Remote Observational Research for Multiple Sclerosis

Abstract

Background and ObjectivesProspective, deeply phenotyped research cohorts monitoring individuals with chronic neurologic conditions, such as multiple sclerosis (MS), depend on continued participant engagement. The COVID-19 pandemic restricted in-clinic research activities, threatening this longitudinal engagement, but also forced adoption of televideo-enabled care. This offered a natural experiment in which to analyze key dimensions of remote research: (1) comparison of remote vs in-clinic visit costs from multiple perspectives and (2) comparison of the remote with in-clinic measures in cross-sectional and longitudinal disability evaluations.MethodsBetween March 2020 and December 2021, 207 MS cohort participants underwent hybrid in-clinic and virtual research visits; 96 contributed 100 “matched visits,” that is, in-clinic (Neurostatus-Expanded Disability Status Scale [NS-EDSS]) and remote (televideo-enabled EDSS [tele-EDSS]; electronic patient-reported EDSS [ePR-EDSS]) evaluations. Clinical, demographic, and socioeconomic characteristics of participants were collected.ResultsThe costs of remote visits were lower than in-clinic visits for research investigators (facilities, personnel, parking, participant compensation) but also for participants (travel, caregiver time) and carbon footprint (p< 0.05 for each). Median cohort EDSS was similar between the 3 modalities (NS-EDSS: 2, tele-EDSS: 1.5, ePR-EDSS: 2, range 0.6.5); the remote evaluations were each noninferior to the NS-EDSS within ±0.5 EDSS point (TOST for noninferiority,p< 0.01 for each). Furthermore, year to year, the % of participants with worsening/stable/improved EDSS scores was similar, whether each annual evaluation used NS-EDSS or whether it switched from NS-EDSS to tele-EDSS.DiscussionAltogether, the current findings suggest that remote evaluations can reduce the costs of research participation for patients, while providing a reasonable evaluation of disability trajectory longitudinally. This could inform the design of remote research that is more inclusive of diverse participants.

Deciphering multiple sclerosis disability with deep learning attention maps on clinical MRI

“…This utterly necessary step of validating the CNN model (although uncommon in the literature) strengthens our findings, laying the foundation for future longitudinal, comprehensive models of MS disease progression. Furthermore, having at hand an excellent tool to discriminate patients according to their disability level may be extremely useful in contexts where the EDSS score is not obtained as part of routine practice ( Bove et al, 2023 ). Such a discriminating tool may allow the fast identification of patients who already present a certain degree of disability and who, therefore, are at high risk of reaching unfavourable clinical outcomes ( Signori et al, 2023 ).…”

Enhancing diversity of clinical trial populations in multiple sclerosis

“…Recommendations to enhance diversity of clinical trials in multiple sclerosis. [40][41][42][43][44][45][46][47][48][49][50][51][52] Target population General recommendation Specific strategies…”

“…For example, use of digital technology, data collection through linked data sources, conducting evening or weekend study visits, and providing appropriate financial supports may help. 45 Table 3 outlines more recommendations (by population group). Generally, multiple strategies are needed to increase inclusion, and the optimal set of strategies will vary across studies and even across sites (both within and between countries) within multi-site studies.…”