[The application of Montreal cognitive assessment in urban Chinese residents of Beijing].
OBJECTIVE
To study the distribution of Montreal cognitive assessment (MoCA) scores in terms of age and educational level in Chinese residents aged 50 years and over and to offer a benchmark for a cutoff score.


METHODS
A total of 281 residents aged 50 years or older was drawn randomly in the urban areas of Beijing, including 215 healthy elderly controls (NC) and 66 patients meeting the clinical criteria of mild cognitive impairment (MCI). The final scores for MoCA were given in the form of mean percentage distributions specific for age, sex and educational level so as to compare the validity of MMSE mini-mental state examination and MoCA in detecting MCI. By a fitting multiple regression model the influence of the factors on MMSE and MoCA was assessed.


RESULTS
Using a cutoff score of 26, MMSE had a sensitivity of 24.2% to detect MCI, whereas MoCA detected 92.4% of the MCI subjects. We found a bivariate correlation between MoCA scores and both the factors of age and educational level (P < 0.001).


CONCLUSIONS
MoCA is a brief cognitive screening tool with high sensitivity and specificity for detecting MCI as currently conceptualized in patients performing normally on MMSE. Our adjustment in the cutoff scores would improve the detection of MCI and Alzheimer's disease by reducing the number of false negatives. MoCA scores should be used to identify current cognitive difficulties but not to make formal diagnoses.
Wen
Hong-Bo
HB
Department of Neurology, Chinese Academy of Medical Sciences, Peking Union Medical College Hospital, Beijing 100730, China.
Zhang
Zhen-Xin
ZX
Niu
Fu-Sheng
FS
Li
Ling
L
D000368:Aged; D000369:Aged, 80 and over; D002681:China; D003072:Cognition Disorders / Q000175:diagnosis*; D005260:Female; D006801:Humans; D008297:Male; D008403:Mass Screening / Q000379:methods; D008609:Mental Status Schedule / Q000706:statistics & numerical data*; D008875:Middle Aged; D012044:Regression Analysis; D014505:Urban Population / Q000706:statistics & numerical data*