ArticleNetherlands heart journal : monthly journal of the Netherlands Society of Cardiology and the Netherlands Heart Foundation2019
Value of machine learning in predicting TAVI outcomes.
Article in Netherlands heart journal : monthly journal of the Netherlands Society of Cardiology and the Netherlands Heart Foundation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Unimodal to multimodal: a systematic review of predictive machine learning models for valvular heart diseases.Frontiers in cardiovascular medicine · 2026Pooled it
- The role of artificial intelligence in aortic valve stenosis: a bibliometric 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
- Identification of patients at risk for adverse events and poor symptom improvement after transcatheter aortic valve implantation.American heart journal plus : cardiology research and practice · 2026Article
- The Application of Multimodal Data Fusion Algorithm MULTINet in Postoperative Risk Assessment of TAVR.Journal of clinical medicine · 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
- Artificial Intelligence in Cardiovascular Imaging and Interventional Cardiology: Emerging Trends and Clinical Implications.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- Generation of a virtual cohort of TAVI patients for in silico trials: a statistical shape and machine learning analysis.Medical & biological engineering & computing · 2025Article
- Predicting mortality after transcatheter aortic valve replacement using preprocedural CT.Scientific reports · 2024Article
- Artificial Intelligence in Transcatheter Aortic Valve Replacement: Its Current Role and Ongoing Challenges.Diagnostics (Basel, Switzerland) · 2024Review
- Artificial Intelligence in the Screening, Diagnosis, and Management of Aortic Stenosis.Reviews in cardiovascular medicine · 2024Review
- Machine learning for prediction of all-cause mortality after transcatheter aortic valve implantation.European heart journal. Quality of care & clinical outcomes · 2023Article
- Article
- Development and validation of explainable machine learning models for risk of mortality in transcatheter aortic valve implantation: TAVI risk machine scores.European heart journal. Digital health · 2023Article
- Aortic valve and vascular calcium score in pre-TAVI CT: correlation with early post-procedural complications.La Radiologia medica · 2023Article
- The evolution of technical prerequisites and local boundary conditions for optimization of mitral valve interventions-Emphasis on skills development and institutional risk performance.Frontiers in cardiovascular medicine · 2023Review
- Machine learning applications in cardiac computed tomography: a composite systematic review.European heart journal open · 2022Review
- Deep Learning in Prediction of Late Major Bleeding After Transcatheter Aortic Valve Replacement.Clinical epidemiology · 2022Article
- Prediction of Atrial Fibrillation Recurrence after Thoracoscopic Surgical Ablation Using Machine Learning Techniques.Diagnostics (Basel, Switzerland) · 2021Article
- Deep learning-based prediction of early cerebrovascular events after transcatheter aortic valve replacement.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
backgroundTranscatheter aortic valve implantation (TAVI) has become a commonly applied procedure for high-risk aortic valve stenosis patients. However, for some patients, this procedure does not result in the expected benefits. Previous studies indicated that it is difficult to predict the beneficial effects for specific patients. We aim to study the accuracy of various traditional machine learning (ML) algorithms in the prediction of TAVI outcomes. METHODS AND
resultsClinical and laboratory data from 1,478 TAVI patients from a single centre were collected. The outcome measures were improvement of dyspnoea and mortality. Three experiments were performed using (1) screening data, (2) laboratory data, and (3) the combination of both. Five well-established ML techniques were implemented, and the models were evaluated based on the area under the curve (AUC). Random forest classifier achieved the highest AUC (0.70) for predicting mortality. Logistic regression had the highest AUC (0.56) in predicting improvement of dyspnoea.
conclusionsIn our single-centre TAVI population, the tree-based models were slightly more accurate than others in predicting mortality. However, ML models performed poorly in predicting improvement of dyspnoea.
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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.