윤형진 (서울대학교 의과대학) | 한빛사논문 > 한빛사

한빛사논문

조회419

서울대학교 의과대학

CV File 93 kb

EClinicalMedicine, Feb 2024, 68 102445 | https://doi.org/10.1016/j.eclinm.2024.102445 Deep learning-based long-term risk evaluation of incident type 2 diabetes using electrocardiogram in a non-diabetic population: a retrospective, multicentre study

Junmo Kim ^a, Hyun-Lim Yang ^b,c, Su Hwan Kim ^b,k, Siun Kim ^b, Jisoo Lee ^a, Jiwon Ryu ^d,e, Kwangsoo Kim ^f,g, Zio Kim ^a, Gun Ahn ^a, Doyun Kwon ^h, Hyung-Jin Yoon ^a,i,j

^aInterdisciplinary Program in Bioengineering, Seoul National University, Seoul, Republic of Korea

^bBiomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea

^cDepartment of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul, Republic of Korea

^dDivision of General Internal Medicine, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea

^eHospital Medicine Center, Seoul National University Bundang Hospital, Seongnam, Republic of Korea

^fDepartment of Transdisciplinary Medicine, Institute of Convergence Medicine with Innovative Technology, Seoul National University Hospital, Seoul, Republic of Korea

^gDepartment of Medicine, College of Medicine, Seoul National University, Seoul, Republic of Korea

^hInterdisciplinary Program of Medical Informatics, Seoul National University College of Medicine, Seoul, Republic of Korea

ⁱMedical Bigdata Research Center, Seoul National University College of Medicine, Seoul, Republic of Korea

^jDepartment of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Republic of Korea

^kDepartment of Information Statistics, Gyeongsang National University, Jinju, Gyeongsangnam-do, Republic of Korea

Corresponding author : Hyung-Jin Yoon

Abstract

Diabetes is a major public health concern. We aimed to evaluate the long-term risk of incident type 2 diabetes in a non-diabetic population using a deep learning model (DLM) detecting prevalent type 2 diabetes using electrocardiogram (ECG). In this retrospective study, participants who underwent health checkups at two tertiary hospitals in Seoul, South Korea, between Jan 1, 2001 and Dec 31, 2022 were included. Type 2 diabetes was defined as glucose ≥126 mg/dL or glycated haemoglobin (HbA1c) ≥ 6.5%. For survival analysis on incident type 2 diabetes, we introduced an additional variable, diabetic ECG, which is determined by the DLM trained on ECG and corresponding prevalent diabetes. It was assumed that non-diabetic individuals with diabetic ECG had a higher risk of incident type 2 diabetes than those with non-diabetic ECG. The one-dimensional ResNet-based model was adopted for the DLM, and the Guided Grad-CAM was used to localise important regions of ECG. We divided the non-diabetic group into the diabetic ECG group (false positive) and the non-diabetic ECG (true negative) group according to the DLM decision, and performed a Cox proportional hazard model, considering the occurrence of type 2 diabetes more than six months after the visit. 190,581 individuals were included in the study with a median follow-up period of 11.84 years. The areas under the receiver operating characteristic curve for prevalent type 2 diabetes detection were 0.816 (0.807-0.825) and 0.762 (0.754-0.770) for the internal and external validations, respectively. The model primarily focused on the QRS duration and, occasionally, P or T waves. The diabetic ECG group exhibited an increased risk of incident type 2 diabetes compared with the non-diabetic ECG group, with hazard ratios of 2.15 (1.82-2.53) and 1.92 (1.74-2.11) for internal and external validation, respectively. In the non-diabetic group, those whose ECG was classified as diabetes by the DLM were at a higher risk of incident type 2 diabetes than those whose ECG was not. Additional clinical research on the relationship between the phenotype of ECG and diabetes to support the results and further investigation with tracked data and various ECG recording systems are suggested for future works. National Research Foundation of Korea.

논문정보

형식Research article
게재일2024년 02월 (BRIC 등록일 2024-02-13)
연구진국내 연구진
분야 바이오·의료융합 > 의료기기

댓글 작성 로그인

CV 열람하기

연락처 보기