Advances in conduction system pacing and implications for congenital heart disease
Bibliographic record
Abstract
INTRODUCTION: Conduction system pacing (CSP) is redefining management of patients requiring permanent pacing by offering a more physiologic alternative to subpulmonary ventricular pacing and conventional cardiac resynchronization therapy (CRT). While most evidence stems from acquired heart disease, CSP is gaining traction in congenital heart disease, where traditional pacing can exacerbate dyssynchrony and lead to long-term ventricular dysfunction. AREAS COVERED: This review examines the role of CSP in congenital heart disease, focusing on anatomical variability of the atrioventricular conduction system and lesion-specific challenges affecting lead placement. It synthesizes early data on the feasibility and safety of His bundle pacing (HBP) and left bundle branch area pacing (LBBAP) across a range of congenital lesions. Technological advances, including CT-based conduction mapping, electroanatomic mapping, and intracardiac echocardiography, enable anatomy-guided implantation strategies. Advances in delivery tools are expanding CSP use in anatomically complex settings. Despite encouraging early results, long-term data are limited, and randomized trials are lacking. EXPERT OPINION: CSP holds the potential to become the default physiologic pacing strategy in congenital heart disease. With advancing tools and operator experience, lesion-specific and hybrid CSP - CRT techniques may optimize outcomes. Key priorities include refining patient selection, defining preventive indications, and developing guidelines to support broader adoption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".