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A risk prediction model for evaluating thrombosis extension of muscle calf venous thrombosis after craniotomy

OBJECTIVE: To explore the risk factors of muscle calf venous thrombosis (MCVT) after craniotomy and construct a risk prediction model, so as to provide tool for evaluating the prognosis of MCVT after craniotomy. METHODS: Retrospective analysis was performed on the data of patients undergoing craniot...

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Autores principales: Li, Juhua, Chen, Huayu, Liu, Mei, Lin, Zheng, Ren, Xingzhen, Wang, Ying, Zou, Xingchen, Gu, Zejuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9614109/
https://www.ncbi.nlm.nih.gov/pubmed/36311954
http://dx.doi.org/10.3389/fsurg.2022.992576
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author Li, Juhua
Chen, Huayu
Liu, Mei
Lin, Zheng
Ren, Xingzhen
Wang, Ying
Zou, Xingchen
Gu, Zejuan
author_facet Li, Juhua
Chen, Huayu
Liu, Mei
Lin, Zheng
Ren, Xingzhen
Wang, Ying
Zou, Xingchen
Gu, Zejuan
author_sort Li, Juhua
collection PubMed
description OBJECTIVE: To explore the risk factors of muscle calf venous thrombosis (MCVT) after craniotomy and construct a risk prediction model, so as to provide tool for evaluating the prognosis of MCVT after craniotomy. METHODS: Retrospective analysis was performed on the data of patients undergoing craniotomy complicated with MCVT from January 1, 2018 to December 31, 2020. A prediction model was established by Logistic regression, and the predictive efficacy of the model was tested by ROC curve. The accuracy of the risk model was evaluated by Hosmer-Lemeshow (H-L) test, and the model was verified internally by cross validation. RESULTS: Among the 446 patients who underwent craniotomy complicated with MCVT, 112 cases (25.11%) had thrombosis extension. D-dimer, Capirini scores, length of hospital stay, malignant tumor, fracture, use of dehydrating agents and hemostatic agents were independently related to thrombosis extension after craniotomy. The area under ROC curve (AUROC) of the prediction model was 0.918 (0.888, 0.942), and the sensitivity and specificity of the maximum Youden index were 85.3% and 78.2%, respectively. H-L test showed that the prediction model was accurate (χ(2) = 12.426, P = 0.133). The internal verification results of the prediction model showed that the AUROC value of the prediction model is 0.892. CONCLUSION: The prediction model has a good prediction efficacy on the prognosis of post-craniotomy patients complicated with MCVT, and can be used as a tool to evaluate the risk of thrombosis extension.
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spelling pubmed-96141092022-10-29 A risk prediction model for evaluating thrombosis extension of muscle calf venous thrombosis after craniotomy Li, Juhua Chen, Huayu Liu, Mei Lin, Zheng Ren, Xingzhen Wang, Ying Zou, Xingchen Gu, Zejuan Front Surg Surgery OBJECTIVE: To explore the risk factors of muscle calf venous thrombosis (MCVT) after craniotomy and construct a risk prediction model, so as to provide tool for evaluating the prognosis of MCVT after craniotomy. METHODS: Retrospective analysis was performed on the data of patients undergoing craniotomy complicated with MCVT from January 1, 2018 to December 31, 2020. A prediction model was established by Logistic regression, and the predictive efficacy of the model was tested by ROC curve. The accuracy of the risk model was evaluated by Hosmer-Lemeshow (H-L) test, and the model was verified internally by cross validation. RESULTS: Among the 446 patients who underwent craniotomy complicated with MCVT, 112 cases (25.11%) had thrombosis extension. D-dimer, Capirini scores, length of hospital stay, malignant tumor, fracture, use of dehydrating agents and hemostatic agents were independently related to thrombosis extension after craniotomy. The area under ROC curve (AUROC) of the prediction model was 0.918 (0.888, 0.942), and the sensitivity and specificity of the maximum Youden index were 85.3% and 78.2%, respectively. H-L test showed that the prediction model was accurate (χ(2) = 12.426, P = 0.133). The internal verification results of the prediction model showed that the AUROC value of the prediction model is 0.892. CONCLUSION: The prediction model has a good prediction efficacy on the prognosis of post-craniotomy patients complicated with MCVT, and can be used as a tool to evaluate the risk of thrombosis extension. Frontiers Media S.A. 2022-10-14 /pmc/articles/PMC9614109/ /pubmed/36311954 http://dx.doi.org/10.3389/fsurg.2022.992576 Text en © 2022 Li, Chen, Liu, Lin, Ren, Wang, Zou and Gu. 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) (https://creativecommons.org/licenses/by/4.0/) . 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 Surgery
Li, Juhua
Chen, Huayu
Liu, Mei
Lin, Zheng
Ren, Xingzhen
Wang, Ying
Zou, Xingchen
Gu, Zejuan
A risk prediction model for evaluating thrombosis extension of muscle calf venous thrombosis after craniotomy
title A risk prediction model for evaluating thrombosis extension of muscle calf venous thrombosis after craniotomy
title_full A risk prediction model for evaluating thrombosis extension of muscle calf venous thrombosis after craniotomy
title_fullStr A risk prediction model for evaluating thrombosis extension of muscle calf venous thrombosis after craniotomy
title_full_unstemmed A risk prediction model for evaluating thrombosis extension of muscle calf venous thrombosis after craniotomy
title_short A risk prediction model for evaluating thrombosis extension of muscle calf venous thrombosis after craniotomy
title_sort risk prediction model for evaluating thrombosis extension of muscle calf venous thrombosis after craniotomy
topic Surgery
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9614109/
https://www.ncbi.nlm.nih.gov/pubmed/36311954
http://dx.doi.org/10.3389/fsurg.2022.992576
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