Machine learning for prediction of transcatheter mitral valve repair outcomes: A systematic review
Bibliographic record
Abstract
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. 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. 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. 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. • TMVR provides a minimally invasive alternative to mitral valve surgery. • Machine learning demonstrates clinical value in TMVR risk stratification. • Machine learning models outperformed established regression scores. • Future development may further improve predictive accuracy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".