Prediction of cognitive progression in Parkinson's disease using three cognitive screening measures

Kim, Hojoong M.; Nazor, Carter; Zabetian, Cyrus P.; Quinn, Joseph F.; Chung, Kathryn A.; Hiller, Amie; Hu, Shu Ching; Leverenz, James B.; Montine, Thomas J.; Edwards, Karen L.; Cholerton, Brenna

doi:10.1016/j.prdoa.2019.08.006

Cited by 27 publications

(32 citation statements)

References 39 publications

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“…Variables for the MoCA and DRS-2 are the total scores and the frequency of scores below PD-specific suggested cutoffs (MoCA ≤ 26; 21-25 DRS-2 ≤ 139). 24,26…”

Section: Cognitive Screening Measures and Proceduresmentioning

confidence: 99%

Multiculturalism: A Challenge for Cognitive Screeners in Parkinson's Disease

Statucka

Cherian

Fasano

et al. 2021

Movement Disord Clin Pract

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BackgroundBackground: The Montreal Cognitive Assessment (MoCA) and the Dementia Rating Scale-2 (DRS-2) are recommended screeners for Parkinson's disease mild cognitive impairment (PD-MCI). Cross-cultural studies examining their diagnostic precision have not addressed cultural bias in a multicultural setting. Objectives Objectives: To compare DRS-2 and MoCA performance between patients born in Canada, the USA, and the UK (Anglosphere group) and immigrant patients born elsewhere (International group). To identify sources of cultural bias by comparing group characteristics, and by assessing the relationships between performance and immigration and socio-development variables. To examine the diagnostic precision of both tools in detecting PD-MCI in each group. Methods Methods: We conducted a clinical chart review of advanced PD patients who completed cognitive screeners (MoCA: n = 288, 30% International group; DRS-2: n = 426, 31% International group). All completed a comprehensive neuropsychological assessment to apply Level II PD-MCI diagnostic criteria. Results Results: The International group performed worse than the Anglosphere group on the MoCA and DRS-2, and the only variable that accounted for some of the group difference was the Historical Index of Human Development, a societal variable, which fully mediated the group effect on the DRS-2. Diagnostic precision of the MoCA was at chance level in the International group, and was poorer than that of the DRS-II in this group and that of the MoCA in the Anglosphere group, although these were considered poor. Conclusions Conclusions: Our results support the recommendation to exert caution in using cognitive screeners to capture PD-MCI in all patients and particularly with first generation immigrants.Cognitive decline is a prevalent symptom of Parkinson's disease (PD). While a comprehensive neuropsychological assessment is the gold standard for PD dementia (PDD) 1 and mild cognitive impairment (PD-MCI) 2 diagnoses, resources are often limited and repeating assessments over time is not feasible. Instead, cognitive screeners are more amenable to routine care and tools such as the Mattis Dementia Rating Scale-2 (DRS-2) 3 and the Montreal Cognitive Assessment (MoCA) 4 are recommended for use in PD. 5 Both scales are translated into numerous languages and are validated for PD around the globe, albeit with variable pass/ fail scores. These cross-cultural initiatives, however, do not address the challenge of cognitive testing in a multicultural setting.Cultural bias on cognitive tasks within multicultural societies is seldom examined in PD research. In other clinical groups, bias was mainly demonstrated in testing of racial/ethnic groups in the USA, with the confounds of education, literacy, socioeconomic status and general health differences. 6,7 Such research may not generalize to multicultural societies such as Toronto, Canada where immigrants represent 50% of the population, 8 and are generally healthy 9,10 and highly educated. 8 Because normative data from immigrants' ...

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“…Variables for the MoCA and DRS-2 are the total scores and the frequency of scores below PD-specific suggested cutoffs (MoCA ≤ 26; 21-25 DRS-2 ≤ 139). 24,26…”

Section: Cognitive Screening Measures and Proceduresmentioning

confidence: 99%

Multiculturalism: A Challenge for Cognitive Screeners in Parkinson's Disease

Statucka

Cherian

Fasano

et al. 2021

Movement Disord Clin Pract

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“…The ACE-R/ACE-III [33] has good psychometric properties and assesses visuospatial function to a larger degree but has incomplete coverage of executive function, limited to only fluency tasks [29]. In contrast to the Mattis Dementia Rating Scale (DRS-2) and the MMSE, the MoCA has been shown to predict progression from MCI to PDD [35] as well as sensitivity to change over time in Parkinson’s disease without dementia [36]. In the latter longitudinal study of 102 patients with PD, lower MoCA scores, postural instability and gait disturbance, and depressive symptoms at baseline were associated with a higher risk of cognitive decline [36].…”

Section: Challenges With Structured Cognitive Tests Used To Diagnose Dementia With Lewy Bodies and To Measure Change Over Timementioning

confidence: 99%

Challenges in Predicting Cognitive Decline in Dementia with Lewy Bodies

Tsamakis

Mueller

2021

Dement Geriatr Cogn Disord

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Despite being the second most common form of neurodegenerative dementia, dementia with Lewy bodies (DLB) is under-recognized and carries a worse prognosis than other subtypes of the condition. Cognitive impairment is a cardinal feature of all types of dementia and DLB presents with a distinct profile with deficits in attention, executive function, and visuoperceptual abilities. This difference from Alzheimer’s disease and the common presence of neuropsychiatric symptoms may lead to challenges in predicting cognitive decline in this patient population. Firstly, the diagnosis of DLB is often delayed in clinical practice leading to variability from which time point in the disease course cognitive decline is measured. Secondly, the most frequently used measurement tools for cognitive difficulties focus on memory and naming rather than the domains affected by DLB. While there is now largely a consensus which tools are useful in diagnosing DLB, their validity in assessing deteriorating cognition is less clear. Thirdly, the presence of fluctuating cognition, the propensity to develop delirium episodes, as well as difficulties in distinguishing the two entities in clinical practice make it difficult to predict the disease course. Sleep disturbances are likely to influence cognitive decline but require further study in patients within established DLB. Fourthly, as in most cases of dementia, neuropathological comorbidities are frequently present in DLB. While the influence of Alzheimer’s pathology on cognitive decline in DLB is comparatively well understood, the impact of other pathologies remains unclear. The recent definition of research criteria for mild cognitive impairment in DLB could facilitate earlier diagnosis and more structured follow-up. Assessment tools measuring cognitive domains predominantly affected in DLB need to be more consistently used in longitudinal studies and clinical practice, as well as concurrent measures of fluctuations in cognition. Greater availability of biomarkers and digital healthcare solutions can play an important role in enabling more accurate monitoring and prediction of cognitive decline in DLB.

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“…However, there has been no study to develop a prediction model based on a nationwide epidemiological survey. Moreover, most of the previous studies [15,16] evaluating the neuropsychological characteristics of patients with PD have used regression models. Regression models are effective in exploring the neuropsychological characteristics of individual risk factors but are limited in analyzing multiple risk factors simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Is the Random Forest Algorithm Suitable for Predicting Parkinson’s Disease with Mild Cognitive Impairment out of Parkinson’s Disease with Normal Cognition?

Byeon

2020

IJERPH

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Because it is possible to delay the progression of dementia if it is detected and treated in an early stage, identifying mild cognitive impairment (MCI) is an important primary goal of dementia treatment. The objectives of this study were to develop a random forest-based Parkinson’s disease with mild cognitive impairment (PD-MCI) prediction model considering health behaviors, environmental factors, medical history, physical functions, depression, and cognitive functions using the Parkinson’s Dementia Clinical Epidemiology Data (a national survey conducted by the Korea Centers for Disease Control and Prevention) and to compare the prediction accuracy of our model with those of decision tree and multiple logistic regression models. We analyzed 96 subjects (PD-MCI = 45; Parkinson’s disease with normal cognition (PD-NC) = 51 subjects). The prediction accuracy of the model was calculated using the overall accuracy, sensitivity, and specificity. Based on the random forest analysis, the major risk factors of PD-MCI were, in descending order of magnitude, Clinical Dementia Rating (CDR) sum of boxes, Untitled Parkinson’s Disease Rating (UPDRS) motor score, the Korean Mini Mental State Examination (K-MMSE) total score, and the K- Korean Montreal Cognitive Assessment (K-MoCA) total score. The random forest method achieved a higher sensitivity than the decision tree model. Thus, it is advisable to develop a protocol to easily identify early stage PDD based on the PD-MCI prediction model developed in this study, in order to establish individualized monitoring to track high-risk groups.

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Prediction of cognitive progression in Parkinson's disease using three cognitive screening measures

Cited by 27 publications

References 39 publications

Multiculturalism: A Challenge for Cognitive Screeners in Parkinson's Disease

Multiculturalism: A Challenge for Cognitive Screeners in Parkinson's Disease

Challenges in Predicting Cognitive Decline in Dementia with Lewy Bodies

Is the Random Forest Algorithm Suitable for Predicting Parkinson’s Disease with Mild Cognitive Impairment out of Parkinson’s Disease with Normal Cognition?

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