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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Adaptation and Validation of the Mini-Addenbrooke's Cognitive Examination in Dementia in Arabic Speakers in Egypt
Tarik Qassem1,2,3, Mohamed S Khater4, Tamer Emara5
1Maudsley Health, Al-Amal Psychiatric Hospital, Al Awir, Dubai, United Arab Emirates.
Background:
The mini-Addenbrooke's Cognitive Examination (m-ACE) is a brief cognitive battery that assesses 5 subdomains of cognition (attention, memory, verbal fluency, visuospatial abilities, and memory recall). It is scored out of 30 and can be administered in under 5 min providing a quick screening tool for assessment of cognition.
Objectives:
We aimed to adapt the m-ACE in Arabic speakers in Egypt and to validate it in dementia patients to provide cutoff scores.
Methods:
We included 37 patients with dementia (Alzheimer's disease [n = 25], vascular dementia [n = 8], and dementia with Lewy body [n = 4]) and 43 controls.
Results:
There was a statistically significant difference (p < 0.001) on the total m-ACE score between dementia patients (mean 10.54 and standard deviation [SD] 5.83) and controls (mean 24.02 and SD 2.75). There was also a statistically significant difference between dementia patients and controls on all sub-score domains of the m-ACE (p < 0.05). Performance on the m-ACE significantly correlated with both the Mini-Mental State Examination (MMSE) and the Addenbrooke's Cognitive Examination-III (ACE-III). Using a receiver operator characteristic curve, the optimal cutoff score for dementia on the m-ACE total score was found to be 18 (92% sensitivity, 95% specificity, and 94% accuracy).
Conclusions:
We adapted the m-ACE in Arabic speakers in Egypt and provided objective validation of it as a screening tool for dementia, with high sensitivity, specificity, and accuracy.
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