Unsupervised Cluster Analysis of Patients With Aortic Stenosis Reveals Distinct Population With Different Phenotypes and Outcomes

Abstract

Background: There is a lack of studies investigating the heterogeneity of patients with aortic stenosis (AS). We explored whether cluster analysis identifies distinct subgroups with different prognostic significances in AS. Methods: Newly diagnosed patients with moderate or severe AS were prospectively enrolled between 2013 and 2016 (n=398, mean 71 years, 55% male). Among demographics, laboratory, and echocardiography parameters (n=32), 11 variables were selected through dimension reduction and used for unsupervised clustering. Phenotypes and causes of mortality were compared between the clusters. Results: Three clusters with markedly different features were identified. Cluster 1 (n=60) was predominantly associated with cardiac dysfunction, cluster 2 (n=86) consisted of elderly with comorbidities, especially end-stage renal disease, whereas cluster 3 (n=252) demonstrated neither cardiac dysfunction nor comorbidities. Although AS severity did not differ, there was a significant difference in adverse outcomes between the clusters during a median 2.4 years follow-up (mortality rate, 13.3% versus 19.8% versus 6.0% for cluster 1, 2, and 3, P <0.001). Particularly, compared with cluster 3, cluster 1 was associated with only cardiac mortality (adjusted hazard ratio, 7.37 [95% CI, 2.00–27.13]; P =0.003), whereas cluster 2 was associated with higher noncardiac mortality (adjusted hazard ratio, 3.35 [95% CI, 1.26–8.90]; P =0.015). Phenotypes and association of clusters with specific outcomes were reproduced in an independent validation cohort (n=262). Conclusions: Unsupervised cluster analysis of patients with AS revealed 3 distinct groups with different causes of death. This provides a new perspective in the categorization of patients with AS that takes into account comorbidities and extravalvular cardiac dysfunction.