Artificial intelligence-enhanced 12-lead electrocardiography for identifying atrial fibrillation during sinus rhythm (AIAFib) trial: protocol for a multicenter retrospective study

Frontiers in Cardiovascular Medicine

Cardiovascular Medicine

Seung Yong Shin¹  Sang-Chul Lee²  Wonik Choi³  Young Ju Suh⁴  Boyoung Joung⁵  Kwang-No Lee⁵  Seng Chan You⁶  Yae Min Park⁷  Dae-Hyeok Kim⁸  Yong-Soo Baek⁹  Soonil Kwon^1,10  Eue-Keun Choi^1,10  So-Ryung Lee^1,10  Hee Tae Yu^1,11  Junbeom Park^1,12  Dong-Hyeok Kim^1,13  Seung-Young Roh^1,14  Dae In Lee^1,15 

[1] Cardiovascular and Arrhythmia Centre, Chung-Ang University Hospital, Chung-Ang University, Seoul, Republic of Korea;Division of Cardiology, Korea University Ansan Hospital, Ansan, Republic of Korea;DeepCardio Inc., Incheon, Republic of Korea;Department of Computer Engineering, Inha University, Republic of Korea;DeepCardio Inc., Incheon, Republic of Korea;Department of Information and Communication Engineering, Inha University, Incheon, Republic of Korea;Department of Biomedical Sciences, Inha University College of Medicine and Inha University Hospital, Incheon, Republic of Korea;Department of Cardiology, Ajou University School of Medicine, Suwon, Republic of Korea;Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea;Division of Cardiology, Department of Internal Medicine, Gachon University Gil Medical Center, Incheon, Republic of Korea;Division of Cardiology, Department of Internal Medicine, Inha University College of Medicine and Inha University Hospital, Incheon, Republic of Korea;DeepCardio Inc., Incheon, Republic of Korea;Division of Cardiology, Department of Internal Medicine, Inha University College of Medicine and Inha University Hospital, Incheon, Republic of Korea;DeepCardio Inc., Incheon, Republic of Korea;School of Computer Science, University of Birmingham, Birmingham, United Kingdom;Division of Cardiology, Department of Internal Medicine, Seoul National University College of Medicine and Seoul National University Hospital, Seoul, Republic of Korea;Division of Cardiology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea;Division of Cardiology, Ewha Womans University Mokdong Hospital, Seoul, Republic of Korea;Division of Cardiology, Ewha Womans University Seoul Hospital, Seoul, Republic of Korea;Division of Cardiology, Korea University Guro Hospital, Seoul, Republic of Korea;Division of Cardiology, Korea University Guro Hospital, Seoul, Republic of Korea;Division of Cardiology, Chungbuk National University Hospital, Cheongju, Republic of Korea;

关键词: atrial fibrillation;    electrocardiography;    artificial intelligence;    deep learning;    neural networks;   

DOI  :  10.3389/fcvm.2023.1258167

 received in 2023-07-13, accepted in 2023-09-27,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionAtrial fibrillation (AF) is the most common arrhythmia, contributing significantly to morbidity and mortality. In a previous study, we developed a deep neural network for predicting paroxysmal atrial fibrillation (PAF) during sinus rhythm (SR) using digital data from standard 12-lead electrocardiography (ECG). The primary aim of this study is to validate an existing artificial intelligence (AI)-enhanced ECG algorithm for predicting PAF in a multicenter tertiary hospital. The secondary objective is to investigate whether the AI-enhanced ECG is associated with AF-related clinical outcomes.Methods and analysisWe will conduct a retrospective cohort study of more than 50,000 12-lead ECGs from November 1, 2012, to December 31, 2021, at 10 Korean University Hospitals. Data will be collected from patient records, including baseline demographics, comorbidities, laboratory findings, echocardiographic findings, hospitalizations, and related procedural outcomes, such as AF ablation and mortality. De-identification of ECG data through data encryption and anonymization will be conducted and the data will be analyzed using the AI algorithm previously developed for AF prediction. An area under the receiver operating characteristic curve will be created to test and validate the datasets and assess the AI-enabled ECGs acquired during the sinus rhythm to determine whether AF is present. Kaplan–Meier survival functions will be used to estimate the time to hospitalization, AF-related procedure outcomes, and mortality, with log-rank tests to compare patients with low and high risk of AF by AI. Multivariate Cox proportional hazards regression will estimate the effect of AI-enhanced ECG multimorbidity on clinical outcomes after stratifying patients by AF probability by AI.DiscussionThis study will advance PAF prediction based on AI-enhanced ECGs. This approach is a novel method for risk stratification and emphasizes shared decision-making for early detection and management of patients with newly diagnosed AF. The results may revolutionize PAF management and unveil the wider potential of AI in predicting and managing cardiovascular diseases.Ethics and disseminationThe study findings will be published in peer-reviewed publications and disseminated at national and international conferences and through social media. This study was approved by the institutional review boards of all participating university hospitals. Data extraction, storage, and management were approved by the data review committees of all institutions.Clinical Trial Registration[cris.nih.go.kr], identifier (KCT0007881).

【 授权许可】

Unknown   
© 2023 Baek, Kwon, You, Lee, Yu, Lee, Roh, Kim, Shin, Lee, Park, Park, Suh, Choi, Lee, Joung, Choi and Kim.