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

Yung-Tsai Lee; Chin-Sheng Lin; Wen-Hui Fang; Chia-Cheng Lee; Ching-Liang Ho; Chih-Hung Wang; Dung-Jang Tsai; Chin Lin

doi:10.3389/fcvm.2022.895201

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

Front Cardiovasc Med. 2022 Jun 13:9:895201. doi: 10.3389/fcvm.2022.895201. eCollection 2022.

Authors

Yung-Tsai Lee¹, Chin-Sheng Lin², Wen-Hui Fang^{3

4

5}, Chia-Cheng Lee^{6

7}, Ching-Liang Ho⁸, Chih-Hung Wang^{9

10}, Dung-Jang Tsai^{5

11}, Chin Lin^{5

11

12}

Affiliations

¹ Division of Cardiovascular Surgery, Cheng Hsin Rehabilitation and Medical Center, Taipei City, Taiwan.
² Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei City, Taiwan.
³ Department of Family and Community Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei City, Taiwan.
⁴ Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei City, Taiwan.
⁵ Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei City, Taiwan.
⁶ Medical Informatics Office, Tri-Service General Hospital, National Defense Medical Center, Taipei City, Taiwan.
⁷ Division of Colorectal Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei City, Taiwan.
⁸ Division of Hematology and Oncology, Tri-Service General Hospital, National Defense Medical Center, Taipei City, Taiwan.
⁹ Department of Otolaryngology-Head and Neck Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei City, Taiwan.
¹⁰ Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei City, Taiwan.
¹¹ School of Public Health, National Defense Medical Center, Taipei City, Taiwan.
¹² Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei City, Taiwan.

Abstract

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.

Keywords: artificial intelligence; deep learning; electrocardiogram; hypoalbuminemia; liver failure events; previvor.