TY - JOUR
T1 - Network Tomography for Understanding Phenotypic Presentations in Aortic Stenosis
AU - Casaclang-Verzosa, Grace
AU - Shrestha, Sirish
AU - Khalil, Muhammad Jahanzeb
AU - Cho, Jung Sun
AU - Tokodi, Márton
AU - Balla, Sudarshan
AU - Alkhouli, Mohamad
AU - Badhwar, Vinay
AU - Narula, Jagat
AU - Miller, Jordan D.
AU - Sengupta, Partho P.
N1 - Publisher Copyright:
© 2019 American College of Cardiology Foundation
PY - 2019/2
Y1 - 2019/2
N2 - 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 < 0.001). These 2 regions were linked through moderate AS; with upper arm of the loop showing patients with predominantly reduced ejection fractions (EFs), and the lower arm showing patients with preserved EFs (p < 0.001). The region of severe AS showed >3 times the increased risk of balloon valvuloplasty, and transcatheter or surgical aortic valve replacement (hazard ratio: 3.88; p < 0.001) compared with the remaining patients in the map. Following aortic valve replacement, patients recovered and moved toward the zone of mild and moderate AS. Topological data analysis in mice showed a similar distribution, with 1 side of the loop corresponding to higher peak aortic velocities than the opposite side (p < 0.0001). The validity of the cross-sectional data that revealed a path of AS progression was confirmed by comparing the locations occupied by 2 groups of mice that were serially imaged. LV systolic and diastolic dysfunction were frequently identified even during moderate AS in both humans and mice. 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.
AB - 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 < 0.001). These 2 regions were linked through moderate AS; with upper arm of the loop showing patients with predominantly reduced ejection fractions (EFs), and the lower arm showing patients with preserved EFs (p < 0.001). The region of severe AS showed >3 times the increased risk of balloon valvuloplasty, and transcatheter or surgical aortic valve replacement (hazard ratio: 3.88; p < 0.001) compared with the remaining patients in the map. Following aortic valve replacement, patients recovered and moved toward the zone of mild and moderate AS. Topological data analysis in mice showed a similar distribution, with 1 side of the loop corresponding to higher peak aortic velocities than the opposite side (p < 0.0001). The validity of the cross-sectional data that revealed a path of AS progression was confirmed by comparing the locations occupied by 2 groups of mice that were serially imaged. LV systolic and diastolic dysfunction were frequently identified even during moderate AS in both humans and mice. 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.
KW - aortic stenosis
KW - left ventricular function
KW - patient similarity
KW - topological data analysis
UR - http://www.scopus.com/inward/record.url?scp=85060523928&partnerID=8YFLogxK
U2 - 10.1016/j.jcmg.2018.11.025
DO - 10.1016/j.jcmg.2018.11.025
M3 - Article
C2 - 30732719
AN - SCOPUS:85060523928
SN - 1936-878X
VL - 12
SP - 236
EP - 248
JO - JACC: Cardiovascular Imaging
JF - JACC: Cardiovascular Imaging
IS - 2
ER -