Predicting New-Onset Atrial Fibrillation in Hypertrophic Cardiomyopathy: A Review

Hypertrophic cardiomyopathy (HCM) is a condition characterized by left ventricular hypertrophy, with physiopathological remodeling that predisposes patients to atrial fibrillation (AF). The electrocardiogram is a basic diagnostic tool for evaluating heart electrical activity. Key electrocardiographic features that correlate with AF onset are P-wave duration, P-wave dispersion, and electromechanical delay in left atrium (LA). Clinical markers, including age, body mass index, New York Heart Association functional class, and heart failure symptoms, are also strong predictors of AF in HCM. Risk scores have been created using multiple variables to better predict AF development. Increasing knowledge of genetic subsets in HCM and cardiovascular pathology in general has provided novel insight in this context. Structural and mechanical LA remodeling, including fibrosis, altered LA function, and changes in atrial size, further contribute to AF risk prediction. Cardiovascular magnetic resonance (CMR) and echocardiographic measures provide accurate information about atrial structure and function. Machine learning models are increasingly being utilized to refine risk prediction, incorporating a wide range of variables. This review highlights the multifaceted approach required to understand and predict AF development in HCM. Such an approach is imperative to enhance prognostic accuracy and improve the quality of life of these patients. Further research is necessary to refine patient outcomes and develop customized management strategies for HCM-associated AF.