![]() ![]() The defective mesostriatal dopaminergic transmission in PD impairs both movement expression and action performing. Nowadays, they represent a challenge for public health worldwide, because of their growing incidence in the population. Parkinson’s disease (PD) and atypical parkinsonian syndromes (APS) define the whole group of parkinsonisms, which are the major subsets of hypokinetic movement disorders. ![]() This study has been registered on (NCT04858893). CoMDA and COMDA-ML are reliable and time-sparing tools, accurate in classifying cognitive profile in parkinsonisms. Considering L2 as a 3-level continuous feature, CoMDA-ML produces accurate and generalizable classifications: micro-average ROC curve, AUC = 0.81 and AUC = 0.85 for NC, 0.67 for MCI, and 0.83 for IC. Among 15 different algorithmic methods, the Quadratic Discriminant Analysis algorithm (CoMDA-ML) showed higher overall-metrics performance levels in predictive performance. The area under the curve (AUC) of CoMDA was significantly higher than that of MMSE, MoCA and FAB ( p < 0.0001, p = 0.028 and p = 0.0007, respectively). The classification accuracy of CoMDA, assessed by ROC analysis, was compared with MMSE, MoCA, and FAB. Machine learning was developed to classify the CoMDA score and obtain an accurate prediction of the cognitive profile along three different classes: normal cognition (NC), mild cognitive impairment (MCI), and impaired cognition (IC). In total, 500 patients (400 with Parkinson’s disease, 41 with vascular parkinsonism, 31 with progressive supranuclear palsy, and 28 with multiple system atrophy) underwent CoMDA (level 1–L1) and in-depth neuropsychological battery (level 2–L2). A new tool, we named CoMDA (Cognition in Movement Disorders Assessment), was composed by merging Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Frontal Assessment Battery (FAB). Low levels of correspondence are observed between evaluations assessed with screening cognitive tests in comparison with those assessed with in-depth neuropsychological batteries. doi: 10.1590/0004-282X20190130.The assessment of cognitive deficits is pivotal for diagnosis and management in patients with parkinsonisms. MoCA test: normative and diagnostic accuracy data for seniors with heterogeneous educational levels in Brazil. The montreal cognitive assessment: normative data from a large Swedish population-based cohort. A subtest analysis of The Montreal Cognitive Assessment (MoCA): which subtests can best discriminate between healthy controls, mild cognitive impairment and Alzheimer’s disease? Int Psychogeriatr. Dementia incidence continues to increase with age in the oldest old: the 90+ study. 2017.Ĭorrada MM, Brookmeyer R, Paganini-Hill A, et al. Revision, custom data acquired via web-site. United Nations Department of Economic and Social Affairs Population Division. The equivalences of the three cognitive tests (MMSE, MoCA-30, MoCA-22) in the oldest-old will facilitate continuity of cognitive tracking of individuals tested with different tests over time and comparison of the studies that use different cognitive tests.ĩ0 + MMSE MoCA-22 MoCA-30 Oldest-old Score conversion. Subtest, domain and MoCA-22 norms will aid in evaluation of the oldest-old who cannot complete the MoCA-30 or are tested over the phone. An MMSE score of 27 is equivalent to a MoCA-30 score of 22 and a MoCA-22 score of 16. MoCA-22 total score norms are: mean = 18.3(standard deviation = 2.2). Second, we derived score equivalences for MMSE to MoCA-30 and MoCA-22, and MoCA-30 to MoCA-22 using equipercentile equating method with log-linear smoothing, based on all 157 participants. These norms were derived from 124 participants with a Mini-Mental State Examination (MMSE) ≥ 27. First, we derived norms for (1) subtests and cognitive domains of the in-person Montreal Cognitive Assessment having a maximum score of 30 (MoCA-30) and (2) the total MoCA-22 score, obtained from the in-person MoCA-30 by summing the subtests that do not require visual input to a maximum score of 22. To provide norms and score equivalence for commonly used cognitive screening tests for the oldest-old.ĭata on 157 participants of the Center for Healthy Aging Longevity Study aged 90 + were analyzed. ![]() However, norms and score equivalence for screening tests are lacking for this group. This age group is the fastest growing and has the highest risk of dementia. Cognitive screening is important for the oldest-old (age 90 +). ![]()
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