Near-field substrate analysis in ventricular tachycardia: a novel approach to identify the critical isthmus
Bibliographic record
Abstract
Abstract Background Mapping and ablation of ventricular tachycardia (VT) integrate data from multiple procedural steps, including substrate and activation mapping. Purpose We applied the Omnipolar Technology Near Field (OTNF) algorithm with the goal of integrating it with traditional substrate metrics, areas of vector field disarray (VFD), and location of VT critical isthmus sites. Methods This single-center retrospective study analyzed 34 patients using the EnSite X System with the Advisor HD Grid catheter for mapping and FlexAbility, TactiCath, or TactiFlex ablation catheters. The analysis integrated the OTNF algorithm with traditional substrate metrics and VFD. Offline analysis was performed through comparison of Emphasis maps. Results The cohort (median age 70.5 years, 52.9% ischaemic) predominantly presented with electrical storm or sustained VT (70.6%). Median left ventricular ejection fraction (EF) was 31% (IQR 24-50), with 22 patients (63%) exhibiting severely reduced EF. Twenty-eight (80%) patients were ICD carriers. Median PF during VT was 435 (IQR 358-510) Hz. In 13 patients (2 female, 15.4%), complete high-density bipolar and omnipolar maps during sinus rhythm, activation maps during VT, and post-radiofrequency maps were available. The median low-voltage area in these patients was 15.6 (IQR 7.40-30.2) cm2. After offline analysis of 39 maps using the "Emphasis" tool (Figure 2), the VT diastolic isthmus was found to match areas of PF > 250 Hz in all patients, whereas it matched areas of increased VFD in 12 of the 13 subjects. Median PF during VT was 435 (IQR 358-510) Hz. VT isthmus sites demonstrated higher baseline PF than non-isthmus locations, with a predictive threshold of 315 Hz (AUC=0.781, 78.1% accuracy). Post-ablation tissue showed significantly reduced PF compared to baseline and VT measurements (123 ± 42 Hz, 372 ± 96 Hz, and 450 ± 99 Hz, respectively). Conclusion OTNF and VFD may enhance the identification of critical VT substrates by detecting wavefront collision zones in low-voltage areas, potentially predicting isthmus locations, and complementing conventional mapping strategies to improve ablation outcomes.Diagnostic performance of OTNF analysis
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 source (direct Gemma or distilled Codex), 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".