Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

Standardization and digitization of clinical data in multiple sclerosis

Nature Reviews Neurology volume 17, pages 119–125 (2021)Cite this article

Subjects

Abstract

Standardization is necessary to ensure the reliability of clinical data and to enable longitudinal and cross-sectional comparisons of data obtained in different centres and countries. In patients with multiple sclerosis (MS), standardized clinical data are needed for monitoring of disability and for collecting real-world evidence for use in research. This Perspective describes attempts to improve the standardization and digitization of clinical data in MS, including digital electronic health recording systems and applications that attempt to offer a comprehensive assessment of patients’ neurological deficits and their effects on daily life. Despite the challenges raised by regulatory, ethical and data-privacy considerations, the standardization and digitization of clinical data in MS is expected to generate new insights into the pathophysiology of the disease and to contribute to personalized patient care.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Neurostatus-eEDSS.
Fig. 2: Tools and initiatives for improving data standardization in multiple sclerosis.

Similar content being viewed by others

References

  1. Thompson, A. J., Baranzini, S. E., Geurts, J., Hemmer, B. & Ciccarelli, O. Multiple sclerosis. Lancet 391, 1622–1636 (2018).

    Article  PubMed  Google Scholar 

  2. Correale, J., Gaitán, M. I., Ysrraelit, M. C. & Fiol, M. P. Progressive multiple sclerosis: from pathogenic mechanisms to treatment. Brain 140, 527–546 (2017).

    PubMed  Google Scholar 

  3. Lublin, F. D. et al. Defining the clinical course of multiple sclerosis: the 2013 revisions. Neurology 83, 278–286 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Peeters, L. M. et al. Multidisciplinary data infrastructures in multiple sclerosis: why they are needed and can be done! Mult. Scler. 25, 500–509 (2019).

    Article  PubMed  Google Scholar 

  5. Gasperini, C. et al. Unraveling treatment response in multiple sclerosis: a clinical and MRI challenge. Neurology 92, 180–192 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Sastre-Garriga, J. et al. MAGNIMS consensus recommendations on the use of brain and spinal cord atrophy measures in clinical practice. Nat. Rev. Neurol. 16, 171–182 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Khalil, M. et al. Neurofilaments as biomarkers in neurological disorders. Nat. Rev. Neurol. 14, 577–589 (2018).

    Article  CAS  PubMed  Google Scholar 

  8. DSouza, M. et al. Accuracy of depth-sensing recordings in classifying expanded disability status scale subscores of motor dysfunction in patients with multiple sclerosis (P2.139). Neurology 86 (Suppl. 16), P2.139 (2016).

    Google Scholar 

  9. Babre, D. K. Clinical data interchange standards consortium: a bridge to overcome data standardisation. Perspect. Clin. Res. 4, 115–116 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Tur, C. et al. Assessing treatment outcomes in multiple sclerosis trials and in the clinical setting. Nat. Rev. Neurol. 14, 75–93 (2018).

    Article  PubMed  Google Scholar 

  11. Kappos, L., D’Souza, M., Lechner-Scott, J. & Lienert, C. On the origin of neurostatus. Mult. Scler. Relat. Disord. 4, 182–185 (2015).

    Article  PubMed  Google Scholar 

  12. Kappos, L. Neurostatus scoring definitions (version 04/10.2) Neurostatus https://www.neurostatus.net/scoring/index.php (2011).

  13. D’Souza, M. et al. Neurostatus e-scoring improves consistency of expanded disability status scale assessments: a proof of concept study. Mult. Scler. 23, 597–603 (2017).

    Article  PubMed  Google Scholar 

  14. D’Souza, M. et al. Electronic neurostatus-EDSS increases the quality of expanded disability status scale assessments: experience from two phase 3 clinical trials. Mult. Scler. 26, 993–996 (2020).

    Article  PubMed  Google Scholar 

  15. Cutter, G. R. et al. Development of a multiple sclerosis functional composite as a clinical trial outcome measure. Brain 122, 871–882 (1999).

    Article  PubMed  Google Scholar 

  16. Fischer, J. S., Rudick, R. A., Cutter, G. R. & Reingold, S. C. The multiple sclerosis functional composite measure (MSFC): an integrated approach to MS clinical outcome assessment. National MS society clinical outcomes assessment task force. Mult. Scler. 5, 244–250 (1999).

    Article  CAS  PubMed  Google Scholar 

  17. Meyer-Moock, S., Feng, Y.-S., Maeurer, M., Dippel, F.-W. & Kohlmann, T. Systematic literature review and validity evaluation of the Expanded Disability Status Scale (EDSS) and the Multiple Sclerosis Functional Composite (MSFC) in patients with multiple sclerosis. BMC Neurol. 14, 58 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Goldman, M. D. et al. Evaluation of multiple sclerosis disability outcome measures using pooled clinical trial data. Neurology 93, e1921–e1931 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Rudick, R. A. et al. The Multiple Sclerosis Performance Test (MSPT): an iPad-based disability assessment tool. J. Vis. Exp. 88, e51318 (2014).

    Google Scholar 

  20. Rhodes, J. K. et al. Multiple sclerosis performance test: technical development and usability. Adv. Ther. 36, 1741–1755 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Traboulsee, A. et al. Revised recommendations of the Consortium of MS Centers Task Force for a Standardized MRI Protocol and Clinical Guidelines for the Diagnosis and Follow-up of Multiple Sclerosis. Am. J. Neuroradiol. 37, 394–401 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Rovira, À. et al. Evidence-based guidelines: MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis—clinical implementation in the diagnostic process. Nat. Rev. Neurol. 11, 471–482 (2015).

    Article  PubMed  Google Scholar 

  23. Feys, P. et al. The Nine-Hole Peg test as a manual dexterity performance measure for multiple sclerosis. Mult. Scler. 23, 711–720 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Motl, R. W. et al. Validity of the timed 25-foot walk as an ambulatory performance outcome measure for multiple sclerosis. Mult. Scler. 23, 704–710 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Ontaneda, D., LaRocca, N., Coetzee, T., Rudick, R. & NMSS MSFC Task Force. Revisiting the multiple sclerosis functional composite: proceedings from the National Multiple Sclerosis Society (NMSS) Task Force on Clinical Disability Measures. Mult. Scler. 18, 1074–1080 (2012).

    Article  CAS  PubMed  Google Scholar 

  26. Behrens, J. et al. Using perceptive computing in multiple sclerosis - the short maximum speed walk test. J. Neuroeng. Rehabil. 11, 89 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Behrens, J. R. et al. Validity of visual perceptive computing for static posturography in patients with multiple sclerosis. Mult. Scler. 22, 1596–1606 (2016).

    Article  PubMed  Google Scholar 

  28. Otte, K. et al. Accuracy and reliability of the Kinect version 2 for clinical measurement of motor function. PLoS ONE 11, e0166532 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Morrison, C. et al. Usability and acceptability of ASSESS MS: assessment of motor dysfunction in multiple sclerosis using depth-sensing computer vision. JMIR Hum. Factors 2, e11 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Otte, K. et al. A Kinect-based perceptive assessment battery for motor dysfunction in multiple sclerosis and other neuroinflammatory disorders. Mult. Scler. J. 23, 473–474 (2017).

    Article  Google Scholar 

  31. The Lancet Neurology. Patient-reported outcomes in the spotlight. Lancet Neurol. 18, 981 (2019).

    Article  CAS  PubMed  Google Scholar 

  32. Brichetto, G. & Zaratin, P. Measuring outcomes that matter most to people with multiple sclerosis: the role of patient-reported outcomes. Curr. Opin. Neurol. 33, 295–299 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Peeters, L. M. et al. Multiple sclerosis data alliance — a global multi-stakeholder collaboration to scale-up real world data research. Mult. Scler. Relat. Disord. 47, 102634 (2020).

    Article  PubMed  Google Scholar 

  34. Wilkinson, M. D. et al. The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Kuklinski, D., Oschmann, L., Pross, C., Busse, R. & Geissler, A. The use of digitally collected patient-reported outcome measures for newly operated patients with total knee and hip replacements to improve post-treatment recovery: study protocol for a randomized controlled trial. Trials 21, 322 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Cohen, J. A. et al. Leveraging real-world data to investigate multiple sclerosis disease behavior, prognosis, and treatment. Mult. Scler. 26, 23–37 (2020).

    Article  PubMed  Google Scholar 

  37. Brichetto, G., Pedullà, L., Podda, J. & Tacchino, A. Beyond center-based testing: understanding and improving functioning with wearable technology in MS. Mult. Scler. 25, 1402–1411 (2019).

    Article  PubMed  Google Scholar 

  38. Torous, J., Rodriguez, J. & Powell, A. The new digital divide for digital biomarkers. Digit. Biomark. 1, 87–91 (2017).

    PubMed  PubMed Central  Google Scholar 

  39. Babrak, L. M. et al. Traditional and digital biomarkers: two worlds apart? Digit. Biomark. 3, 92–102 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Midaglia, L. et al. Adherence and satisfaction of smartphone- and smartwatch-based remote active testing and passive monitoring in people with multiple sclerosis: nonrandomized interventional feasibility study. J. Med. Internet Res. 21, e14863 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Montalban, X. et al. FLOODLIGHT: smartphone-based self-monitoring is accepted by patients and provides meaningful, continuous digital outcomes augmenting conventional in-clinic multiple sclerosis measures (poster P3.2-024). Neurology 92 (Suppl. 15), P3.2–024 (2019).

    Google Scholar 

  42. Pelletier, D., Rennert, T., Chang, M., Decunto, S. & Cen, S. myMS; a comprehensive patient-centered mobile app to monitor MS at home (poster P3.2-003). Neurology 92 (Suppl. 15), P3.2–003 (2019).

    Google Scholar 

  43. US National Library of Medicine. ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/NCT03111394 (2019).

  44. Wendrich, K. et al. Toward digital self-monitoring of multiple sclerosis: investigating first experiences, needs, and wishes of people with MS. Int. J. MS Care 21, 282–291 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Maillart, E. et al. MSCopilot, a new multiple sclerosis self-assessment digital solution: results of a comparative study versus standard tests. Eur. J. Neurol. 27, 429–436 (2020).

    Article  CAS  PubMed  Google Scholar 

  46. US National Library of Medicine. ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/NCT03148938 (2018).

  47. Guo, C. et al. KonectomTM smartphone-based digital outcome assessment of cognitive and motor function in multiple sclerosis. P0102. Mult. Scler. 26 (Suppl. 3), P0102 (2020).

    Google Scholar 

  48. Naegelin, Y. et al. Dreams: developing a comprehensive, sensitive and validated set of digital biomarkers for MS. P0069. Mult. Scler. 26 (Suppl. 3), P0069 (2020).

    Google Scholar 

  49. US National Library of Medicine. ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/NCT04413032 (2020).

  50. Gourraud, P.-A. et al. Precision medicine in chronic disease management: the multiple sclerosis BioScreen. Ann. Neurol. 76, 633–642 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Ziemssen, T. et al. Multiple sclerosis documentation system (MSDS): moving from documentation to management of MS patients. J. Neural Transm. 120, S61–S66 (2013).

    Article  PubMed  Google Scholar 

  52. Voigt, I. et al. A digital patient portal for patients with multiple sclerosis. Front. Neurol. 11, 400 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Ziemssen, T., Kern, R., Voigt, I. & Haase, R. Data collection in multiple sclerosis: the MSDS approach. Front. Neurol. 11, 445 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Haase, R. et al. Improving multiple sclerosis management and collecting safety information in the real world: the MSDS3D software approach. Expert Opin. Drug Saf. 17, 369–378 (2018).

    Article  PubMed  Google Scholar 

  55. Kalincik, T. & Butzkueven, H. The MSBase registry: informing clinical practice. Mult. Scler. 25, 1828–1834 (2019).

    Article  PubMed  Google Scholar 

  56. Butzkueven, H. et al. MSBase: an international, online registry and platform for collaborative outcomes research in multiple sclerosis. Mult. Scler. 12, 769–774 (2006).

    Article  CAS  PubMed  Google Scholar 

  57. Dolin, R. H., Rogers, B. & Jaffe, C. Health level seven interoperability strategy: big data, incrementally structured. Methods Inf. Med. 54, 75–82 (2015).

    Article  CAS  PubMed  Google Scholar 

  58. Jiang, G., Kiefer, R. C., Sharma, D. K., Prud’hommeaux, E. & Solbrig, H. R. A consensus-based approach for harmonizing the OHDSI common data model with HL7 FHIR. Stud. Health Technol. Inform. 245, 887–891 (2017).

    PubMed  PubMed Central  Google Scholar 

  59. Stewart, J. P. & Software as a Medical Device Working Group. Software as a medical device (SaMD): clinical evaluation http://www.imdrf.org/docs/imdrf/final/technical/imdrf-tech-170921-samd-n41-clinical-evaluation_1.pdf (2017).

  60. Digital Health Software Precertification (Pre-Cert) Program: https://www.fda.gov/medical-devices/digital-health-center-excellence/digital-health-software-precertification-pre-cert-program (2020).

  61. He, J. et al. The practical implementation of artificial intelligence technologies in medicine. Nat. Med. 25, 30–36 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Bruffaerts, R. Machine learning in neurology: what neurologists can learn from machines and vice versa. J. Neurol. 265, 2745–2748 (2018).

    Article  PubMed  Google Scholar 

  63. United States Food and Drug Administration. Proposed regulatory framework for modifications to artificial intelligence/machine learning (AI/ML)-based software as a medical device (SaMD): discussion paper and request for feedback US FDA https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device (2019).

  64. The European Parliament & The Council of the European Union. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) https://eur-lex.europa.eu/eli/reg/2016/679/oj (2016).

  65. Clinical Trials Transformation Initiative. CTTI recommendations: developing novel endpoints generated by mobile technologies for use in clinical trials. https://www.ctti-clinicaltrials.org/files/novelendpoints-recs.pdf (2017).

Download references

Acknowledgements

The authors thank T. Ziemssen for providing support in drafting the manuscript.

Author information

Authors and Affiliations

  1. Neurologic Clinic and Policlinic, Departments of Medicine, Biomedicine and Clinical Research, University Hospital Basel, Basel, Switzerland

    Marcus D’Souza, Athina Papadopoulou & Ludwig Kappos

  2. Research Center for Clinical Neuroimmunology and Neuroscience, University of Basel, Basel, Switzerland

    Marcus D’Souza, Athina Papadopoulou & Ludwig Kappos

  3. Office of the Chief Medical Informatics Officer, Digitalisierung & Information and Communication Technology Department, University Hospital Basel, Basel, Switzerland

    Marcus D’Souza

  4. Wega Informatik AG, Basel, Switzerland

    Christophe Girardey

Authors
  1. Marcus D’Souza
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Athina Papadopoulou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christophe Girardey
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ludwig Kappos
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.D.S. researched data for the article and wrote the manuscript. A.P. researched data for the article and wrote parts of the manuscript regarding standardization in clinical routine care. C.G. researched data for the article and wrote parts of the manuscript regarding regulatory and ethical challenges. M.D.S., A.P. and L.K. additionally contributed substantially to discussions of the article content. All authors reviewed and edited the manuscript before submission.

Corresponding author

Correspondence to Marcus D’Souza.

Ethics declarations

Competing interests

University Hospital Basel, the employer of M.D.S., A.P. and L.K., receives licence fees for Neurostatus products and uses these funds to support research. C.G. is an employee of Wega Informatik AG.

Additional information

Peer review information

Nature Reviews Neurology thanks L. Peeters and J. Sastre-Garriga for their contribution to the peer review of this work.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

Health Level Seven International (HL7) initiative: http://www.hl7.org

International Medical Device Regulators Forum: http://www.imdrf.org/about/about.asp

MSBase: https://www.msbase.org

MS BRIDGE: https://bridge.ucsf.edu

MS Data Alliance: https://msdataalliance.com/

ODHSI (Observational Health Data Sciences and Informatics) initiative: https://www.ohdsi.org

PROMS (Patient Reported Outcomes for MS) initiative: https://www.charcot-ms.org/initiatives/proms

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

D’Souza, M., Papadopoulou, A., Girardey, C. et al. Standardization and digitization of clinical data in multiple sclerosis. Nat Rev Neurol 17, 119–125 (2021). https://doi.org/10.1038/s41582-020-00448-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41582-020-00448-7

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research