Evaluation of qt prolongation in patients following conduction system pacing implantation
Bibliographic record
Abstract
Abstract Background Conduction system pacing is becoming increasingly popular as a pacing technique; however, its impact on repolarization, particularly on QT interval prolongation, remains unclear. Purpose This study aimed to evaluate the effect of conduction system pacing on the QT interval. Methods We conducted a retrospective review of all patients who received a left bundle branch lead at two centers between January 1, 2022, and June 30, 2024. Patients with left bundle pacing were included if they: identified as having deep septal or left bundle pacing (satisfying 2 of 3 markers—LVAT <80 ms, R’ in V1, PR1R6 >30 ms) and had a prolonged corrected QT interval (QTc >550 ms). Demographic and clinical data were collected through electronic medical records. Results From 597 patients who received a 3830 lead, 14 patients with left bundle branch pacing were identified as having QTc >550 ms (mean age 82 ± 6 years, 5 females). Nine patients received their device for complete heart block, 3 for tachy-brady and one after AV [MG1] node ablation. The average ejection fraction was 47 ± 13%. Post-implant ECG analysis showed an average QRS width of 127 ± 14 ms, an average QTc interval of 584 ± 28 ms, and an average heart rate of 74 ± 14 bpm. Notably, one patient experienced Torsades de Pointes three hours post-AV node ablation, with a QT interval of 600 ms that persisted for two weeks post-implant. All patients survived, with a mean follow-up period of 609 ± 470 days. Conclusion QT prolongation can occur following the placement of a conduction system pacing lead, which may elevate the risk of Torsades de Pointes post-implantation. In cases of prolonged QT, increasing heart rate may help to reduce the QT interval, though it may not fully normalize it. QT prolongation may persist for more than two weeks post-implant. Clinicians should be mindful of the potential for QT prolongation with conduction system pacing and consider closer follow-up until QT normalizes.
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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 | 0.001 | 0.000 |
| 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 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".