ArticleBMC medical informatics and decision making2023
Liver function test indices-based prediction model for post-stroke depression: a multicenter, retrospective study.
Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it, 17 citations in OpenAlex.
- Accuracy of Machine Learning in Predicting Post-Stroke Depression: A Systematic Review and Meta-Analysis.Brain and behavior · 2025Pooled it
- Clinical prediction models for post-stroke depression: a systematic review and meta-analysis.Frontiers in psychiatry · 2025Pooled it
- Development and validation of nomograms for predicting depression and suicidal ideation in stroke survivors: a community-based study.BMC psychiatry · 2026Article
- Predicting generalized anxiety disorder among Chinese depressed adolescents: an explainable machine learning approach.BMC medical informatics and decision making · 2025Article
- Development and interpretation of a machine learning risk prediction model for post-stroke depression in a Chinese population.Scientific reports · 2025Article
- Machine learning-based prediction model for patients with recurrent Staphylococcus aureus bacteremia.BMC medical informatics and decision making · 2025Article
- Analysis of influencing factors and nursing interventions of patients with mental abnormality after orthotopic liver transplantation.Frontiers in surgery · 2025Article
- Predictive models for secondary epilepsy in patients with acute ischemic stroke within one year.eLife · 2024Article
- Association between depression and liver function biomarkers among US cancer survivors in NHANES 2005-2020.Scientific reports · 2024Article
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Authors and funding
6 authors at 4 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundPost-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.
methodsAll 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.
resultsA 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.
conclusionsThe 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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