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Aortic Distensibility Measured by Automated Analysis of Magnetic Resonance Imaging Predicts Adverse Cardiovascular Events in UK Biobank
BACKGROUND: Automated analysis of cardiovascular magnetic resonance images provides the potential to assess aortic distensibility in large populations. The aim of this study was to compare the prediction of cardiovascular events by automated cardiovascular magnetic resonance with those of other simp...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9851433/ https://www.ncbi.nlm.nih.gov/pubmed/36444831 http://dx.doi.org/10.1161/JAHA.122.026361 |
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author | Cecelja, Marina Ruijsink, Bram Puyol‐Antón, Esther Li, Ye Godwin, Harriet King, Andrew P. Razavi, Reza Chowienczyk, Phil |
author_facet | Cecelja, Marina Ruijsink, Bram Puyol‐Antón, Esther Li, Ye Godwin, Harriet King, Andrew P. Razavi, Reza Chowienczyk, Phil |
author_sort | Cecelja, Marina |
collection | PubMed |
description | BACKGROUND: Automated analysis of cardiovascular magnetic resonance images provides the potential to assess aortic distensibility in large populations. The aim of this study was to compare the prediction of cardiovascular events by automated cardiovascular magnetic resonance with those of other simple measures of aortic stiffness suitable for population screening. METHODS AND RESULTS: Aortic distensibility was measured from automated segmentation of aortic cine cardiovascular magnetic resonance using artificial intelligence in 8435 participants. The associations of distensibility, brachial pulse pressure, and stiffness index (obtained by finger photoplethysmography) with conventional risk factors was examined by multivariable regression and incident cardiovascular events by Cox proportional‐hazards regression. Mean (±SD) distensibility values for men and women were 1.77±1.15 and 2.10±1.45 (P<0.0001) 10(−3) mm Hg(−1), respectively. There was a good correlation between automatically and manually obtained systolic and diastolic aortic areas (r=0.980 and r=0.985, respectively). In regression analysis, distensibility associated with age, mean arterial pressure, heart rate, weight, and plasma glucose but not male sex, cholesterol or current smoking. During an average follow‐up of 2.8±1.3 years, 86 participants experienced cardiovascular events 6 of whom died. Higher distensibility was associated with reduced risk of cardiovascular events (adjusted hazard ratio [HR], 0.61 per log unit of distensibility; P=0.016). There was no evidence of an association between pulse pressure (adjusted HR 1.00; P=0.715) or stiffness index (adjusted HR, 1.02; P=0.535) and risk of cardiovascular events. CONCLUSIONS: Automated cardiovascular magnetic resonance‐derived aortic distensibility may be incorporated into routine clinical imaging. It shows a similar association to cardiovascular risk factors as other measures of arterial stiffness and predicts new‐onset cardiovascular events, making it a useful tool for the measurement of vascular aging and associated cardiovascular risk. |
format | Online Article Text |
id | pubmed-9851433 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98514332023-01-24 Aortic Distensibility Measured by Automated Analysis of Magnetic Resonance Imaging Predicts Adverse Cardiovascular Events in UK Biobank Cecelja, Marina Ruijsink, Bram Puyol‐Antón, Esther Li, Ye Godwin, Harriet King, Andrew P. Razavi, Reza Chowienczyk, Phil J Am Heart Assoc Original Research BACKGROUND: Automated analysis of cardiovascular magnetic resonance images provides the potential to assess aortic distensibility in large populations. The aim of this study was to compare the prediction of cardiovascular events by automated cardiovascular magnetic resonance with those of other simple measures of aortic stiffness suitable for population screening. METHODS AND RESULTS: Aortic distensibility was measured from automated segmentation of aortic cine cardiovascular magnetic resonance using artificial intelligence in 8435 participants. The associations of distensibility, brachial pulse pressure, and stiffness index (obtained by finger photoplethysmography) with conventional risk factors was examined by multivariable regression and incident cardiovascular events by Cox proportional‐hazards regression. Mean (±SD) distensibility values for men and women were 1.77±1.15 and 2.10±1.45 (P<0.0001) 10(−3) mm Hg(−1), respectively. There was a good correlation between automatically and manually obtained systolic and diastolic aortic areas (r=0.980 and r=0.985, respectively). In regression analysis, distensibility associated with age, mean arterial pressure, heart rate, weight, and plasma glucose but not male sex, cholesterol or current smoking. During an average follow‐up of 2.8±1.3 years, 86 participants experienced cardiovascular events 6 of whom died. Higher distensibility was associated with reduced risk of cardiovascular events (adjusted hazard ratio [HR], 0.61 per log unit of distensibility; P=0.016). There was no evidence of an association between pulse pressure (adjusted HR 1.00; P=0.715) or stiffness index (adjusted HR, 1.02; P=0.535) and risk of cardiovascular events. CONCLUSIONS: Automated cardiovascular magnetic resonance‐derived aortic distensibility may be incorporated into routine clinical imaging. It shows a similar association to cardiovascular risk factors as other measures of arterial stiffness and predicts new‐onset cardiovascular events, making it a useful tool for the measurement of vascular aging and associated cardiovascular risk. John Wiley and Sons Inc. 2022-12-06 /pmc/articles/PMC9851433/ /pubmed/36444831 http://dx.doi.org/10.1161/JAHA.122.026361 Text en © 2022 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Research Cecelja, Marina Ruijsink, Bram Puyol‐Antón, Esther Li, Ye Godwin, Harriet King, Andrew P. Razavi, Reza Chowienczyk, Phil Aortic Distensibility Measured by Automated Analysis of Magnetic Resonance Imaging Predicts Adverse Cardiovascular Events in UK Biobank |
title | Aortic Distensibility Measured by Automated Analysis of Magnetic Resonance Imaging Predicts Adverse Cardiovascular Events in UK Biobank |
title_full | Aortic Distensibility Measured by Automated Analysis of Magnetic Resonance Imaging Predicts Adverse Cardiovascular Events in UK Biobank |
title_fullStr | Aortic Distensibility Measured by Automated Analysis of Magnetic Resonance Imaging Predicts Adverse Cardiovascular Events in UK Biobank |
title_full_unstemmed | Aortic Distensibility Measured by Automated Analysis of Magnetic Resonance Imaging Predicts Adverse Cardiovascular Events in UK Biobank |
title_short | Aortic Distensibility Measured by Automated Analysis of Magnetic Resonance Imaging Predicts Adverse Cardiovascular Events in UK Biobank |
title_sort | aortic distensibility measured by automated analysis of magnetic resonance imaging predicts adverse cardiovascular events in uk biobank |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9851433/ https://www.ncbi.nlm.nih.gov/pubmed/36444831 http://dx.doi.org/10.1161/JAHA.122.026361 |
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