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Updated: Dec 29, 2025

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Predictive medicine in multiple sclerosis: A systematic review.

Julie Havas1, Emmanuelle Leray2, Fabien Rollot3

  • 1SPHERE (methodS in Patient-centered outcomes & HEalth ResEarch) U1246, INSERM, Nantes University, Tours University, Nantes, France.

Multiple Sclerosis and Related Disorders
|February 1, 2020
PubMed
Summary
This summary is machine-generated.

Predicting multiple sclerosis progression is challenging. This review found a lack of validated predictive tools, highlighting the need for better development and external validation of existing methods for clinical practice.

Keywords:
Multiple sclerosisPrecision medicinePrognostic toolsSystematic review

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Area of Science:

  • Neurology
  • Clinical Epidemiology

Background:

  • Predicting disease progression in multiple sclerosis (MS) is a significant clinical challenge.
  • Accurate prediction aids in tailoring therapeutic strategies and managing patient care.

Purpose of the Study:

  • To systematically review and critically appraise existing composite tools for predicting multiple sclerosis progression.
  • To identify methodological limitations in the development and validation of these predictive tools.

Main Methods:

  • Conducted comprehensive electronic database searches (MEDLINE, EMBASE, Web of Science, Cochrane Library).
  • Included studies developing and/or validating predictive models for MS patients, screened by multiple reviewers.
  • Assessed full-text articles for eligibility and methodological quality.

Main Results:

  • Identified 15 eligible articles from over 6,000 initial studies.
  • Found numerous methodological pitfalls, particularly in validation of discrimination and calibration.
  • Only Rio and modified Rio scores were externally validated multiple times, with variable accuracy (65%-91%).

Conclusions:

  • There is a significant lack of validated predictive tools for multiple sclerosis.
  • Further external validation and demonstration of clinical utility are essential before widespread adoption.
  • The field requires improved standards for developing and validating predictive models in MS research.