Unsupervised phenotype clustering of non-ischemic dilated cardiomyopathy with AI-assisted T1 mapping cardiac MR
Authors
Affiliations (1)
Affiliations (1)
- Seoul National University Bundang Hospital
Abstract
AimsPrognostic stratification and individual management are essential in the heterogeneous population of non-ischemic dilated cardiomyopathy (NIDCM). We applied unsupervised machine learning (ML) clustering in NIDCM cohorts, using semi-automated artificial intelligence (AI)-based cardiac magnetic resonance imaging (CMR) measurements with multimodal data to identify distinct phenotypes, characterize echocardiographic remodeling trajectories, and evaluate prognostic significance. Methods and resultsWe analyzed 347 patients with NIDCM from two tertiary centers who underwent CMR and echocardiography at baseline, with follow-up echocardiography at a median 12 months. The cohort was randomly divided into derivation (n=242) and validation (n=105) sets using stratification by the composite outcome. Remodeling trajectories were evaluated using follow-up echocardiographic changes, and associations with outcomes were assessed by multivariable Cox regression adjusted for age and sex. Using eleven comprehensive clinical, laboratory, echocardiographic, and CMR-derived variables, partitioning around medoids clustering identified three phenotypes: (i) a younger, male-predominant preserved phenotype; (ii) a metabolic, fibrotic-remodeling phenotype; and (iii) an atrial fibrillation-predominant biventricular dysfunction phenotype. Cluster 1 showed the most favorable prognosis, whereas Cluster 3 had the highest risk of the composite outcome. Although LV reverse remodeling occurred across all clusters, Cluster 3 was characterized by attenuated LA reverse remodeling, suggesting persistent LA dysfunction. ConclusionUnsupervised ML-based clustering of NIDCM patients, integrating AI-derived CMR parameters with multimodal data, identified three clusters exhibiting distinct patterns in longitudinal echocardiographic trajectories and outcomes. This strategy may enable more individualized management in heterogeneous NIDCM.