Journal article
Electrocardiogram-Based Machine Learning Emulator Model for Predicting Novel Echocardiography-Derived Phenogroups for Cardiac Risk-Stratification: A Prospective Multicenter Cohort Study
Journal of patient-centered research and reviews, Vol.9(2), pp.98-107
01/01/2022
DOI: 10.17294/2330-0698.1893
PMCID: PMC9022713
PMID: 35600228
Abstract
Purpose Electrocardiography (ECG)-derived machine learning models can predict echocardiography (echo)derived indices of systolic or diastolic function. However, systolic and diastolic dysfunction frequently coexists, which necessitates an integrated assessment for optimal risk-stratification. We explored an ECG-derived model that emulates an echo-derived model that combines multiple parameters for identifying patient phenogroups at risk for major adverse cardiac events (MACE).
Methods In this substudy of a prospective, multicenter study, patients from 3 institutions (n=727) formed an internal cohort, and the fourth institution was reserved as an external test set (n=518). A previously validated patient similarity analysis model was used for labeling the patients as low-/high-risk phenogroups. These labels were utilized for training an ECG-derived deep neural network model to predict MACE risk per phenogroup. After 5-fold cross-validation training, the model was tested on the reserved external dataset.
Results Our ECG-derived model showed robust classification of patients, with area under the receiver operating characteristic curve of 0.86 (95% CI: 0.79-0.91) and 0.84 (95% CI: 0.80-0.87), sensitivity of 80% and 76%, and specificity of 88% and 75% for the internal and external test sets, respectively. The ECG-derived model demonstrated an increased probability for MACE in high-risk vs low-risk patients (21% vs 3%; P<0.001), which was similar to the echo-trained model (21% vs 5%; P<0.001), suggesting comparable utility.
Conclusions This novel ECG-derived machine learning model provides a cost-effective strategy for predicting patient subgroups in whom an integrated milieu of systolic and diastolic dysfunction is associated with a high risk of MACE.
Details
- Title: Subtitle
- Electrocardiogram-Based Machine Learning Emulator Model for Predicting Novel Echocardiography-Derived Phenogroups for Cardiac Risk-Stratification: A Prospective Multicenter Cohort Study
- Creators
- Heenaben B. Patel - West Virginia UniversityNaveena Yanamala - Carnegie Mellon UniversityBrijesh Patel - West Virginia UniversitySameer Raina - West Virginia UniversityPeter D. Farjo - West Virginia UniversitySrinidhi Sunkara - West Virginia UniversityMarton Tokodi - West Virginia UniversityNobuyuki Kagiyama - West Virginia UniversityGrace Casaclang-Verzosa - West Virginia UniversityPartho P. Sengupta - West Virginia University
- Resource Type
- Journal article
- Publication Details
- Journal of patient-centered research and reviews, Vol.9(2), pp.98-107
- Publisher
- Aurora Health Care, Inc
- DOI
- 10.17294/2330-0698.1893
- PMID
- 35600228
- PMCID
- PMC9022713
- ISSN
- 2330-068X
- eISSN
- 2330-0698
- Number of pages
- 11
- Grant note
- HeartSciences Heart Test Laboratories, Inc. d/b/a HeartSciences 1920920 / National Science Foundation (NSF)
- Language
- English
- Date published
- 01/01/2022
- Academic Unit
- Internal Medicine
- Record Identifier
- 9984695825802771
Metrics
2 Record Views