ArticleScientific reports2024
Machine learning for pacemaker implantation prediction after TAVI using multimodal imaging data.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Unimodal to multimodal: a systematic review of predictive machine learning models for valvular heart diseases.Frontiers in cardiovascular medicine · 2026Pooled it
- 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
- Artificial Intelligence in Risk Stratification and Outcome Prediction for Transcatheter Aortic Valve Replacement: A Systematic Review and Meta-Analysis.Journal of personalized medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pacemaker implantation (PMI) after transcatheter aortic valve implantation (TAVI) is a common complication. While computed tomography (CT) scan data are known predictors of PMI, no machine learning (ML) model integrating CT with clinical, ECG, and transthoracic echocardiography (TTE) data has been proposed. This study investigates the contribution of ML methods to predict PMI after TAVI, with a focus on the role of CT imaging data. A retrospective analysis was conducted on a cohort of 520 patients who underwent TAVI. Recursive feature elimination with SHAP values was used to select key variables from clinical, ECG, TTE, and CT data. Six ML models, including Support Vector Machines (SVM), were trained using these selected variables. The model's performance was evaluated using AUC-ROC, F1 score, and accuracy metrics. The PMI rate was 18.8%. The best-performing model achieved an AUC-ROC of 92.1% ± 4.7, an F1 score of 71.8% ± 9.9, and an accuracy of 87.9% ± 4.7 using 22 variables, 9 of which were CT-based. Membranous septum measurements and their dynamic variations were critical predictors. Our ML model provides robust PMI predictions, enabling personalized risk assessments. The model is implemented online for broad clinical use.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.