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

作者全名："Gong, Jun; Zhang, Yalian; Zhong, Xiaogang; Zhang, Yi; Chen, Yanhua; Wang, Huilai"

作者地址："[Gong, Jun; Wang, Huilai] Chongqing Med Univ, Univ Town Hosp, Dept Informat Ctr, Chongqing, Peoples R China; [Gong, Jun; Wang, Huilai] Chongqing Med Univ, Med Data Sci Acad, Chongqing, Peoples R China; [Zhang, Yalian] Chongqing Med Univ, Dept Rehabil, Childrens Hosp, Chongqing, Peoples R China; [Zhang, Yalian] Natl Clin Res Ctr Child Hlth & Disorders, Chongqing, Peoples R China; [Zhong, Xiaogang] Chongqing Med Univ, NHC Key Lab Diag & Treatment Brain Funct Dis, Affiliated Hosp 1, Chongqing, Peoples R China; [Zhong, Xiaogang] Chongqing Med Univ, Coll Basic Med, Chongqing, Peoples R China; [Zhang, Yi] Chongqing Med Univ, Dept Informat Ctr, Rehabil Hosp, Chongqing, Peoples R China; [Chen, Yanhua] Seventh Peoples Hosp Chongqing, Dept Pain & Rehabil, Chongqing, Peoples R China"

通信作者："Wang, HL (通讯作者)，Chongqing Med Univ, Univ Town Hosp, Dept Informat Ctr, Chongqing, Peoples R China.; Wang, HL (通讯作者)，Chongqing Med Univ, Med Data Sci Acad, Chongqing, Peoples R China.; Chen, YH (通讯作者)，Seventh Peoples Hosp Chongqing, Dept Pain & Rehabil, Chongqing, Peoples R China."

来源：BMC MEDICAL INFORMATICS AND DECISION MAKING

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001029439800001

JCR分区：Q2

影响因子：3.3

年份：2023

卷号：23

期号：1

开始页：　

结束页：　

文献类型：Article

关键词：Post-stroke depression; Liver function test; Relationship; Predictors; Prediction model

摘要："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."

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