ArticleEuropean heart journal. Quality of care & clinical outcomes2023
Machine learning for prediction of all-cause mortality after transcatheter aortic valve implantation.
Article in European heart journal. Quality of care & clinical outcomes, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 5 of them syntheses 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
18 citing papers in PubMed, 5 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
- Early predictive value of prediction models for mortality after transcatheter aortic valve replacement: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2026Pooled it
- Pooled it
- The role of artificial intelligence in aortic valve stenosis: a bibliometric analysis.Frontiers in cardiovascular medicine · 2025Pooled it
- Harnessing the power of artificial intelligence in predicting all-cause mortality in transcatheter aortic valve replacement: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2024Pooled it
- Prognostic Value of the C-Reactive Protein-Albumin-Lymphocyte (CALLY) Index for 1-Year Mortality After Transcatheter Aortic Valve Implantation.Journal of cardiovascular development and disease · 2026Article
- Superiority of the Rockwood Clinical Frailty Scale for Assessing Long-Term Outcome in Patients Undergoing Transcatheter Aortic Valve Implantation.Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions · 2026Article
- Comprehensive Unified Regimen for Eliminating Undiagnosed/Untreated Aortic Valve Stenosis: Algorithm Validation for Identifying Aortic Stenosis and Treatment Disparities.Structural heart : the journal of the Heart Team · 2026Article
- A Reproducible Post-Valve-Replacement EHR Cohort for Comparative AI Studies.Diagnostics (Basel, Switzerland) · 2026Article
- The Application of Multimodal Data Fusion Algorithm MULTINet in Postoperative Risk Assessment of TAVR.Journal of clinical medicine · 2025Article
- Applications of Artificial Intelligence in Transcatheter Aortic Valve Replacement: A Review of the Literature.Life (Basel, Switzerland) · 2025Review
- Artificial Intelligence in Risk Stratification and Outcome Prediction for Transcatheter Aortic Valve Replacement: A Systematic Review and Meta-Analysis.Journal of personalized medicine · 2025Review
- Risk factors and predictive models for post-operative moderate-to-severe mitral regurgitation following transcatheter aortic valve replacement: a machine learning approach.BMC cardiovascular disorders · 2025Article
- Prediction of Major Adverse Cardiac Events After Transcatheter Aortic Valve Implantation: A Machine Learning Approach with GRACE Score.Sisli Etfal Hastanesi tip bulteni · 2024Article
- Artificial intelligence in detecting left atrial appendage thrombus by transthoracic echocardiography and clinical features: the Left Atrial Thrombus on Transoesophageal Echocardiography (LATTEE) registry.European heart journal · 2024Article
- Advanced Cardiac Imaging in the Assessment of Aortic Stenosis.Journal of cardiovascular development and disease · 2023Review
- Preprocedural CT angiography and machine learning for mortality prediction after transcatheter aortic valve replacement.Progress in cardiovascular diseasesArticle
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Authors and funding
19 authors.
Funding
Abstract
aimsPrediction of adverse events in mid-term follow-up after transcatheter aortic valve implantation (TAVI) is challenging. We sought to develop and validate a machine learning model for prediction of 1-year all-cause mortality in patients who underwent TAVI and were discharged following the index procedure. METHODS AND
resultsThe model was developed on data of patients who underwent TAVI at a high-volume centre between January 2013 and March 2019. Machine learning by extreme gradient boosting was trained and tested with repeated 10-fold hold-out testing using 34 pre- and 25 peri-procedural clinical variables. External validation was performed on unseen data from two other independent high-volume TAVI centres. Six hundred four patients (43% men, 81 ± 5 years old, EuroSCORE II 4.8 [3.0-6.3]%) in the derivation and 823 patients (46% men, 82 ± 5 years old, EuroSCORE II 4.7 [2.9-6.0]%) in the validation cohort underwent TAVI and were discharged home following the index procedure. Over the 12 months of follow-up, 68 (11%) and 95 (12%) subjects died in the derivation and validation cohorts, respectively. In external validation, the machine learning model had an area under the receiver-operator curve of 0.82 (0.78-0.87) for prediction of 1-year all-cause mortality following hospital discharge after TAVI, which was superior to pre- and peri-procedural clinical variables including age 0.52 (0.46-0.59) and the EuroSCORE II 0.57 (0.51-0.64), P < 0.001 for a difference.
conclusionMachine learning based on readily available clinical data allows accurate prediction of 1-year all-cause mortality following a successful TAVI.
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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.