| Title: | Explainable temporal machine learning of multimorbidity trajectories after acute myocardial infarction: complementing clinical risk scores with mechanistic phenotypes |
| Journal: | Journal of the American Medical Informatics Association |
| Published: | 6 Aug 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/42562770/ |
| DOI: | https://doi.org/10.1093/jamia/ocag135 |
Publication 21068
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Abstract
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.</p>
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.</p>
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.</p>
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.</p>
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.</p>
4 Authors
- Anthony Onoja
- Kris Elomaa
- Anthony D Whetton
- Nophar Geifman
1 Application
| Application ID | Title |
|---|---|
| 83988 | Endotypes and Trajectories of Multi-morbidity for Stratification and Biomarker Analysis |
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