Faculty, Staff and Student Publications
Publication Date
10-15-2024
Journal
Cell Reports Medicine
Abstract
We develop a machine learning (ML) model using electrocardiography (ECG) to predict myocardial blood flow reserve (MFR) and assess its prognostic value for major adverse cardiovascular events (MACEs). Using 3,639 ECG-positron emission tomography (PET) and 17,649 ECG-single-photon emission computed tomography (SPECT) data pairs, the ML model is trained with a swarm intelligence approach and support vector regression (SVR). The model achieves a receiver-operator curve (ROC) area under the curve (AUC) of 0.83, with a sensitivity and specificity of 0.75. An ECG-MFR value below 2 is significantly associated with MACE, with hazard ratios (HRs) of 3.85 and 3.70 in the discovery and validation phases, respectively. The model's C-statistic is 0.76, with a net reclassification improvement (NRI) of 0.35. Validated in an independent cohort, the ML model using ECG data offers superior MACE prediction compared to baseline clinical models, highlighting its potential for risk stratification in patients with coronary artery disease (CAD) using the accessible 12-lead ECG.
Keywords
Humans, Electrocardiography, Machine Learning, Female, Male, Middle Aged, Aged, Coronary Circulation, Prognosis, Coronary Artery Disease, ROC Curve, Positron-Emission Tomography, Tomography, Emission-Computed, Single-Photon, artificial intelligence, coronary artery disease, electrocardiography, machine learning, major adverse cardiovascular events, myocardial blood flow, positron emission tomography
DOI
10.1016/j.xcrm.2024.101746
PMID
39326409
PMCID
PMC11513811
PubMedCentral® Posted Date
9-25-2024
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Included in
Bioinformatics Commons, Biomedical Informatics Commons, Genetic Phenomena Commons, Medical Genetics Commons, Oncology Commons