In mitral valve prolapse, myocardial fibrosis at cardiac MRI was associated with an increased risk of unexplained syncope, sustained ventricular tachycardia, or sudden cardiac death. Background Patients with mitral valve prolapse (MVP) may develop adverse outcomes even in the absence of mitral regurgitation or left ventricular (LV) dysfunction. Purpose To investigate the progno...

The use of apical views focused on the left atrium (LA) has improved the accuracy of LA volume evaluation by two-dimensional (2D) echocardiography. However, routine cardiovascular magnetic resonance (CMR) evaluation of LA volumes still uses standard 2- and 4-chamber cine images focused on the left ventricle (LV). To investigate the potential of LA-focused CMR cine images, we compared LA maximuml (LAVmax) and minimum (LAVmin) volumes, and emptying fraction (LAEF), calculated on both standard and LA-focused long-axis cine images, with LA volumes and LAEF obtained by short-axis cine stacks covering the LA. LA strain was also calculated and compared between standard and LA-focused images. LA volumes and LAEF were obtained from 108 consecutive patients by applying the biplane area-length algorithm to both standard and LA-focused 2- and 4-chamber cine images. Manual segmentation of a short-axis cine stack covering the LA was used as the reference method. In addition, LA strain reservoir (εs), conduit (εe) and booster pump (εa) were calculated using CMR feature-tracking. Compared to the reference method, the standard approach significantly underestimated LA volumes (LAVmax: bias − 13 ml; LOA = + 11, − 37 ml; LAVmax i: bias − 7 ml/m2; LOA = + 7, − 21 ml/m2; LAVmin; bias − 10 ml, LOA: + 9, − 28 ml; LAVmin i: bias − 5 ml/m2, LOA: + 5, − 16 ml/m2), and overestimated LA-EF (bias 5%, LOA: + 23, − 14%). Conversely, LA volumes (LAVmax: bias 0 ml; LOA: + 10, − 10 ml; LAVmax i: bias 0 ml/m2; LOA: + 5, − 6 ml/m2; LAVmin: bias − 2 ml; LOA: + 7, − 10 ml; LAVmin i: bias − 1 ml/m2; LOA: + 3, − 5 ml/m2) and LAEF (bias 2%, LOA: + 11, − 7%) by LA-focused cine images were similar to those measured using the reference method. LA volumes by LA-focused images were obtained faster than using the reference method (1.2 vs 4.5 min, p < 0.001). LA strain (εs: bias 7%, LOA = 25, − 11%; εe: bias 4%, LOA = 15, − 8%; εa: bias 3%, LOA = 14, − 8%) was significantly higher in standard vs. LA-focused images (p < 0.001). LA volumes and LAEF measured using dedicated LA-focused long-axis cine images are more accurate than using standard LV-focused cine images. Moreover, LA strain is significantly lower in LA-focused vs. standard images.

Purpose To use unsupervised machine learning to identify phenotypic clusters with increased risk of arrhythmic mitral valve prolapse (MVP). Materials and Methods This retrospective study included patients with MVP without hemodynamically significant mitral regurgitation or left ventricular (LV) dysfunction undergoing late gadolinium enhancement (LGE) cardiac MRI between October 2007 and June 2020 in 15 European tertiary centers. The study end point was a composite of sustained ventricular tachycardia, (aborted) sudden cardiac death, or unexplained syncope. Unsupervised data-driven hierarchical k-mean algorithm was utilized to identify phenotypic clusters. The association between clusters and the study end point was assessed by Cox proportional hazards model. Results A total of 474 patients (mean age, 47 years ± 16 [SD]; 244 female, 230 male) with two phenotypic clusters were identified. Patients in cluster 2 (199 of 474, 42%) had more severe mitral valve degeneration (ie, bileaflet MVP and leaflet displacement), left and right heart chamber remodeling, and myocardial fibrosis as assessed with LGE cardiac MRI than those in cluster 1. Demographic and clinical features (ie, symptoms, arrhythmias at Holter monitoring) had negligible contribution in differentiating the two clusters. Compared with cluster 1, the risk of developing the study end point over a median follow-up of 39 months was significantly higher in cluster 2 patients (hazard ratio: 3.79 [95% CI: 1.19, 12.12], P = .02) after adjustment for LGE extent. Conclusion Among patients with MVP without significant mitral regurgitation or LV dysfunction, unsupervised machine learning enabled the identification of two phenotypic clusters with distinct arrhythmic outcomes based primarily on cardiac MRI features. These results encourage the use of in-depth imaging-based phenotyping for implementing arrhythmic risk prediction in MVP. Keywords: MR Imaging, Cardiac, Cardiac MRI, Mitral Valve Prolapse, Cluster Analysis, Ventricular Arrhythmia, Sudden Cardiac Death, Unsupervised Machine Learning Supplemental material is available for this article. © RSNA, 2024.