Asymptomatic severe degenerative mitral regurgitation

Area of Science:

• Cardiology
• Medical Technology
• Artificial Intelligence in Medicine

Background:

• Degenerative mitral valve disease (DMVD) is prevalent, with up to 25% of patients with severe regurgitation being asymptomatic.
• Current guidelines for intervention in asymptomatic patients focus on ventricular size and function, but carry risks of post-operative heart failure and mortality.
• Existing criteria may not adequately identify all patients who could benefit from early intervention.

Purpose of the Study:

• To explore novel risk markers for degenerative mitral valve disease.
• To investigate the potential of machine learning and artificial intelligence in identifying high-risk patient phenotypes.
• To inform clinical practice regarding timely intervention for severe mitral regurgitation.

Main Methods:

• Review of newer risk stratification markers including ventricular strain, myocardial fibrosis (late gadolinium enhancement), mitral annular disjunction, and ventricular arrhythmia burden.
• Discussion on the integration of these markers using machine learning and artificial intelligence.
• Consideration of advancements in surgical techniques like robotic surgery for mitral valve repair.

Main Results:

• Newer markers like strain, fibrosis, and arrhythmias offer potential for improved risk stratification in asymptomatic severe mitral regurgitation.
• Machine learning and AI hold promise for identifying complex high-risk phenotypes.
• Mitral valve repair is preferred, but success depends on operator/center volume and valve characteristics.

Conclusions:

• Current guidelines for intervention in asymptomatic severe mitral regurgitation may be insufficient.
• Integrating advanced imaging and electrophysiological markers with AI can refine patient selection for early intervention.
• Establishing high-volume centers of excellence for mitral valve repair is crucial for optimizing outcomes.