Multidisciplinary data infrastructures in multiple sclerosis: Why they are needed and can be done!

Abstract

Personalized treatment is highly desirable in multiple sclerosis (MS). We believe that multidisciplinary measurements including clinical, functional and patient-reported outcome measures in combination with extensive patient profiling can enhance personalized treatment and rehabilitation strategies. We elaborate on four reasons behind this statement: (1) MS disease activity and progression are complex and multidimensional concepts in nature and thereby defy a one-size-fits-all description, (2) functioning, progression, treatment, and rehabilitation effects are interdependent and should be investigated together, (3) personalized healthcare is based on the dynamics of system biology and on technology that confirms a patient’s fundamental biology and (4) inclusion of patient-reported outcome measures can facilitate patient-relevant healthcare. We discuss currently available multidisciplinary MS data initiatives and introduce joint actions to further increase the overall success. With this topical review, we hope to drive the MS community to invest in expanding towards more multidisciplinary and longitudinal data collection.

Data harmonization for collaborative research among MS registries: A case study in employment

“…The extension of these and other efforts to share individual-level data must address issues related to ethics and privacy considerations which vary across jurisdictions. 31…”

Beyond center-based testing: Understanding and improving functioning with wearable technology in MS

“…To this aim, the collection of continuous mobile health streaming data from multiple wearable sources in a secure, highly scalable, interoperable, and extensible platform is of high interest. MS DataConnect 61 (consortium leads by the University of Hasselt) connects national and international partners involved in MS care, rehabilitation, and research with partners involved in IT development, database management, data-sharing procedures, statistics, machine learning, and prediction modeling in order to move forward in developing standards for interoperability of different data sources.…”

Observational designs in clinical multiple sclerosis research: Particulars, practices and potentialities

“…There is a trend to combine data from various data bases into large data sets, as it is thought that information from multidisciplinary and longitudinal assessmentsincluding clinical, functional and PRO measureswill facilitate the development of personalized treatment and disease management (Peeters et al, 2018;Peeters, 2017). The rationale being that, as disease activity and treatment response result from multiple interactions between various factors, classification of individuals into subpopulations with differences in prognosis or response to a specific treatment will require a vast amount of data (Peeters, 2017).…”

“…Conceivably, the use of large data sets in combination with deep learning approaches may lead to evidence-and practicebased treatment algorithms (Butzkueven et al, 2006). To optimally realize the potential of existing and future data, the application of the Findable, Accessible, Interoperable and Reusable (FAIR) data concept has been proposed, as well as the development of multidisciplinary data infrastructures (Peeters, 2017;Wilkinson et al, 2016;Peeters et al, 2018). In all, these concepts and their implementation will expectedly lead to sophisticated decision-support systems, and may also provide health insurers and regulators with long-term data on (relative) effectiveness and costs of treatments (Peeters, 2017).…”