Electrocardiographic characterization of non-selective His bundle pacing. Validation of novel diagnostic criteria
Bibliographic record
Abstract
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. Our aim was to identify ECG criteria for loss of HB capture during ns-HB pacing. Methods Consecutive patients with permanent HB pacing were recruited. Surface 12-lead ECGs during ns-HB pacing and loss of HB capture (RV-only capture) were obtained. ECG criteria for loss/presence of HB capture were identified. In the validation phase these criteria and the “HB ECG algorithm” were tested by two blinded observers using a separate, sizable set of ECGs. Results 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 in lead V6 were identified as the best features for differentiation. The 2-step HB ECG algorithm based on these features correctly classified 87.1% of cases with sensitivity and specificity of 93.2% and 83.9%, respectively. Moreover, the proposed criteria for definitive diagnosis of ns-HB capture (no QRS slur/notch in leads I, V1, V4-V6 and the R-wave peak time in V6 ≤ 100 ms) presented 100% specificity. Conclusion A novel ECG algorithm for the diagnosis of loss of HB capture and novel criteria for definitive confirmation of HB capture were formulated and validated. Practical application of these criteria during implant and follow-up of patients with HB pacing devices is feasible. Condensed Abstract The 2-step ECG algorithm for loss of His bundle capture based on surface ECG analysis is proposed and validated. This method correctly classified 87.1% of cases with a sensitivity and specificity of 93.2% and 83.9%, respectively. What’s New This is the first study that analyzes QRS characteristics during non-selective His bundle pacing in a sizable cohort of patients. Precise criteria and a novel algorithm for electrocardiographic diagnosis of loss of HB capture during presumed non-selective HB pacing were validated. QRS notch/slur in left ventricular leads was identified as a simple and reproducible feature indicating loss of HB capture or lack/loss of correction of intraventricular conduction disturbances. Assessment of R-wave peak time in lead V6 rather than QRS duration for diagnosis of ns-HB pacing was validated.
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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 | 0.000 | 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".