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Record W3023569998 · doi:10.1136/heartjnl-2019-ics.2

2 Ablation of scar-related ventricular tachycardia: paced electrogram feature analysis (PEFA) is a novel and effective substrate based strategy

2019· article· en· W3023569998 on OpenAlexaff
Derek Crinion, Mohammad Hassan Shariat, Adrián Baranchuk, Chris Simpson, Dhiraj Gupta, Javad Hashemi, E.E. Gül, Hoshiar Abdollah, Andrés Enríquez, Benedict M. Glover, Damian Redfearn

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrhythmias and Treatments
Canadian institutionsKingston General HospitalQueen's University
Fundersnot available
KeywordsMedicineAblationVentricular tachycardiaCardiologyInternal medicineTachycardiaLatency (audio)Computer science

Abstract

fetched live from OpenAlex

Background Ablation of scar related ventricular tachycardia (VT) has been shown to be superior to escalation of drug therapy. However, the incremental benefit remains modest, with 42% experiencing recurrent shocks and 64% appropriate anti-tachycardia pacing (ATP) in the VANISH study. Furthermore, VT induction to allow activation mapping and entrainment is not tolerated in 70% of cases. Improved substrate based ablation strategies are needed. Paced electrogram feature analysis (PEFA) is a novel and promising technique. This method utilizes close coupled extra-stimuli to reveal latency and increased electrogram (EGM) duration (figure 1) that is evident at critical VT isthmus(es) (figure 2). Purpose To investigate the effectiveness of PEFA based VT ablation. Methods A single centre, prospective study. Consecutive cases of scar related VT that had an implantable cardiac defibrillator (ICD) and no prior ablations were recruited. Close coupled pacing was performed at the right ventricular apex (VERP + 50 ms) and the VT isthmus(es) identified on high density mapping catheters (HD Grid™ or Reflexion™, St Jude) by increased electrogram (EGM) duration and latency (figure 1). An algorithm was developed to identify the latest EGM component after the S2 pacing artefact (St Jude EnSite Precision Electroanatomic Mapping). The amplitude sensitivity was set at 0.05 mV and manual assessment was used to correct the automated annotation when visibly inaccurate. This millisecond value was displayed on the geometry as a colour (PEFA map) (figure 2). PEFA identified VT isthmus sites were targeted for ablation (figure 1). The PEFA map was repeated to ensure comprehensive abolition. VT stim protocol with three extra-stimuli was performed at the end of each case. Follow up ICD interrogation data was utilized to assess for VT recurrence and mortality. Results A total of 23 cases were recruited. table 1 provides an overview of baseline characteristics. table 2 includes procedure details and outcomes. Conclusion PEFA based VT ablation is feasible and effective. A high proportion of cases were non-inducible, with low VT recurrence rates. This is the largest dataset to date on PEFA based VT ablation, the first to include non-ischaemic aetiologies, and reports a longer mean follow up. PEFA appears to be a promising substrate based VT ablation strategy.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.004
GPT teacher head0.239
Teacher spread0.235 · 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
Published2019
Admission routes1
Has abstractyes

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