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The S ymbol D igit M odalities T est as sentinel test for cognitive impairment in multiple sclerosis
Author(s) -
Van Schependom J.,
D'hooghe M. B.,
Cleynhens K.,
D'hooge M.,
Haelewyck M.C.,
De Keyser J.,
Nagels G.
Publication year - 2014
Publication title -
european journal of neurology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.881
H-Index - 124
eISSN - 1468-1331
pISSN - 1351-5101
DOI - 10.1111/ene.12463
Subject(s) - logistic regression , multiple sclerosis , medicine , neuropsychology , test (biology) , cognitive impairment , population , psychology , clinical psychology , cognition , audiology , psychiatry , biology , paleontology , environmental health
Background and purpose Cognitive impairment ( CI ) is found in about half of the multiple sclerosis ( MS ) population and is an important contributor to employment status and social functioning. CI is encountered in all disease stages and correlates only moderately with disease duration or Expanded Disability Status Scale scores. Most present neuropsychological test batteries are time‐demanding and expensive. The Symbol Digit Modalities Test ( SDMT ) has been suggested as a screening tool for CI in MS . In this paper, we aim to assess the performance of the SDMT in predicting the outcome of an extensive battery. Methods Neuropsychological test results from 359 patients were assessed in a multidisciplinary MS center (National MS Center Melsbroek, Belgium). Using receiver operating characteristic curves, the performance of the SDMT in predicting the general cognitive outcome of the extensive Neuropsychological Screening Battery for MS (NSBMS) could be assessed. The performance of the SDMT was assessed for different levels of CI and compared with other cognitive tests. Finally, useful covariates were included in a logistic regression model. Results At a specificity of 0.60 a high sensitivity (0.91) was obtained indicating the potential of the SDMT as a sentinel test for CI in MS . The SDMT outperformed the individual tests included in the NSBMS , used as benchmark. As the logistic regression model did not result in a relevant improvement, it is concluded that most clinical variables influence both the SDMT and the NSBMS in a similar way. Excluding patients with possible practice effects, an optimal cutoff of 40 was found for the SDMT . Conclusion As the SDMT is an easy, low‐cost and fast test, this result may help to detect CI in everyday clinical practice.