Development and validation of a deep-learning algorithm for rule-in and rule-out coronary artery disease based on electrocardiogram without evidence of myocardial ischemia
Year: 2025
Type: article
Abstract: Background: Current coronary artery disease (CAD) guidelines recommend to rule-out or rule-in patients for further examination by assessing a pretest probability (PTP) ≤ 5 % or ≥ 15 %. We developed and validated a deep-learning algorithm for rule-in or rule-out based on electrocardiogram (ECG) without myocardial ischemia evidence. Methods: Between October 2019 and June 2022, data from two centers (Fuwai Hospital [Beijing] and Yunnan Fuwai Hospital) of CAD-suspected patients undergoing either coronary angiography or coronary computed tomography were used. Data from the Fuwai Hospital (Beijing) were used to train (randomly 90 %) and internally validate (randomly 10 %) a deep-learning algorithm to detect CAD (≥ 70 % stenosis) based on 12-lead ECGs. An algorithm-based decision-making protocol was established for rule-out or rule-in based on a predefined threshold allowing for a 95 % negative predictive value (NPV). Data from the Yunnan Fuwai Hospital were used to externally validate the performance of the decision-making protocol. The CAD prevalence was calculated in patients who were recommended to rule-in or rule-out. Results: In internal validation set, area under the receiver operating characteristic curve (AUC) was 0.81 and the CAD prevalence of patients who were recommended rule-out and rule-in were 5 % (40/790) and 23 % (527/2253), respectively. In external validation set, the CAD prevalence of patients who were recommended rule-out and rule-in were 0 % (0/661) and 15 % (255/1699), respectively. Conclusions: Our algorithm based on ECG without myocardial ischemia evidence performed good in CAD detection. An algorithm-based decision-making protocol could achieve the guideline-recommended performance in guiding rule-out or rule-in for further examination.
Source: IJC Heart & Vasculature
Authors: Runchen Sun, Xiangqian Zhu, Shen Lin, Mengnan Shi, Xuexin Yu, Chang Liu, Yaoguan Yue, Juntong Zeng, Yan Zhao, Xiaoqi Wang, Xiaocong Lian, Xin Jin, Zhe Zheng, Xiangyang Ji
Institutions: Chinese Academy of Medical Sciences & Peking Union Medical College, Fu Wai Hospital, State Key Laboratory of Cardiovascular Disease, Tsinghua Shenzhen International Graduate School, Tsinghua University, Fuwai Yunnan Cardiovascular Hospital, Beijing National Research Center for Information Science and Technology
Language: English
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,330
OpenAlex ID: W4413290269