Artificial intelligence-estimated biological heart age using a 12-lead electrocardiogram predicts mortality and cardiovascular outcomes
© 2023 Baek, Lee, Jo, Lee, Choi and Kim..
Background: There is a paucity of data on artificial intelligence-estimated biological electrocardiography (ECG) heart age (AI ECG-heart age) for predicting cardiovascular outcomes, distinct from the chronological age (CA). We developed a deep learning-based algorithm to estimate the AI ECG-heart age using standard 12-lead ECGs and evaluated whether it predicted mortality and cardiovascular outcomes.
Methods: We trained and validated a deep neural network using the raw ECG digital data from 425,051 12-lead ECGs acquired between January 2006 and December 2021. The network performed a holdout test using a separate set of 97,058 ECGs. The deep neural network was trained to estimate the AI ECG-heart age [mean absolute error, 5.8 ± 3.9 years; R-squared, 0.7 (r = 0.84, p < 0.05)].
Findings: In the Cox proportional hazards models, after adjusting for relevant comorbidity factors, the patients with an AI ECG-heart age of 6 years older than the CA had higher all-cause mortality (hazard ratio (HR) 1.60 [1.42-1.79]) and more major adverse cardiovascular events (MACEs) [HR: 1.91 (1.66-2.21)], whereas those under 6 years had an inverse relationship (HR: 0.82 [0.75-0.91] for all-cause mortality; HR: 0.78 [0.68-0.89] for MACEs). Additionally, the analysis of ECG features showed notable alterations in the PR interval, QRS duration, QT interval and corrected QT Interval (QTc) as the AI ECG-heart age increased.
Conclusion: Biological heart age estimated by AI had a significant impact on mortality and MACEs, suggesting that the AI ECG-heart age facilitates primary prevention and health care for cardiovascular outcomes.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
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| Enthalten in: |
Frontiers in cardiovascular medicine - 10(2023) vom: 14., Seite 1137892 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Baek, Yong-Soo [VerfasserIn] |
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| Links: |
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| Themen: |
Artificial intelligence |
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| Anmerkungen: |
Date Revised 02.05.2023 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.3389/fcvm.2023.1137892 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM356268608 |
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| 245 | 1 | 0 | |a Artificial intelligence-estimated biological heart age using a 12-lead electrocardiogram predicts mortality and cardiovascular outcomes |
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| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © 2023 Baek, Lee, Jo, Lee, Choi and Kim. | ||
| 520 | |a Background: There is a paucity of data on artificial intelligence-estimated biological electrocardiography (ECG) heart age (AI ECG-heart age) for predicting cardiovascular outcomes, distinct from the chronological age (CA). We developed a deep learning-based algorithm to estimate the AI ECG-heart age using standard 12-lead ECGs and evaluated whether it predicted mortality and cardiovascular outcomes | ||
| 520 | |a Methods: We trained and validated a deep neural network using the raw ECG digital data from 425,051 12-lead ECGs acquired between January 2006 and December 2021. The network performed a holdout test using a separate set of 97,058 ECGs. The deep neural network was trained to estimate the AI ECG-heart age [mean absolute error, 5.8 ± 3.9 years; R-squared, 0.7 (r = 0.84, p < 0.05)] | ||
| 520 | |a Findings: In the Cox proportional hazards models, after adjusting for relevant comorbidity factors, the patients with an AI ECG-heart age of 6 years older than the CA had higher all-cause mortality (hazard ratio (HR) 1.60 [1.42-1.79]) and more major adverse cardiovascular events (MACEs) [HR: 1.91 (1.66-2.21)], whereas those under 6 years had an inverse relationship (HR: 0.82 [0.75-0.91] for all-cause mortality; HR: 0.78 [0.68-0.89] for MACEs). Additionally, the analysis of ECG features showed notable alterations in the PR interval, QRS duration, QT interval and corrected QT Interval (QTc) as the AI ECG-heart age increased | ||
| 520 | |a Conclusion: Biological heart age estimated by AI had a significant impact on mortality and MACEs, suggesting that the AI ECG-heart age facilitates primary prevention and health care for cardiovascular outcomes | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a ECG age | |
| 650 | 4 | |a MACE | |
| 650 | 4 | |a artificial intelligence | |
| 650 | 4 | |a biological ageing | |
| 650 | 4 | |a heart age | |
| 650 | 4 | |a hospitalization | |
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| 700 | 1 | |a Lee, Dong-Ho |e verfasserin |4 aut | |
| 700 | 1 | |a Jo, Yoonsu |e verfasserin |4 aut | |
| 700 | 1 | |a Lee, Sang-Chul |e verfasserin |4 aut | |
| 700 | 1 | |a Choi, Wonik |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Dae-Hyeok |e verfasserin |4 aut | |
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