ArticleBioengineering (Basel, Switzerland)2021
Machine Learning Prediction Models for Mitral Valve Repairability and Mitral Regurgitation Recurrence in Patients Undergoing Surgical Mitral Valve Repair.
Article in Bioengineering (Basel, Switzerland), 2021. 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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Who cites it
11 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
- Accuracy of machine learning models for mitral regurgitation severity assessment: A systematic review and meta-analysis.International journal of cardiology. Cardiovascular risk and prevention · 2026Review
- Prognostic Modeling of Tricuspid Valve Regurgitation Outcomes Using Machine Learning-Based Survival Analysis.Journal of clinical medicine · 2026Article
- Echocardiographic Assessment of Patients Undergoing Mitral Valve Repair.Journal of cardiovascular development and disease · 2025Review
- Comments and evaluation on "Preoperative mitral valve annulus area size is an important factor in avoiding functional mitral stenosis after mitral valve repair".Journal of echocardiography · 2025Article
- Automatic Segmentation and Evaluation of Mitral Regurgitation Using Doppler Echocardiographic Images.Bioengineering (Basel, Switzerland) · 2024Article
- Mitral valve repair for degenerative mitral regurgitation with Carpentier's functional classification type II in elderly patients: a single center experience.Journal of cardiothoracic surgery · 2024Article
- Machine-learning-based prediction of survival and mitral regurgitation recurrence in patients undergoing mitral valve repair.Interdisciplinary cardiovascular and thoracic surgery · 2023Article
- Review
- Multimodality Imaging of Sudden Cardiac Death and Acute Complications in Acute Coronary Syndrome.Journal of clinical medicine · 2022Review
- 3D echocardiography in mitral valve prolapse.Frontiers in cardiovascular medicine · 2022Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMitral valve regurgitation (MR) is the most common valvular heart disease and current variables associated with MR recurrence are still controversial. We aim to develop a machine learning-based prognostic model to predict causes of mitral valve (MV) repair failure and MR recurrence.
methods1000 patients who underwent MV repair at our institution between 2008 and 2018 were enrolled. Patients were followed longitudinally for up to three years. Clinical and echocardiographic data were included in the analysis. Endpoints were MV repair surgical failure with consequent MV replacement or moderate/severe MR (>2+) recurrence at one-month and moderate/severe MR recurrence after three years.
results817 patients (DS1) had an echocardiographic examination at one-month while 295 (DS2) also had one at three years. Data were randomly divided into training (DS1: n = 654; DS2: n = 206) and validation (DS1: n = 164; DS2 n = 89) cohorts. For intra-operative or early MV repair failure assessment, the best area under the curve (AUC) was 0.75 and the complexity of mitral valve prolapse was the main predictor. In predicting moderate/severe recurrent MR at three years, the best AUC was 0.92 and residual MR at six months was the most important predictor.
conclusionsMachine learning algorithms may improve prognosis after MV repair procedure, thus improving indications for correct candidate selection for MV surgical repair.
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