Influence of Pacing Site Characteristics on Response to Cardiac Resynchronization Therapy
Bibliographic record
Abstract
BACKGROUND: Transmural scar occupying left ventricular (LV) pacing regions has been associated with reduced response to cardiac resynchronization therapy (CRT). However, spatial influences of lead tip delivery relative to scar at both pacing sites remain poorly explored. This study evaluated scar distribution relative to LV and right ventricular (RV) lead tip placement through coregistration of late gadolinium enhancement MRI and cardiac computed tomographic (CT) findings. Influences on CRT response were assessed by serial echocardiography. METHODS AND RESULTS: Sixty patients receiving CRT underwent preimplant late gadolinium enhancement MRI, postimplant cardiac CT, and serial echocardiography. Blinded segmental evaluations of mechanical delay, percentage scar burden, and lead tip location were performed. Response to CRT was defined as a reduction in LV end-systolic volume ≥15% at 6 months. The mean age and LV ejection fraction were 64±9 years and 25±7%, respectively. Mean scar volume was higher among CRT nonresponders for both the LV (23±23% versus 8±14% [P=0.01]) and RV pacing regions (40±32% versus 24±30% [P=0.04]). Significant pacing region scar was identified in 13% of LV pacing regions and 37% of RV pacing regions. Absence of scar in both regions was associated with an 81% response rate compared with 55%, 25%, and 0%, respectively, when the RV, LV, or both pacing regions contained scar. LV pacing region dyssynchrony was not predictive of response. CONCLUSIONS: Myocardial scar occupying the LV pacing region is associated with nonresponse to CRT. Scar occupying the RV pacing region is encountered at higher frequency and seems to provide a more intermediate influence on CRT response.
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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 | 0.003 |
| 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".