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Objective: The effect of mitral valve (MV) surgery on the natural history of ventricular arrhythmia (VA) in patients with arrhythmic MV prolapse remains unknown. We sought to evaluate the cumulative incidence of VA at 1 year after surgical mitral repair. Methods: A retrospective review of progressively captured data identified 204 consecutive patients who underwent elective MV repair for significant degenerative mitral regurgitation as a first-time cardiovascular intervention in a quaternary reference center between January 2018 and December 2020. A subset of 62 consecutive patients with diagnosed arrhythmic MV prolapse was further evaluated for recurrent VA after MV repair. Results: The median age was 62 years (range, 27-77 years) and 26 of 62 (41.9%) were female. The median time from initial mitral regurgitation/MV prolaspe diagnosis-to-referral was 13.8 years (interquartile range [IQR], 5.4-25) and from VA diagnosis-to-referral was 8 years (IQR, 3-10.6). Using the Lown-Wolf classification, complex VA (Lown grade ≥3) was identified in 36 of 62 patients (58%) at baseline, whereas 8 of 62 (13%) had a cardioverter/defibrillator implanted for primary (4/8) or secondary (4/8) prevention. Left ventricular myocardial scar was confirmed in 23 of 34 (68%) of patients scanned at baseline. The prevailing valve phenotype was bileaflet Barlow (59/62; 95.2%). All patients underwent surgical MV repair by the same team. Surgical repair was stabilized with an annuloplasty prosthesis (median size 36 mm [IQR, 34-38]). Concomitant procedures included tricuspid valve repair (51/62; 82.3%), cryo-maze ± left atrial appendage exclusion (14/62, 23%), and endocardial cryoablation of VA ectopy (4/62; 6.5%). The 30-day and 1-year freedom from recurrent VA were 98.4% and 75.9%, respectively. Absent VA after mitral repair was uniformly observed in patients with minor VA at baseline. Absent VA after mitral repair was uniformly observed in patients with minor VA preoperatively. Complex baseline VA was the strongest predictor of recurrent VA (hazard ratio, 10.8; 95% confidence interval, 1.4-84.2; P = .024), irrespective of myocardial fibrosis. Conclusions: In a series of 62 consecutive patients operated electively for arrhythmic mitral prolapse, VA remained undetected in 75.9% of patients at 1 year. Freedom from recurrent VA was greater among patients without complex VA preoperatively, whereas baseline Lown grade ≥3 was the strongest independent risk factor for recurrent VA at 1 year. These findings attest to the importance of early recognition and prompt referral of patients with mitral prolapse and progressive VA to specialty interdisciplinary care.

BackgroundVisual interpretation of PET and CMR may fail to identify cardiac sarcoidosis (CS) with high specificity. This study aimed to evaluate the role of [18F]FDG PET and late gadolinium enhancement (LGE)-CMR radiomic features in differentiating CS from another cause of myocardial inflammation, in this case patients with cardiac-related clinical symptoms following COVID-19.Methods[18F]FDG PET and LGE-CMR were treated separately in this work. There were 35 post-COVID-19 (PC) and 40 CS datasets. Regions of interest were delineated manually around the entire left ventricle for the PET and LGE-CMR datasets. Radiomic features were then extracted. The ability of individual features to correctly identify image data as CS or PC was tested to predict the clinical classification of CS vs. PC using Mann–Whitney U-tests and logistic regression. Features were retained if the P-value was 0.5, and the accuracy was >0.7. After applying the correlation test, uncorrelated features were used as a signature (joint features) to train machine learning classifiers. For LGE-CMR analysis, to further improve the results, different classifiers were used for individual features besides logistic regression, and the results of individual features of each classifier were screened to create a signature that included all features that followed the previously mentioned criteria and used it them as input for machine learning classifiers.ResultsThe Mann–Whitney U-tests and logistic regression were trained on individual features to build a collection of features. For [18F]FDG PET analysis, the maximum target-to-background ratio (TBRmax) showed a high area under the curve (AUC) and accuracy with small P-values (