Optimization of Chronic Cardiac Resynchronization Therapy Using Fusion Pacing Algorithm Improves Echocardiographic Response
Bibliographic record
Abstract
Background Whether reprogramming of cardiac resynchronization therapy (CRT) to increase electrical synchrony translates into echocardiographic improvement remains unclear. SyncAV is an algorithm that allows fusion of intrinsic conduction with biventricular pacing. We aimed to assess whether reprogramming chronically implanted CRT devices with SyncAV is associated with improved echocardiographic parameters. Methods Patients at a quaternary center with previously implanted CRT devices with a programmable SyncAV algorithm underwent routine electrocardiography-based SyncAV optimization during regular device clinic visits. This analysis included only patients who could be programmed to the SyncAV algorithm (i.e., in sinus rhythm with intrinsic atrioventricular conduction). Echocardiography was performed before and 6 months after CRT optimization. Results Of 64 consecutive, potentially eligible patients who underwent assessment, 34 who were able to undergo SyncAV programming were included. Their mean age was 74 ± 9 years, 41% were female, and 59% had ischemic cardiomyopathy. The mean time from CRT implant to SyncAV optimization was 17.8 ± 8.5 months. At 6-month follow-up, SyncAV optimization was associated with a significant increase in left ventricular ejection fraction (LVEF) (mean LVEF 36.5% ± 13.3% vs 30.9% ± 13.3%; P < 0.001) and a reduction in left ventricular end-systolic volume (LVESV) (mean LVESV 110.5 ± 57.5 mL vs 89.6 ± 52.4 mL; P < 0.001) compared with baseline existing CRT programming. Conclusion CRT reprogramming to maximize biventricular fusion pacing significantly increased LVEF and reduced LVESV in patients with chronic CRT devices. Further studies are needed to assess if a continuous fusion pacing algorithm improves long-term clinical outcomes and to identify which patients are most likely to derive benefit.
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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 | 0.001 |
| 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) | 0.001 | 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".