Author + information
- Received August 16, 2018
- Revision received November 15, 2018
- Accepted November 28, 2018
- Published online February 4, 2019.
- Grace Casaclang-Verzosa, MDa,∗,
- Sirish Shrestha, MSca,∗,
- Muhammad Jahanzeb Khalil, MDa,
- Jung Sun Cho, MDa,
- Márton Tokodi, MDa,
- Sudarshan Balla, MDa,
- Mohamad Alkhouli, MDa,
- Vinay Badhwar, MDb,
- Jagat Narula, MD, PhDc,
- Jordan D. Miller, PhDd and
- Partho P. Sengupta, MD, DMa,∗ ()
- aDivision of Cardiology, West Virginia University Heart & Vascular Institute, Morgantown, West Virginia
- bDepartment of Cardiovascular & Thoracic Surgery, West Virginia University Heart & Vascular Institute, Morgantown, West Virginia
- cDivision of Cardiology, Icahn School of Medicine at Mount Sinai, New York, New York
- dDepartments of Surgery, Cardiovascular Surgery, Physiology, and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota
- ↵∗Address for correspondence:
Dr. Partho P. Sengupta, Heart and Vascular Institute, West Virginia University, 1 Medical Center Drive, Morgantown, West Virginia 26506-8059.
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.
↵∗ Dr. Casaclang-Verzosa and Mr. Shrestha contributed equally to this work and are joint first authors.
Dr. Sengupta has been a consultant for Heartsciences, Hitachi Aloka Ltd., and Ultromics. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. Dr. Blase A. Carabello served as Guest Editor for this paper.
- Received August 16, 2018.
- Revision received November 15, 2018.
- Accepted November 28, 2018.
- 2019 American College of Cardiology Foundation
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