Artificial Intelligence–Derived Electrocardiographic Age Predicts Mortality in Adults With Congenital Heart Disease
Background: Artificial intelligence (AI) can be used to estimate age from the electrocardiogram (AI-ECG age). The difference between AI-ECG age and chronological age (delta-age) is an independent predictor of mortality in the general population. Objectives: The purpose of this study was to assess th...
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Elsevier
2025-06-01
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| Series: | JACC: Advances |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772963X25001954 |
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| author | Scott Anjewierden, MD Donnchadh O'Sullivan, MB, BCh, BAO Kathryn E. Mangold, PhD Itzhak Zachi Attia, PhD Francisco Lopez-Jimenez, MD Paul A. Friedman, MD Alexander C. Egbe, MBBS, MPH Heidi M. Connolly, MD William R. Miranda, MD Samuel J. Asirvatham, MD Jennifer Dugan Katia Bravo-Jaimes, MD Talha Niaz, MBBS Malini Madhavan, MBBS Luke J. Burchill, MBBS, PhD |
| author_facet | Scott Anjewierden, MD Donnchadh O'Sullivan, MB, BCh, BAO Kathryn E. Mangold, PhD Itzhak Zachi Attia, PhD Francisco Lopez-Jimenez, MD Paul A. Friedman, MD Alexander C. Egbe, MBBS, MPH Heidi M. Connolly, MD William R. Miranda, MD Samuel J. Asirvatham, MD Jennifer Dugan Katia Bravo-Jaimes, MD Talha Niaz, MBBS Malini Madhavan, MBBS Luke J. Burchill, MBBS, PhD |
| author_sort | Scott Anjewierden, MD |
| collection | DOAJ |
| description | Background: Artificial intelligence (AI) can be used to estimate age from the electrocardiogram (AI-ECG age). The difference between AI-ECG age and chronological age (delta-age) is an independent predictor of mortality in the general population. Objectives: The purpose of this study was to assess the relationship between delta-age and mortality among adults with congenital heart disease (ACHD). Methods: A previously validated neural network was used to analyze standard digital 12-lead ECGs in a cohort of ACHD (age >18 years) between 1992 and 2023. A single ECG from each patient, collected during the first visit to the ACHD clinic, was analyzed to compute the delta-age. The relationship between the delta-age and mortality was evaluated using Cox proportional hazard models adjusting for influential clinical factors. Results: Of 5,780 subjects tested (50% females), the mean chronological age was 39.1 ± 15.0 years. AI-ECG age was 52.3 ± 16.6 years. CHD complexity was classified as mild, moderate, and severe in 7.4%, 73.9%, and 18.7% of patients, respectively. Patients with severe CHD had the highest median delta-age of 15.8 (IQR: 3.5-31.2) years followed by moderate 11.5 (IQR: 3.5-21.3) years and simple 6.7 (IQR: 0.3-14.2) years. During a median follow-up of 6.4 years (IQR: 1.58-13.7 years), 839 (14.5%) patients died. After adjusting for chronologic age, CHD complexity, and other clinical variables, delta-age was associated with increased mortality risk (HR: 1.06 [1.03-1.09] per 5-year increment in delta-age, P < 0.05). Conclusions: Delta-age, the difference between AI-ECG and chronological age, is an independent predictor of all-cause mortality in ACHD. |
| format | Article |
| id | doaj-art-4428bbaca3de47d8951069fcc9ccf264 |
| institution | DOAJ |
| issn | 2772-963X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | JACC: Advances |
| spelling | doaj-art-4428bbaca3de47d8951069fcc9ccf2642025-08-20T03:10:21ZengElsevierJACC: Advances2772-963X2025-06-014610177710.1016/j.jacadv.2025.101777Artificial Intelligence–Derived Electrocardiographic Age Predicts Mortality in Adults With Congenital Heart DiseaseScott Anjewierden, MD0Donnchadh O'Sullivan, MB, BCh, BAO1Kathryn E. Mangold, PhD2Itzhak Zachi Attia, PhD3Francisco Lopez-Jimenez, MD4Paul A. Friedman, MD5Alexander C. Egbe, MBBS, MPH6Heidi M. Connolly, MD7William R. Miranda, MD8Samuel J. Asirvatham, MD9Jennifer Dugan10Katia Bravo-Jaimes, MD11Talha Niaz, MBBS12Malini Madhavan, MBBS13Luke J. Burchill, MBBS, PhD14Department of Pediatric and Adolescent Medicine, Division of Pediatric Cardiology, Mayo Clinic, Rochester, Minnesota, USADepartment of Pediatric and Adolescent Medicine, Division of Pediatric Cardiology, Mayo Clinic, Rochester, Minnesota, USADepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USADepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USADepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USADepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USADepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USADepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USADepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USADepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USADepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USADepartment of Cardiovascular Medicine, Mayo Clinic, Jacksonville, Florida, USADepartment of Pediatric and Adolescent Medicine, Division of Pediatric Cardiology, Mayo Clinic, Rochester, Minnesota, USADepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USADepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA; Address for correspondence: Dr Luke J. Burchill, Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, Minnesota 55902, USA.Background: Artificial intelligence (AI) can be used to estimate age from the electrocardiogram (AI-ECG age). The difference between AI-ECG age and chronological age (delta-age) is an independent predictor of mortality in the general population. Objectives: The purpose of this study was to assess the relationship between delta-age and mortality among adults with congenital heart disease (ACHD). Methods: A previously validated neural network was used to analyze standard digital 12-lead ECGs in a cohort of ACHD (age >18 years) between 1992 and 2023. A single ECG from each patient, collected during the first visit to the ACHD clinic, was analyzed to compute the delta-age. The relationship between the delta-age and mortality was evaluated using Cox proportional hazard models adjusting for influential clinical factors. Results: Of 5,780 subjects tested (50% females), the mean chronological age was 39.1 ± 15.0 years. AI-ECG age was 52.3 ± 16.6 years. CHD complexity was classified as mild, moderate, and severe in 7.4%, 73.9%, and 18.7% of patients, respectively. Patients with severe CHD had the highest median delta-age of 15.8 (IQR: 3.5-31.2) years followed by moderate 11.5 (IQR: 3.5-21.3) years and simple 6.7 (IQR: 0.3-14.2) years. During a median follow-up of 6.4 years (IQR: 1.58-13.7 years), 839 (14.5%) patients died. After adjusting for chronologic age, CHD complexity, and other clinical variables, delta-age was associated with increased mortality risk (HR: 1.06 [1.03-1.09] per 5-year increment in delta-age, P < 0.05). Conclusions: Delta-age, the difference between AI-ECG and chronological age, is an independent predictor of all-cause mortality in ACHD.http://www.sciencedirect.com/science/article/pii/S2772963X25001954adults with congenital heart diseaseartificial intelligencecongenital heart diseasemortalityrisk factor |
| spellingShingle | Scott Anjewierden, MD Donnchadh O'Sullivan, MB, BCh, BAO Kathryn E. Mangold, PhD Itzhak Zachi Attia, PhD Francisco Lopez-Jimenez, MD Paul A. Friedman, MD Alexander C. Egbe, MBBS, MPH Heidi M. Connolly, MD William R. Miranda, MD Samuel J. Asirvatham, MD Jennifer Dugan Katia Bravo-Jaimes, MD Talha Niaz, MBBS Malini Madhavan, MBBS Luke J. Burchill, MBBS, PhD Artificial Intelligence–Derived Electrocardiographic Age Predicts Mortality in Adults With Congenital Heart Disease JACC: Advances adults with congenital heart disease artificial intelligence congenital heart disease mortality risk factor |
| title | Artificial Intelligence–Derived Electrocardiographic Age Predicts Mortality in Adults With Congenital Heart Disease |
| title_full | Artificial Intelligence–Derived Electrocardiographic Age Predicts Mortality in Adults With Congenital Heart Disease |
| title_fullStr | Artificial Intelligence–Derived Electrocardiographic Age Predicts Mortality in Adults With Congenital Heart Disease |
| title_full_unstemmed | Artificial Intelligence–Derived Electrocardiographic Age Predicts Mortality in Adults With Congenital Heart Disease |
| title_short | Artificial Intelligence–Derived Electrocardiographic Age Predicts Mortality in Adults With Congenital Heart Disease |
| title_sort | artificial intelligence derived electrocardiographic age predicts mortality in adults with congenital heart disease |
| topic | adults with congenital heart disease artificial intelligence congenital heart disease mortality risk factor |
| url | http://www.sciencedirect.com/science/article/pii/S2772963X25001954 |
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