Improving detection of obstructive coronary artery disease with an artificial intelligence-enabled electrocardiogram algorithm

Yin-Hao Lee; Ming-Tsung Hsieh; Chun-Chin Chang; Yi-Lin Tsai; Ruey-Hsing Chou; Henry Hong-Shing Lu; Po-Hsun Huang

doi:10.1016/j.atherosclerosis.2023.117238

Improving detection of obstructive coronary artery disease with an artificial intelligence-enabled electrocardiogram algorithm

Atherosclerosis. 2023 Sep:381:117238. doi: 10.1016/j.atherosclerosis.2023.117238. Epub 2023 Aug 12.

Authors

Yin-Hao Lee¹, Ming-Tsung Hsieh², Chun-Chin Chang³, Yi-Lin Tsai⁴, Ruey-Hsing Chou⁵, Henry Hong-Shing Lu⁶, Po-Hsun Huang⁷

Affiliations

¹ Division of Cardiology, Department of Medicine, Taipei City Hospital, Yang Ming Branch, Taipei, Taiwan; Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.
² Institute of Statistics, National Yang Ming Chiao Tung University, Taipei, Taiwan.
³ Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan; Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
⁴ Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.
⁵ Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan; Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Critical Care Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.
⁶ Institute of Statistics, National Yang Ming Chiao Tung University, Taipei, Taiwan. Electronic address: henryhslu@nycu.edu.tw.
⁷ Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan; Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan. Electronic address: hunagbsvgh@gmail.com.

PMID: 37607462
DOI: 10.1016/j.atherosclerosis.2023.117238

Abstract

Background and aims: To evaluate the risk of coronary artery disease (CAD), the traditional approach involves assessing the patient's symptoms, traditional cardiovascular risk factors (CVRFs), and a 12-lead electrocardiogram (ECG). However, currently, there are no established criteria for interpreting an ECG to diagnose CAD. Therefore, we sought to develop an artificial intelligence (AI)-enabled ECG model to assist in identifying patients with CAD.

Methods: In this study, we included patients who underwent coronary angiography (CAG) at a single center between 2017 and 2019. Preprocedural 12-lead ECG performed within 24 h was obtained. Obstructive CAD was defined as ≥ 50% diameter stenosis. Using age, gender and ECG data, we developed stacking models using both deep learning and machine learning. Then we compared the performance of our models with CVRFs and with cardiologists' ECG interpretation. Additionally, we validated our model on an external cohort from a different hospital.

Results: We included 4951 patients with a mean age of 65.5 ± 12.5 years, of whom 67.0% were men. Based on CAG, obstructive CAD was confirmed in 2637 patients (53.2%). Our best AI model demonstrated comparable performance to CVRFs in predicting CAD, with an AUC of 0.70 (95% CI: 0.66-0.75) compared to 0.71 (95% CI: 0.66-0.76). The sensitivity and specificity of the AI model were 0.75 and 0.54, respectively, while those of CVRFs were 0.67 and 0.63. Compared to cardiologists, the AI model showed better performance with an F1 score of 0.68 vs 0.41. The external validation showed generally consistent diagnostic findings, although there was a slightly lower level of agreement observed in the external cohort. Incorporating ECG and CVRFs improved the AUC to 0.72.

Conclusions: Our study suggests that an AI-enabled ECG model can assist in identifying patients with obstructive CAD, with diagnostic performance similar to that of the traditional approach based on CVRFs. This model could serve as a useful clinical tool in an outpatient setting to identify patients who require further diagnostic tests.

Keywords: Artificial intelligence; Coronary artery disease; ECG.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Aged
Algorithms
Artificial Intelligence*
Coronary Angiography
Coronary Artery Disease* / diagnosis
Electrocardiography
Female
Humans
Male
Middle Aged