Aims: While factors associated with adverse events are well elucidated in setting of isolated left ventricular dysfunction, clinical and imaging-based prognosticators of adverse outcomes are lacking in context of biventricular dysfunction. The purpose of this study was to establish role of clinical variables in prognosis of biventricular heart failure (HF), as assessed by cardiac magnetic resonance imaging.
Methods: Study cohort consisted of 840 patients enrolled in DERIVATE registry with coexisting CMR-derived right ventricular (RV) and left ventricular (LV) dysfunction, as defined by RV and LV ejection fractions ≤45 % and ≤ 50 %, respectively.
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.
View Article and Find Full Text PDF