The study analysed hospital diagnoses for five years after incident acute myocardial infarction in 12 701 UK Biobank participants. Dynamic Time Warping clustering grouped patients whose diagnoses built up in a similar pattern, even at different speeds. The groups were cardiometabolic (63.4%), heart-valve, atrial fibrillation and kidney-metabolic disease (13.5%), and smoking-related multisystem disease (23.1%; five-year mortality 43.9%). XGBoost predicted the group from pre-infarction diagnoses with an AUC of 0.906, but only 0.635 without the final pre-infarction week. The SMART score predicted mortality better (C-index 0.65 vs 0.59); trajectory effects lost significance after full adjustment. UK Biobank volunteers are healthier and mostly White British, and only hospital diagnosis codes were used.
Why it is interesting: Diagnosis clusters after a heart attack separated survival before adjustment but added little beyond an established vascular risk score. Their predictive accuracy depended heavily on diagnoses recorded in the week before infarction.
Abstract — the authors' own words, CC BY
Objective
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. Materials and methods
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. Results
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. Discussion
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. Conclusion
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.
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