Abstract 4117438: Machine Learning Predicts Successful Transcatheter Mitral Valve Edge to Edge Repair: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: Transcatheter Mitral Valve Edge to Edge Repair (TEER) is an established percutaneous treatment for patients with severe symptomatic Mitral Regurgitation (MR). The current AHA/ACC guidelines recommend TEER for inoperable patients with severe primary MR or patients with symptomatic severe secondary MR despite medical therapy. Machine learning (ML) has emerged as a tool for TEER risk stratification due to the paucity of established risk scores. To address the lack of consensus on its efficacy, we conducted a systematic review and meta-analysis of studies that utilized ML to predict the success of TEER. Methods: Electronic databases, including Embase, MEDLINE, and the Cochrane Library, were searched from inception through April 2024. We included studies that used TEER and employed at least one ML model to predict the success of TEER. The Area Under the Receiver Operating Characteristic Curve (AUC) was used to measure the accuracy of ML risk stratification algorithms. Results: 102 publications were screened, with seven eventually included in this analysis. Two studies employed clustering techniques, two utilized extreme gradient boosting, and three used multiple ML algorithms to predict outcomes. Of the four studies that compared the accuracy of ML with traditional Cox regression, all four demonstrated higher accuracy with ML, and this difference was statistically significant in three of the four studies. The mean AUC of the aggregated ML data was 0.737 [95% CI: 0.717, 0.758], compared to 0.627 [95% CI: 0.600, 0.653] for the pooled traditional methods. Conclusions: To our knowledge, we conducted the first systematic review and meta-analysis of ML methods for prediction of TEER success. ML outperformed established risk scores, demonstrating promising potential. Future ML models, trained on larger patient datasets, may further improve predictive accuracy in this patient population.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.025 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".