Study on Heart Block Risks Using Outcome-Based Modeling
NCT ID: NCT07065123
Last Updated: 2025-07-15
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
112 participants
OBSERVATIONAL
2017-01-01
2025-06-26
Brief Summary
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Detailed Description
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However, post-TAVR cardiac conduction disturbances are among the most frequently observed complications, particularly complete left bundle branch block (CLBBB) and high-degree atrioventricular block (AVB) requiring permanent pacemaker implantation (PPI). These complications are associated with an increased risk of heart failure and cardiac mortality. Identified risk factors include patient-specific characteristics, electrocardiographic features, intraoperative procedural techniques, and the type of device used.
Current research on post-TAVR conduction disorders predominantly focuses on Western populations. However, Chinese AS patients exhibit distinct clinical features-such as higher degrees of valve calcification and a higher prevalence of bicuspid aortic valves-which may lead to differences in risk factors for conduction abnormalities. Moreover, prior studies have largely focused on isolated risk factors rather than developing integrated models to quantify the risk of such complications. Against this backdrop, the purpose of this study is to address the knowledge gap in postoperative risk prediction for Chinese patients. By constructing a risk prediction model tailored to Chinese AS patients for post-TAVR cardiac conduction block, we aim to improve preoperative risk assessment and individualized clinical management, thereby providing actionable guidance for clinical decision-making.
This study included 112 aortic stenosis patients undergoing TAVR at Sun Yat-sen Memorial Hospital (2017-2024), divided into internal (2017-2022) and external (2023-2024) validation cohorts.
Data covered demographics, medical history, ECG, echocardiography, biomarkers, and valve parameters. Outcomes were permanent pacemaker implantation (PPI) ≤48h post-TAVR and complete left bundle branch block (CLBBB).
Univariate logistic regression (p\<0.20 threshold) and stepwise multivariate analysis (R "MASS" package, AIC-based) identified predictors for nomogram development. Model performance was assessed via AUC, C-index, ROC, and Hosmer-Lemeshow test. Internal validation used 1000 bootstrap resamples; external validation employed temporal validation. Analyses were performed in R 4.2.3 (significance: p\<0.05).
Conditions
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Study Design
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COHORT
RETROSPECTIVE
Study Groups
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Permanent pacemaker implantation after TAVR surgery
The population undergoing TAVR for aortic valve stenosis with outcomes of permanent pacemaker implantation and complete left bundle branch block after surgery
The population undergoing TAVR for aortic valve stenosis with outcomes of permanent pacemaker implantation and complete left bundle branch block after surgery
Complete left bundle branch block occurred after TAVR surgery
The population undergoing TAVR for aortic valve stenosis with outcomes of permanent pacemaker implantation and complete left bundle branch block after surgery
The population undergoing TAVR for aortic valve stenosis with outcomes of permanent pacemaker implantation and complete left bundle branch block after surgery
Interventions
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The population undergoing TAVR for aortic valve stenosis with outcomes of permanent pacemaker implantation and complete left bundle branch block after surgery
The population undergoing TAVR for aortic valve stenosis with outcomes of permanent pacemaker implantation and complete left bundle branch block after surgery
Eligibility Criteria
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Inclusion Criteria
* Scheduled to undergo transcatheter aortic valve replacement (TAVR) in Sun Yat-Sen Memorial Hospital
Exclusion Criteria
* Patients with pre-existing permanent pacemaker implantation
* Inability to comply with follow-up protocol
ALL
No
Sponsors
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Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
OTHER
Responsible Party
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Jingfeng Wang
Professor
Other Identifiers
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AS-TAVR-2023-04-10
Identifier Type: -
Identifier Source: org_study_id
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