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Full-Text Articles in Cardiovascular Diseases
Electrocardiogram-Based Machine Learning Emulator Model For Predicting Novel Echocardiography-Derived Phenogroups For Cardiac Risk-Stratification: A Prospective Multicenter Cohort Study, Heenaben B. Patel, Naveena Yanamala, Brijesh Patel, Sameer Raina, Peter D. Farjo, Srinidhi Sunkara, Márton Tokodi, Nobuyuki Kagiyama, Grace Casaclang-Verzosa, Partho P. Sengupta
Electrocardiogram-Based Machine Learning Emulator Model For Predicting Novel Echocardiography-Derived Phenogroups For Cardiac Risk-Stratification: A Prospective Multicenter Cohort Study, Heenaben B. Patel, Naveena Yanamala, Brijesh Patel, Sameer Raina, Peter D. Farjo, Srinidhi Sunkara, Márton Tokodi, Nobuyuki Kagiyama, Grace Casaclang-Verzosa, Partho P. Sengupta
Journal of Patient-Centered Research and Reviews
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 …