Electrocardiogram-Based Heart Age Estimation by a Deep Learning Model Provides More Information on the Incidence of Cardiovascular Disorders
OBJECTIVE: The biological age progression of the heart varies from person to person. We developed a deep learning model (DLM) to predict the biological age via ECG to explore its contribution to future cardiovascular diseases (CVDs). METHODS: There were 71,741 cases ranging from 20 to 80 years old r...
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ftpubmed:oai:pubmedcentral.nih.gov:8860826 2023-05-15T18:12:09+02:00 Electrocardiogram-Based Heart Age Estimation by a Deep Learning Model Provides More Information on the Incidence of Cardiovascular Disorders Chang, Chiao-Hsiang Lin, Chin-Sheng Luo, Yu-Sheng Lee, Yung-Tsai Lin, Chin 2022-02-08 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8860826/ https://doi.org/10.3389/fcvm.2022.754909 en eng Frontiers Media S.A. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8860826/ http://dx.doi.org/10.3389/fcvm.2022.754909 Copyright © 2022 Chang, Lin, Luo, Lee and Lin. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. CC-BY Front Cardiovasc Med Cardiovascular Medicine Text 2022 ftpubmed https://doi.org/10.3389/fcvm.2022.754909 2022-02-27T01:42:51Z OBJECTIVE: The biological age progression of the heart varies from person to person. We developed a deep learning model (DLM) to predict the biological age via ECG to explore its contribution to future cardiovascular diseases (CVDs). METHODS: There were 71,741 cases ranging from 20 to 80 years old recruited from the health examination center. The development set used 32,707 cases to train the DLM for estimating the ECG-age, and 8,295 cases were used as the tuning set. The validation set included 30,469 ECGs to follow the outcomes, including all-cause mortality, cardiovascular-cause mortality, heart failure (HF), diabetes mellitus (DM), chronic kidney disease (CKD), acute myocardial infarction (AMI), stroke (STK), coronary artery disease (CAD), atrial fibrillation (AF), and hypertension (HTN). Two independent external validation sets (SaMi-Trop and CODE15) were also used to validate our DLM. RESULTS: The mean absolute errors of chronologic age and ECG-age was 6.899 years (r = 0.822). The higher difference between ECG-age and chronological age was related to more comorbidities and abnormal ECG rhythm. The cases with the difference of more than 7 years had higher risk on the all-cause mortality [hazard ratio (HR): 1.61, 95% CI: 1.23–2.12], CV-cause mortality (HR: 3.49, 95% CI: 1.74–7.01), HF (HR: 2.79, 95% CI: 2.25–3.45), DM (HR: 1.70, 95% CI: 1.53–1.89), CKD (HR: 1.67, 95% CI: 1.41–1.97), AMI (HR: 1.76, 95% CI: 1.20–2.57), STK (HR: 1.65, 95% CI: 1.42–1.92), CAD (HR: 1.24, 95% CI: 1.12–1.37), AF (HR: 2.38, 95% CI: 1.86–3.04), and HTN (HR: 1.67, 95% CI: 1.51–1.85). The external validation sets also validated that an ECG-age >7 years compare to chronologic age had 3.16-fold risk (95% CI: 1.72–5.78) and 1.59-fold risk (95% CI: 1.45–1.74) on all-cause mortality in SaMi-Trop and CODE15 cohorts. The ECG-age significantly contributed additional information on heart failure, stroke, coronary artery disease, and atrial fibrillation predictions after considering all the known risk factors. CONCLUSIONS: The ECG-age ... Text sami PubMed Central (PMC) Frontiers in Cardiovascular Medicine 9 |
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Cardiovascular Medicine |
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Cardiovascular Medicine Chang, Chiao-Hsiang Lin, Chin-Sheng Luo, Yu-Sheng Lee, Yung-Tsai Lin, Chin Electrocardiogram-Based Heart Age Estimation by a Deep Learning Model Provides More Information on the Incidence of Cardiovascular Disorders |
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Cardiovascular Medicine |
description |
OBJECTIVE: The biological age progression of the heart varies from person to person. We developed a deep learning model (DLM) to predict the biological age via ECG to explore its contribution to future cardiovascular diseases (CVDs). METHODS: There were 71,741 cases ranging from 20 to 80 years old recruited from the health examination center. The development set used 32,707 cases to train the DLM for estimating the ECG-age, and 8,295 cases were used as the tuning set. The validation set included 30,469 ECGs to follow the outcomes, including all-cause mortality, cardiovascular-cause mortality, heart failure (HF), diabetes mellitus (DM), chronic kidney disease (CKD), acute myocardial infarction (AMI), stroke (STK), coronary artery disease (CAD), atrial fibrillation (AF), and hypertension (HTN). Two independent external validation sets (SaMi-Trop and CODE15) were also used to validate our DLM. RESULTS: The mean absolute errors of chronologic age and ECG-age was 6.899 years (r = 0.822). The higher difference between ECG-age and chronological age was related to more comorbidities and abnormal ECG rhythm. The cases with the difference of more than 7 years had higher risk on the all-cause mortality [hazard ratio (HR): 1.61, 95% CI: 1.23–2.12], CV-cause mortality (HR: 3.49, 95% CI: 1.74–7.01), HF (HR: 2.79, 95% CI: 2.25–3.45), DM (HR: 1.70, 95% CI: 1.53–1.89), CKD (HR: 1.67, 95% CI: 1.41–1.97), AMI (HR: 1.76, 95% CI: 1.20–2.57), STK (HR: 1.65, 95% CI: 1.42–1.92), CAD (HR: 1.24, 95% CI: 1.12–1.37), AF (HR: 2.38, 95% CI: 1.86–3.04), and HTN (HR: 1.67, 95% CI: 1.51–1.85). The external validation sets also validated that an ECG-age >7 years compare to chronologic age had 3.16-fold risk (95% CI: 1.72–5.78) and 1.59-fold risk (95% CI: 1.45–1.74) on all-cause mortality in SaMi-Trop and CODE15 cohorts. The ECG-age significantly contributed additional information on heart failure, stroke, coronary artery disease, and atrial fibrillation predictions after considering all the known risk factors. CONCLUSIONS: The ECG-age ... |
format |
Text |
author |
Chang, Chiao-Hsiang Lin, Chin-Sheng Luo, Yu-Sheng Lee, Yung-Tsai Lin, Chin |
author_facet |
Chang, Chiao-Hsiang Lin, Chin-Sheng Luo, Yu-Sheng Lee, Yung-Tsai Lin, Chin |
author_sort |
Chang, Chiao-Hsiang |
title |
Electrocardiogram-Based Heart Age Estimation by a Deep Learning Model Provides More Information on the Incidence of Cardiovascular Disorders |
title_short |
Electrocardiogram-Based Heart Age Estimation by a Deep Learning Model Provides More Information on the Incidence of Cardiovascular Disorders |
title_full |
Electrocardiogram-Based Heart Age Estimation by a Deep Learning Model Provides More Information on the Incidence of Cardiovascular Disorders |
title_fullStr |
Electrocardiogram-Based Heart Age Estimation by a Deep Learning Model Provides More Information on the Incidence of Cardiovascular Disorders |
title_full_unstemmed |
Electrocardiogram-Based Heart Age Estimation by a Deep Learning Model Provides More Information on the Incidence of Cardiovascular Disorders |
title_sort |
electrocardiogram-based heart age estimation by a deep learning model provides more information on the incidence of cardiovascular disorders |
publisher |
Frontiers Media S.A. |
publishDate |
2022 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8860826/ https://doi.org/10.3389/fcvm.2022.754909 |
genre |
sami |
genre_facet |
sami |
op_source |
Front Cardiovasc Med |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8860826/ http://dx.doi.org/10.3389/fcvm.2022.754909 |
op_rights |
Copyright © 2022 Chang, Lin, Luo, Lee and Lin. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
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CC-BY |
op_doi |
https://doi.org/10.3389/fcvm.2022.754909 |
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Frontiers in Cardiovascular Medicine |
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9 |
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1766184711295598592 |