Abstract 14246: Automated Detection of Arrhythmogenic VT Substrate: Performance of a 2019 HRS VT Consensus Document Recommended Strategy
Bibliographic record
Abstract
Background: During ventricular tachycardia (VT) ablation, clinical VT is often non-inducible or hemodynamically unstable. Hence, a substrate-based approach is often necessary. Although many strategies have been proposed, none have been automated nor incorporated into current day electro-anatomical mapping (EAM) systems. One strategy, recommended in 2019 HRS guideline, is Decrement Evoked Potential (DeEP) mapping for identification of critical VT substrate. We have developed an automated method to identify critical DeEP VT substrate in an EAM system (CARTO ® 3). Objective: The performance of this novel automated algorithm was retrospectively evaluated in patients who underwent substrate-based VT ablation. Methods: Entire VT ablation using DeEP mapping was performed and recorded in 12 consecutive patients. CARTO ® 3 case data was downloaded and analyzed by a novel automated algorithm for DeEP detection. A blinded electrophysiologist verified the automated electrogram DeEP annotations and assessed its performance in a dichotomous fashion. The effect of bipolar voltage threshold (0.01, 0.02, 0.05, 0.075, 0.1, 0.20, 0.25 and 0.30 mV) on algorithm performance was evaluated. Sensitivity, specificity and ROC curve of the algorithms were calculated. Finally, results from the analysis were merged with voltage map to produce DeEP maps. Results: Results from the ROC curve shows that a bipolar threshold of 0.075mV optimizes specificity and sensitivity. At that threshold the algorithm found 848 true positive and 626 true negatives out of a total of 1975 EGMS being analyzed. Sensitivity and specificity were found to be 65.8% and 82.3% respectively. Conclusions: We have developed an automatic detection algorithm that identifies and locates critical VT substrates. The performance characteristics have been evaluated for a multimodal VT substrate map to provide clinicians mechanistic substrate map in CARTO 3 ® for VT ablation.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".