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Background: Detecting cognitive impairment in Parkinson's disease (PD) is challenging due to diverse manifestations and outdated diagnostic criteria. Cognitive screening tools, as Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA), are adopted worldwide, but despite several cutoffs has been proposed for PD, no consensus has been reached, hindered by limited sample sizes, lack of validation, and inconsistent age- and education-adjustments., Objectives: Determine the optimal MMSE and MoCA cutoffs in a large PD cohort, spanning from normal cognition (PD-NC), mild cognitive impairment (PD-MCI) to dementia (PDD), and develop a decision tree model to assist physicians in cognitive workups., Methods: Our retrospective Italian multicenter study involves 1780 PD, cognitively diagnosed with a level-II assessment: PD-NC(n = 700), PD-MCI(n = 706), and PDD(n = 374). Optimal cutoffs (for raw scores) were determined through ROC analysis. Then, a machine learning approach-decision trees-was adopted to validate and analyze the possible inclusion of other relevant clinical features., Results: The decision tree model selected as primary feature a MMSE cutoff ≤24 to predict dementia, and a score ≤ 27 for PD-MCI. To enhance PD-MCIvs.PD-NC accuracy, it also recommends including a MoCA score ≤ 22 for PD-MCI, and > 22 for PD-NC. Age and education were not selected as relevant features for the cognitive workup. Both MoCA and MMSE cutoffs exhibited high sensitivity and specificity in detecting PD cognitive statues., Conclusions: For the first time, a clinical decision tree model based on robust MMSE and MoCA cutoffs has been developed, allowing to diagnose PD-MCI and/or PDD with a high accuracy and short administration time., Competing Interests: Declaration of competing interest The authors declare no competing financial interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD) not only differ for the time of onset of cognitive deficits but also present variability in affected functions which are relevant in understanding underlying pathology. Cognitive performance of two global cognitive screening scales, the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA), as well as of a neuropsychological test battery, was evaluated in 18 DLB and 21 PDD patients. Feasibility for each cognitive test was investigated. Both MMSE and MoCA are feasible assessments in PDD and DLB patients. MoCA was more sensitive in discriminating groups as higher number of DLB patients showed pathological performances on the Digit Span Forward subitem (p = 0.049). The Stroop test in PDD and the Trail Making Tests-A and B, and the Benton's judgment of line orientation tests in both groups were considered not feasible. Among feasible cognitive tests in at least one group, Rey-Osterrieth complex figure test copy (p = 0.013) and semantic fluency (p = 0.038) are sensitive in discriminating DLB from PDD cognitive profile. Trail Making Tests-A and B, the Benton's judgment of line orientation and the Stroop tests are not feasible for assessing patients with frank dementia. Longitudinal studies should not include those tasks to reduce the risk of missing data once disease progresses and dementia develops. DLB patients present more severe and widespread cognitive dysfunction than PDD, particularly in attentive, visuospatial, and language domains.