Identifying clinical phenotype clusters for coronary artery disease: a novel approach using clustering by unsupervised machine learning

Abstract Background Guideline recommendations for the prevention of cardiovascular (CV) events in patients with coronary artery disease (CAD) are one-size-fits-all. Clinically identifiable phenotypes needing specific considerations might exist. Purpose To identify phenotypes in patients with CAD based on clinical characteristics and their relationship with the risk of new CV events and treatment benefits. Methods Unsupervised machine learning through latent class analysis (LCA) was performed on patients with CAD included in the UCC-SMART cohort (n = 5,888) and SWEDEHEART registry (n = 67,428), to identify patient clusters (i.e., phenotypes) based on clinical characteristics: age, sex, body-mass-index, diastolic blood pressure, history of diabetes, myocardial infarction, atrial fibrillation, polyvascular disease, current smoking, non-high density lipoprotein cholesterol, estimated glomerular filtration rate, and C-reactive protein. Number of clusters was determined using the Bayesian information criterion. Event free survival and hazard ratios (HR) for major adverse cardiovascular events (MACE) as primary endpoint, including non-fatal myocardial infarction, non-fatal stroke, and cardiovascular death, were estimated per cluster. The LCA were performed in each respective cohort and cross-validated in the other cohort to assess impact of variations in clustering variables on cluster reproducibility. In UCC-SMART, bootstrapping was performed the assess stability of the LCA model and impact on cluster assignment. Results In UCC-SMART four distinct phenotypes within the CAD population could be distinguished: Phenotype A (16%) of mostly males with isolated CAD, phenotype B (30%) of older patients with polyvascular disease, Phenotype C (28%) of only women with limited traditional risk factors and Phenotype D (27%) young, current smokers with a premature event (Figure 1). Cross-validation in SWEDEHEART identified roughly the same four phenotypes, i.e., phenotype A (39%) with 100% males and 3.8% polyvascular disease, phenotype B (8%) with average age of 71 years and 30% polyvascular disease, phenotype C (19%) with 100% women, and phenotype D (34%) 54 years and 25% current smokers. Notably, although age was comparable between phenotype A and C, MACE-free survival was worse for the female-only cluster. Similarly, although patients where on average 9 years younger in phenotype D when compared to phenotype A, MACE-free survival was worse (Figure 2). No significant changes in clustering models were observed in the bootstrap samples. Conclusion Phenotypes can be distinguished within patients with CAD. These phenotypes are related to differences in the risk of recurrent events. These findings suggest that specific guideline recommendations for these groups could be considered.Cluster interpretation in SMARTSurvival along cluster lines