Network Tomography for Understanding Phenotypic Presentations in Aortic Stenosis

Abstract Objectives This study sought to build a patient−patient similarity network using multiple features of left ventricular (LV) structure and function in patients with aortic stenosis (AS). The study further validated the observations in an experimental murine model of AS. Background The LV response in AS is variable and results in heterogeneous phenotypic presentations. Methods The patient similarity network was developed using topological data analysis (TDA) from cross-sectional echocardiographic data collected from 246 patients with AS. Multivariate features of AS were represented on the map, and the network topology was compared with that of a murine AS model by imaging 155 animals at 3, 6, 9, or 12 months of age. Results The topological map formed a loop in which patients with mild and severe AS were aggregated on the right and left sides, respectively (p  3 times the increased risk of balloon valvuloplasty, and transcatheter or surgical aortic valve replacement (hazard ratio: 3.88; p  Conclusions Multifeature assessments of patient similarity by machine-learning processes may allow precise phenotypic recognition of the pattern of LV responses during the progression of AS.