Quantification of Mitral Regurgitation in Mitral Valve Prolapse by Three-Dimensional Vena Contracta Area: Derived Cutoff Values and Comparison With Two-Dimensional Multiparametric Approach.

BACKGROUND: Echocardiographic grading of mitral regurgitation (MR) in mitral valve prolapse (MVP) is challenging. Three-dimensional (3D) vena contracta area (VCA) has been proposed as a valuable method. However, data defining the cutoff values of severity and validation in the subset of patients with MVP are scarce. The aim of this study was to validate the 3D VCA by 3D color-Doppler transesophageal echocardiography (TEE) in patients with MVP and to define the cutoff values of severity grading. The secondary aim was to compare 3D VCA to the effective regurgitant orifice area estimation by proximal isovelocity surface area (EROA-PISA) method. METHODS: A total of 1,138 patients with at least moderate MR who underwent TEE were included. Three-dimensional VCA was measured, and the cutoff value and area under the curve (AUC) for the prediction of severe MR were estimated by receiver operating characteristic curve using a guideline-suggested multiparametric approach as the reference standard. In a subgroup of patients, 3D regurgitant volume (RV) and 3D fraction were calculated from mitral and left ventricular outflow tract stroke volumes to further validate 3D VCA against a 3D volumetric reference standard. RESULTS: The optimal 3D VCA cutoff value for predicting severe MR was 0.45 cm2 (specificity, 0.87; sensitivity, 0.90) with an AUC of 0.95 using a multiparametric approach as reference. Three-dimensional VCA had a good linear correlation with EROA-PISA (r = 0.62, P < .05) with larger values compared to EROA-PISA (0.63 cm2 vs 0.44 cm2, P < .05). A cutoff of 0.50 cm2 (AUC of 0.84; sensitivity, 0.78; specificity, 0.78) predicts an EROA-PISA of 0.40 cm2. Three-dimensional VCA had a good linear correlation with 3D RV (r = 0.56, P < .01), with an AUC of 0.86 to predict a 3D fraction >50%. CONCLUSIONS: The present study suggests 0.45 cm2 as the best cutoff value of 3D VCA to define severe MR in patients with MVP, showing an optimal agreement with the reference standard multiparametric approach and 3D RV.

Tricuspid regurgitation in the context of severe left-sided valvular disease: Patients characteristics and outcome.

Background: We aimed to assess the characteristics, management and long-term prognosis of a cohort of patients with multiple valvular disease, focusing on the context of severe mitral or aortic disease with concomitant significant tricuspid regurgitation (TR). Methods: After using a propensity score matching for age, 975 patients with ≥ moderate TR, diagnosed at our centers from 2012 to 2020, were included and divided in four groups, including isolated TR patients as reference group. Primary endpoint was all-cause death (ACD), secondary endpoint was the composite of heart failure (HF) hospitalization + any valvular intervention. Results: Patients with isolated TR (356, 37 %) had more history of atrial fibrillation and were more often asymptomatic and with preserved left-ventricular ejection fraction (LVEF). Patients with severe mitral regurgitation (MR) + TR (466, 48 %) showed higher rates of concomitant coronary artery disease, advanced functional class symptoms and larger left atrial volumes. Severe aortic stenosis (AS) patients (131, 13 %) were older, with more comorbidities and lower LVEF. Patients with severe aortic regurgitation and TR (22, 2 %) were younger, with larger LV dimensions and higher pulmonary arterial pressures.After a median follow-up of 2.8 years, both endpoints were univariably more frequent in patients with severe AS + TR (all p < 0.001), but after comprehensive adjustment difference in the primary endpoint became insignificant, underscoring the serious outcomes of all significant TR groups significantly. Overall, in 44 (5 %) patients tricuspid intervention was performed, with no differences between groups in term of frequency of concomitant or staged tricuspid valve surgical treatment. Conclusions: In the context of severe left-sided VD, concomitant significant TR is common, and each subtype presents with different clinical and echocardiographic features: patients with severe AS and TR have considerable worse prognosis, although comprehensive adjustment reflected the poor outcomes affecting all types of patients with significant TR. In this scenario, TR was profoundly undertreated.

Transcatheter Ablation of Atrial Fibrillation in Patients With Hypertrophic Cardiomyopathy: A Multicenter Propensity Score-Based Analysis.

Background: The prognostic impact of catheter ablation (CA) of atrial fibrillation (AF) in hypertrophic cardiomyopathy (HCM) patients has not yet been satisfactorily elucidated. Objectives: The aim of the study was to assess the impact of CA of AF on clinical outcomes in a large cohort of HCM patients. Methods: In this retrospective multicenter study, 555 HCM patients with AF were enrolled, 140 undergoing CA and 415 receiving medical therapy. 1:1 propensity score matching led to the inclusion of 226 patients (113 medical group, 113 intervention group) in the final analysis. The primary outcome was a composite of all-cause mortality, heart transplant and acute heart failure exacerbations. Secondary outcomes included AF recurrence and transition to permanent AF. Additionally, an inverse probability weighted (IPW) model was examined. Results: At propensity score matching analysis, after a median follow-up of 58.1 months, the primary endpoint occurred in 29 (25.7%) patients in intervention group vs 42 (37.2%) in medical group (P = 0.9). Thromboembolic strokes and major arrhythmic events in intervention vs medical group were 9.7% vs 7.1% (P = 0.144) and 4.4 vs 8.0% (P = 0.779), respectively. Fewer patients in intervention vs medical group experienced AF recurrences (63.7% vs 84.1%, P = 0.001) and transition to permanent AF pattern (20.4% vs 33.6%, P = 0.026). IPW analysis showed consistent results. Severe complications related to CA were uncommon (0.7%). Conclusions: After 5 years of follow-up, CA did not improve major adverse cardiac outcomes in a large cohort of patients with HCM and AF. Nevertheless, CA seems to facilitate the maintenance of sinus rhythm and slow the progression to permanent AF, without significant safety concerns.

Arrhythmic Mitral Valve Prolapse Phenotype: An Unsupervised Machine Learning Analysis Using a Multicenter Cardiac MRI Registry.

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.