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Structural and Functional Characterization of the Aorta in Hypertrophic Obstructive Cardiomyopathy.
Circ Heart Fail
January 2025
Aswan Heart Center, Magdi Yacoub Heart Foundation, Egypt (A.M.I., M.R., A. Elsawy, M.H., S.H., W.E., A. Elaithy, A. Elguindy, A. Afifi, Y.A., M.Y.).
Background: Changes in the phenotype and genotype in hypertrophic cardiomyopathy (HCM) are thought to involve the myocardium as well as extracardiac tissues. Here, we describe the structural and functional changes in the ascending aorta of obstructive patients with HCM.
Methods: Changes in the aortic wall were studied in a cohort of 101 consecutive patients with HCM undergoing myectomy and 9 normal controls.
Anonymize or synthesize? Privacy-preserving methods for heart failure score analytics.
Eur Heart J Digit Health
January 2025
Klaus Tschira Institute for Integrative Computational Cardiology, University Hospital Heidelberg, Im Neuenheimer Feld 669, 69120 Heidelberg, Germany.
Aims: Data availability remains a critical challenge in modern, data-driven medical research. Due to the sensitive nature of patient health records, they are rightfully subject to stringent privacy protection measures. One way to overcome these restrictions is to preserve patient privacy by using anonymization and synthetization strategies.
View Article and Find Full Text PDFUse of artificial intelligence to predict outcomes in mild aortic valve stenosis.
Eur Heart J Digit Health
January 2025
Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
Aims: Aortic stenosis (AS) is a common and progressive disease, which, if left untreated, results in increased morbidity and mortality. Monitoring and follow-up care can be challenging due to significant variability in disease progression. This study aimed to develop machine learning models to predict the risks of disease progression and mortality in patients with mild AS.
View Article and Find Full Text PDFMachine learning approaches for risk prediction after percutaneous coronary intervention: a systematic review and meta-analysis.
Eur Heart J Digit Health
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Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114, USA.
Aims: Accurate prediction of clinical outcomes following percutaneous coronary intervention (PCI) is essential for mitigating risk and peri-procedural planning. Traditional risk models have demonstrated a modest predictive value. Machine learning (ML) models offer an alternative risk stratification that may provide improved predictive accuracy.
View Article and Find Full Text PDFMultimodal data integration to predict atrial fibrillation.
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Cardiovascular Division, Department of Medicine, University of Minnesota Medical School, 401 East River Parkway, Minneapolis, MN, USA.
Aims: Many studies have utilized data sources such as clinical variables, polygenic risk scores, electrocardiogram (ECG), and plasma proteins to predict the risk of atrial fibrillation (AF). However, few studies have integrated all four sources from a single study to comprehensively assess AF prediction.
Methods And Results: We included 8374 (Visit 3, 1993-95) and 3730 (Visit 5, 2011-13) participants from the Atherosclerosis Risk in Communities Study to predict incident AF and prevalent (but covert) AF.
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