scholarly journals Psychometric Properties of the Persian Montreal Cognitive Assessment in Mild Cognitive Impairment and Alzheimer Disease

Author(s):  
Vahid Rashedi ◽  
Mahshid Foroughan ◽  
Negin Chehrehnegar

Introduction: The Montreal Cognitive Assessment (MoCA) is a cognitive screening test widely used in clinical practice and suited for the detection of Mild Cognitive Impairment (MCI). The aims were to evaluate the psychometric properties of the Persian MoCA as a screening test for mild cognitive dysfunction in Iranian older adults and to assess its accuracy as a screening test for MCI and mild Alzheimer disease (AD). Method: One hundred twenty elderly with a mean age of 73.52 ± 7.46 years participated in this study. Twenty-one subjects had mild AD (MMSE score ≤21), 40 had MCI, and 59 were cognitively healthy controls. All the participants were administered the Mini-Mental State Examination (MMSE) to evaluate their general cognitive status. Also, a battery of comprehensive neuropsychological assessments was administered. Results: The mean score on the Persian version of the MoCA and the MMSE were 19.32 and 25.62 for MCI and 13.71 and 22.14 for AD patients, respectively. Using an optimal cutoff score of 22 the MoCA test detected 86% of MCI subjects, whereas the MMSE with a cutoff score of 26 detected 72% of MCI subjects. In AD patients with a cutoff score of 20, the MoCA had a sensitivity of 94% whereas the MMSE detected 61%. The specificity of the MoCA was 70% and 90% for MCI and AD, respectively. Discussion: The results of this study show that the Persian version of the MoCA is a reliable screening tool for detection of MCI and early stage AD. The MoCA is more sensitive than the MMSE in screening for cognitive impairment, proving it to be superior to MMSE in detecting MCI and mild AD.

2018 ◽  
Vol 46 (5-6) ◽  
pp. 335-345 ◽  
Author(s):  
Ales Bartos ◽  
Dan Fayette

Background: The Czech version of the Montreal Cognitive Assessment (MoCA-CZ) and delayed recall of 5 words have not been validated in patients with mild cognitive impairment (MCI) due to Alzheimer disease (AD) and compared to norms of a large population. Method: The MoCA-CZ was administered to 1,600 elderly individuals in 2 groups consisting of 48 patients with MCI due to AD (AD-MCI) and 1,552 normal elderly adults. Results: MoCA-CZ scores were significantly lower in the AD-MCI patients than in the normal elderly (21 ± 4 vs. 26 ± 3 points; p = 0.03). Under the recommended cutoff score of ≤25, the MoCA-CZ demonstrated an excellent sensitivity of 94% but a low specificity of 62%. When the score was reduced to ≤24, the MoCA-CZ showed an optimal sensitivity of 87% for AD-MCI and a specificity of 72%. Normal elderly persons should recall at least 2 words after delay (sensitivity 80%, specificity 74%). Several cutoff points were derived from normative data stratified by age and education. Conclusions: The cutoff for AD-MCI and stratified norms are available for the MoCA total score and delayed recall of the Czech version. The cut-off scores of the MoCA-CZ, sensitivity, and specificity are lower than in the original study.


2018 ◽  
Vol 45 (1-2) ◽  
pp. 49-55 ◽  
Author(s):  
Felicia C. Goldstein ◽  
Aaron Milloy ◽  
David W. Loring ◽  

Background/Aims: The aim of this paper was to evaluate the incremental validity of the Montreal Cognitive Assessment (MoCA) index scores and the MoCA total score in differentiating individuals with normal cognition versus mild cognitive impairment (MCI) or Alzheimer disease (AD). Methods: Effect sizes were calculated for Alzheimer’s Disease Neuroimaging Initiative research participants with normal cognition (n = 295), MCI (n = 471), or AD (n = 150). Results: Effect sizes for the total score were large (> 0.80) and exceeded the index scores in differentiating those with MCI versus normal cognition, MCI versus AD, and AD versus normal cognition. A combined score incorporating the Memory, Executive, and Orientation indexes also improved incremental validity for all 3 group comparisons. Conclusion: Administration of the entire MoCA is more informative than the index scores, especially in distinguishing normal cognition versus MCI. A combined score has stronger incremental validity than the individual index scores.


CNS Spectrums ◽  
2020 ◽  
pp. 1-19
Author(s):  
Elena Cecilia Rosca ◽  
Mihaela Simu

Abstract Objective This study aims to systematically review evidence of the accuracy of the Montreal Cognitive Assessment (MoCA) for evaluating the presence of cognitive impairment in patients with Huntington’s disease (HD) and to outline the quality and quantity of research evidence available about the use of the MoCA in this population. Methods We conducted a systematic literature review, searching four databases from inception until April 2020. Results We identified 26 studies that met the inclusion criteria: two case–control studies comparing the MoCA to a battery of tests, three studies comparing MoCA to Mini-Mental State Examination, two studies estimating the prevalence of cognitive impairment in individuals with HD and 19 studies or clinical trials in which the MoCA was used as an instrument for the cognitive assessment of participants with HD. We found no cross-sectional studies in which participants received the index test (MoCA) and a reference standard diagnostic assessment composed of an extensive neuropsychological battery. The publication period ranged from 2010 to 2020. Conclusions In patients with HD, the MoCA provides information about disturbances in general cognitive function. Even if the MoCA demonstrated good sensitivity and specificity when used at the recommended threshold score of 26, further cross-sectional studies are required to examine the optimum cutoff score for detecting cognitive impairments in patients with HD. Moreover, more studies are necessary to determine whether the MoCA adequately assesses cognitive status in individuals with HD.


2015 ◽  
Vol 11 (7S_Part_9) ◽  
pp. P442-P443 ◽  
Author(s):  
Parunyou Julayanont ◽  
Sookjaroen Tangwongchai ◽  
Solaphat Hemrungrojn ◽  
Chawit Tunvirachaisakul ◽  
Kammant Phanthumchinda ◽  
...  

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