SynthesisFrontiers in cardiovascular medicine2025
Risk prediction models for permanent pacemaker implantation following transcatheter aortic valve replacement: a systematic review and meta-analysis.
Synthesis in Frontiers in cardiovascular medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
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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.
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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.
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Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Permanent pacemaker rate following Commando and Hemi-Commando procedures: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2026Pooled it
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To systematically evaluate the methodological quality and predictive performance of risk prediction models for permanent pacemaker implantation (PPMI) following transcatheter aortic valve replacement (TAVR), identify key predictive factors, and assess the risk of bias and clinical applicability of these models. Methods: A comprehensive search was conducted across multiple databases, including PubMed, Web of Science, The Cochrane Library, Embase, Cumulative Index to Nursing and Allied Health Literature (CINAHL), China National Knowledge Infrastructure (CNKI), Wanfang Database, China Science and Technology Journal Database (VIP), and SinoMed. The search included all records from database inception to January 1, 2025. Two independent researchers screened studies and extracted relevant data. Results: A total of 11 studies were included, covering 11 risk prediction models with sample sizes ranging from 184-35,410. The incidence of PPMI after TAVR varied between 7.3% and 31.0%. Frequently identified predictors (present in at least two studies) included right bundle branch block (RBBB), self-expandable valves, PR interval, QRS interval, and atrioventricular block (AVB). All models reported the area under the receiver operating characteristic curve (AUROC), ranging from 0.660-0.916, with seven studies providing calibration metrics. Internal validation was performed in three studies, while one study included both internal and external validation. Ten studies were assessed as having a high risk of bias, primarily due to deficiencies in data analysis. The pooled AUROC for the nine validated models was 0.76 (95% confidence interval: 0.72-0.80), indicating moderate discriminatory ability. Conclusion: Existing risk prediction models for PPMI after TAVR demonstrate moderate predictive performance but are limited by a high risk of bias, as assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Future research should focus on developing more robust models through larger sample sizes, rigorous methodologies, and multi-center external validation. Systematic Review Registration: The protocol for this study is registered with https://www.crd.york.ac.uk/PROSPERO/view/CRD42025629869, PROSPERO CRD42025629869.
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