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

Abstract

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.