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Record W4410621871 · doi:10.1093/europace/euaf085.739

New peak frequency maps' usefulness to enhance localization and ablation of premature ventricular contractions

2025· article· en· W4410621871 on OpenAlexaff
Francesco Raffaele Spera, R Falcetti, Antonio Bisignani, M.L. Loricchio, M Volpicelli, Mary Anne Conti, Zefferino Palamà, Giuseppe Tricarico, G Coluccia, P Palmisano, Maria Lucia Narducci, Gemma Pelargonio, Emanuele Barbato

Bibliographic record

VenueEP Europace · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrhythmias and Treatments
Canadian institutionsCentre Casa
Fundersnot available
KeywordsCardiologyAblationInternal medicineMaterials scienceMedicine

Abstract

fetched live from OpenAlex

Abstract Background Premature ventricular contractions (PVCs) are a common type of idiopathic ventricular arrhythmia. Catheter ablation has emerged as a first-line therapy with a high success rate and few complications. Traditional ablation targets the earliest local activation time (LAT) with typical QS signal morphology on unipolar electrogram, but often manual signals reannotation is needed to identify the best target for ablation. Peak frequency (PF) map is a new mapping tool that help to discriminate ‘far-field’ from ‘near-field’ signals thanks to the analysis of the electrograms’ sharpness. Several studies investigated its usefulness for signal detection and for identification of 3-dimensional critical isthmus during ventricular tachycardia ablation. In this regard, there are no studies that evaluated the role of PF mapping in PVC ablation Purpose To evaluate the concordance between earliest LAT and highest PF signals in PVC ablation and assess the potential of PF mapping to improve procedural targeting. Methods Patients who underwent monomorphic PVCs catheter ablation using the Ensite X mapping system at 7 different Italian hospitals between 2023 and 2024 were retrospectively included. Patients were excluded if no ablation was performed or the map had fewer than 50 points in the region of interest. Electroanatomical activation maps were created using a 16-pole grid catheter (Advisor HD Grid) with Omnipolar technology and PF maps were overlapped to local activation maps to emphasize a visual target within the earliest signal area (Figure 1). Acute success was defined as suppression of the targeted PVC at the end of the procedure. ROC analysis curve assessed PF’s predictive value for earliest reannotated LAT (rLAT) point where radiofrequency ablation was deployed. Results Sixty patients (44 men [73%]; mean age 52 ± 11 years) were included. An average of 656±553 electrograms for patient were acquired. The frequency distribution across different PVC origin sites are reported in Figure 2A. The highest frequencies were located close to the tricuspid valve (TV) and aortomitral continuity (AMC) with 420 and 415 Hz respectively, while the lowest were found in PVC originating from left anterior fascicle and Moderator band (MB) with an average of 265 Hz. In 95% of cases (57/60), the earliest rLAT and highest PF signals were concordant. The discordance was found in two cases of RVOT and one PVC located on the left anterior fascicle. As shown in Figure 2B, the ROC curve (AUC = 0.789, p<0.001) demonstrates the predictive value of PF and rLAT concordance. The acute success of ablation was achieved in 57 (95%) procedures. In these unsuccessful 3 cases there was an overlapping between PF and rLAT maps. Conclusion PF mapping offers an effective method to enhance PVC ablation target by identifying high-frequency sites that correlate with successful suppression. Site-specific frequency mapping cutoff should be delineated to enhance procedural success.RVOT-LVOT PVC with PF-LAT maps overlap PF average and ROC curve

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score0.281

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 head0.006
GPT teacher head0.273
Teacher spread0.268 · 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
Published2025
Admission routes1
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