Identifying the High-Risk Arrhythmic Mitral Valve Prolapse Phenotype by Imaging.
Authors
Affiliations (4)
Affiliations (4)
- University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA. Electronic address: [email protected].
- Oslo University Hospital, Oslo, Norway.
- Massachusetts General Hospital, Boston, Massachusetts, USA.
- Department of Cardiology, University of California San Francisco, San Francisco, California, USA.
Abstract
Within arrhythmogenic mitral valve prolapse (AMVP), a distinct subset of mitral valve prolapse (MVP), there is a small group that carries a higher risk of malignant ventricular arrhythmias and sudden cardiac death called malignant MVP. The malignant MVP phenotype is over-represented in young women with unexplained sudden cardiac death on autopsy. AMVP is defined by myxomatous, often bileaflet MVP with mitral annular disjunction, abnormal valvular-myocardial mechanics, complex ventricular ectopy, and nonsustained ventricular tachycardia. Excessive mechanical force from systolic annular expansion, papillary muscle traction, and basal inferolateral "curling" promotes focal replacement and diffuse interstitial fibrosis, creating an arrhythmogenic substrate. Premature ventricular contractions act as triggers, whereas autonomic modulation influences the timing of ventricular arrhythmia initiation and progression to ventricular fibrillation or sustained monomorphic ventricular tachycardia. Baseline evaluation with an electrocardiogram and transthoracic echocardiography is necessary to identify AMVP, exclude severe mitral regurgitation, and identify high-risk markers. If no premature ventricular contractions are found on a resting electrocardiogram, exercise stress testing and extended rhythm monitoring can help bring out ventricular premature contractions, which are triggers of ventricular arrhythmias. Cardiac magnetic resonance provides incremental prognostic value with late gadolinium enhancement reflecting replacement fibrosis and T1 mapping identifying diffuse fibrosis, even in those with no late gadolinium enhancement. Machine-learning analysis may further refine risk stratification by integrating high-risk features such as mitral annular disjunction, fibrosis extent, and location that outperform symptom-based assessment. As transcatheter and surgical mitral interventions expand, accurate phenotyping of AMVP is increasingly important, as hemodynamic correction of mitral regurgitation alone does not eliminate the underlying arrhythmogenic substrate. Comprehensive multimodality imaging and integrated rhythm assessment are therefore central to individualized risk stratification and management in this vulnerable population.