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Updated: Sep 1, 2025
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Validation of a brief cross-cultural cognitive screening test in Multiple Sclerosis
Alfonso Delgado-Álvarez1, Cristina Delgado-Alonso2, Miriam Goudsmit3
1Department of Neurology, Hospital Clinico San Carlos, San Carlos Institute for Health Research (IdiSSC), Universidad Complutense, Profesor Martin Lagos St. ES-28040, Madrid, Spain; Faculty of Psychology, Universidad Autónoma de Madrid, Madrid, Spain.
Background:
Several batteries have been developed for the cognitive assessment of people with multiple sclerosis (MS). However, all these tests have some limitations in general clinical practice and from a cross-cultural perspective. In this study, we aimed to validate a novel cognitive screening test, the Cross-Cultural Dementia screening test (CCD), in pwMS.
Methods:
Seventy-five participants with relapsing-remitting MS and 75 healthy controls were enrolled and completed a comprehensive neuropsychological battery and the CCD. Intergroup comparisons, effect sizes, and correlations with previously validated tests were calculated for a majority and a pilot study of a minority sample. ROC curves were estimated, and random forest classification models were developed.
Results:
There were statistically significant differences between cognitively impaired MS (MS-CI) group and healthy controls, and between MS-CI and non-cognitively impaired MS group in all subtests of CCD with medium to large effect sizes. Correlations with standardized neuropsychological tests were moderate to high, supporting concurrent validity. These results were replicated in the minority sample. The random forest models showed a very accurate classification using the CCD. This test showed good psychometric properties compared with SDMT.
Conclusions:
Our study validates the CCD for cognitive impairment screening in MS, showing advantages over other routinely used cognitive tests.
