Abstract
SURFACE ECG-BASED MACHINE LEARNING MODEL FOR PREDICTING PATIENT SUBGROUP AT A HIGH RISK FOR MAJOR ADVERSE CARDIAC EVENTS
Journal of the American College of Cardiology, Vol.77(18 Supplement 1), pp.3227-3227
05/11/2021
DOI: 10.1016/S0735-1097(21)04582-4
Abstract
Background
This study explores the feasibility of using surface electrocardiograms (ECG) as a diagnostic tool for predicting echocardiographically defined patients subgroups at a high-risk of major cardiac adverse events (MACE).
Methods
A total of 1,166 ECG features including signal-processed (spECG) and conventional ECG data were obtained in 1,461 patients as part of multicenter prospective study conducted at 4 institutions in North America. Patients from 3 institutions (n = 943) formed an internal cohort and data from the fourth institution was reserved as an external test set (n = 518). Patients subgroups delineated using high-throughput cardiac imaging data were used to train an ECG based bagged decision tree (DT) model to identify patient groups at low or high risk for MACE.
Results
The DT model showed robust classification of patients with an area under the receiver operating curve (AUC) of 76% (precision = 73% & recall = 72%) and 78% (precision = 71% & recall = 68%) for the internal and external testset respectively (Fig 1A). A combination of spECG and conventional ECG features such as depolarization average measures in lead V6, V1, I, and aVR, and LVH score were important for the classification and provided a robust prediction of MACE in the external test set (Fig 1B).
Conclusion
ECG-derived machine-learning models provide a cost-effective strategy for predicting echocardiographically defined patients subgroups at a high-risk of MACE and may aid in optimizing intervention strategies.
Details
- Title: Subtitle
- SURFACE ECG-BASED MACHINE LEARNING MODEL FOR PREDICTING PATIENT SUBGROUP AT A HIGH RISK FOR MAJOR ADVERSE CARDIAC EVENTS
- Creators
- Heenaben Patel - West Virginia UniversityNaveena Yanamala - West Virginia UniversityMarton Tokodi - West Virginia UniversityNobuyaki Kagiyama - West Virginia UniversityMarco Piccirilli - West Virginia UniversitySirish Shrestha - West Virginia UniversityPeter Farjo - West Virginia UniversityGrace Casaclang-VerzosaWadea Tarhuni - West Virginia UniversityNegin Nezarat - West Virginia UniversityMatthew Budoff - West Virginia UniversityJagat Narula - West Virginia UniversityPartho Sengupta - West Virginia University
- Resource Type
- Abstract
- Publication Details
- Journal of the American College of Cardiology, Vol.77(18 Supplement 1), pp.3227-3227
- DOI
- 10.1016/S0735-1097(21)04582-4
- ISSN
- 0735-1097
- eISSN
- 1558-3597
- Language
- English
- Date published
- 05/11/2021
- Academic Unit
- Internal Medicine
- Record Identifier
- 9984695671502771
Metrics
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