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

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.