Subphenotyping of Patients With Aortic Stenosis by Unsupervised Agglomerative Clustering of Echocardiographic and Hemodynamic Data.

OBJECTIVES: The aim of this retrospective analysis was to categorize patients with severe aortic stenosis (AS) according to clinical presentation by applying unsupervised machine learning. BACKGROUND: Patients with severe AS present with heterogeneous clinical phenotypes, depending on disease progression and comorbidities. METHODS: Unsupervised agglomerative clustering was applied to preprocedural data from echocardiography and right heart catheterization from 366 consecutively enrolled patients undergoing transcatheter aortic valve replacement for severe AS. RESULTS: Cluster analysis revealed 4 distinct phenotypes. Patients in cluster 1 (n = 164 [44.8%]), serving as a reference, presented with regular cardiac function and without pulmonary hypertension (PH). Accordingly, estimated 2-year survival was 90.6% (95% CI: 85.8%-95.6%). Clusters 2 (n = 66 [18.0%]) and 4 (n = 91 [24.9%]) both comprised patients with postcapillary PH. Yet patients in cluster 2 with preserved left and right ventricular structure and function showed a similar survival as those in cluster 1 (2-year survival 85.8%; 95% CI: 76.9%-95.6%), whereas patients in cluster 4 with dilatation of all heart chambers and a high prevalence of mitral and tricuspid regurgitation (12.5% and 14.8%, respectively) died more often (2-year survival 74.9% [95% CI: 65.9%-85.2%]; HR for 2-year mortality: 2.8 [95% CI: 1.4-5.5]). Patients in cluster 3, the smallest (n = 45 [12.3%]), displayed the most extensive disease characteristics (ie, left and right heart dysfunction together with combined pre- and postcapillary PH), and 2-year survival was accordingly reduced (77.3% [95% CI: 65.2%-91.6%]; HR for 2-year mortality: 2.6 [95% CI: 1.1-6.2]). CONCLUSIONS: Unsupervised machine learning aids in capturing complex clinical presentations as observed in patients with severe AS. Importantly, structural alterations in left and right heart morphology, possibly due to genetic predisposition, constitute an equally sensitive indicator of poor prognosis compared with high-grade PH.      «

OBJECTIVES: The aim of this retrospective analysis was to categorize patients with severe aortic stenosis (AS) according to clinical presentation by applying unsupervised machine learning. BACKGROUND: Patients with severe AS present with heterogeneous clinical phenotypes, depending on disease progression and comorbidities. METHODS: Unsupervised agglomerative clustering was applied to preprocedural data from echocardiography and right heart catheterization from 366 consecutively enrolled patients...     »