Programming Cardiac Resynchronization Therapy for Electrical Synchrony: Reaching Beyond Left Bundle Branch Block and Left Ventricular Activation Delay
Bibliographic record
Abstract
Background QRS narrowing following cardiac resynchronization therapy with biventricular (BiV) or left ventricular (LV) pacing is likely affected by patient‐specific conduction characteristics ( PR , qLV, LV ‐paced propagation interval), making a universal programming strategy likely ineffective. We tested these factors using a novel, device‐based algorithm (Sync AV ) that automatically adjusts paced atrioventricular delay (default or programmable offset) according to intrinsic atrioventricular conduction. Methods and Results Seventy‐five patients undergoing cardiac resynchronization therapy (age 66±11 years; 65% male; 32% with ischemic cardiomyopathy; LV ejection fraction 28±8%; QRS duration 162±16 ms) with intact atrioventricular conduction (PR interval 194±34, range 128–300 ms), left bundle branch block, and optimized LV lead position were studied at implant. QRS duration ( QRS d) reduction was compared for the following pacing configurations: nominal simultaneous BiV (Mode I: paced/sensed atrioventricular delay=140/110 ms), BiV+Sync AV with 50 ms offset (Mode II ), BiV+Sync AV with offset that minimized QRS d (Mode III ), or LV ‐only pacing+Sync AV with 50 ms offset (Mode IV ). The intrinsic QRS d (162±16 ms) was reduced to 142±17 ms (−11.8%) by Mode I, 136±14 ms (−15.6%) by Mode IV , and 132±13 ms (−17.8%) by Mode II . Mode III yielded the shortest overall QRS d (123±12 ms, −23.9% [ P <0.001 versus all modes]) and was the only configuration without QRS d prolongation in any patient. QRS narrowing occurred regardless of QRS d, PR , or LV ‐paced intervals, or underlying ischemic disease. Conclusions Post‐implant electrical optimization in already well‐selected patients with left bundle branch block and optimized LV lead position is facilitated by patient‐tailored BiV pacing adjusted to intrinsic atrioventricular timing using an automatic device–based algorithm.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".