Phenotypic clustering using cardiovascular magnetic resonance for risk stratification in beta-thalassemia major

Motivation: Machine learning algorithms provide a means to uncover hidden patterns within complex and heterogeneous datasets. Goal(s): We aimed to identify phenogroups among patients with β-thalassemia major (TM) using an unsupervised clustering approach based on demographic, clinical, and CMR data. Approach: We considered 356 β-TM patients who underwent MR for the assessment of iron overload, biventricular function and atrial, and replacement myocardial fibrosis. Results: We identified three mutually exclusive phenogroups characterized by different biventricular function parameters and frequency of replacement myocardial fibrosis and by a different prospective risk of cardiovascular complications. Impact: In TM, unsupervised clustering integrating routinely measured CMR parameters conveys the potential to significantly impact patient care and improve cardiovascular outcomes by enabling early detection of cardiac remodeling and damage, as well as improved risk stratification.