Artificial Intelligence-Enabled Electrocardiography Detects Hypoalbuminemia and Identifies the Mechanism of Hepatorenal and Cardiovascular Events

Frontiers in Cardiovascular Medicine

article

Yung-Tsai Lee¹  Chin-Sheng Lin²  Wen-Hui Fang³  Chia-Cheng Lee⁶  Ching-Liang Ho⁸  Chih-Hung Wang⁹  Dung-Jang Tsai⁵  Chin Lin⁵ 

[1] Division of Cardiovascular Surgery, Cheng Hsin Rehabilitation and Medical Center;Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center;Department of Family and Community Medicine, Tri-Service General Hospital, National Defense Medical Center;Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center;Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center;Medical Informatics Office, Tri-Service General Hospital, National Defense Medical Center;Division of Colorectal Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center;Division of Hematology and Oncology, Tri-Service General Hospital, National Defense Medical Center;Department of Otolaryngology-Head and Neck Surgery, Tri-Service General Hospital, National Defense Medical Center;Graduate Institute of Medical Sciences, National Defense Medical Center;School of Public Health, National Defense Medical Center;Medical Technology Education Center, School of Medicine, National Defense Medical Center

关键词: artificial intelligence;    electrocardiogram;    deep learning;    hypoalbuminemia;    previvor;    liver failure events;   

DOI  :  10.3389/fcvm.2022.895201

学科分类：地球科学(综合)

来源: Frontiers

【 摘 要 】

Background Albumin, an important component of fluid balance, is associated with kidney, liver, nutritional, and cardiovascular diseases (CVD) and is measured by blood tests. Since fluid balance is associated with electrocardiography (ECG) changes, we established a deep learning model (DLM) to estimate albumin via ECG. Objective This study aimed to develop a DLM to estimate albumin via ECG and explored its contribution to future complications. Materials and Methods A DLM was trained for estimating ECG-based albumin (ECG-Alb) using 155,078 ECGs corresponding to albumin from 79,111 patients, and another independent 13,335 patients from an academic medical center and 11,370 patients from a community hospital were used for internal and external validation. The primary analysis focused on distinguishing patients with mild to severe hypoalbuminemia, and the secondary analysis aimed to provide additional prognostic value from ECG-Alb for future complications, which included mortality, new-onset hypoalbuminemia, chronic kidney disease (CKD), new onset hepatitis, CVD mortality, new-onset acute myocardial infarction (AMI), new-onset stroke (STK), new-onset coronary artery disease (CAD), new-onset heart failure (HF), and new-onset atrial fibrillation (Afib). Results The AUC to identify hypoalbuminemia was 0.8771 with a sensitivity of 56.0% and a specificity of 90.7% in the internal validation set, and the Pearson correlation coefficient was 0.69 in the continuous analysis. The most important ECG features contributing to ECG-Alb were ordered in terms of heart rate, corrected QT interval, T wave axis, sinus rhythm, P wave axis, etc. The group with severely low ECG-Alb had a higher risk of all-cause mortality [hazard ratio (HR): 2.45, 95% CI: 1.81–3.33] and the other hepatorenal and cardiovascular events in the internal validation set. The external validation set yielded similar results. Conclusion Hypoalbuminemia and its complications can be predicted using ECG-Alb as a novel biomarker, which may be a non-invasive tool to warn asymptomatic patients.

【 授权许可】

CC BY