ArticleBrain and behavior2025
Early Detection and Management of Cognitive Impairment in Parkinson's Disease: A Predictive Model Approach.
Article in Brain and behavior, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning methods for the detection and prediction of cognitive impairment in Parkinson's disease: a systematic review and meta-analysis.Frontiers in aging neuroscience · 2025Pooled it
- Proceedings of the world summit on parkinson's disease.NPJ Parkinson's disease · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
objectiveThe study aims to identify risk factors associated with cognitive impairment in Parkinson's disease and to develop a predictive model to facilitate early clinical detection, diagnosis, and management, thereby enhancing patient prognosis and quality of life.
methodsA total of 351 PD patients were enrolled from the Department of Neurology at the First Hospital of Shanxi Medical University between January 2022 and December 2023. Cognitive function was evaluated using the Montreal Cognitive Assessment (MoCA) scale, and patients were subsequently categorized into cognitively normal (PD-NC) and cognitively impaired (PD-CI) groups. A logistic regression analysis was conducted to identify risk factors, and a predictive model was constructed and validated.
resultsAmong the 351 patients with PD, 188 cases were in the PD⁃NC group and 163 cases were in the PD⁃CI group, with an incidence of cognitive impairment of 46.4%. Logistic regression analysis indicated that H-Y classification, HAMA score, homocysteine, uric acid, and folic acid were significant predictors and were incorporated into the regression equation. The constructed prediction model had an area under the receiver operating characteristic curve of 0.738.
conclusionsThe cognitive function of PD patients is influenced by H-Y classification, HAMA score, homocysteine, uric acid, and folic acid. The constructed prediction model demonstrates good discrimination and calibration, providing a reference basis for early clinical identification and intervention of cognitive impairment in Parkinson's disease patients.
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Registered trials
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