Incremental predictive value of CMR phenotyping for major adverse cardiovascular events: an interpretable machine learning study from UK Biobank.

Aims: To characterize cardiac magnetic resonance (CMR)-derived phenotypes in a population-based cohort and to evaluate their incremental prognostic value for major adverse cardiovascular events (MACE) beyond traditional risk factors. Methods: Participants from the UK Biobank imaging cohort without prior cardiovascular diseases were included. Unsupervised k-means clustering and Kaplan-Meier analyses identified distinct CMR-derived phenotypes. Sequential XGBoost models were developed to evaluate the incremental predictive value of CMR features over conventional risk factors, with SHAP used for interpretation. Cross-modality transportability was assessed in an independent Asian cohort using echocardiographic data as a surrogate for CMR-derived variables. Results: Among 27,254 participants, 785 (2.88%) experienced MACE over a median follow-up of approximately 5 years. Two phenotypes were identified, with the higher-risk phenotype characterized by increased cardiac volumes, impaired cardiac function, a tendency toward myocardial hypertrophy, and reduced myocardial strain. The CMR-enhanced model improved MACE prediction compared with traditional risk factors alone (AUC: 0.76, 95% CI: 0.73-0.79; vs. AUC: 0.61, 95% CI: 0.58-0.64, Conclusion: CMR-derived phenotypes provide incremental prognostic value beyond traditional risk factors and may improve early identification of individuals at elevated cardiovascular risk.