Programmed deep septal pacing for the diagnosis of left bundle branch capture
Bibliographic record
Abstract
Abstract Background During permanent deep septal pacing, it is important to confirm left bundle branch (LBB) capture. Objective The effective refractory period (ERP) of the working myocardium is different than the ERP of the LBB; we hypothesized that it should be possible to differentiate LBB capture from septal myocardial capture using programmed extra-stimulus technique. Methods In consecutive patients undergoing pacemaker implantation who received pacing lead in a deep septal position programmed pacing was delivered from this lead. Responses to programmed pacing were categorized on the basis of QRS morphology of the extrastimuli as: myocardial (broader QRS, often slurred), selective (narrower QRS, preceded by an isoelectric interval) or non-diagnostic (unequivocal change). Results Programmed deep septal pacing was performed 269 times in 143 patients; in every patient with the use of an 8-beat basic drive train of 600 ms and when possible also during supraventricular rhythm. Responses diagnostic for LBB capture were observed in 114 (79.7%) of patients. Selective LBB paced QRS was more often seen when premature beats were introduced during the intrinsic rhythm rather than after the basic drive train. The average septal-myocardial refractory period was significantly shorter than the LBB refractory period: 263.0±34.4 ms vs. 318.0±37.4 ms. Conclusions A novel maneuver for the diagnosis of LBB capture during deep septal pacing, was formulated, assessed and found as diagnostically valuable. This method, based on the differences in refractoriness between LBB and the septal myocardium is unique in enabling the visualization of components of the usually fused, non-selective LBB paced QRS complex. Graphical abstract
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".