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Prescreening and treatment of aortic dissection through an analysis of infinite-dimension data
BACKGROUND: Aortic dissection (AD) is one of the most catastrophic aortic diseases associated with a high mortality rate. In contrast to the advances in most cardiovascular diseases, both the incidence and in-hospital mortality rate of AD have experienced deviant increases over the past 20 years, hi...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8015064/ https://www.ncbi.nlm.nih.gov/pubmed/33794946 http://dx.doi.org/10.1186/s13040-021-00249-8 |
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author | Qiu, Peng Li, Yixuan Liu, Kai Qin, Jinbao Ye, Kaichuang Chen, Tao Lu, Xinwu |
author_facet | Qiu, Peng Li, Yixuan Liu, Kai Qin, Jinbao Ye, Kaichuang Chen, Tao Lu, Xinwu |
author_sort | Qiu, Peng |
collection | PubMed |
description | BACKGROUND: Aortic dissection (AD) is one of the most catastrophic aortic diseases associated with a high mortality rate. In contrast to the advances in most cardiovascular diseases, both the incidence and in-hospital mortality rate of AD have experienced deviant increases over the past 20 years, highlighting the need for fresh prospects on the prescreening and in-hospital treatment strategies. METHODS: Through two cross-sectional studies, we adopt image recognition techniques to identify pre-disease aortic morphology for prior diagnoses; assuming that AD has occurred, we employ functional data analysis to determine the optimal timing for BP and HR interventions to offer the highest possible survival rate. RESULTS: Compared with the healthy control group, the aortic centerline is significantly more slumped for the AD group. Further, controlling patients’ blood pressure and heart rate according to the likelihood of adverse events can offer the highest possible survival probability. CONCLUSIONS: The degree of slumpness is introduced to depict aortic morphological changes comprehensively. The morphology-based prediction model is associated with an improvement in the predictive accuracy of the prescreening of AD. The dynamic model reveals that blood pressure and heart rate variations have a strong predictive power for adverse events, confirming this model’s ability to improve AD management. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s13040-021-00249-8). |
format | Online Article Text |
id | pubmed-8015064 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-80150642021-04-01 Prescreening and treatment of aortic dissection through an analysis of infinite-dimension data Qiu, Peng Li, Yixuan Liu, Kai Qin, Jinbao Ye, Kaichuang Chen, Tao Lu, Xinwu BioData Min Research BACKGROUND: Aortic dissection (AD) is one of the most catastrophic aortic diseases associated with a high mortality rate. In contrast to the advances in most cardiovascular diseases, both the incidence and in-hospital mortality rate of AD have experienced deviant increases over the past 20 years, highlighting the need for fresh prospects on the prescreening and in-hospital treatment strategies. METHODS: Through two cross-sectional studies, we adopt image recognition techniques to identify pre-disease aortic morphology for prior diagnoses; assuming that AD has occurred, we employ functional data analysis to determine the optimal timing for BP and HR interventions to offer the highest possible survival rate. RESULTS: Compared with the healthy control group, the aortic centerline is significantly more slumped for the AD group. Further, controlling patients’ blood pressure and heart rate according to the likelihood of adverse events can offer the highest possible survival probability. CONCLUSIONS: The degree of slumpness is introduced to depict aortic morphological changes comprehensively. The morphology-based prediction model is associated with an improvement in the predictive accuracy of the prescreening of AD. The dynamic model reveals that blood pressure and heart rate variations have a strong predictive power for adverse events, confirming this model’s ability to improve AD management. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s13040-021-00249-8). BioMed Central 2021-04-01 /pmc/articles/PMC8015064/ /pubmed/33794946 http://dx.doi.org/10.1186/s13040-021-00249-8 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Qiu, Peng Li, Yixuan Liu, Kai Qin, Jinbao Ye, Kaichuang Chen, Tao Lu, Xinwu Prescreening and treatment of aortic dissection through an analysis of infinite-dimension data |
title | Prescreening and treatment of aortic dissection through an analysis of infinite-dimension data |
title_full | Prescreening and treatment of aortic dissection through an analysis of infinite-dimension data |
title_fullStr | Prescreening and treatment of aortic dissection through an analysis of infinite-dimension data |
title_full_unstemmed | Prescreening and treatment of aortic dissection through an analysis of infinite-dimension data |
title_short | Prescreening and treatment of aortic dissection through an analysis of infinite-dimension data |
title_sort | prescreening and treatment of aortic dissection through an analysis of infinite-dimension data |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8015064/ https://www.ncbi.nlm.nih.gov/pubmed/33794946 http://dx.doi.org/10.1186/s13040-021-00249-8 |
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