Abstract
This study aimed to identify trajectories of multimorbidity following acute myocardial infarction (AMI), using explainable temporal machine-learning methods, and assess their clinical, prognostic, and biological significance.
Dynamic Time Warping k-means clustering was applied to post-AMI diagnostic sequences from 12 701 UK-Biobank participants. Latent Dirichlet Allocation characterised cluster themes. Multiclass classifiers (CatBoost, XGBoost, random forest, logistic regression) trained on pre-AMI diagnoses and demographics predicted trajectory membership, with SHAP interpretability. SMART scores and Cox models evaluated 5-year mortality; Phenotype-Wide Association (PheWAS) and Reactome pathway enrichment were used to identify associated biological mechanisms.
Three trajectories of multimorbidity were identified: acute cardiorenal-respiratory with metabolic disease (ACUTE-CARD; 63.4%), cardiometabolic disease with arrhythmic-ischemic burden (CARDIOMIX; 13.5%), and smoking-related multisystem multimorbidity (SMO-CARD; 23.1%). XGBoost achieved the highest discrimination (AUC-ROC 0.906; 95% CI, 0.895-0.916), with CatBoost showing comparable performance (AUC-ROC 0.900; 95% CI, 0.889-0.910). SMO-CARD had the highest 5-year mortality (43.9%). The established SMART cardiovascular risk score remained the dominant predictor of mortality while the identified trajectories provided complementary prognostic signals; these associations were weaker after full adjustment for potential confounders with each profile displaying distinct genetic and pathway signatures.
The SMART score outperformed in capturing mortality risk, whereas the trajectories complement it by revealing nuanced clustering of risk factors across organ systems and in identifying trajectory-specific intervention priorities.
Explainable temporal modeling of EHR data reveals clinically interpretable, biologically grounded multimorbidity trajectories after AMI that complement established risk scores and provide a reproducible approach to mechanistic phenotyping and precision care.