ArticleInterdisciplinary cardiovascular and thoracic surgery2023
Machine-learning-based prediction of survival and mitral regurgitation recurrence in patients undergoing mitral valve repair.
Article in Interdisciplinary cardiovascular and thoracic surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 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 in mitral valve repair: promise, precision, and the challenge of clinical translation.Journal of thoracic disease · 2026Article
- Lifetime management of primary mitral regurgitation through integrated surgical and transcatheter reinterventions.NPJ cardiovascular health · 2026Review
- Prognostic Modeling of Tricuspid Valve Regurgitation Outcomes Using Machine Learning-Based Survival Analysis.Journal of clinical medicine · 2026Article
- Construction and validation of explainable machine learning models to predict in-hospital mortality for patients with acute type A aortic dissection surgery.Frontiers in medicine · 2026Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study was conducted to assess long-term clinical outcomes after mitral valve repair using machine-learning techniques.
methodsWe retrospectively evaluated 436 consecutive patients (mean age: 54.7 ± 15.4; 235 males) who underwent mitral valve repair between January 2000 and December 2017. Actuarial survival and freedom from significant (≥ moderate) mitral regurgitation (MR) were clinical end points. To evaluate the independent risk factors, random survival forest (RSF), extreme gradient boost (XGBoost), support vector machine, Cox proportional hazards model and general linear models with elastic net regularization were used. Concordance indices (C-indices) of each model were estimated.
resultsThe operative mortality was 0.9% (N = 4). Reoperation was required in 15 patients (3.5%). In terms of C-index, the overall performance of the XGBoost (C-index 0.806) and RSF models (C-index 0.814) was better than that of the Cox model (C-index 0.733) in overall survival. For the recurrent MR, the C-index for XGBoost was 0.718, which was the highest among the 5 models. Compared to the Cox model (C-index 0.545), the C-indices of the XGBoost (C-index 0.718) and RSF models (C-index 0.692) were higher.
conclusionsMachine-learning techniques can be a useful tool for both prediction and interpretation in the survival and recurrent MR. From the machine-learning techniques examined here, the long-term clinical outcomes of mitral valve repair were excellent. The complexity of MV increased the risk of late mitral valve-related reoperation.
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