Hierarchical physiologic pacing to enhance clinical outcomes in conduction system pacing
Bibliographic record
Abstract
BACKGROUND: Left bundle branch area pacing (LBBAP) includes left bundle branch pacing (LBBP) (truncal or fascicular) and left ventricular septal pacing (LVSP). Studies show that LBBP provides better outcomes than LVSP in heart failure. OBJECTIVES: We classified the lead placement with computed tomographic angiography (CTA) based method (CARA-Metis; CARA Medical Ltd) into LBBP or LVSP and assessed the effect of pacing lead location relative to the conduction tree hierarchy and the LV endocardium on clinical outcomes in patients with successful LBBAP. METHODS: Two-center, nonrandomized, observational study that included patients with LBBAP undergoing CTA. The CTA parameters analyzed (1) the distance between the lead tip and the anterior position of the floor of the membranous septum (LH) and (2) the distance between the lead tip and LV endocardium (LE). RESULTS: Among 264 patients analyzed, 172 (65%) with interpretable CTAs were included. Pacing lead tip was categorized as LBBP in 153 (89%) and LVSP in 19 patients (11%) (median values were 27.7 mm [interquartile range, 9.21] for LH and 0.80 mm [interquartile range, 2.59] for LE). Shorter LH was associated with greater improvement in left ventricular ejection fraction (LVEF) [each 1 mm shorter LH increased LVEF by 0.25% when adjusted for baseline LVEF (P = 0.00637)]. Closer to the LV endocardium (larger LE), increased LVEF response by 0.63% for each additional 1 mm (P = 0.0133) in the entire cohort. Patients with depressed LVEF accrued significantly larger improvement in LVEF (P < 0.0001 for both). CONCLUSION: Hierarchical physiologic pacing with improved LVEF response is achieved with a closer position of the pacing lead to the His bundle and the LV endocardium.
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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 | 0.004 |
| 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 | 0.000 |
| 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".