A method to identify which patients will have VT substrate ablation targets prior to ablation procedure
Bibliographic record
Abstract
Abstract Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): Dr Nanthakumar is a recipient of the Mid-career Investigator Award from the Heart & Stroke Foundation of Ontario Background Recent trials in ventricular tachycardia (VT) ablation suggest that early ablation could reduce VT burden. A method to predict which patients will have substrate ablation targets based on non-invasive testing prior to the procedure does not exist and could be of value in selecting ideal patients for early VT substrate ablation. Decrement-evoked potential (DeEP) mapping is used to identify VT substrate ablation targets.1 A method for predicting which patients would have DeEP targets on a pre-ablation 12-lead ECG has not yet been developed. Objective To develop a metric to predict the presence of physiological substrate VT ablation targets from the 12-lead ECG. Method 22 electrophysiology (EP) lab VT cases that had undergone DeEP mapping were extracted from the CARTO® 3 system and were analysed retrospectively. For each case, DeEP was calculated by subtracting last component of the nearfield evoked response in the pacing train (S1) from the latest component of the nearfield evoked decremented response of the extra stimulus (S2). By inference, 12-lead ECG prediction was calculated by subtracting the width of the 12-lead surface ECG envelope of S1 from the width of the 12-lead ECG envelope of S2. Cases were divided into three categories: - Cases displaying intracardiac DeEP between 0-10ms – no ablation targets - Cases displaying intracardiac DeEP between 10-50ms – DeEP positive - Cases displaying intracardiac DeEP >50ms – prominent DeEP present Results Out of the 22 cases, 8 were characterised as no ablation targets, 12 as DeEP positive, and 2 as prominent DeEP present. The 12-lead surface ECG measurement means were 3.5±4.6ms for the no ablation target group, 23.2±9.1ms for positive DeEP group, and 80.9±8.1ms for prominent DeEP group. The surface ECG decrement was significantly different P<0.0001 between the three groups. Consequently, we propose that ECG QRS widening of envelope width of 20ms or more may indicate the presence of DeEP targets during invasive mapping. Conclusion It may be possible to detect which patients will have substrate ablation targets based on 12-lead ECG, prior to the VT ablation procedure. Additionally, this prediction rule, based on its association with arrhythmogenic potentials, may be able to predict which ambulatory patients who have not had manifest VT, may be prone to develop VT thus help identify early VT ablation candidates. This concept should be validated in larger data sets and is ideal for training and testing using surface ECG envelopes with artificial intelligence algorithms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 0.001 |
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 teacher head, 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".