Dynamic atrioventricular delay programming improves ventricular electrical synchronization as evaluated by 3D vectorcardiography
Bibliographic record
Abstract
BACKGROUND: Optimal timing of the atrioventricular delay in cardiac resynchronization therapy (CRT) can improve synchrony in patients suffering from heart failure. The purpose of this study was to evaluate the impact of SyncAV™ on electrical synchrony as measured by vectorcardiography (VCG) derived QRS metrics during bi-ventricular (BiV) pacing. METHODS: Patients implanted with a cardiac resynchronization therapy (CRT) device and quadripolar left ventricular (LV) lead underwent 12‑lead ECG recordings. VCG metrics, including QRS duration (QRSd) and area, were derived from the ECG by a blinded observer during: intrinsic conduction, BiV with nominal atrioventricular delays (BiV Nominal), and BiV with SyncAV programmed to the optimal offset achieving maximal synchronization (BiV + SyncAV Opt). RESULTS: One hundred patients (71% male, 40% ischemic, 65% LBBB, 32 ± 9% ejection fraction) completed VCG assessment. QRSd during intrinsic conduction (166 ± 25 ms) was narrowed successively by BiV Nominal (137 ± 23 ms, p < .05 vs. intrinsic) and BiV + SyncAV Opt (122 ± 22 ms, p < .05 vs. BiV Nominal). Likewise, 3D QRS area during intrinsic conduction (90 ± 42 mV ∗ ms) was reduced by BiV Nominal (65 ± 39 mV ∗ ms, p < .05 vs. intrinsic) and further by BiV + SyncAV Opt (53 ± 30 mV ∗ ms, p = .06 vs. BiV Nominal). CONCLUSION: With VCG-based, patient-specific optimization of the programmable offset, SyncAV reduced electrical dyssynchrony beyond conventional CRT.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".