An artificial intelligence and machine learning model for personalized prediction of long-term mitral valve repair durability
Bibliographic record
Abstract
OBJECTIVE: The study objective was to compare Random Survival Forest, a machine learning method, with Cox proportional hazards models in predicting long-term mitral valve repair durability, focusing on clinical utility and personalized decision-making. METHODS: We analyzed 444 patients undergoing primary mitral valve repair for degenerative mitral regurgitation (2008-2024). The primary outcome was mitral repair failure, defined as recurrent regurgitation/stenosis or reintervention. Random Survival Forest and penalized Cox proportional hazards models were compared for predictive accuracy and interpretability. A web-based application was created to demonstrate the Random Survival Forest model. RESULTS: The failure end point, mitral repair failure, occurred in 13 individuals (3%) during the study period. Random Survival Forest showed superior discrimination (Concordance index: 0.874 vs 0.796) and identified both coaptation length and early mean mitral gradient as key predictors. Cox proportional hazards identified coaptation length alone, with each 1-mm increase reducing failure by approximately 40%. Random Survival Forest-predicted freedom from mitral repair failure at 5, 10, and 15 years was 94%, 74%, and 51% for coaptation length of 6 mm; 98%, 94%, and 91% for 9 mm; and 99%, 98%, and 96% for 12 mm, respectively. Mean gradients of 2 to 5 mm Hg were linked to 90% or greater durability at 5 to 10 years, whereas 8 mm Hg predicted worse outcomes (68% at 10 years, 64% at 15 years). Random Survival Forest further provided nuanced interpretation of temporal risk patterns and generated patient-specific survival estimates to improve repair durability forecasting. CONCLUSIONS: Machine learning outperforms traditional methods by modeling complex, nonlinear associations and identifying clinically actionable predictors. Integrating machine learning into surgical practice may support more personalized, data-driven mitral repair strategies and improve long-term outcomes.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".