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Record W4378233344 · doi:10.1093/europace/euad122.327

Determining physiological VT ablation targets in the era of early VT ablation

2023· article· en· W4378233344 on OpenAlexaffabout
M Burg, R. David Anderson, Hanney Gonna, Abdullah Al-Shaheen, E Shapira, Amir Ben‐Dor, Gal Hayam, Tomasz Baron, Stéphane Massé, Kumaraswamy Nanthakumar

Bibliographic record

VenueEP Europace · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsToronto General HospitalUniversity Health Network
Fundersnot available
KeywordsMedicineAblationCardiologyVentricular tachycardiaStimulus (psychology)Internal medicineAnesthesia

Abstract

fetched live from OpenAlex

Abstract Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): Professor Nanthakumar is a recipient of the Mid-career Investigator Award from the Heart & Stroke Foundation of Ontario. Background Ablation targets in the era of early ventricular tachycardia (VT) ablation will likely be driven by substrate-based physiological target approaches rather than activation mapping. Decrement evoked potentials (DeEP) have been co-localized to the diastolic VT circuit and are used as a method to perform VT ablation when VT cannot or should not be induced.1 Traditionally these potentials are evoked by a single extrastimulus. The incremental utility of a second extrastimulus in detecting these potentials has not been evaluated. Purpose We studied the utility of the second extra stimulus (S3) in detecting substrate ablation over the first extra stimulus (S2) following a drivetrain (S1) of 8 beats at 600ms. Methods DeEP was measured as the time difference between the pacing stimulus and the nearfield local activation delay on the bipolar electrogram during the S1 drivetrain and after the S2 or S3 respectively. Decrement of ≥10ms was considered a positive DeEP site. A total of 157 sites from 6 patients with VT were analysed. The incremental value of S3-induced DeEP was compared to the S2-induced DeEP. Results The mean decrement was significantly greater on the S3 compared to the S2 (41.8±35.6ms for S3 vs 25.8±20.4ms for S2, P <0.0001). The mean incremental value of the S3 was 15.9ms (95% CI, 11.5-20.3ms). From the 157 intracardiac electrograms studied, 79.1% had a positive DeEP with S2 alone. At the same electrode locations, an additional S3 evoked DeEP in 82.2% of sites. Conclusion When physiological substrate is present on the first extra stimulus, the second extra stimulus magnifies the identification of the target. However, we found in our study that the second extrastimulus uncovered a hidden arrhythmogenic substrate in only 3.1% additional mapped sites. Consequently, the utility of the second extrastimulus in identification of hidden substrate needs to be balanced with the risk of induction of potentially unstable rhythms.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.955
Threshold uncertainty score0.246

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.284
Teacher spread0.261 · 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 teacher head, 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 routes2
Has abstractyes

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