Related Experiment Video
Updated: Aug 6, 2025
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Accuracy of the Montreal Cognitive Assessment tool for detecting mild cognitive impairment: A systematic review and
Nayaar Islam1, Rola Hashem1, Maryse Gad1
1School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, Canada.
Introduction:
This systematic review evaluates the accuracy of the Montreal Cognitive Assessment (MoCA) for detecting mild cognitive impairment (MCI).
Methods:
We searched MEDLINE, PSYCInfo, EMBASE, and Cochrane CENTRAL (1995-2021) for studies comparing the MoCA with validated diagnostic criteria to identify MCI in general practice. Screening, data extraction, and risk of bias assessment were performed independently, in duplicate. Pooled sensitivity and specificity for MoCA cutoffs were estimated using bivariate meta-analysis.
Results:
Thirteen studies [2158 participants, 948(44%) with MCI] were included; 10 used Petersen criteria as the reference standard. Risk of bias of studies were high or unclear for all domains except reference standard. Sensitivity and specificity were 73.5%(95% confidence interval: 56.7-85.5) and 91.3%(84.6-95.3) at cutoff <23; 79.5%(67.1-88.0) and 83.7%(75.4-89.6) at cutoff <24; and 83.8%(75.6-89.6) and 70.8(62.1-78.3) at cutoff <25.
Discussion:
MoCA cutoffs <23 to <25 maximized the sum of sensitivity and specificity for detecting MCI. The risk of bias of included studies limits confidence in these findings.
More Related Videos
06:58Highlighting and Reducing the Impact of Negative Aging Stereotypes During Older Adults' Cognitive Testing
Published on: January 24, 2020
06:23The 4 Mountains Test: A Short Test of Spatial Memory with High Sensitivity for the Diagnosis of Pre-dementia Alzheimer's Disease
Published on: October 13, 2016