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Composite Marker of Cognitive Dysfunction and Brain Atrophy is Highly Accurate in Discriminating Between Relapsing-Remitting and Secondary Progressive Multiple Sclerosis

BACKGROUND: With the advent of numerous new-generation disease-modifying drugs for multiple sclerosis (MS), the discrimination between relapsing-remitting MS (RRMS) and secondary progressive MS (SPMS) has become a problem of high importance. The aim of our study was to find a simple way to accuratel...

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Detalles Bibliográficos
Autores principales: Kizlaitienandedot, Rasa, Kaubrys, Gintaras, Giedraitienandedot, Nataandscaron;a, Ramanauskas, Naglis, Dementaviandccaron;ienandedot;, Jūratandedot;
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Scientific Literature, Inc. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5301955/
https://www.ncbi.nlm.nih.gov/pubmed/28145395
http://dx.doi.org/10.12659/MSM.903234
Descripción
Sumario:BACKGROUND: With the advent of numerous new-generation disease-modifying drugs for multiple sclerosis (MS), the discrimination between relapsing-remitting MS (RRMS) and secondary progressive MS (SPMS) has become a problem of high importance. The aim of our study was to find a simple way to accurately discriminate between RRMS and SPMS that is applicable in clinical practice as a composite marker, using the linear measures of magnetic resonance imaging (MRI) and the results of cognitive tests. MATERIAL/METHODS: We included 88 MS patients in the study: 43 participants had RRMS and 45 had SPMS. A battery consisting of 11 tests was used to evaluate cognitive function. We used 11 linear MRI measures and 7 indexes to assess brain atrophy. RESULTS: Four cognitive tests and 3 linear MRI measures were able to distinguish RRMS from SPMS with the AUC >0.8 based on ROC analysis. Multiple logistic regression models were constructed to identify the best set of cognitive and MRI markers. The model, using the Rey Auditory Verbal Learning Test (RAVLT), Digit Symbol Substitution Test (DSST), and Huckman Index, showed the highest predictive ability: AUC=0.921 (p<0.001). We constructed a simple remission-progression index from the same 3 variables, which discriminated well between RRMS and SPMS: AUC=0.920 (p<0.001), maximal Youden Index=0.702, cut-off=1.68, sensitivity=79.1%, and specificity=91.1%. CONCLUSIONS: The composite remission-progression index, using the RAVLT test, DSST test, and MRI Huckman Index, is highly accurate in discriminating between RRMS and SPMS.