A web-based tool for personalized prediction of long-term disease course in patients with multiple sclerosis
- PMID: 23379849
- PMCID: PMC4491319
- DOI: 10.1111/ene.12016
A web-based tool for personalized prediction of long-term disease course in patients with multiple sclerosis
Abstract
Background and purpose: The Evidence-Based Decision Support Tool in Multiple Sclerosis (EBDiMS) is the first web-based prognostic calculator in multiple sclerosis (MS) capable of delivering individualized estimates of disease progression. It has recently been extended to provide long-term predictions based on the data from a large natural history cohort.
Methods: We compared the predictive accuracy and consistency of EBDiMS with that of 17 neurologists highly specialized in MS.
Results: We show that whilst the predictive accuracy was similar, neurologists showed a significant intra-rater and inter-rater variability.
Conclusions: Because EBDiMS was consistent, it is of superior utility in a specialist setting. Further field testing of EBDiMS in non-specialist settings, and investigation of its usefulness for counselling patients in treatment decisions, is warranted.
© 2012 The Author(s) European Journal of Neurology © 2012 EFNS.
Figures
Comment in
-
Can we predict the evolution of an unpredictable disease like multiple sclerosis?Eur J Neurol. 2013 Jul;20(7):995-6. doi: 10.1111/ene.12020. Epub 2012 Oct 31. Eur J Neurol. 2013. PMID: 23114082 No abstract available.
Similar articles
-
Can we predict the evolution of an unpredictable disease like multiple sclerosis?Eur J Neurol. 2013 Jul;20(7):995-6. doi: 10.1111/ene.12020. Epub 2012 Oct 31. Eur J Neurol. 2013. PMID: 23114082 No abstract available.
-
Long-term prognostic counselling in people with multiple sclerosis using an online analytical processing tool.Mult Scler. 2021 Aug;27(9):1442-1450. doi: 10.1177/1352458520964774. Epub 2020 Oct 26. Mult Scler. 2021. PMID: 33103987
-
A web-based decision support tool for prognosis simulation in multiple sclerosis.Mult Scler Relat Disord. 2014 Sep;3(5):575-83. doi: 10.1016/j.msard.2014.04.005. Epub 2014 May 2. Mult Scler Relat Disord. 2014. PMID: 26265269
-
Personalized medicine in multiple sclerosis: hope or reality?BMC Med. 2012 Oct 4;10:116. doi: 10.1186/1741-7015-10-116. BMC Med. 2012. PMID: 23035757 Free PMC article. Review.
-
Multiple sclerosis: clinical profiling and data collection as prerequisite for personalized medicine approach.BMC Neurol. 2016 Aug 2;16:124. doi: 10.1186/s12883-016-0639-7. BMC Neurol. 2016. PMID: 27484848 Free PMC article. Review.
Cited by
-
Machine Learning Use for Prognostic Purposes in Multiple Sclerosis.Life (Basel). 2021 Feb 5;11(2):122. doi: 10.3390/life11020122. Life (Basel). 2021. PMID: 33562572 Free PMC article. Review.
-
Intelligent Computer Systems for Multiple Sclerosis Diagnosis: a Systematic Review of Reasoning Techniques and Methods.Acta Inform Med. 2018 Dec;26(4):258-264. doi: 10.5455/aim.2018.26.258-264. Acta Inform Med. 2018. PMID: 30692710 Free PMC article.
-
Scoping review of clinical decision support systems for multiple sclerosis management: Leveraging information technology and massive health data.Eur J Neurol. 2025 Jan;32(1):e16363. doi: 10.1111/ene.16363. Epub 2024 Jun 11. Eur J Neurol. 2025. PMID: 38860844 Free PMC article.
-
How Do People with Multiple Sclerosis Experience Prognostic Uncertainty and Prognosis Communication? A Qualitative Study.PLoS One. 2016 Jul 19;11(7):e0158982. doi: 10.1371/journal.pone.0158982. eCollection 2016. PLoS One. 2016. PMID: 27434641 Free PMC article.
-
Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis.PLoS One. 2020 Mar 20;15(3):e0230219. doi: 10.1371/journal.pone.0230219. eCollection 2020. PLoS One. 2020. PMID: 32196512 Free PMC article.
References
-
- Degenhardt A, Ramagopalan SV, Scalfari A, Ebers GC. Clinical prognostic factors in multiple sclerosis: a natural history review. Nat Rev Neurol. 2009;5:672–682. - PubMed
-
- Boeije HR, Janssens AC. ‘It might happen or it might not’: how patients with multiple sclerosis explain their perception of prognostic risk. Soc Sci Med. 2004;59:861–868. - PubMed
-
- Heesen C, Kopke S, Fischer K, et al. Perception of evidence-based patient information on prognostic risk in multiple sclerosis. Mult Scler. 2009;15:S149.
Publication types
MeSH terms
Grants and funding
LinkOut - more resources
Full Text Sources
Medical