ArticleCanadian geriatrics journal : CGJ2023
Dementia Risk Prediction in a Longitudinal Geriatric Parkinson's Disease Cohort: Evaluation and Application of the Montreal Parkinson Risk of Dementia Scale.
Article in Canadian geriatrics journal : CGJ, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Dementia risk prediction in early Parkinson's disease: Validation and genetic integration of the Montreal Parkinson risk of dementia scale (MoPaRDS).Journal of Parkinson's disease · 2025Article
- Risk factors and predictors for Lewy body dementia: a systematic review.NPJ dementia · 2025Article
- Anticipating Tomorrow: Tailoring Parkinson's Symptomatic Therapy Using Predictors of Outcome.Movement disorders clinical practice · 2024Article
- Identifying key multi-modal predictors of incipient dementia in Parkinson's disease: a machine learning analysis and Tree SHAP interpretation.Frontiers in aging neuroscience · 2023Article
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Authors and funding
6 authors.
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Abstract
Background: Parkinson's disease (PD) increases risk for dementia and cascading adverse outcomes. The eight-item Montreal Parkinson Risk of Dementia Scale (MoPaRDS) is a rapid, in-office dementia screening tool. We examine predictive validity and other characteristics of the MoPaRDS in a geriatric PD cohort by testing a series of alternative versions and modelling risk score change trajectories. Methods: Participants were 48 initially non-demented PD patients (Mage = 71.6 years, range = 65-84) from a three-year, three-wave prospective Canadian cohort study. A dementia diagnosis at Wave 3 was used to stratify two baseline groups: PD with Incipient Dementia (PDID) and PD with No Dementia (PDND). We aimed to predict dementia three years prior to diagnosis using baseline data for eight indicators that harmonized with the original report, plus education. Results: Three MoPaRDS items (age, orthostatic hypotension, mild cognitive impairment [MCI]) discriminated the groups both independently and as a composite three-item scale (area under the curve [AUC] = 0.88). The eight-item MoPaRDS reliably discriminated PDID from PDND (AUC = 0.81). Education did not improve predictive validity (AUC = 0.77). Performance of the eight-item MoPaRDS varied across sex (AUCfemales = 0.91; AUCmales = 0.74), whereas the three-item configuration did not (AUCfemales = 0.88; AUCmales = 0.91). Risk scores of both configurations increased over time. Conclusions: We report new data on the application of the MoPaRDS as a dementia prediction tool for a geriatric PD cohort. Results support the viability of the full MoPaRDS, and indicate that an empirically determined brief version is a promising complement.
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