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Prediction of in-hospital death following acute type A aortic dissection
BACKGROUND: Our goal was to create a prediction model for in-hospital death in Chinese patients with acute type A aortic dissection (ATAAD). METHODS: A retrospective derivation cohort was made up of 340 patients with ATAAD from Tianjin, and the retrospective validation cohort was made up of 153 pati...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10090540/ https://www.ncbi.nlm.nih.gov/pubmed/37064704 http://dx.doi.org/10.3389/fpubh.2023.1143160 |
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author | Chen, Junquan Bai, Yunpeng Liu, Hong Qin, Mingzhen Guo, Zhigang |
author_facet | Chen, Junquan Bai, Yunpeng Liu, Hong Qin, Mingzhen Guo, Zhigang |
author_sort | Chen, Junquan |
collection | PubMed |
description | BACKGROUND: Our goal was to create a prediction model for in-hospital death in Chinese patients with acute type A aortic dissection (ATAAD). METHODS: A retrospective derivation cohort was made up of 340 patients with ATAAD from Tianjin, and the retrospective validation cohort was made up of 153 patients with ATAAD from Nanjing. For variable selection, we used least absolute shrinkage and selection operator analysis, and for risk scoring, we used logistic regression coefficients. We categorized the patients into low-, middle-, and high-risk groups and looked into the correlation with in-hospital fatalities. We established a risk classifier based on independent baseline data using a multivariable logistic model. The prediction performance was determined based on the receiver operating characteristic curve (ROC). Individualized clinical decision-making was conducted by weighing the net benefit in each patient by decision curve analysis (DCA). RESULTS: We created a risk prediction model using risk scores weighted by five preoperatively chosen variables [AUC: 0.7039 (95% CI, 0.643–0.765)]: serum creatinine (Scr), D-dimer, white blood cell (WBC) count, coronary heart disease (CHD), and blood urea nitrogen (BUN). Following that, we categorized the cohort's patients as low-, intermediate-, and high-risk groups. The intermediate- and high-risk groups significantly increased hospital death rates compared to the low-risk group [adjusted OR: 3.973 (95% CI, 1.496–10.552), P < 0.01; 8.280 (95% CI, 3.054–22.448), P < 0.01, respectively). The risk score classifier exhibited better prediction ability than the triple-risk categories classifier [AUC: 0.7039 (95% CI, 0.6425–0.7652) vs. 0.6605 (95% CI, 0.6013–0.7197); P = 0.0022]. The DCA showed relatively good performance for the model in terms of clinical application if the threshold probability in the clinical decision was more than 10%. CONCLUSION: A risk classifier is an effective strategy for predicting in-hospital death in patients with ATAAD, but it might be affected by the small number of participants. |
format | Online Article Text |
id | pubmed-10090540 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100905402023-04-13 Prediction of in-hospital death following acute type A aortic dissection Chen, Junquan Bai, Yunpeng Liu, Hong Qin, Mingzhen Guo, Zhigang Front Public Health Public Health BACKGROUND: Our goal was to create a prediction model for in-hospital death in Chinese patients with acute type A aortic dissection (ATAAD). METHODS: A retrospective derivation cohort was made up of 340 patients with ATAAD from Tianjin, and the retrospective validation cohort was made up of 153 patients with ATAAD from Nanjing. For variable selection, we used least absolute shrinkage and selection operator analysis, and for risk scoring, we used logistic regression coefficients. We categorized the patients into low-, middle-, and high-risk groups and looked into the correlation with in-hospital fatalities. We established a risk classifier based on independent baseline data using a multivariable logistic model. The prediction performance was determined based on the receiver operating characteristic curve (ROC). Individualized clinical decision-making was conducted by weighing the net benefit in each patient by decision curve analysis (DCA). RESULTS: We created a risk prediction model using risk scores weighted by five preoperatively chosen variables [AUC: 0.7039 (95% CI, 0.643–0.765)]: serum creatinine (Scr), D-dimer, white blood cell (WBC) count, coronary heart disease (CHD), and blood urea nitrogen (BUN). Following that, we categorized the cohort's patients as low-, intermediate-, and high-risk groups. The intermediate- and high-risk groups significantly increased hospital death rates compared to the low-risk group [adjusted OR: 3.973 (95% CI, 1.496–10.552), P < 0.01; 8.280 (95% CI, 3.054–22.448), P < 0.01, respectively). The risk score classifier exhibited better prediction ability than the triple-risk categories classifier [AUC: 0.7039 (95% CI, 0.6425–0.7652) vs. 0.6605 (95% CI, 0.6013–0.7197); P = 0.0022]. The DCA showed relatively good performance for the model in terms of clinical application if the threshold probability in the clinical decision was more than 10%. CONCLUSION: A risk classifier is an effective strategy for predicting in-hospital death in patients with ATAAD, but it might be affected by the small number of participants. Frontiers Media S.A. 2023-03-29 /pmc/articles/PMC10090540/ /pubmed/37064704 http://dx.doi.org/10.3389/fpubh.2023.1143160 Text en Copyright © 2023 Chen, Bai, Liu, Qin and Guo. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Public Health Chen, Junquan Bai, Yunpeng Liu, Hong Qin, Mingzhen Guo, Zhigang Prediction of in-hospital death following acute type A aortic dissection |
title | Prediction of in-hospital death following acute type A aortic dissection |
title_full | Prediction of in-hospital death following acute type A aortic dissection |
title_fullStr | Prediction of in-hospital death following acute type A aortic dissection |
title_full_unstemmed | Prediction of in-hospital death following acute type A aortic dissection |
title_short | Prediction of in-hospital death following acute type A aortic dissection |
title_sort | prediction of in-hospital death following acute type a aortic dissection |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10090540/ https://www.ncbi.nlm.nih.gov/pubmed/37064704 http://dx.doi.org/10.3389/fpubh.2023.1143160 |
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