Correlation of cavotricuspid isthmus dynamics with clinical parameters: insights from interventional cardiac magnetic resonance imaging
Bibliographic record
Abstract
Abstract Aims Ablation of typical atrial flutter (AFL) within an interventional cardiac magnetic resonance (iCMR) is a novel treatment modality. This study aims to describe the segmental kinetics of the cavotricuspid isthmus (CTI) during iCMR-guided ablation for AFL, to evaluate the impact of CTI dynamics on procedural time, to assess the utility of machine learning (ML) for clustering patient profiles based on CTI kinetics, and to identify clinical factors influencing CTI kinetics. Methods and results A cohort of 32 patients underwent first-time iCMR-guided CTI ablation while in sinus rhythm, of whom 15 (47%) underwent a successful electrical cardioversion (EC) within 12 h before the procedure. CTI delineation and measurements were retrospectively performed using TOMTEC-ARENA™ software. Normalized elongation (NE) was defined as the ratio between CTI elongation and CTI length during right atrial systole. Unsupervised ML (K-means clustering) was used for patients’ classification. Segmental analysis revealed greater displacements for CTI segments near the tricuspid valve compared with those near the Eustachian valve. K-means clustering identified three patient groups: low, intermediate, and high NE. Prior EC was significantly associated with low NE (P < 0.05), suggesting myocardial stunning. Hypokinetic CTIs were more prevalent among patients with dyslipidaemia, smoking history, and elevated BMI. Conclusion This study provides the first detailed description of segmental CTI dynamics during iCMR-guided AFL ablation. NE emerged as a valuable metric for characterizing CTI kinetics. A clinical profile including a history of EC, smoking status, elevated BMI, and dyslipidaemia, was linked to reduced CTI kinetics suggestive of right atrial cardiomyopathy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".