Literature DB >> 33465758

Brief international cognitive assessment for multiple sclerosis (BICAMS) cut-off scores for detecting cognitive impairment in multiple sclerosis.

Artemios Artemiadis1, Christos Bakirtzis2, Andreas Chatzittofis3, Constantinos Christodoulides4, George Nikolaou5, Marina Kleopatra Boziki6, Nikolaos Grigoriadis7.   

Abstract

BACKGROUND: Cognitive impairment (CI) affects 35-65% of multiple sclerosis (MS) patients. The Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS) has been proposed as a highly feasible and cost-effective tool for cognitive impairment (CI) screening in MS. The tool yields scores that should, ideally, readily convey patients' cognitive status to the clinicians.
METHODS: To this aim, this study sought for cut-off scores of the three BICAMS test in a sample of 960 MS patients. We used three definitions for CI: 1.5, 1,65 and 2 standard deviations below the mean. Receiver operating characteristic (ROC) statistics helped us determine the capacity of BICAMS to diagnose CI. Optimal cut-offs were determined by the delta distance. Positive and negative predictive values, along with overall accuracy were also calculated.
RESULTS: Symbol Digit Modalities Test (SDMT) and California Verbal Learning Test-II (CVLT-II) showed a diagnostic accuracy ranging from 74.6 to 77.4%, across the three CI definitions. The accuracy of Brief Visuospatial Memory Test-Revised (BVMT-R) was over 88%. SDMT had a balanced sensitivity, while CVLT-II and BVMT-R had higher specificities than sensitivities at detecting CI. More specifically, BVMT-R showed 100% specificity for all CI definitions. Raw cut-off scores for BICAMS tests are also provided within the manuscript, along with the diagnostic calculations.
CONCLUSIONS: In this study, we confirmed that BICAMS is a good screening tool for CI and that simple cut-offs can be used in the everyday neurological practice.
Copyright © 2021 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  BICAMS; Cognitive; Multiple sclerosis; Sensitivity; Specificity

Year:  2021        PMID: 33465758     DOI: 10.1016/j.msard.2021.102751

Source DB:  PubMed          Journal:  Mult Scler Relat Disord        ISSN: 2211-0348            Impact factor:   4.339


  2 in total

1.  Smell as a Disease Marker in Multiple Sclerosis.

Authors:  Athanasia Printza; Marina Boziki; Constantinos Valsamidis; Christos Bakirtzis; Jannis Constantinidis; Nikolaos Grigoriadis; Stefanos Triaridis
Journal:  J Clin Med       Date:  2022-09-03       Impact factor: 4.964

2.  Reliability, construct and concurrent validity of a smartphone-based cognition test in multiple sclerosis.

Authors:  K H Lam; P van Oirschot; B den Teuling; H E Hulst; B A de Jong; Bmj Uitdehaag; V de Groot; J Killestein
Journal:  Mult Scler       Date:  2021-05-26       Impact factor: 6.312

  2 in total

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