Electrocardiographic characterization of non-selective His-bundle pacing: validation of novel diagnostic criteria
Bibliographic record
Abstract
AIMS: Permanent His-bundle (HB) pacing is usually accompanied by simultaneous capture of the adjacent right ventricular (RV) myocardium-this is described as a non-selective (ns)-HB pacing. It is of clinical importance to confirm HB capture using standard electrocardiogram (ECG). Our aim was to identify ECG criteria for loss of HB capture during ns-HB pacing. METHODS AND RESULTS: Patients with permanent HB pacing were recruited. Electrocardiograms during ns-HB pacing and loss of HB capture (RV-only capture) were obtained. Electrocardiogram criteria for loss/presence of HB capture were identified. In the validation phase, these criteria and the 'HB ECG algorithm' were tested using a separate, sizable set of ECGs. A total of 353 ECG (226 ns-HB and 128 RV-only) were obtained from 226 patients with permanent HB pacing devices. QRS notch/slur in left ventricular leads and R-wave peak time (RWPT) in lead V6 were identified as the best features for differentiation. The 'HB ECG algorithm' based on these features correctly classified 87.1% of cases with sensitivity and specificity of 93.2% and 83.9%, respectively. The criteria for definitive diagnosis of ns-HB capture (no QRS slur/notch in Leads I, V1, V4-V6, and the V6 RWPT ≤ 100 ms) presented 100% specificity. CONCLUSION: A novel ECG algorithm for the diagnosis of loss of HB capture and criteria for definitive confirmation of HB capture were formulated and validated. The algorithm might be useful during follow-up and the criteria for definitive confirmation of ns-HB capture offer a simple and reliable ancillary procedural endpoint during HB device implantation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".