Machine learning for prediction of transcatheter mitral valve repair outcomes: A systematic review

Abstract

Background: Transcatheter mitral valve repair (TMVR) has evolved as a minimally invasive alternative to traditional mitral valve surgery. Meanwhile, machine learning (ML) offers a promising tool for TMVR risk stratification due to the lack of established risk scores specifically tailored for TMVR patients. To address the absence of consensus on its efficacy, we conducted a systematic review of primary studies that have utilized ML to predict the success of TMVR. Methods: Embase, MEDLINE, Scopus, Web of Science, PubMed, Google Scholar, and the Cochrane Library were systematically searched from inception through April 2024. We included primary studies that used TMVR as the sole interventional technique for adult MR patients. These studies also had to employ at least one ML model to predict the success of TMVR. Results: 244 publications were screened, with seven eventually included in this review. Two studies employed clustering techniques, two utilized extreme gradient boosting, and three used multiple ML algorithms to predict TMVR outcomes. Of the four studies that compared the accuracy of ML with traditional regression models, all four demonstrated higher accuracy with ML, and this difference was statistically significant in three of the four studies. Conclusions: To our knowledge, we conducted the first systematic review of ML methods for prediction of TMVR success in MR treatment. ML outperformed established risk scores, demonstrating promising potential in interventional cardiology. Future ML models, trained on larger patient datasets, may further improve predictive accuracy, and enhance risk stratification in this population.

Keywords

• Machine learning
• TMVR
• Transcatheter mitral valve repair
• TEER
• Transcatheter edge-to-edge repair