ArticleFrontiers in aging neuroscience2022
A new nomogram including total cerebral small vessel disease burden for individualized prediction of early-onset depression in patients with acute ischemic stroke.
Article in Frontiers in aging neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- Accuracy of Machine Learning in Predicting Post-Stroke Depression: A Systematic Review and Meta-Analysis.Brain and behavior · 2025Pooled it
- Magnetic Resonance Imaging-Defined Cerebral Small Vessel Disease Burden and Longitudinal Neuropsychiatric and Functional Recovery After Stroke: A Prospective Cohort Study.Neurology international · 2026Article
- Development and validation of nomograms for predicting depression and suicidal ideation in stroke survivors: a community-based study.BMC psychiatry · 2026Article
- Network-based predictive models for artificial intelligence: an interpretable application of machine learning techniques in the assessment of depression in stroke patients.BMC geriatrics · 2025Article
- Using individualized structural covariance networks to analyze the heterogeneity of cerebral small vessel disease with depressive states.Frontiers in neurology · 2025Article
- Different implications of daytime and nighttime heart rate variability on total burden of cerebral small vascular disease in patients with nondisabling ischemic cerebrovascular events.Frontiers in cardiovascular medicine · 2024Article
- Leukoaraiosis as a Predictor of Depression and Cognitive Impairment among Stroke Survivors: A Systematic Review.Neurology international · 2023Review
- Regional white matter hyperintensity volume predicts persistent cognitive impairment in acute lacunar infarct patients.Frontiers in neurology · 2023Article
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Authors and funding
7 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Objectives: The present study was designed to evaluate the effects of total cerebral small vessel disease (CSVD) on early-onset depression after acute ischemic stroke (AIS), and to develop a new nomogram including total CSVD burden to predict early-onset post-stroke depression (PSD). Methods: We continuously enrolled patients with AIS who were hospitalized at the First Affiliated Hospital of Soochow University between October 2017 and June 2019. All patients were assessed for depressive symptoms using the 17-item Hamilton Depression Scale (HAMD-17) at 14 ± 2 days after the onset of AIS. The diagnosis for depression was made according to the American Diagnostic and Statistical Manual of Mental Disorders Version 5 (DSM-5). The demographic and clinical data were collected including total CSVD burden. On the basis of a multivariate logistic model, the independent factors of early-onset PSD were identified and the predictive nomogram was generated. The performance of the nomogram was evaluated by Harrell's concordance index (C-index) and calibration plot. Results: A total of 346 patients were enrolled. When contrasted to a 0 score of total CSVD burden, the score ≥2 (moderate to severe total CSVD burden) was an independent risk factor for early-onset PSD. Besides, gender, cognitive impairments, baseline Barthel Index (BI), and plasma fibrinogen were independently associated with early-onset PSD. The nomogram based on all these five independent risk factors was developed and validated with an Area Under Curve (AUC) of 0.780. In addition, the calibration plot revealed an adequate fit of the nomogram in predicting the risk of early-onset depression in patients with AIS. Conclusions: Our study found the total CSVD burden score of 2-4 points was an independent risk factor of early-onset PSD. The proposed nomogram based on total CSVD burden, gender, cognitive impairments, baseline BI, and plasma fibrinogen concentration gave rise to a more accurate and more comprehensive prediction for early-onset PSD.
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