Artificial Intelligence-Enabled Electrocardiography Detects Hypoalbuminemia and Identifies the Mechanism of Hepatorenal and Cardiovascular Events
BackgroundAlbumin, 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 estimat...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-06-01
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| Series: | Frontiers in Cardiovascular Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcvm.2022.895201/full |
| _version_ | 1828333976732827648 |
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| author | Yung-Tsai Lee Chin-Sheng Lin Wen-Hui Fang Wen-Hui Fang Wen-Hui Fang Chia-Cheng Lee Chia-Cheng Lee Ching-Liang Ho Chih-Hung Wang Chih-Hung Wang Dung-Jang Tsai Dung-Jang Tsai Chin Lin Chin Lin Chin Lin |
| author_facet | Yung-Tsai Lee Chin-Sheng Lin Wen-Hui Fang Wen-Hui Fang Wen-Hui Fang Chia-Cheng Lee Chia-Cheng Lee Ching-Liang Ho Chih-Hung Wang Chih-Hung Wang Dung-Jang Tsai Dung-Jang Tsai Chin Lin Chin Lin Chin Lin |
| author_sort | Yung-Tsai Lee |
| collection | DOAJ |
| description | BackgroundAlbumin, 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.ObjectiveThis study aimed to develop a DLM to estimate albumin via ECG and explored its contribution to future complications.Materials and MethodsA 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).ResultsThe 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.ConclusionHypoalbuminemia and its complications can be predicted using ECG-Alb as a novel biomarker, which may be a non-invasive tool to warn asymptomatic patients. |
| first_indexed | 2024-04-13T21:27:49Z |
| format | Article |
| id | doaj.art-78b0eb8c20e14ca88c9463f721439e83 |
| institution | Directory Open Access Journal |
| issn | 2297-055X |
| language | English |
| last_indexed | 2024-04-13T21:27:49Z |
| publishDate | 2022-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cardiovascular Medicine |
| spelling | doaj.art-78b0eb8c20e14ca88c9463f721439e832022-12-22T02:29:16ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2022-06-01910.3389/fcvm.2022.895201895201Artificial Intelligence-Enabled Electrocardiography Detects Hypoalbuminemia and Identifies the Mechanism of Hepatorenal and Cardiovascular EventsYung-Tsai Lee0Chin-Sheng Lin1Wen-Hui Fang2Wen-Hui Fang3Wen-Hui Fang4Chia-Cheng Lee5Chia-Cheng Lee6Ching-Liang Ho7Chih-Hung Wang8Chih-Hung Wang9Dung-Jang Tsai10Dung-Jang Tsai11Chin Lin12Chin Lin13Chin Lin14Division of Cardiovascular Surgery, Cheng Hsin Rehabilitation and Medical Center, Taipei City, TaiwanDivision of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei City, TaiwanDepartment of Family and Community Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei City, TaiwanDepartment of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei City, TaiwanArtificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei City, TaiwanMedical Informatics Office, Tri-Service General Hospital, National Defense Medical Center, Taipei City, TaiwanDivision of Colorectal Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei City, TaiwanDivision of Hematology and Oncology, Tri-Service General Hospital, National Defense Medical Center, Taipei City, TaiwanDepartment of Otolaryngology-Head and Neck Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei City, Taiwan0Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei City, TaiwanArtificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei City, Taiwan1School of Public Health, National Defense Medical Center, Taipei City, TaiwanArtificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei City, Taiwan1School of Public Health, National Defense Medical Center, Taipei City, Taiwan2Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei City, TaiwanBackgroundAlbumin, 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.ObjectiveThis study aimed to develop a DLM to estimate albumin via ECG and explored its contribution to future complications.Materials and MethodsA 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).ResultsThe 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.ConclusionHypoalbuminemia and its complications can be predicted using ECG-Alb as a novel biomarker, which may be a non-invasive tool to warn asymptomatic patients.https://www.frontiersin.org/articles/10.3389/fcvm.2022.895201/fullartificial intelligenceelectrocardiogramdeep learninghypoalbuminemiaprevivorliver failure events |
| spellingShingle | Yung-Tsai Lee Chin-Sheng Lin Wen-Hui Fang Wen-Hui Fang Wen-Hui Fang Chia-Cheng Lee Chia-Cheng Lee Ching-Liang Ho Chih-Hung Wang Chih-Hung Wang Dung-Jang Tsai Dung-Jang Tsai Chin Lin Chin Lin Chin Lin Artificial Intelligence-Enabled Electrocardiography Detects Hypoalbuminemia and Identifies the Mechanism of Hepatorenal and Cardiovascular Events Frontiers in Cardiovascular Medicine artificial intelligence electrocardiogram deep learning hypoalbuminemia previvor liver failure events |
| title | Artificial Intelligence-Enabled Electrocardiography Detects Hypoalbuminemia and Identifies the Mechanism of Hepatorenal and Cardiovascular Events |
| title_full | Artificial Intelligence-Enabled Electrocardiography Detects Hypoalbuminemia and Identifies the Mechanism of Hepatorenal and Cardiovascular Events |
| title_fullStr | Artificial Intelligence-Enabled Electrocardiography Detects Hypoalbuminemia and Identifies the Mechanism of Hepatorenal and Cardiovascular Events |
| title_full_unstemmed | Artificial Intelligence-Enabled Electrocardiography Detects Hypoalbuminemia and Identifies the Mechanism of Hepatorenal and Cardiovascular Events |
| title_short | Artificial Intelligence-Enabled Electrocardiography Detects Hypoalbuminemia and Identifies the Mechanism of Hepatorenal and Cardiovascular Events |
| title_sort | artificial intelligence enabled electrocardiography detects hypoalbuminemia and identifies the mechanism of hepatorenal and cardiovascular events |
| topic | artificial intelligence electrocardiogram deep learning hypoalbuminemia previvor liver failure events |
| url | https://www.frontiersin.org/articles/10.3389/fcvm.2022.895201/full |
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