Understanding LOT-CRT: Current Insights, Limitations, and Our Center’s Experience
Bibliographic record
Abstract
Cardiac resynchronization therapy (CRT) using biventricular (BiV) pacing is the standard treatment for heart failure (HF) patients with reduced left ventricular ejection fraction (LVEF) and electrical dyssynchrony. However, one in three patients remains a non-responder. Left bundle branch area pacing (LBBAP) could represent a more physiological alternative, but its effectiveness is limited in cases of atypical left bundle branch block (LBBB) or intraventricular conduction delay (IVCD). Left Bundle Branch Pacing Optimized cardiac resynchronization therapy (LOT-CRT) integrates LBBAP with coronary sinus (CS) lead pacing to improve electrical synchrony and clinical outcomes. This review evaluates the feasibility, advantages, disadvantages, and clinical outcomes of LOT-CRT. Additionally, we describe our center's experience and propose an evidence-based implantation algorithm. A review of published studies investigating LOT-CRT was conducted, comparing its effectiveness with BiV-CRT and LBBAP alone using QRS narrowing, LVEF improvement, left ventricular remodeling, New York Heart Association (NYHA) class changes and NT-proBNP levels. It was found that LOT-CRT outperforms BiV-CRT or LBBAP alone in selected populations, at the cost of higher clinical skills, longer procedural times, and specific device setups. Randomized trials are underway to further define its role in clinical practice.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".