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Circulating biomarker-based risk stratifications individualize arch repair strategy of acute Type A aortic dissection via the XGBoosting algorithm
AIMS: The incremental usefulness of circulating biomarkers from different pathological pathways for predicting mortality has not been evaluated in acute Type A aortic dissection (ATAAD) patients. We aim to develop a risk prediction model and investigate the impact of arch repair strategy on mortalit...
Autores principales: | , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9779759/ https://www.ncbi.nlm.nih.gov/pubmed/36710897 http://dx.doi.org/10.1093/ehjdh/ztac068 |
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author | Liu, Hong Qian, Si-Chong Han, Lu Zhang, Ying-Yuan Wu, Ying Hong, Liang Yang, Ji-Nong Zhong, Ji-Sheng Wang, Yu-Qi Wu, Dong-Kai Fan, Guo-Liang Chen, Jun-Quan Zhang, Sheng-Qiang Peng, Xing-Xing Tang, Zhi-Wei Hamzah, Al-Wajih Shao, Yong-Feng Li, Hai-Yang Zhang, Hong-Jia |
author_facet | Liu, Hong Qian, Si-Chong Han, Lu Zhang, Ying-Yuan Wu, Ying Hong, Liang Yang, Ji-Nong Zhong, Ji-Sheng Wang, Yu-Qi Wu, Dong-Kai Fan, Guo-Liang Chen, Jun-Quan Zhang, Sheng-Qiang Peng, Xing-Xing Tang, Zhi-Wei Hamzah, Al-Wajih Shao, Yong-Feng Li, Hai-Yang Zhang, Hong-Jia |
author_sort | Liu, Hong |
collection | PubMed |
description | AIMS: The incremental usefulness of circulating biomarkers from different pathological pathways for predicting mortality has not been evaluated in acute Type A aortic dissection (ATAAD) patients. We aim to develop a risk prediction model and investigate the impact of arch repair strategy on mortality based on distinct risk stratifications. METHODS AND RESULTS: A total of 3771 ATAAD patients who underwent aortic surgery retrospectively included were randomly divided into training and testing cohorts at a ratio of 7:3 for the development and validation of the risk model based on multiple circulating biomarkers and conventional clinical factors. Extreme gradient boosting was used to generate the risk models. Subgroup analyses were performed by risk stratifications (low vs. middle–high risk) and arch repair strategies (proximal vs. extensive arch repair). Addition of multiple biomarkers to a model with conventional factors fitted an ABC risk model consisting of platelet–leucocyte ratio, mean arterial pressure, albumin, age, creatinine, creatine kinase-MB, haemoglobin, lactate, left ventricular end-diastolic dimension, urea nitrogen, and aspartate aminotransferase, with adequate discrimination ability {area under the receiver operating characteristic curve (AUROC): 0.930 [95% confidence interval (CI) 0.906–0.954] and 0.954, 95% CI (0.930–0.977) in the derivation and validation cohort, respectively}. Compared with proximal arch repair, the extensive repair was associated with similar mortality risk among patients at low risk [odds ratio (OR) 1.838, 95% CI (0.559–6.038); P = 0.316], but associated with higher mortality risk among patients at middle–high risk [OR 2.007, 95% CI (1.460–2.757); P < 0.0001]. CONCLUSION: In ATAAD patients, the simultaneous addition of circulating biomarkers of inflammatory, cardiac, hepatic, renal, and metabolic abnormalities substantially improved risk stratification and individualized arch repair strategy. |
format | Online Article Text |
id | pubmed-9779759 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-97797592023-01-27 Circulating biomarker-based risk stratifications individualize arch repair strategy of acute Type A aortic dissection via the XGBoosting algorithm Liu, Hong Qian, Si-Chong Han, Lu Zhang, Ying-Yuan Wu, Ying Hong, Liang Yang, Ji-Nong Zhong, Ji-Sheng Wang, Yu-Qi Wu, Dong-Kai Fan, Guo-Liang Chen, Jun-Quan Zhang, Sheng-Qiang Peng, Xing-Xing Tang, Zhi-Wei Hamzah, Al-Wajih Shao, Yong-Feng Li, Hai-Yang Zhang, Hong-Jia Eur Heart J Digit Health Original Article AIMS: The incremental usefulness of circulating biomarkers from different pathological pathways for predicting mortality has not been evaluated in acute Type A aortic dissection (ATAAD) patients. We aim to develop a risk prediction model and investigate the impact of arch repair strategy on mortality based on distinct risk stratifications. METHODS AND RESULTS: A total of 3771 ATAAD patients who underwent aortic surgery retrospectively included were randomly divided into training and testing cohorts at a ratio of 7:3 for the development and validation of the risk model based on multiple circulating biomarkers and conventional clinical factors. Extreme gradient boosting was used to generate the risk models. Subgroup analyses were performed by risk stratifications (low vs. middle–high risk) and arch repair strategies (proximal vs. extensive arch repair). Addition of multiple biomarkers to a model with conventional factors fitted an ABC risk model consisting of platelet–leucocyte ratio, mean arterial pressure, albumin, age, creatinine, creatine kinase-MB, haemoglobin, lactate, left ventricular end-diastolic dimension, urea nitrogen, and aspartate aminotransferase, with adequate discrimination ability {area under the receiver operating characteristic curve (AUROC): 0.930 [95% confidence interval (CI) 0.906–0.954] and 0.954, 95% CI (0.930–0.977) in the derivation and validation cohort, respectively}. Compared with proximal arch repair, the extensive repair was associated with similar mortality risk among patients at low risk [odds ratio (OR) 1.838, 95% CI (0.559–6.038); P = 0.316], but associated with higher mortality risk among patients at middle–high risk [OR 2.007, 95% CI (1.460–2.757); P < 0.0001]. CONCLUSION: In ATAAD patients, the simultaneous addition of circulating biomarkers of inflammatory, cardiac, hepatic, renal, and metabolic abnormalities substantially improved risk stratification and individualized arch repair strategy. Oxford University Press 2022-11-01 /pmc/articles/PMC9779759/ /pubmed/36710897 http://dx.doi.org/10.1093/ehjdh/ztac068 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of the European Society of Cardiology. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Original Article Liu, Hong Qian, Si-Chong Han, Lu Zhang, Ying-Yuan Wu, Ying Hong, Liang Yang, Ji-Nong Zhong, Ji-Sheng Wang, Yu-Qi Wu, Dong-Kai Fan, Guo-Liang Chen, Jun-Quan Zhang, Sheng-Qiang Peng, Xing-Xing Tang, Zhi-Wei Hamzah, Al-Wajih Shao, Yong-Feng Li, Hai-Yang Zhang, Hong-Jia Circulating biomarker-based risk stratifications individualize arch repair strategy of acute Type A aortic dissection via the XGBoosting algorithm |
title | Circulating biomarker-based risk stratifications individualize arch repair strategy of acute Type A aortic dissection via the XGBoosting algorithm |
title_full | Circulating biomarker-based risk stratifications individualize arch repair strategy of acute Type A aortic dissection via the XGBoosting algorithm |
title_fullStr | Circulating biomarker-based risk stratifications individualize arch repair strategy of acute Type A aortic dissection via the XGBoosting algorithm |
title_full_unstemmed | Circulating biomarker-based risk stratifications individualize arch repair strategy of acute Type A aortic dissection via the XGBoosting algorithm |
title_short | Circulating biomarker-based risk stratifications individualize arch repair strategy of acute Type A aortic dissection via the XGBoosting algorithm |
title_sort | circulating biomarker-based risk stratifications individualize arch repair strategy of acute type a aortic dissection via the xgboosting algorithm |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9779759/ https://www.ncbi.nlm.nih.gov/pubmed/36710897 http://dx.doi.org/10.1093/ehjdh/ztac068 |
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