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A nomogram for predicting in-hospital mortality in acute type A aortic dissection patients

BACKGROUND: Although there are several biomarkers for identifying in-hospital mortality in acute type A aortic dissection (AAD), timely as well as perfect prediction in-hospital mortality is still not attained. Herein, we intend to develop as well to validate an in-hospital mortality risk independen...

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Autores principales: Yang, Guifang, Zhou, Yang, He, Huaping, Pan, Xiaogao, Li, Xizhao, Chai, Xiangping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7139052/
https://www.ncbi.nlm.nih.gov/pubmed/32274093
http://dx.doi.org/10.21037/jtd.2020.01.41
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author Yang, Guifang
Zhou, Yang
He, Huaping
Pan, Xiaogao
Li, Xizhao
Chai, Xiangping
author_facet Yang, Guifang
Zhou, Yang
He, Huaping
Pan, Xiaogao
Li, Xizhao
Chai, Xiangping
author_sort Yang, Guifang
collection PubMed
description BACKGROUND: Although there are several biomarkers for identifying in-hospital mortality in acute type A aortic dissection (AAD), timely as well as perfect prediction in-hospital mortality is still not attained. Herein, we intend to develop as well to validate an in-hospital mortality risk independent predictive nomogram for AAD patients. METHODS: From January 2014 to December 2018, 703 individuals with AAD were involved in this study. They were indiscriminately categorized into training (n=520) and validation (n=183) sets. The univariate and multivariate analyses were used to screen in-hospital mortality predictors from the entire training set data. The predictors were used to establish a nomogram which was confirmed via internal as well as external authentication. This validation included discriminative capacity defined by the receiver operating characteristic (ROC) curve area under the curve (AUC) and the predictive precision via calibration curves. RESULTS: There was 33.43% in-hospital mortality overall incidence. The uric acid, D-dimer, C-reactive protein and management were individually related to in-hospital mortality as per multivariate logistic regression. On the basis of four variables with internal of AUC 0.901 and external validation of AUC 0.903, a nomogram was established. Calibration plots showed that the predicted and actual in-hospital mortality probabilities were fitted well on both internal and external validation. CONCLUSIONS: This recommended nomogram can calculate the specific possibility of in-hospital mortality with good precision, high discrimination, and probable clinical application in AAD patients.
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spelling pubmed-71390522020-04-09 A nomogram for predicting in-hospital mortality in acute type A aortic dissection patients Yang, Guifang Zhou, Yang He, Huaping Pan, Xiaogao Li, Xizhao Chai, Xiangping J Thorac Dis Original Article BACKGROUND: Although there are several biomarkers for identifying in-hospital mortality in acute type A aortic dissection (AAD), timely as well as perfect prediction in-hospital mortality is still not attained. Herein, we intend to develop as well to validate an in-hospital mortality risk independent predictive nomogram for AAD patients. METHODS: From January 2014 to December 2018, 703 individuals with AAD were involved in this study. They were indiscriminately categorized into training (n=520) and validation (n=183) sets. The univariate and multivariate analyses were used to screen in-hospital mortality predictors from the entire training set data. The predictors were used to establish a nomogram which was confirmed via internal as well as external authentication. This validation included discriminative capacity defined by the receiver operating characteristic (ROC) curve area under the curve (AUC) and the predictive precision via calibration curves. RESULTS: There was 33.43% in-hospital mortality overall incidence. The uric acid, D-dimer, C-reactive protein and management were individually related to in-hospital mortality as per multivariate logistic regression. On the basis of four variables with internal of AUC 0.901 and external validation of AUC 0.903, a nomogram was established. Calibration plots showed that the predicted and actual in-hospital mortality probabilities were fitted well on both internal and external validation. CONCLUSIONS: This recommended nomogram can calculate the specific possibility of in-hospital mortality with good precision, high discrimination, and probable clinical application in AAD patients. AME Publishing Company 2020-03 /pmc/articles/PMC7139052/ /pubmed/32274093 http://dx.doi.org/10.21037/jtd.2020.01.41 Text en 2020 Journal of Thoracic Disease. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Original Article
Yang, Guifang
Zhou, Yang
He, Huaping
Pan, Xiaogao
Li, Xizhao
Chai, Xiangping
A nomogram for predicting in-hospital mortality in acute type A aortic dissection patients
title A nomogram for predicting in-hospital mortality in acute type A aortic dissection patients
title_full A nomogram for predicting in-hospital mortality in acute type A aortic dissection patients
title_fullStr A nomogram for predicting in-hospital mortality in acute type A aortic dissection patients
title_full_unstemmed A nomogram for predicting in-hospital mortality in acute type A aortic dissection patients
title_short A nomogram for predicting in-hospital mortality in acute type A aortic dissection patients
title_sort nomogram for predicting in-hospital mortality in acute type a aortic dissection patients
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7139052/
https://www.ncbi.nlm.nih.gov/pubmed/32274093
http://dx.doi.org/10.21037/jtd.2020.01.41
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