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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Mini-Mental State Examination: Optimal Cut-Off Levels for Mild and Severe Cognitive Impairment
Francesco Salis1, Diego Costaggiu1, Antonella Mandas1
1Department of Medical Sciences, and Public Health, University of Cagliari, SS 554 Bvio Sestu, 09042 Monserrato, Italy.
Abstract:
Considering the need to intercept neurocognitive damage as soon as possible, it would be useful to extend cognitive test screening throughout the population. Here, we propose differential cut-off levels that can be used to identify mild and severe cognitive impairment with a simple and widely used first-level neurocognitive screening test: the Mini-Mental State Examination (MMSE). We studied a population of 262 patients referred for cognitive impairment testing using the MMSE and Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), a neuropsychological battery. The sample consisted of 262 participants with mean age 73.8 years (60-87), of whom 154 (58.8%) women. No significant gender-related differences in cognitive ability were identified. The two tests (MMSE and RBANS) showed a moderate correlation in identifying cognitive deficit. We used RBANS as a categorial variable to identify different degrees of cognitive impairment. Youden's J indexes were used to consider the better sensitivity/specificity balance in the 24-point cut-off score for severe cognitive deficit, 29.7-point score for mild cognitive deficit, and 26.1-point score for both mild and severe cognitive deficit. The study shows that the MMSE does not identify early cognitive impairment. Though different cut-offs are needed to discriminate different impairment degrees, the 26.1-point score seems to be preferable to the others.
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