Evidence map›Paper›PMID 42505907›Full record

ArticleJournal of cardiovascular development and disease2026

Predictors of Unfavorable Therapeutic Response to Left Bundle Branch Area Pacing and Atrioventricular Node Ablation in Patients with Atrial Fibrillation and Heart Failure.

Lishui Shen, Hailing Li, Wenliang Che, Kai Tang, Dongdong Zhao

Abstract read
In one paragraph

Article in Journal of cardiovascular development and disease, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Lishui ShenDepartment of Cardiology, Shanghai Tenth People's Hospital, Tongji University, Shanghai 200072, China.
Hailing LiDepartment of Cardiology, Shanghai Tenth People's Hospital, Tongji University, Shanghai 200072, China.
Wenliang CheDepartment of Cardiology, Shanghai Tenth People's Hospital, Tongji University, Shanghai 200072, China.
Kai TangDepartment of Cardiology, Shanghai Tenth People's Hospital, Tongji University, Shanghai 200072, China.
Dongdong ZhaoDepartment of Cardiology, Shanghai Tenth People's Hospital, Tongji University, Shanghai 200072, China.

Funding

Central Universities Fundamental Research Grant of China 3332020092National Center for Chronic and Noncommunicable Disease Control and Prevention 2024ZD0521900National Natural Science Foundation of China 82200347
6 · The paper itself

Abstract

backgroundLeft bundle branch area pacing (LBBAP) with atrioventricular node (AVN) ablation has emerged as a promising therapy for patients with atrial fibrillation (AF) and symptomatic heart failure (HF). However, it remains unclear which patients are likely responders and which may derive limited benefit.

objectiveThis study aimed to identify predictors of unfavorable therapeutic outcomes following this 'ablate and pace' therapy.

methodsIn this retrospective analysis, consecutive patients with successful LBBAP and AVN ablation were included. Baseline characteristics and long-term outcomes were assessed. Multivariable Cox regression and reclassification analysis were used to evaluate the predictors of heart failure hospitalization (HFH).

resultsNinety-eight patients (age 73.1 ± 8.9 years; ventricular rate 93.6 ± 18.7 bpm; 41.8% male) were enrolled. During 43.9 ± 19.4 months of follow-up, 19 (19.4%) patients experienced HFH. Three independent risk factors were identified for HFH: impaired renal function (eGFR ≤ 60 mL/min/1.73 m

conclusionsThe eGFR ≤ 60 mL/min/1.73 m

Indexed as

atrial fibrillationatrioventricular node ablationheart failureleft bundle branch area pacing

Identifiers

PMID42505907
PMCPMC13411699

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.