Unsupervised Cluster Analysis Reveals Different Phenotypes in Patients after Transcatheter Aortic Valve Replacement

Abstract

The aim of this study was to identify phenotypes with potential prognostic significance in aortic stenosis (AS) patients post-transcatheter aortic valve replacement (TAVR) through a clustering approach. This multicenter retrospective study included 1,365 patients with severe AS who underwent TAVR between January 2015 and March 2019. Among demographics, laboratory, and echocardiography parameters, 20 variables were selected through dimension reduction and used for unsupervised clustering. Phenotypes and outcomes were compared between clusters. Patients were randomly divided into a derivation cohort (n = 1092: 80%) and a validation cohort (n = 273: 20%). Three clusters with markedly different features were identified. Cluster 1 was associated predominantly with elderly age, a high aortic valve gradient, and left ventricular (LV) hypertrophy; cluster 2 consisted of preserved LV ejection fraction, larger aortic valve area, and high blood pressure; and cluster 3 demonstrated tachycardia and low flow/low gradient AS. Adverse outcomes differed significantly among clusters during a median of 2.2 years of follow-up (P<0.001). After adjustment for clinical and echocardiographic data in a Cox proportional-hazards model, cluster 3 (hazard ratio, 4.18; 95% CI, 1.76-9.94; P=0.001) was associated with increased risk of adverse outcomes. In sequential Cox models, a model based on clinical data and echocardiographic variables (χ2, 18.4) was improved by cluster 3 (χ2, 31.5; P=0.001) in the validation cohort. Unsupervised cluster analysis of patients after TAVR revealed 3 different groups for assessment of prognosis. This provides a new perspective in the categorization of patients after TAVR that considers comorbidities and extravalvular cardiac dysfunction.