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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Cognitive Assessment Test: Validation of a Short Cognitive Test for the Detection of Mild Cognitive Disorder
Kelly Estrada-Orozco1,2, Kely Bonilla-Vargas1, Francy Cruz1
1Clinical Research Institute, National University of Colombia, Colombia.
Introduction:
Cognitive disorders are a clinical and research challenge; in particular, the mild cognitive disorder (MiCD) requires diagnostic suspicion and tools with adequate performance for its detection. The objective of this study was the validation of a short cognitive test (CATest) for the detection of MiCD in population of 50 years or more.
Methods:
A diagnostic accuracy study was assembled and performed in a prospective cohort. A consecutive sample of 200 Colombian subjects who represented the whole spectrum of the condition of interest allowed us to reach the objective. Validity was determined by concurrent criteria. The cut points were determined by the ROC curves considering the best overall performance and accuracy of the test.
Results:
CATest was validated to detection of MiCD at a cut-off point of 18. As a result, scores lower than 18 classified the participants as MiCD. At this cut-off point, CATest showed sensitivity of 84.3% (CI 76 to 90.16), specificity of 71.4% (CI 95% 61.8 to 79.43), positive predictive value of 75% ( 95% CI 66.79 to 82.42), and area under curve AUC 0.8518 (standard error SE 0.0265).
Discussion:
CATest has an adequate performance as a short cognitive test for the detection of MiCD. Its performance is superior to MiniMental and similar to Montreal Cognitive test (MoCA) according to the data reported in the literature. The advantages over other tests are the evaluation of all cognitive domains, time of application, and easy interpretation of results. CATest is a free use alternative for MiCD detection.
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