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, P < 0.001), with strong performance for heart failure (AUC: 0.90, 95% CI: 0.86-0.94). In the cross-modality validation cohort, the simplified model yielded an AUC of 0.69 (95% CI: 0.56-0.83). Higher age, left ventricular mass index, global wall thickness, and lower levels of high-density lipoprotein cholesterol, left atrial ejection fraction, right atrial stroke volume were the key contributors to increased MACE risk. CONCLUSION: CMR-derived phenotypes provide incremental prognostic value beyond traditional risk factors and may improve early identification of individuals at elevated cardiovascular risk.