Faculty Bibliography

BACKGROUND:The Expanded Disability Status Scale (EDSS) is widely utilized in clinical trials and routine care to evaluate disease burden and progression among people with multiple sclerosis (pwMS). However, instrumental gait measures may be more suitable than EDSS to track walking disability in pwMS. In this cross-sectional study, we aimed to quantify the variability of spatiotemporal gait measures within homologous EDSS categories. METHODS:A total of 205 pwMS (age=46.5[SD=10.5] years, 72.2% female, EDSS range=1.0-6.5) were studied in this retrospective analysis. Participants underwent walking assessments through the GAITRite system and the following spatiotemporal gait measures were recorded: gait speed, mean normalized velocity (MNV), base of support, stride length, step length, percentage of gait cycle spent in double support and single support, and functional ambulation profile. The EDSS was evaluated by a certified neurologist. RESULTS:≤0.17). Overall, the percent variability of gait measures increased across EDSS categories, with coefficients of variation ranging from 6.9% to 37.2% in the minimal disability group (EDSS≤2.5), 8.1% to 33.4% and 22.3% to 53.8% in the moderate (2.5<EDSS≤4.5) and severe (EDSS>4.5) disability groups, respectively. CONCLUSION/CONCLUSIONS:Spatiotemporal gait measures have great variability within homologous EDSS categories. The high percent variability of gait speed and MNV (up to more than 50%) suggests that walking ability varies substantially within and across disability levels. Therefore, in addition to the EDSS, more comprehensive (multidimensional), objective patient-centric metrics would be needed to accurately evaluate disability in pwMS.

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: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: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: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.