Unsupervised Learning for Automated Detection of Coronary Artery Disease Subgroups

Abstract

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.

Keywords: cluster analysis; coronary artery disease; machine learning; phenotype discovery.

Figures

Figure 1. Schematic for generalized low rank modeling.

A, Patient data are condensed to fewer dimensions to allow for analysis using unsupervised K‐means clustering. The “features” matrix is a high‐dimensional data set that includes patient information on demographics and clinical, lifestyle, angiographic, and cardiovascular genetic risk markers. This data set is transformed into a lower dimensional “latent feature” space by approximating the features matrix as the product of 2 matrices, shown as the X (containing each observation) and Y representations (containing the definition for each observation). L,r indicates the loss function that accounts for the accuracy in the data approximation and regularizes the latent feature representation to prevent overfitting. B, After cluster analysis, data are then transformed back to their original form and analyzed to discover subgroup characteristics and compare long‐term outcomes across clusters.

Figure 2. Distinct subgroups of patients with coronary artery disease identified by unsupervised clustering.

Plot showing 4 distinct groups of patients identified by K‐means clustering. Data are plotted based on the top 20 principal components across the first 2 discriminant functions to form a 2‐dimensional plot.

Figure 3. Schematic representation of the 4 CAD clusters and their major features.

ABI indicates ankle‐brachial index; BMI, body mass index; CAD, coronary artery disease; CHF, congestive heart failure; CVA, cerebrovascular accident; LDL, low‐density lipoprotein; MACCE, major adverse cardiovascular and cerebrovascular events; MI, myocardial infarction; and PAD, peripheral artery disease.

Figure 4. Long‐term outcomes of the 4 coronary artery disease clusters.

Kaplan–Meier curves showing (A) MACCE* and (B) all‐cause mortality. *Primary MACCE composite included myocardial infarction, stroke, coronary revascularization, and peripheral revascularization. MACCE indicates major adverse cardiovascular and cerebrovascular events.

Figure 5. Comparison of clustering to PCE risk groups for prediction of MACCE* and all‐cause mortality.

*PCE‐consistent MACCE included myocardial infarction, stroke, and death. MACCE indicates major adverse cardiovascular and cerebrovascular events; and PCE, pooled cohort equations.