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Machine Learning Approaches in Primary Mitral Regurgitation
Quick Takes
- An AI/machine learning (ML) model integrating standard, quantitative, and objective echo parameters has the ability to predict a patient population with primary MR that would benefit from mitral valve surgery.
- These data suggest the potential value of a more robust and global integration of echocardiographic data to enhance risk stratification in patients with primary MR and to potentially guide treatment by determining interventional benefit.
- There is a need for further trials with large, diverse populations and systematic feature selection to validate and improve model prediction and clinical utility.
Study Questions:
What is the utility of machine learning (ML) to identify pathophysiologically and prognostically informative primary mitral regurgitation (MR) patient subgroups based on standard echocardiographic measurements?
Methods:
The investigators used unsupervised and supervised ML and explainable artificial intelligence (AI) to integrate 24 echocardiographic parameters in 400 primary MR subjects from France (n = 243; development cohort) and Canada (n = 157; validation cohort) followed up during a median time of 3.2 (interquartile range [IQR], 1.3-5.3) years and 6.8 (IQR, 4.0-8.5) years, respectively. The authors compared the phenogroups’ incremental prognostic value over conventional MR profiles and for the primary endpoint of all-cause mortality incorporating time-to-mitral valve repair/replacement surgery as a covariate for survival analysis (time-dependent exposure). The association of phenogroups with time-to-event (i.e., death) was examined using Cox-proportional hazard regression analysis.
Results:
High-severity (HS) phenogroups from the French cohort (HS, n = 117; low-severity [LS], n = 126) and the Canadian cohort (HS, n = 87; LS, n = 70) showed improved event-free survival in surgical HS subjects over nonsurgical subjects (p = 0.047 and p = 0.020, respectively). A similar benefit of surgery was not seen in the LS phenogroup in both cohorts (p = 0.7 and p = 0.5, respectively). Phenogrouping showed incremental prognostic value in conventionally severe or moderate-severe MR subjects (Harrell C-statistic improvement, p = 0.480; and categorical net reclassification improvement, p = 0.002). Explainable AI specified how each echocardiographic parameter contributed to phenogroup distribution.
Conclusions:
The authors report that novel data-driven phenogrouping and explainable AI aided in improved integration of echocardiographic data to identify patients with primary MR and improved event-free survival after mitral valve repair/replacement surgery.
Perspective:
This preliminary study with an AI/ML model integrating standard, quantitative, and objective echocardiographic parameters reports the ability to predict a patient population with primary MR that would benefit from mitral valve surgery and incrementally improved the prognostic value over the conventional classification method in subjects with moderate-severe and severe MR. These data suggest the potential value of a more robust and global integration of echocardiographic data to enhance risk stratification in patients with primary MR and to potentially guide treatment by determining interventional benefit. However, there is a need for further trials with large, diverse populations and systematic feature selection to validate and improve model prediction and clinical utility.
Clinical Topics: Cardiac Surgery, Invasive Cardiovascular Angiography and Intervention, Noninvasive Imaging, Valvular Heart Disease, Cardiac Surgery and VHD, Interventions and Imaging, Interventions and Structural Heart Disease, Echocardiography/Ultrasound, Mitral Regurgitation
Keywords: Cardiac Surgical Procedures, Echocardiography, Heart Valve Diseases, Machine Learning, Mitral Valve Insufficiency
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