Key result
Six-feature Random Forest model predicts moderate/severe MR six months post-NeoChord surgery with ~0.82 AUC.
Why the study?
Patient selection for the minimally invasive NeoChord procedure to treat mitral regurgitation remains challenging, and machine learning may help identify subtle predictive patterns.
Does a Random Forest machine learning model accurately predict moderate or severe mitral regurgitation at six months post-NeoChord surgery in patients with MR type A or B?
Does a Random Forest machine learning model accurately predict moderate or severe mitral regurgitation at six months post-NeoChord surgery in patients with MR type A or B?
A Random Forest machine learning model using six preoperative clinical and echocardiographic features can accurately predict the risk of moderate or severe mitral regurgitation at six months following NeoChord surgery, potentially improving patient selection.
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May support preoperative stratification for NeoChord; leaves open prospective validation before clinical adoption.
Serra et al. (2025) studied this question. Random Forest model using 6 preoperative features predicted moderate/severe mitral regurgitation at 6 months post-NeoChord surgery with AUC 0.817, precision 0.875.
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