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

• Published in: Atherosclerosis 2023-09, Vol.381, p.117238-117238, Article 117238
• Main Authors: Lee, Yin-Hao, Hsieh, Ming-Tsung, Chang, Chun-Chin, Tsai, Yi-Lin, Chou, Ruey-Hsing, Lu, Henry Hong-Shing, Huang, Po-Hsun
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Coronary artery disease; ECG
• ISSN: 0021-9150; 1879-1484
• DOI: 10.1016/j.atherosclerosis.2023.117238

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. 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. 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. 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. [Display omitted] •We built an AI-enabled ECG stacking model to assist in identifying obstructive CAD.•Our model had comparable diagnostic performance to the traditional risk factor-based approach for identifying obstructive CAD.•This model could be readily used in outpatient settings as a clinical tool to assist in patient diagnosis.