Unsupervised machine learning identified 4 distinct coronary artery disease clusters, with major adverse cardiovascular and cerebrovascular event rates ranging from 23% to 41% across subgroups.
Cohort (n=1,329)
Does unsupervised machine learning clustering improve risk assessment for major adverse cardiovascular and cerebrovascular events and mortality compared to pooled cohort equations in patients with coronary artery disease?
Unsupervised machine learning can identify distinct clinical subgroups of coronary artery disease patients, offering improved risk stratification for adverse cardiovascular events compared to traditional pooled cohort equations.
Background The promise of precision population health includes the ability to use robust patient data to tailor prevention and care to specific groups. Advanced analytics may allow for automated detection of clinically informative subgroups that account for clinical, genetic, and environmental variability. This study sought to evaluate whether unsupervised machine learning approaches could interpret heterogeneous and missing clinical data to discover clinically important coronary artery disease subgroups. Methods and Results The Genetic Determinants of Peripheral Arterial Disease study is a prospective cohort that includes individuals with newly diagnosed and/or symptomatic coronary artery disease. We applied generalized low rank modeling and K-means cluster analysis using 155 phenotypic and genetic variables from 1329 participants. Cox proportional hazard models were used to examine associations between clusters and major adverse cardiovascular and cerebrovascular events and all-cause mortality. We then compared performance of risk stratification based on clusters and the American College of Cardiology/American Heart Association pooled cohort equations. Unsupervised analysis identified 4 phenotypically and prognostically distinct clusters. All-cause mortality was highest in cluster 1 (oldest/most comorbid; 26%), whereas major adverse cardiovascular and cerebrovascular event rates were highest in cluster 2 (youngest/multiethnic; 41%). Cluster 4 (middle-aged/healthiest behaviors) experienced more incident major adverse cardiovascular and cerebrovascular events (30%) than cluster 3 (middle-aged/lowest medication adherence; 23%), despite apparently similar risk factor and lifestyle profiles. In comparison with the pooled cohort equations, cluster membership was more informative for risk assessment of myocardial infarction, stroke, and mortality. Conclusions Unsupervised clustering identified 4 unique coronary artery disease subgroups with distinct clinical trajectories. Flexible unsupervised machine learning algorithms offer the ability to meaningfully process heterogeneous patient data and provide sharper insights into disease characterization and risk assessment. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT00380185.
Flores et al. (2021) conducted a cohort in Coronary artery disease (n=1,329). Unsupervised machine learning (K-means cluster analysis) vs. American College of Cardiology/American Heart Association pooled cohort equations was evaluated on Major adverse cardiovascular and cerebrovascular events and all-cause mortality. Unsupervised machine learning identified 4 distinct coronary artery disease clusters, with major adverse cardiovascular and cerebrovascular event rates ranging from 23% to 41% across subgroups.