Abstract 5926: Cardiac Electrical Resynchronization Therapy Combining HIS Bundle and Left Ventricular Pacing is Effective in the Treatment of Severe Heart Failure
Bibliographic record
Abstract
Introduction: It is known that Cardiac Resynchronization Therapy (CRT) combining right ventricular (RV) apex pacing and left ventricular pacing (LVP) is ineffective in up to 35% of heart failure (HF) patients. Our hypothesis is that RV apical pacing bypasses the rapidly conducting right bundle and may further impede ventricular activation with LBBB hence a more physiological CRT is via HIS Bundle pacing (HBP) and LVP. We report of our early experience with Cardiac Electrical Resynchronization Therapy (CERT) incorporating HBP with LVP in patients with severe HF. Methods: Patients indicated for CRT were approached for CERT. In addition to an atrial and LV lead, patients also received a HBP lead. An active fixation lead (SelectSecure/Site® Medtronic) was used directly for HIS Bundle mapping and pacing. HBP implant thresholds, procedure and fluoroscopy times, pre and post implant QRS, PR interval, and NYHA class were collected. Student t -test was used for analysis. Results: 15 patients (13 male, mean age 70yrs) referred for CRT underwent successful CERT with mean follow up of 3.5±4.3 months. The mean implant HBP threshold, procedure and fluoroscopy time were 1.6V/0.6ms, 150min, and 26min respectively. All patients had QRS shortening with a mean of 64±21ms (pre CERT 182ms, post CERT 118ms, p<0.0001, figure ) and mean PR shortening of 81±101ms (pre CERT 254ms, post CERT 173ms, p<0.05). 13 patients had improvement of at least one NYHA class. Conclusions: HBP with LVP is effective in achieving electrical resynchronization, and has resulted in early improvement of HF symptoms. Evidence of reverse remodeling is pending. CERT needs to be further validated before considering for wide adoption.
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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 | 0.000 | 0.000 |
| 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) | 0.004 | 0.001 |
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".