Transmural temporospatial left ventricular activation during pacing from different sites: potential implications for optimal pacing
Bibliographic record
Abstract
AIMS: Previous studies showed that right ventricular (RV) endocardial pacing can be deleterious even in individuals with initially normal left ventricular (LV) function. The mechanism(s) by which RV endocardial pacing may cause LV dysfunction is unknown. This study compares the temporospatial LV transmyocardial activation profiles during sinus rhythm with normal His/Purkinje conduction vs. currently utilized and proposed cardiac pacing sites. METHODS AND RESULTS: Mongrel dogs were instrumented with transmural electrodes that tracked transmyocardial activation sequences at five sites in the LV. Pacing/recording catheters were positioned in the RV apex and on the RV and LV sides of the ventricular septum. An epicardial pacing electrode was also sewn to the mid-lateral LV epicardium. Electrograms were recorded during sinus rhythm and pacing from the RV endocardium, LV septum, LV epicardium and during biventricular pacing. Compared to normal sinus/His/Purkinje rhythm (NSR), RV endocardial pacing significantly (P < 0.05) prolonged transmural activation (NSR endocardium 6.1 +/- 1 ms vs. RV endocardium 23.0 +/- 2.6 ms). The highly ordered temporospatial pattern of transmural activation during sinus rhythm was replaced with dispersion and intermingling of endo-, mid-, and epicardial activation. LV epicardial and biventricular pacing did not correct these abnormalities. Only LV septal pacing achieved the transmural and transseptal activation sequences similar to sinus rhythm. CONCLUSION: Clinically utilized pacing modalities, including biventricular pacing, cause abnormal transmyocardial activation. LV septal pacing results in transmyocardial activation patterns that closely resemble those seen in sinus rhythm.
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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 | 0.001 |
| 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) | 0.000 | 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 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".