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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Validation of a New Screening Tool for Dementia: The Simple Observation Checklist for Activities of Daily Living
Jinse Park1, Hojin Choi2, Jea Won Jang3
1Department of Neurology, Haeundae Paik Hospital, Inje University, Busan, Korea.
Background And Purpose:
Screening tests for dementia such as the Mini Mental State Examination (MMSE) and the Montreal Cognitive Assessment are widely used, but there are drawbacks to their efficient use. There remains a need for a brief and easy method of assessing the activities of daily living (ADL) that can be administered to elderly individuals by healthcare workers. We have therefore developed a new scale named the Simple Observation Checklist for Activities of Daily Living (SOC-ADL).
Methods:
We developed the SOC-ADL scale as a team of experts engaged in caring for individuals with dementia. This scale comprises eight items and was designed based on the Korean instrumental activities of daily living (K-IADL) scale and the Barthel activities of daily living scale (Barthel Index). The new scale was validated by enrolling 176 patients with cognitive dysfunction across 6 centers. Confirmatory factor analysis (CFA) and exploratory factor analysis (EFA) were performed. We assessed its concurrent validity by performing comparisons with the Korean-MMSE, Clinical Dementia Rating, Clinical Dementia Rating-Sum of Boxes, K-IADL, and Barthel Index, and its criterion validity by performing comparisons between mild cognitive impairment (MCI) and dementia. We also used Cronbach's alpha to assess the interitem reliability. The appropriate cutoff values were determined by analyzing receiver operating characteristic curves, including the areas underneath them.
Results:
EFA extracted one factor and CFA revealed that all of the model fits exceeded the minimum acceptable criteria. The SOC-ADL scores were strongly correlated with those of the other tools for dementia and could be used to differentiate MCI from dementia. Cronbach's alpha values indicated that the results were reliable. The optimal cutoff value of the SOC-ADL for discriminating dementia from MCI was 3 points, which provided a sensitivity and specificity of 74.5% and 75.7%, respectively.
Conclusions:
Our results demonstrate that the SOC-ADL is a valid and reliable tool for differentiating dementia from MCI based on an assessment of ADL. This new tool can be used for screening ADL in elderly subjects who have difficulty communicating, and to increase the efficiency of dementia screening at the population level.
