Adjusting the timing of left-ventricular pacing using electrocardiogram and device electrograms
Bibliographic record
Abstract
AIMS: Left-ventricular (LV) pacing with optimized atrio-ventricular (AV) timing may provide similar or greater benefit in comparison with bi-ventricular (BiV) pacing in a subset of cardiac resynchronization therapy (CRT) patients with sinus rhythm and preserved AV conduction. We hypothesized that the optimal device AV delays during LV pacing can be predicted using electrocardiogram (ECG) and device electrograms. METHODS AND RESULTS PATIENTS: (n= 55) with sinus rhythm and PR interval < 300 ms had their CRT devices programmed to atrial and LV pacing with a range of AVs as well as to echocardiographically optimized BiV and no ventricular pacing. At each setting, LV function was evaluated using echocardiography and AVs corresponding to the highest LV ejection fraction (LVEF), lowest LV end-systolic volume (LVESV), and the average of the two (by EF and ESV) were determined. Correlation between the optimal AVs and the following intervals was investigated: intrinsic QRS duration (QRSs), intervals from atrial pacing (Ap) to right-ventricular (RV) sensing (Ap-RVs), from RV sensing to LV activation (RVs-LVs), and from LV pacing to RV sensing (LVp-RVs). Optimal AVs moderately correlated with intrinsic Ap-RVs interval, whereas other parameters showed weak or no correlation. The best correlation (R = 0.66, P< 0.0001) was between the optimal AV delay according to EF and ESV, and Ap-RVs interval. Programming of AVs during LV pacing to the shortest of 70% of the intrinsic Ap-RVs interval, or Ap-RVs--40 ms resulted in significant improvement in LV function similar to that in case of BiV. CONCLUSION: Optimal AV during LV pacing can be approximated from the intrinsic AV conduction time.
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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 | 0.000 |
| 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".