The Movement Disorders Society (MDS) Task Force has proposed diagnostic criteria guidelines for PD-MCI and PDD and recommends cognitive assessments using abbreviated (level I) or comprehensive assessments (level II) comprising neuropsychological tests with at least two tests in each of the five cognitive domains. Crucially, the occurrence of dementia in PD has a major impact on functional independence, nursing home placement, mortality, psychiatric morbidities, and caregiver burden. The cumulative prevalence of dementia in PD over an 8- to 12-year follow-up has been reported to be 60%–83%. More than 25% of cases of newly diagnosed PD with MCI (PD-MCI) progress to PD dementia (PDD) within 3 years. Mild cognitive impairment (MCI) affects 20%–50% of patients with PD. Keywords: Depression Machine learning Mild cognitive impairment Montreal Cognitive Assessment Parkinson’s disease Regression analysisĬognitive impairment is common in Parkinson’s disease (PD).Conclusion Machine learning analysis using MoCA domain scores is a valid method for screening cognitive impairment in PD.Inclusion of cognitive complaints as an additional variable improved the accuracy of classification using the machine learning method (0.87–0.89). Using a more stringent dataset that excluded MoCA results ( n = 101 per group) from the same patients, the accuracy of the cutoff method (0.66 ± 0.05), but not that of machine learning (0.74 ± 0.07), was significantly reduced. Results Based on cognitive status classification using a dataset that permitted sampling of MoCA results from the same individual ( n = 221 per group), no difference was observed in accuracy between the cutoff value method (0.74 ± 0.03) and machine learning (0.78 ± 0.03).Using the same number of MoCA results randomly sampled from patients with PD with normal cognition or PD-CI, discriminant validity was compared between machine learning (logistic regression, support vector machine, or random forest) with domain scores and a cutoff method. Methods In total, 2,069 MoCA results were obtained from 397 patients with PD enrolled in the Parkinson’s Progression Markers Initiative database with a diagnosis of cognitive status based on comprehensive neuropsychological assessments.This study investigated the utility of machine learning algorithms using MoCA cognitive domain scores for improving diagnostic performance for PD-CI. Several cutoffs of MoCA scores for diagnosing PD with cognitive impairment (PD-CI) have been proposed, with varying sensitivity and specificity. Objective The Montreal Cognitive Assessment (MoCA) is recommended for assessing general cognition in Parkinson’s disease (PD).Conclusion: The MoCA's ability to discriminate MCI from NC was modest in this Chinese population, because it was far more sensitive to the effect of education than MCI diagnosis. In multivariate analyses controlling for age and gender, MCI diagnosis was associated with a <1-point decrement in MoCA score (η 2 = 0.010), but lower (1-6 years) and no education was associated with a 3- to 5-point decrement (η 2 = 0.115 and η 2 = 0.162, respectively). Overall, the MoCA's test performance was not better than that of the MMSE. The MoCA's test performance was least satisfactory in the highest (>6 years) education group: AUC = 0.50 (p = 0.98), Sn = 0.54, and Sp = 0.51 at a cut-off of 27/28. Results: The MoCA modestly discriminated MCI from NC in both study samples (AUC = 0.63 and 0.65): Sn = 0.64 and Sp = 0.36 at a cut-off of 28/29 in the clinic-based sample, and Sn = 0.65 and Sp = 0.55 at a cut-off of 22/23 in the community-based sample. Method: The MoCA and Mini-Mental State Examination (MMSE) were evaluated in two independent studies (clinic-based sample and community-based sample) of MCI and normal cognition (NC) controls, using receiver operating characteristic curve analyses: area under the curve (AUC), sensitivity (Sn), and specificity (Sp). We evaluated the MoCA's test performance by educational groups among older Singaporean Chinese adults. Background: The Montreal Cognitive Assessment (MoCA) was developed as a screening instrument for mild cognitive impairment (MCI).
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