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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
An Ultra-Brief Informant Questionnaire for Case Finding of Cognitive Impairment Across Diverse Literacy: Diagnostic
Tau Ming Liew1,2,3,4, King Fan Yip5, Kaavya Narasimhalu6
1Department of Psychiatry, Singapore General Hospital, Outram Road, Singapore, 169608, Singapore, 65 62223322.
Background:
Undiagnosed cognitive impairment poses a global challenge, prompting recent interest in ultra-brief screening questionnaires (comprising <2 to 3 items) to efficiently identify individuals needing further evaluation. However, evidence on ultra-brief questionnaires remains limited, particularly regarding their validity across diverse literacy levels.
Objective:
This study aimed to develop an ultra-brief questionnaire that performs well in detecting mild cognitive impairment or dementia (MCI/dementia) across diverse literacy levels and to compare its performance with an established questionnaire (the 8-item Informant Interview to Differentiate Aging and Dementia [AD8]).
Methods:
This diagnostic study involved 1856 participants aged ≥65 years (median education 10 y, range 0-23 y), prospectively recruited from community settings in Singapore. Participants and informants completed 15 cognition-related questions. MCI/dementia was diagnosed via a comprehensive assessment and consensus conference. The sample was randomly split 70/30-the training sample (70%) was used to derive an ultra-brief questionnaire from the 15 cognition-related questions (using an exhaustive search approach), and the test sample (30%) evaluated the area under the receiver operating characteristic curve (AUC).
Results:
The new questionnaire comprised 2 informant questions (ie, assistance with medications and worry about cognition), plus age and years of education. It demonstrated excellent performance in detecting MCI/dementia (AUC 85%, 95% CI 80%-90%), significantly better (P=.003) than a nested baseline model (comprising age and years of education; AUC 78%, 95% CI 73%-83%). In contrast, the AD8 had an AUC of 76% (95% CI 70%-83%), not significantly different (P>.99) from the baseline model. The questionnaire's performance was consistent across education subgroups and varying prevalence scenarios. Two optimal cutoffs were used-the lower cutoff provided 80% sensitivity and 96% negative predictive value, and the upper cutoff provided 99% specificity and 81% positive predictive value. A web-based calculator is available for public use.
Conclusions:
This ultra-brief questionnaire enables rapid screening for cognitive impairment (in <1 min) by family members or as part of community geriatric assessments. Its excellent performance across literacy levels supports its utility for case finding in diverse populations, including underserved communities and lower- and middle-income countries.
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