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Liver function test indices-based prediction model for post-stroke depression: a multicenter, retrospective study

BACKGROUND: Post-stroke depression (PSD) was one of the most prevalent and serious neuropsychiatric effects after stroke. Nevertheless, the association between liver function test indices and PSD remains elusive, and there is a lack of effective prediction tools. The purpose of this study was to exp...

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Autores principales: Gong, Jun, Zhang, Yalian, Zhong, Xiaogang, Zhang, Yi, Chen, Yanhua, Wang, Huilai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10357817/
https://www.ncbi.nlm.nih.gov/pubmed/37468891
http://dx.doi.org/10.1186/s12911-023-02241-0
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author Gong, Jun
Zhang, Yalian
Zhong, Xiaogang
Zhang, Yi
Chen, Yanhua
Wang, Huilai
author_facet Gong, Jun
Zhang, Yalian
Zhong, Xiaogang
Zhang, Yi
Chen, Yanhua
Wang, Huilai
author_sort Gong, Jun
collection PubMed
description BACKGROUND: Post-stroke depression (PSD) was one of the most prevalent and serious neuropsychiatric effects after stroke. Nevertheless, the association between liver function test indices and PSD remains elusive, and there is a lack of effective prediction tools. The purpose of this study was to explore the relationship between the liver function test indices and PSD, and construct a prediction model for PSD. METHODS: All patients were selected from seven medical institutions of Chongqing Medical University from 2015 to 2021. Variables including demographic characteristics and liver function test indices were collected from the hospital electronic medical record system. Univariate analysis, least absolute shrinkage and selection operator (LASSO) and logistic regression analysis were used to screen the predictors. Subsequently, logistic regression, random forest (RF), extreme gradient boosting (XGBoost), gradient boosting decision tree (GBDT), categorical boosting (CatBoost) and support vector machine (SVM) were adopted to build the prediction model. Furthermore, a series of evaluation indicators such as area under curve (AUC), sensitivity, specificity, F1 were used to assess the performance of the prediction model. RESULTS: A total of 464 PSD and 1621 stroke patients met the inclusion criteria. Six liver function test items, namely AST, ALT, TBA, TBil, TP, ALB/GLB, were closely associated with PSD, and included for the construction of the prediction model. In the test set, logistic regression model owns the AUC of 0.697. Compared with the other four machine learning models, the GBDT model has the best predictive performance (F1 = 0.498, AUC = 0.761) and was chosen to establish the prediction tool. CONCLUSIONS: The prediction model constructed using these six predictors with GBDT algorithm displayed a promising prediction ability, which could be used for the participating hospital units or individuals by mobile phone or computer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12911-023-02241-0.
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spelling pubmed-103578172023-07-21 Liver function test indices-based prediction model for post-stroke depression: a multicenter, retrospective study Gong, Jun Zhang, Yalian Zhong, Xiaogang Zhang, Yi Chen, Yanhua Wang, Huilai BMC Med Inform Decis Mak Research BACKGROUND: Post-stroke depression (PSD) was one of the most prevalent and serious neuropsychiatric effects after stroke. Nevertheless, the association between liver function test indices and PSD remains elusive, and there is a lack of effective prediction tools. The purpose of this study was to explore the relationship between the liver function test indices and PSD, and construct a prediction model for PSD. METHODS: All patients were selected from seven medical institutions of Chongqing Medical University from 2015 to 2021. Variables including demographic characteristics and liver function test indices were collected from the hospital electronic medical record system. Univariate analysis, least absolute shrinkage and selection operator (LASSO) and logistic regression analysis were used to screen the predictors. Subsequently, logistic regression, random forest (RF), extreme gradient boosting (XGBoost), gradient boosting decision tree (GBDT), categorical boosting (CatBoost) and support vector machine (SVM) were adopted to build the prediction model. Furthermore, a series of evaluation indicators such as area under curve (AUC), sensitivity, specificity, F1 were used to assess the performance of the prediction model. RESULTS: A total of 464 PSD and 1621 stroke patients met the inclusion criteria. Six liver function test items, namely AST, ALT, TBA, TBil, TP, ALB/GLB, were closely associated with PSD, and included for the construction of the prediction model. In the test set, logistic regression model owns the AUC of 0.697. Compared with the other four machine learning models, the GBDT model has the best predictive performance (F1 = 0.498, AUC = 0.761) and was chosen to establish the prediction tool. CONCLUSIONS: The prediction model constructed using these six predictors with GBDT algorithm displayed a promising prediction ability, which could be used for the participating hospital units or individuals by mobile phone or computer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12911-023-02241-0. BioMed Central 2023-07-19 /pmc/articles/PMC10357817/ /pubmed/37468891 http://dx.doi.org/10.1186/s12911-023-02241-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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
Gong, Jun
Zhang, Yalian
Zhong, Xiaogang
Zhang, Yi
Chen, Yanhua
Wang, Huilai
Liver function test indices-based prediction model for post-stroke depression: a multicenter, retrospective study
title Liver function test indices-based prediction model for post-stroke depression: a multicenter, retrospective study
title_full Liver function test indices-based prediction model for post-stroke depression: a multicenter, retrospective study
title_fullStr Liver function test indices-based prediction model for post-stroke depression: a multicenter, retrospective study
title_full_unstemmed Liver function test indices-based prediction model for post-stroke depression: a multicenter, retrospective study
title_short Liver function test indices-based prediction model for post-stroke depression: a multicenter, retrospective study
title_sort liver function test indices-based prediction model for post-stroke depression: a multicenter, retrospective study
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10357817/
https://www.ncbi.nlm.nih.gov/pubmed/37468891
http://dx.doi.org/10.1186/s12911-023-02241-0
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