Author + information
- Received August 2, 2019
- Revision received October 31, 2019
- Accepted December 19, 2019
- Published online May 4, 2020.
- Márton Tokodi, MDa,b,
- Sirish Shrestha, MSca,
- Christopher Bianco, MDa,
- Nobuyuki Kagiyama, MD, PhDa,
- Grace Casaclang-Verzosa, MDa,
- Jagat Narula, MD, PhDc and
- Partho P. Sengupta, MD, DMa,∗ ()
- aDivision of Cardiology, West Virginia University Heart & Vascular Institute, Morgantown, West Virginia
- bHeart and Vascular Center, Semmelweis University, Budapest, Hungary
- cDivision of Cardiology, Icahn School of Medicine at Mount Sinai, New York, New York
- ↵∗Address for correspondence:
Dr. Partho P. Sengupta, Heart & Vascular Institute, West Virginia University, 1 Medical Center Drive, Morgantown, West Virginia 26506-8059.
Objectives The authors applied unsupervised machine-learning techniques for integrating echocardiographic features of left ventricular (LV) structure and function into a patient similarity network that predicted major adverse cardiac event(s) (MACE) in an individual patient.
Background Patient similarity analysis is an evolving paradigm for precision medicine in which patients are clustered or classified based on their similarities in several clinical features.
Methods A retrospective cohort of 866 patients was used to develop a network architecture using 9 echocardiographic features of LV structure and function. The data for 468 patients from 2 prospective cohort registries were then added to test the model’s generalizability.
Results The map of cross-sectional data in the retrospective cohort resulted in a looped patient network that persisted even after the addition of data from the prospective cohort registries. After subdividing the loop into 4 regions, patients in each region showed unique differences in LV function, with Kaplan-Meier curves demonstrating significant differences in MACE-related rehospitalization and death (both p < 0.001). Addition of network information to clinical risk predictors resulted in significant improvements in net reclassification, integrated discrimination, and median risk scores for predicting MACE (p < 0.05 for all). Furthermore, the network predicted the cardiac disease cycle in each of the 96 patients who had second echocardiographic evaluations. An improvement or remaining in low-risk regions was associated with lower MACE-related rehospitalization rates than worsening or remaining in high-risk regions (3% vs. 37%; p < 0.001).
Conclusions Patient similarity analysis integrates multiple features of cardiac function to develop a phenotypic network in which patients can be mapped to specific locations associated with specific disease stage and clinical outcomes. The use of patient similarity analysis may have relevance for automated staging of cardiac disease severity, personalized prediction of prognosis, and monitoring progression or response to therapies.
Dr. Sengupta has been a consultant for Heartsciences, Ultromics Ltd., and Kencor Health. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
The authors attest they are in compliance with human studies committees and animal welfare regulations of the authors’ institutions and Food and Drug Administration guidelines, including patient consent where appropriate. For more information, visit the JACC: Cardiovascular Imaging author instructions page.
- Received August 2, 2019.
- Revision received October 31, 2019.
- Accepted December 19, 2019.
- 2020 American College of Cardiology Foundation
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