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A preoperative mortality risk assessment model for Stanford type A acute aortic dissection

BACKGROUND: Acute aortic dissection type A is a life-threatening disease required emergency surgery during acute phase. Different clinical manifestations, laboratory tests, and imaging features of patients with acute aortic dissection type A are the risk factors of preoperative mortality. This study...

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Autores principales: Kuang, Juntao, Yang, Jue, Wang, Qiuji, Yu, Changjiang, Li, Ying, Fan, Ruixin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7712615/
https://www.ncbi.nlm.nih.gov/pubmed/33272195
http://dx.doi.org/10.1186/s12872-020-01802-9
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author Kuang, Juntao
Yang, Jue
Wang, Qiuji
Yu, Changjiang
Li, Ying
Fan, Ruixin
author_facet Kuang, Juntao
Yang, Jue
Wang, Qiuji
Yu, Changjiang
Li, Ying
Fan, Ruixin
author_sort Kuang, Juntao
collection PubMed
description BACKGROUND: Acute aortic dissection type A is a life-threatening disease required emergency surgery during acute phase. Different clinical manifestations, laboratory tests, and imaging features of patients with acute aortic dissection type A are the risk factors of preoperative mortality. This study aims to establish a simple and effective preoperative mortality risk assessment model for patients with acute aortic dissection type A. METHODS: A total of 673 Chinese patients with acute aortic dissection type A who were admitted to our hospital were retrospectively included. All patients were unable to receive surgically treatment within 3 days from the onset of disease. The patients included were divided into the survivor and deceased groups, and the endpoint event was preoperative death. Multivariable analysis was used to investigate predictors of preoperative mortality and to develop a prediction model. RESULTS: Among the 673 patients, 527 patients survived (78.31%) and 146 patients died (21.69%). The developmental dataset had 505 patients, calibration by Hosmer Lemeshow was significant (χ(2) = 3.260, df = 8, P = 0.917) and discrimination by area under ROC curve was 0.8448 (95% CI 0.8007–0.8888). The validation dataset had 168 patients, calibration was significant (χ(2) = 5.500, df = 8, P = 0.703) and the area under the ROC curve was 0.8086 (95% CI 0.7291–0.8881). The following independent variables increased preoperative mortality: age (OR = 1.008, P = 0.510), abrupt chest pain (OR = 3.534, P < 0.001), lactic in arterial blood gas ≥ 3 mmol/L (OR = 3.636, P < 0.001), inotropic support (OR = 8.615, P < 0.001), electrocardiographic myocardial ischemia (OR = 3.300, P = 0.001), innominate artery involvement (OR = 1.625, P = 0.104), right common carotid artery involvement (OR = 3.487, P = 0.001), superior mesenteric artery involvement (OR = 2.651, P = 0.001), false lumen / true lumen of ascending aorta ≥ 0.75 (OR = 2.221, P = 0.007). Our data suggest that a simple and effective preoperative death risk assessment model has been established. CONCLUSIONS: Using a simple and effective risk assessment model can help clinicians quickly identify high-risk patients and make appropriate medical decisions.
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spelling pubmed-77126152020-12-03 A preoperative mortality risk assessment model for Stanford type A acute aortic dissection Kuang, Juntao Yang, Jue Wang, Qiuji Yu, Changjiang Li, Ying Fan, Ruixin BMC Cardiovasc Disord Research Article BACKGROUND: Acute aortic dissection type A is a life-threatening disease required emergency surgery during acute phase. Different clinical manifestations, laboratory tests, and imaging features of patients with acute aortic dissection type A are the risk factors of preoperative mortality. This study aims to establish a simple and effective preoperative mortality risk assessment model for patients with acute aortic dissection type A. METHODS: A total of 673 Chinese patients with acute aortic dissection type A who were admitted to our hospital were retrospectively included. All patients were unable to receive surgically treatment within 3 days from the onset of disease. The patients included were divided into the survivor and deceased groups, and the endpoint event was preoperative death. Multivariable analysis was used to investigate predictors of preoperative mortality and to develop a prediction model. RESULTS: Among the 673 patients, 527 patients survived (78.31%) and 146 patients died (21.69%). The developmental dataset had 505 patients, calibration by Hosmer Lemeshow was significant (χ(2) = 3.260, df = 8, P = 0.917) and discrimination by area under ROC curve was 0.8448 (95% CI 0.8007–0.8888). The validation dataset had 168 patients, calibration was significant (χ(2) = 5.500, df = 8, P = 0.703) and the area under the ROC curve was 0.8086 (95% CI 0.7291–0.8881). The following independent variables increased preoperative mortality: age (OR = 1.008, P = 0.510), abrupt chest pain (OR = 3.534, P < 0.001), lactic in arterial blood gas ≥ 3 mmol/L (OR = 3.636, P < 0.001), inotropic support (OR = 8.615, P < 0.001), electrocardiographic myocardial ischemia (OR = 3.300, P = 0.001), innominate artery involvement (OR = 1.625, P = 0.104), right common carotid artery involvement (OR = 3.487, P = 0.001), superior mesenteric artery involvement (OR = 2.651, P = 0.001), false lumen / true lumen of ascending aorta ≥ 0.75 (OR = 2.221, P = 0.007). Our data suggest that a simple and effective preoperative death risk assessment model has been established. CONCLUSIONS: Using a simple and effective risk assessment model can help clinicians quickly identify high-risk patients and make appropriate medical decisions. BioMed Central 2020-12-03 /pmc/articles/PMC7712615/ /pubmed/33272195 http://dx.doi.org/10.1186/s12872-020-01802-9 Text en © The Author(s) 2020 Open AccessThis 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 Article
Kuang, Juntao
Yang, Jue
Wang, Qiuji
Yu, Changjiang
Li, Ying
Fan, Ruixin
A preoperative mortality risk assessment model for Stanford type A acute aortic dissection
title A preoperative mortality risk assessment model for Stanford type A acute aortic dissection
title_full A preoperative mortality risk assessment model for Stanford type A acute aortic dissection
title_fullStr A preoperative mortality risk assessment model for Stanford type A acute aortic dissection
title_full_unstemmed A preoperative mortality risk assessment model for Stanford type A acute aortic dissection
title_short A preoperative mortality risk assessment model for Stanford type A acute aortic dissection
title_sort preoperative mortality risk assessment model for stanford type a acute aortic dissection
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7712615/
https://www.ncbi.nlm.nih.gov/pubmed/33272195
http://dx.doi.org/10.1186/s12872-020-01802-9
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