Dweck, Marc Richard

A vendor-neutral Agatston score enabled consistent aortic valve calcium quantification across CT systems and improved identification of severe aortic stenosis in patients with discordant echocardiographic findings.

Abstract The persistent global rise in atherosclerotic cardiovascular disease (ASCVD) challenges the effectiveness of current risk factor-based algorithms in primary prevention. Most myocardial infarctions occur in individuals without prior symptoms or recognized standard modifiable risk factors, underscoring the need to improve risk stratification. Coronary CT imaging, including coronary artery calcium scoring (CACS) and coronary computed tomography angiography (CCTA), enables direct non-invasive visualization of coronary atherosclerosis, supporting a shift from population-based prevention towards personalized, disease-based risk assessment and targeted preventive treatment. CCTA provides comprehensive evaluation of plaque burden, composition, stenosis severity, and high-risk plaque (HRP) features, thereby refining risk stratification and guiding personalized treatment decisions. The presence and total burden of coronary atherosclerotic plaque are considered the primary CCTA-derived markers for guiding preventive therapy, while noncalcified plaque burden and HRP features may support treatment intensification. Advances in CT technology, including photon-counting CT, and the application of artificial intelligence-based tools may further refine risk assessment by enabling accurate and reproducible plaque quantification and characterization. Although no randomized trials have yet demonstrated that CCTA-based screening improves clinical outcomes in asymptomatic individuals compared with standard care, the same is true of traditional cardiovascular risk scores. Three large ongoing randomized trials are currently evaluating whether CCTA-guided, personalized prevention strategies can reduce ASCVD events. The results are eagerly awaited and may redefine the role of CCTA in asymptomatic individuals.

Abstract Heart involvement in eosinophilia represents a rare entity with substantial cardiac morbidity and mortality. This disorder is often overlooked and diagnosed in its advanced stages. In a subgroup of patients with eosinophilic heart disease, peripheral blood eosinophilia may not be present, and the biopsy of the affected organ or tissue is essential to establish the correct diagnosis. In this clinical consensus statement, an expert consensus group reviews the epidemiology, aetiology, diagnosis, and management of eosinophilic heart disease. The aim of the document is to increase awareness about the cardiac involvement associated with eosinophilia in order to diagnose the disease in early phases and prevent deleterious heart damage and adverse outcomes of affected individuals. The spectrum of physiological and immunological derangements requires a multidisciplinary approach for optimal management of affected individuals.

Abstract Background Aortic valve calcification (AVC), as measured by gold-standard computed tomography (CT) Agatston score, provides an anatomic assessment of aortic stenosis (AS) severity and is a key predictor of AS progression and need for valve replacement. AVC detection and quantification from transthoracic echocardiography (TTE) could expand AS early diagnosis and risk stratification, currently limited by CT availability and radiation exposure. Methods A multi-view video-based deep learning framework was developed using 1166 TTE aortic valve videos from 187 TTE studies acquired in 110 patients with available AVC score by CT. EchoAVC architecture includes a feature extraction model followed by a quality-aware model that aggregates video-level information to obtain patient-level predictions for AVC detection and quantification (score). The framework was validated internally with 173 TTE studies from 86 patients across seven centres, and externally using 430 TTE studies from 280 patients across four different centres. The associations between EchoAVC estimations and AS severity, progression, and need for aortic valve replacement were examined. Results EchoAVC demonstrated excellent performance in AVC detection (AUROC 0.98, accuracy 94.4%), and quantification (R = 0.64) in the external multi-centre testing set. EchoAVC score was correlated with echocardiographic AS severity descriptors, including aortic valve mean pressure gradient (ρ = 0.749) and peak velocity (ρ = 0.757), and predicted future increase in mean pressure gradient (ρ = 0.382), peak velocity (ρ = 0.433) and calcium score by CT (ρ = 0.650). In 361 patients followed for a median of 3.8 years, 139 underwent aortic valve replacement. Baseline presence and extent of AVC as predicted by EchoAVC showed strong risk-stratification power for aortic valve replacement, remarkably in line with those obtained by CT, and incremental over TTE AS descriptors. EchoAVC was further tested in routine clinical practice images, confirming strong associations with AS severity and progression, including stratification for incident AS in previously unaffected individuals (p<0.001). Conclusions EchoAVC enables accurate and non-invasive detection and quantification of AVC, offering substantial diagnostic and prognostic value for aortic stenosis progression and need for valve replacement. This technique holds promise as a scalable tool for early detection and clinical management of aortic valve stenosis. Clinical Perspective What Is New? * Aortic valve calcification can be detected and quantified on standard, two-dimensional transthoracic echocardiography * EchoAVC score is associated with echocardiographic metrics of aortic stenosis severity * EchoAVC predicts progressive increase in aortic stenosis severity and need for aortic valve replacement What Are the Clinical Implications? * This openly available deep learning framework can be used to estimate aortic valve calcium in patients at risk of or presenting aortic stenosis to predict aortic stenosis progression * EchoAVC may help identify patients likely to have high aortic valve calcium and prioritize them for confirmatory CT assessment, thereby supporting earlier identification of calcific aortic valve disease in clinical pathways.