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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Screening properties of the updated normative framework for the Italian MMSE in MCI and dementia
Edoardo Nicolò Aiello1, Federico Verde2,3, Beatrice Curti1
1Department of Neurology and Laboratory of Neuroscience, IRCCS Istituto Auxologico Italiano, Milano, Italy.
Background:
This study aimed to assess the screening properties of Foderaro et al.s' updated normative framework for the Italian MMSE in detecting mild cognitive impairment (MCI) and dementia due to neurodegenerative, chronic cerebrovascular, and mixed etiologies, as well as in differentiating between these two syndromes.
Methods:
Data on 234 patients with either MCI (N = 83) or dementia (N = 151) due to Alzheimer's disease (N = 112), Lewy body disease (N = 11), frontotemporal lobar degeneration (N = 20), chronic cerebrovascular disease (N = 39), or mixed (N = 47) etiologies having been administered Foderaro et al.'s version of the MMSE were retrospectively recruited. Moreover, N = 247 healthy controls (HCs) with a normal Montreal Cognitive Assessment performance were prospectively recruited. Receiver-operating characteristics analyses were run to test the capability of both raw and demographically adjusted MMSE scores to discriminate both HCs from MCI/dementia and MCI from dementia. For these comparisons, screening metrics were also computed at Foderaro et al.'s cut-off (<26.02).
Results:
The capability of demographically adjusted MMSE scores to discriminate both HCs from dementia and MCI from dementia was excellent (AUC = 0.91 and 0.93, respectively), whilst good for MCI case-finding (AUC = 0.85). Consistently, the screening metrics associated with the cut-off at hand were optimal-to-excellent for dementia case-finding (sensitivity = 0.95; specificity = 0.99) and for the differentiation between MCI and dementia (sensitivity = 0.95; specificity = 0.64), whilst imbalanced for detecting MCI (sensitivity = 0.35; specificity = 0.99).
Discussion:
Foderaro et al.'s updated normative framework for the Italian MMSE has optimal screening properties for both dementia case-finding and the discrimination between MCI and dementia, being at variance unbalanced towards specificity when it comes to detecting MCI.
Insights
The Italian MMSE, using Foderaro et al.'s framework, effectively screens for dementia and differentiates it from mild cognitive impairment (MCI). However, its ability to detect MCI is less balanced, favoring specificity over sensitivity.
Area of Science:
- Neurology
- Cognitive Science
- Geriatrics
Background:
- Assessing the diagnostic accuracy of cognitive screening tools is crucial for early detection of neurodegenerative diseases.
- The Mini-Mental State Examination (MMSE) is widely used, but its performance can vary with normative frameworks.
- Foderaro et al. proposed an updated normative framework for the Italian MMSE.
Purpose of the Study:
- To evaluate the screening performance of the updated Italian MMSE framework by Foderaro et al.
- To assess its ability to detect mild cognitive impairment (MCI) and dementia.
- To differentiate between MCI and dementia across various etiologies.
Main Methods:
- Retrospective analysis of 234 patients (MCI/dementia) and 247 healthy controls (HCs).
- Utilized Foderaro et al.'s version of the MMSE.
- Performed receiver-operating characteristics (ROC) analyses on raw and demographically adjusted MMSE scores.
- Computed screening metrics at a cut-off of <26.02.
Main Results:
- Demographically adjusted MMSE scores showed excellent discrimination between HCs and dementia (AUC=0.91) and between MCI and dementia (AUC=0.93).
- Good performance was observed for MCI case-finding (AUC=0.85).
- The cut-off <26.02 yielded optimal-to-excellent metrics for dementia detection (sensitivity=0.95, specificity=0.99) and MCI/dementia differentiation (sensitivity=0.95, specificity=0.64), but was imbalanced for MCI detection (sensitivity=0.35, specificity=0.99).
Conclusions:
- The updated Italian MMSE framework by Foderaro et al. demonstrates optimal screening properties for dementia detection.
- It effectively discriminates between MCI and dementia.
- The framework shows a bias towards specificity when identifying MCI, potentially missing milder cases.
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