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Individualized prediction of risk of ascending aortic syndromes
OBJECTIVES: Although ascending aortic diameter changes acutely after dissection, recommendation for prophylactic surgery of thoracic aortic aneurysms rely on data from dissected aortas. In this case-control study we aim to identify risk markers for acute and chronic aortic syndromes of the ascending...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9236241/ https://www.ncbi.nlm.nih.gov/pubmed/35759492 http://dx.doi.org/10.1371/journal.pone.0270585 |
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author | Saleh, Qais Waleed Diederichsen, Axel Cosmus Pyndt Lindholt, Jes Sanddal |
author_facet | Saleh, Qais Waleed Diederichsen, Axel Cosmus Pyndt Lindholt, Jes Sanddal |
author_sort | Saleh, Qais Waleed |
collection | PubMed |
description | OBJECTIVES: Although ascending aortic diameter changes acutely after dissection, recommendation for prophylactic surgery of thoracic aortic aneurysms rely on data from dissected aortas. In this case-control study we aim to identify risk markers for acute and chronic aortic syndromes of the ascending aorta (ACAS-AA). Furthermore, to develop a predictive model for ACAS-AA. METHODS: We collected data of 188 cases of ACAS-AA and 376 controls standardized to age- and sex of the background population. Medical history and CT-derived aortic morphology were collected. For the dependent outcome ACAS-AA, potential independent risk factors were identified by univariate logistic regression and confirmed in multivariate logistic regression. As post-dissection tubular ascending aortic diameter is prone to expand, this factor was not included in the first model. The individual calculated adjusted odds ratios were then used in ROC-curve analysis to evaluate the diagnostic accuracy of the model. To test the influence of post-ACAS-AA tubular ascending aortic diameter, this was added to the model. RESULTS: The following risk factors were identified as independent risk factors for ACAS-AA in multivariate analysis: bicuspid aortic valve (OR 20.41, p = 0.03), renal insufficiency (OR 2.9, p<0.01), infrarenal abdominal aortic diameter (OR 1.08, p<0.01), left common carotid artery diameter (OR 1.40, p<0.01) and aortic width (OR 1.07, p<0.01). Area under the curve was 0.88 (p<0.01). Adding post-ACAS-AA tubular ascending aortic diameter to the model, negated the association of bicuspid aortic valve, renal insufficiency, and left common carotid artery diameter. Area under the curve changed to 0.98 (p<0.01). CONCLUSIONS: A high performing predictive model for ACAS-AA, free of ascending aortic diameter, can be achieved. Furthermore, we have identified abdominal aortic ectasia as an independent risk factor of ACAS-AA. Integration of potential biomarkers and morphologic variables, derived from undissected aortas, would probably improve the model. |
format | Online Article Text |
id | pubmed-9236241 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-92362412022-06-28 Individualized prediction of risk of ascending aortic syndromes Saleh, Qais Waleed Diederichsen, Axel Cosmus Pyndt Lindholt, Jes Sanddal PLoS One Research Article OBJECTIVES: Although ascending aortic diameter changes acutely after dissection, recommendation for prophylactic surgery of thoracic aortic aneurysms rely on data from dissected aortas. In this case-control study we aim to identify risk markers for acute and chronic aortic syndromes of the ascending aorta (ACAS-AA). Furthermore, to develop a predictive model for ACAS-AA. METHODS: We collected data of 188 cases of ACAS-AA and 376 controls standardized to age- and sex of the background population. Medical history and CT-derived aortic morphology were collected. For the dependent outcome ACAS-AA, potential independent risk factors were identified by univariate logistic regression and confirmed in multivariate logistic regression. As post-dissection tubular ascending aortic diameter is prone to expand, this factor was not included in the first model. The individual calculated adjusted odds ratios were then used in ROC-curve analysis to evaluate the diagnostic accuracy of the model. To test the influence of post-ACAS-AA tubular ascending aortic diameter, this was added to the model. RESULTS: The following risk factors were identified as independent risk factors for ACAS-AA in multivariate analysis: bicuspid aortic valve (OR 20.41, p = 0.03), renal insufficiency (OR 2.9, p<0.01), infrarenal abdominal aortic diameter (OR 1.08, p<0.01), left common carotid artery diameter (OR 1.40, p<0.01) and aortic width (OR 1.07, p<0.01). Area under the curve was 0.88 (p<0.01). Adding post-ACAS-AA tubular ascending aortic diameter to the model, negated the association of bicuspid aortic valve, renal insufficiency, and left common carotid artery diameter. Area under the curve changed to 0.98 (p<0.01). CONCLUSIONS: A high performing predictive model for ACAS-AA, free of ascending aortic diameter, can be achieved. Furthermore, we have identified abdominal aortic ectasia as an independent risk factor of ACAS-AA. Integration of potential biomarkers and morphologic variables, derived from undissected aortas, would probably improve the model. Public Library of Science 2022-06-27 /pmc/articles/PMC9236241/ /pubmed/35759492 http://dx.doi.org/10.1371/journal.pone.0270585 Text en © 2022 Saleh et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Saleh, Qais Waleed Diederichsen, Axel Cosmus Pyndt Lindholt, Jes Sanddal Individualized prediction of risk of ascending aortic syndromes |
title | Individualized prediction of risk of ascending aortic syndromes |
title_full | Individualized prediction of risk of ascending aortic syndromes |
title_fullStr | Individualized prediction of risk of ascending aortic syndromes |
title_full_unstemmed | Individualized prediction of risk of ascending aortic syndromes |
title_short | Individualized prediction of risk of ascending aortic syndromes |
title_sort | individualized prediction of risk of ascending aortic syndromes |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9236241/ https://www.ncbi.nlm.nih.gov/pubmed/35759492 http://dx.doi.org/10.1371/journal.pone.0270585 |
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