact catheter mapping to study the resolution of this technique, the influence of myocardial scar, and the ability to map VT. METHODS AND RESULTS: For 4 patients undergoing epicardial catheter mapping and ablation of VT, 120-lead body surface potential mappings were obtained during implantable defibrillator pacing, catheter pacing from 79 epicardial sites, and induced VT. Inverse epicardial electrograms computed using individualized torso/epicardial surface geometries extracted from computed tomography images were compared with registered electroanatomic contact maps. The distance between estimated and actual epicardial pacing sites was 13 ± 9 mm over normal myocardium with no stimulus-QRS delay but increased significantly over scar (P=0.013) or was close to scar (P=0.014). Contact maps during right ventricular pacing correlated closely to inverse solution isochrones. Maps of inverse epicardial potentials during 6 different induced VTs indicated areas of earliest activation, which correlated closely with clinically identified VT exit sites for 2 epicardial VTs. CONCLUSIONS: Inverse solution maps can identify sites of epicardial pacing with good accuracy, which diminishes over myocardial scar or over slowly conducting tissue. This approach can also identify epicardial VT exit sites and ventricular activation sequences.
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".