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...

Family history for CAD (coronary artery disease) is an established cardiovascular (CV) risk factor and it is progressively acquiring importance in patients’ CV risk stratification. Numerous studies have demonstrated that individuals with a first-degree relative affected by CAD have a significantly higher risk of developing the condition themselves; in particular, when CAD occurs at an early age in relatives. Indeed, recently published CCS (chronic coronary syndrome) ESC (European Society of Cardiology) guidelines include family history (FH) as a risk factor to consider when calculating pre-test risk for CAD. ESC guidelines on preventive cardiology (2021) only suggested CV risk assessment in the presence of a positive FH for CV disease, not considering it in the actual risk scores. Evidence suggests that positive anamnesis for relatives affected by CAD correlates with ACS (acute coronary syndrome) and CAD, with slight differences in relative risk as far as the degree of kinship is concerned. Genetic factors contribute to this correlation by influencing key processes that affect heart health, such as cholesterol metabolism, blood pressure regulation, and inflammatory responses. New technologies in the genetics field are increasing the availability of genome sequencing, and new polymorphism panels are being tested as predictive for CAD, objectifying familiarity. Advances in imaging techniques allow the assessment of coronary atherosclerosis and its composition, and these are acquiring strength in evidence and recommendations in ESC guidelines as a way to define coronary disease in low and low-to-intermediate risk patients and to guide medical therapy and interventional procedures. Use of these emerging tools to guide screening is likely to be extended, beyond high CV risk patients, to individuals with FH for early CAD and/or specific genetic profiles, as recent evidence in the literature is suggesting.

Purpose: Chronic mitral regurgitation promotes left atrial (LA) remodeling. However, the significance of LA dysfunction in the setting of ventricular functional mitral regurgitation (FMR) has not been fully investigated. Our aim was to assess the prognostic impact of peak atrial longitudinal strain (PALS), a surrogate of LA function, in patients with FMR and reduced left ventricular ejection fraction (LVEF). Methods: Patients with at least mild ventricular FMR and LVEF < 50% under optimized medical therapy who underwent transthoracic echocardiography at a single center were retrospectively identified in the laboratory database. PALS was assessed by 2D speckle tracking in the apical 4-chamber view and the study population was divided in two groups according to the best cut-off value of PALS, using receiver operating characteristics (ROC) curve analysis. The primary endpoint-point was all-cause mortality. Results: A total of 307 patients (median age 70 years, 77% male) were included. Median LVEF was 35% (IQR: 27 – 40%) and median effective regurgitant orifice area (EROA) was 15mm² (IQR: 9 – 22mm²). According to current European guidelines, 32 patients had severe FMR (10%). During a median follow-up of 3.5 years (IQR 1.4 – 6.6), 148 patients died. The unadjusted mortality incidence per 100 persons-years increased with progressively lower values of PALS. On multivariable analysis, PALS remained independently associated with all-cause mortality (adjusted hazard ratio 1.052 per % decrease; 95% CI: 1.010 – 1.095; P = 0.016), even after adjustment for several (n = 14) clinical and echocardiographic confounders. Conclusion: PALS is independently associated with all-cause mortality in patients with reduced LVEF and ventricular FMR. Graphical

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.

We describe a case of fulminant eosinophilic myocarditis as the first presentation of eosinophilic granulomatosis with polyangiitis, promptly managed with extracorporeal membrane oxygenation. This case highlights the multidisciplinary work involving all health care professionals in the acute management of these patients and discusses it from an educational point of view. (Level of Difficulty: Intermediate.)

The 2019 ESC guidelines on chronic coronary syndromes updated the method for estimating the pre-test probability (PTP) of obstructive coronary artery disease (CAD). We aimed to compare the performance of the new PTP method against the 2013 prediction model in patients with stable chest pain undergoing coronary computed tomography angiography (CCTA) for suspected CAD. We conducted a single-center cross-sectional study enrolling 320 consecutive patients undergoing CCTA for suspected CAD. Obstructive CAD was defined as any ≥50% luminal stenosis on CCTA. Whenever invasive coronary angiography was subsequently performed, patients were reclassified accordingly. The two PTP prediction models were assessed for calibration, discrimination and the ability to change the downstream diagnostic pathway. The observed prevalence of obstructive CAD was 16.3% (n=52). The 2013 prediction model significantly overestimated the likelihood of obstructive CAD (relative overestimation of 130%, p=0.005), while the updated 2019 method showed good calibration (relative underestimation of 6.5%, p=0.712). The two approaches showed similar discriminative power, with C-statistics of 0.73 (95% CI: 0.66-0.80) and 0.74 (95% CI: 0.66-0.81) for the 2013 and 2019 methods, respectively (p=0.933). Reclassification of PTP using the new method resulted in a net reclassification improvement of 0.10 (p=0.001). The updated 2019 prediction model provides a more accurate estimation of pre-test probabilities of obstructive CAD than the previous model. Adoption of this new score may improve disease prediction and influence the selection of non-invasive testing.