The potential role of vectorcardiographic QRS-area in determining capture type in left bundle branch area pacing
Bibliographic record
Abstract
Abstract Background Confirming conduction system capture during left bundle branch area pacing (LBBAP) primarily relies on ECG criteria obtained at the time of implantation. Post-implant ECG interpretation, however, is often subjective to clinical variability. In this context, objective and more quantitative parameters could aid implanting cardiologists in identifying LBBAP capture. Purpose To investigate the potential role of vectorcardiographic QRS-area in identifying capture type during LBBAP, compared to clinical judgment. Methods Unipolar paced ECGs during LBBAP implantation from 48 patients with preserved ejection fraction and baseline narrow QRS were collected. Conduction system capture was attempted in all patients and assessed using QRS morphology transitions during threshold testing following the EHRA 2023 consensus statement (gold standard). Eight experienced LBBAP-implanting cardiologists from six tertiary referral centers were asked to classify the ECGs into capture types. The vectorcardiographic QRS area was calculated from the synthesized vectorcardiogram using the Kors transformation matrix, with custom-developed code programmed in MATLAB. The accuracy of QRS area in differentiating left ventricular septal pacing (LVSP) from non-selective (nsLBBP), compared to clinical judgment of each individual observer, was evaluated using sensitivity, specificity, and accuracy analyses. Differences between QRS-area and QRS duration between LVSP and nsLBBP were assessed with Mann-Whitney U tests. Results QRS area was significantly higher for LVSP than for nsLBBP (LVSP median: 38, range 28–49; nsLBBP median: 22, range 15–28; p <0.001). QRS duration (determined from onset stimulus) was not significantly different between LVSP and nsLBBP (LVSP median: 133, range 125–143; nsLBBP median: 131, range 110–141; p <0.108). A cutoff point of 26 mV.ms (determined from ROC curves) for QRS-area yielded a sensitivity of 81%, specificity of 69%, and accuracy of 77% for distinguishing LVSP from nsLBBP. Clinical judgement of the 8 observers yielded an average sensitivity of 82% (range 71-100%), specificity of 64% (range 52-85%), and accuracy of 72% (range 62-78%). An overview of the accompanying sensitivity and specificity analyses is provided in the figure. Conclusion Vectorcardiographic QRS-area is a promising parameter for distinguishing LVSP from nsLBBP. Its ability to differentiate capture types slightly outperforms clinical judgment of experienced cardiologists and may serve as a valuable quantitative adjunct to clinical assessment, enhancing the accuracy of conduction system capture determination during LBBAP.Accuracy analyses
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".