ArticleFrontiers in aging neuroscience2025
Nomogram for predicting mild cognitive impairment in Chinese elder CSVD patients based on Boruta algorithm.
Article in Frontiers in aging neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Risk prediction models for cognitive impairment in patients with cerebral small vessel disease: a systematic review and meta-analysis.Frontiers in aging neuroscience · 2026Pooled it
- Prediction Model for Mild Cognitive Impairment in Older Chinese Patients With Cerebral Small Vessel Disease Based on XGBoost Algorithms and Shapley Additive Explanations.CNS neuroscience & therapeutics · 2026Article
- Machine learning-based prediction of 3-6-month post-stroke cognitive impairment using acute-phase clinical data: a two-center retrospective prognostic modeling study.BMC medical informatics and decision making · 2026Article
- Sleep Traits and Cognitive Function: A Prospective Cohort Study With Exploration of Inflammatory Biomarkers.Brain and behavior · 2026Article
- GEMap: A comprehensive gene essentiality map for drug discovery.Acta pharmaceutica Sinica. B · 2026Article
- Oral and gut microbiota dysbiosis with strengthened oral-gut connectivity in post-stroke cognitive impairment.Frontiers in immunology · 2026Article
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Authors and funding
9 authors.
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Abstract
Background: The number of patients with cerebral small vessel disease is increasing, especially among the elderly population. With the continuous improvement of detection techniques, the positivity rate keeps increasing. Our goal is to develop a nomogram for early identification of PSCI and PSCID in stroke patients. Methods: In a retrospective cohort, chained data imputation was performed to ensure no statistical differences from the original dataset. Subsequently, Boruta algorithm was utilized for variable selection based on their importance, followed by logistic regression employing backward stepwise regression. Finally, the regression results were visualized as a Nomogram. Results: The nomogram chart in this study achieves clinical utility in a concise and user-friendly manner, passing the Hosmer-Lemeshow goodness-of-fit test. ROC and calibration curves indicate its high discriminative ability. Conclusion: While CSVD is prevalent among middle-aged and older individuals, cognitive decline trajectories differ. Endocrine metabolic indicators like IGF-1 offer early predictive value. This study has produced a succinct nomogram integrating demographic and clinical indicators for medical application.
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