ArticleWorld journal of cardiology2023
Prediction of permanent pacemaker implantation after transcatheter aortic valve replacement: The role of machine learning.
Article in World journal of cardiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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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
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Risk prediction models for permanent pacemaker implantation following transcatheter aortic valve replacement: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2025Pooled it
- Machine Learning for Predicting Mortality Following Transcatheter Aortic Valve Replacement: A Scoping Review of Current Literature.CJC open · 2026Review
- Machine Learning-Driven Probability of Permanent Pacemaker Implantation After Transcatheter Aortic Valve Replacement.Diagnostics (Basel, Switzerland) · 2026Article
- Clinical Applications of Artificial Intelligence in Structural Heart Disease.Journal of the Society for Cardiovascular Angiography & Interventions · 2026Review
- A Reproducible Post-Valve-Replacement EHR Cohort for Comparative AI Studies.Diagnostics (Basel, Switzerland) · 2026Article
- Preprocedural CT and ECG Markers for Predicting Post-TAVR Pacemaker Requirement in High-Risk Patients.Structural heart : the journal of the Heart Team · 2025Article
- Artificial Intelligence in Risk Stratification and Outcome Prediction for Transcatheter Aortic Valve Replacement: A Systematic Review and Meta-Analysis.Journal of personalized medicine · 2025Review
- Harnessing Artificial Intelligence for Innovation in Interventional Cardiovascular Care.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- One-year Outcomes of Permanent Pacemaker Implantation after Transcatheter Aortic Valve Replacement: CONDUCT Registry: A Propensity Score-Matched Comparison.Arrhythmia & electrophysiology review · 2025Article
- Machine learning for pacemaker implantation prediction after TAVI using multimodal imaging data.Scientific reports · 2024Article
- Risk Factors, Management, and Avoidance of Conduction System Disease after Transcatheter Aortic Valve Replacement.Journal of clinical medicine · 2023Review
Corrections and comments
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Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAtrioventricular block requiring permanent pacemaker (PPM) implantation is an important complication of transcatheter aortic valve replacement (TAVR). Application of machine learning could potentially be used to predict pre-procedural risk for PPM.
aimTo apply machine learning to be used to predict pre-procedural risk for PPM.
methodsA retrospective study of 1200 patients who underwent TAVR (January 2014-December 2017) was performed. 964 patients without prior PPM were included for a 30-d analysis and 657 patients without PPM requirement through 30 d were included for a 1-year analysis. After the exclusion of variables with near-zero variance or ≥ 50% missing data, 167 variables were included in the random forest gradient boosting algorithm (GBM) optimized using 5-fold cross-validations repeated 10 times. The receiver operator curve (ROC) for the GBM model and PPM risk score models were calculated to predict the risk of PPM at 30 d and 1 year.
resultsOf 964 patients included in the 30-d analysis without prior PPM, 19.6% required PPM post-TAVR. The mean age of patients was 80.9 ± 8.7 years. 42.1 % were female. Of 657 patients included in the 1-year analysis, the mean age of the patients was 80.7 ± 8.2. Of those, 42.6% of patients were female and 26.7% required PPM at 1-year post-TAVR. The area under ROC to predict 30-d and 1-year risk of PPM for the GBM model (0.66 and 0.72) was superior to that of the PPM risk score (0.55 and 0.54) with a
conclusionThe GBM model has good discrimination and calibration in identifying patients at high risk of PPM post-TAVR.
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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.