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Record W4377984309 · doi:10.1093/europace/euad122.326

A method to identify which patients will have VT substrate ablation targets prior to ablation procedure

2023· article· en· W4377984309 on OpenAlexafffundabout
M Burg, R. David Anderson, Hanney Gonna, Abdullah Al-Shaheen, Ahmed Niri, E Shapira, Amir Ben‐Dor, Gal Hayam, Tomasz Baron, Stéphane Massé, Kumaraswamy Nanthakumar

Bibliographic record

VenueEP Europace · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrhythmias and Treatments
Canadian institutionsToronto General HospitalUniversity Health Network
FundersHeart and Stroke Foundation of Canada
KeywordsAblationMedicineIntracardiac injectionVentricular tachycardiaCatheter ablationCardiologyInternal medicine

Abstract

fetched live from OpenAlex

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.003

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.

Opus teacher head0.018
GPT teacher head0.337
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes3
Has abstractyes

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