ArticleOncotarget2017
A risk prediction model for post-stroke depression in Chinese stroke survivors based on clinical and socio-psychological features.
Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it, 29 citations in OpenAlex.
- Accuracy of Machine Learning in Predicting Post-Stroke Depression: A Systematic Review and Meta-Analysis.Brain and behavior · 2025Pooled it
- Role of social support in poststroke depression: A meta-analysis.Frontiers in psychiatry · 2022Pooled it
- Prognosis prediction models for post-stroke depression: a protocol for systematic review, meta-analysis, and critical appraisal.Systematic reviews · 2024Article
- Social Support and Depression among Stroke Patients: A Topical Review.International journal of environmental research and public health · 2023Review
- A machine learning approach for predicting suicidal ideation in post stroke patients.Scientific reports · 2022Article
- Psychosis Relapse Prediction Leveraging Electronic Health Records Data and Natural Language Processing Enrichment Methods.Frontiers in psychiatry · 2022Article
- Prediction of Major Depressive Disorder Following Beta-Blocker Therapy in Patients with Cardiovascular Diseases.Journal of personalized medicine · 2020Article
- Mild to Severe Depressive Symptoms in Elderly Stroke Survivors and Its Associated Factors: Evidence From a Cross-Sectional Study in Zhejiang Province, China.Frontiers in psychiatry · 2020Article
- Use of Patient-Reported Data to Match Depression Screening Intervals With Depression Risk Profiles in Primary Care Patients With Diabetes: Development and Validation of Prediction Models for Major Depression.JMIR formative research · 2019Article
- Article
- Predictors of early-onset post-ischemic stroke depression: a cross-sectional study.BMC neurology · 2017Article
Corrections and comments
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Authors and funding
15 authors at 8 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPost-stroke depression (PSD) is a frequent complication that worsens rehabilitation outcomes and patient quality of life. This study developed a risk prediction model for PSD based on patient clinical and socio-psychology features for the early detection of high risk PSD patients.
resultsRisk predictors included a history of brain cerebral infarction (odds ratio [OR], 3.84; 95% confidence interval [CI], 2.22-6.70;
methodsThis study recruited 562 stroke patients in China who were assessed for demographic data, medical history, vascular risk factors, functional status post-stroke, and socio-psychological factors. Multivariate backward logistic regression was used to extract risk factors for depression in 1-month after stroke. We converted the logistic model to a visible tree model using the decision tree method. Receiver operating characteristic (ROC) was used to evaluate the performance of the model.
conclusionThis study provided an effective risk model for PSD and indicated that the socio-psychological factors were important risk factors of PSD.
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