Measuring cognitive function by the SDMT across functional domains: Useful but not sufficient.

Cognitive function Cognitive impairment Computerized cognitive assessment Multiple sclerosis Screening Symbol digit modalities test

Journal

Multiple sclerosis and related disorders
ISSN: 2211-0356
Titre abrégé: Mult Scler Relat Disord
Pays: Netherlands
ID NLM: 101580247

Informations de publication

Date de publication:
Apr 2022
Historique:
received: 18 11 2021
revised: 21 01 2022
accepted: 19 02 2022
pubmed: 9 3 2022
medline: 18 5 2022
entrez: 8 3 2022
Statut: ppublish

Résumé

The Symbol Digit Modalities Test (SDMT) is a common screen of cognitive function for people with Multiple Sclerosis (pwMS) but growing acknowledgement that people with cognitive impairment are a heterogeneous population suggests that a single screen may provide limited information. To assess the adequacy of the SDMT in capturing impairment across specific cognitive domains as measured by a multi-domain cognitive assessment battery (CAB, NeuroTrax). 113 pwMS were assessed with SDMT and the CAB. Cognitive impairment in each CAB domain was defined as ≥1.5 SD below the normalized mean. Logistic regression models were fit for each CAB domain with domain-specific cognitive impairment as the outcome and SDMT as the predictor, and a classifier created by selecting cutpoints using the Youden Index. Model performance was assessed by predicting domain-specific cognitive impairment in an independent data set consisting of 81 pwMS. SDMT was a significant predictor of cognitive impairment in all outcomes considered (Odds Ratio: 0.885-0.950), but prediction metrics such as area under the receiver operating curve (AUC) were modest (0.623-0.778), and the alignment between observed/predicted impairment was less than optimal. The SDMT is not sufficient to differentiate between impaired and non-impaired pwMS across several cognitive domains.

Sections du résumé

BACKGROUND BACKGROUND
The Symbol Digit Modalities Test (SDMT) is a common screen of cognitive function for people with Multiple Sclerosis (pwMS) but growing acknowledgement that people with cognitive impairment are a heterogeneous population suggests that a single screen may provide limited information.
OBJECTIVE OBJECTIVE
To assess the adequacy of the SDMT in capturing impairment across specific cognitive domains as measured by a multi-domain cognitive assessment battery (CAB, NeuroTrax).
METHODS METHODS
113 pwMS were assessed with SDMT and the CAB. Cognitive impairment in each CAB domain was defined as ≥1.5 SD below the normalized mean. Logistic regression models were fit for each CAB domain with domain-specific cognitive impairment as the outcome and SDMT as the predictor, and a classifier created by selecting cutpoints using the Youden Index. Model performance was assessed by predicting domain-specific cognitive impairment in an independent data set consisting of 81 pwMS.
RESULTS RESULTS
SDMT was a significant predictor of cognitive impairment in all outcomes considered (Odds Ratio: 0.885-0.950), but prediction metrics such as area under the receiver operating curve (AUC) were modest (0.623-0.778), and the alignment between observed/predicted impairment was less than optimal.
CONCLUSION CONCLUSIONS
The SDMT is not sufficient to differentiate between impaired and non-impaired pwMS across several cognitive domains.

Identifiants

pubmed: 35259683
pii: S2211-0348(22)00219-X
doi: 10.1016/j.msard.2022.103704
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

103704

Informations de copyright

Copyright © 2022. Published by Elsevier B.V.

Auteurs

Justin M Leach (JM)

Department of Biostatistics, University of Alabama at Birmingham, 1665 University Blvd., 1665 University Blvd. Suite #327, Birmingham, Alabama 35294, United States. Electronic address: jleach@uab.edu.

Gary Cutter (G)

Department of Biostatistics, University of Alabama at Birmingham, 1665 University Blvd., 1665 University Blvd. Suite #327, Birmingham, Alabama 35294, United States.

Daniel Golan (D)

Department of Neurology, Lady Davis Carmel Medical Center, Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel.

Glen Doniger (G)

Department of Clinical Research, NeuroTrax Corporation, Modiin, Israel.

Myassar Zarif (M)

South Shore Neurologic Associates, Patchogue, NY, United States.

Barbara Bumstead (B)

South Shore Neurologic Associates, Patchogue, NY, United States.

Marijean Buhse (M)

South Shore Neurologic Associates, Patchogue, NY, United States; South Shore Neurologic Associates, Stony Brook University, Stony Brook, NY, United States.

Olivia Kaczmarek (O)

South Shore Neurologic Associates, Patchogue, NY, United States.

Avtej Sethi (A)

South Shore Neurologic Associates, Patchogue, NY, United States.

Thomas Covey (T)

Division of Cognitive and Behavioral Neurosciences, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Neuroscience Program, 955 Main St, Buffalo, NY 14203, United States.

Iris-Katharina Penner (IK)

Center for Applied Neurocognition and Neuropsychological Research, Department of Neurology, Heinrich Heine University, Moorenstrasse 5, Düsseldorf 40225, Germany.

Jeffrey Wilken (J)

Georgetown University Dept of Neurology, Washington Neuropsychology Research Group, LLC., Fairfax, United States.

Mark Gudesblatt (M)

South Shore Neurologic Associates, Patchogue, NY, United States.

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Classifications MeSH