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Related Concept Videos

Multiple Sclerosis l: Introduction01:19

Multiple Sclerosis l: Introduction

Multiple sclerosis is a chronic autoimmune disease of the central nervous system (CNS) that affects the brain, spinal cord, and optic nerves. It is an inflammatory demyelinating disorder and a leading cause of neurological disability in young adults.EpidemiologyMS commonly begins between 20 and 40 years of age and is twice as common in women. Its exact cause remains unclear, but genetic susceptibility contributes, with higher risk in first-degree relatives and identical twins. A greater...

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Related Experiment Video

Updated: Jun 5, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

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Published on: December 9, 2015

Validation of rule-based detection methods for relapse in multiple sclerosis.

Almaha Alfakhri1, Ohoud Almadani1, Adel Alrwisan1

  • 1Saudi Food and Drug Authority, Riyadh, Saudi Arabia.

BMC Medical Informatics and Decision Making
|June 4, 2026
PubMed
Summary
This summary is machine-generated.

This study validated rule-based methods to identify multiple sclerosis (MS) relapses in Saudi electronic health records (EHRs). The methods showed high sensitivity and negative predictive value for detecting MS relapses.

Keywords:
DetectionDiagnosisIdentificationMultiple sclerosisRWERelapseValidation

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Area of Science:

  • Neurology
  • Health Informatics
  • Clinical Research

Background:

  • Identifying multiple sclerosis (MS) relapses within electronic health records (EHRs) presents significant challenges.
  • This study focused on validating rule-based detection methods using structured EHR data from Saudi Arabia.

Purpose of the Study:

  • To assess the efficacy of two distinct rule-based algorithms for identifying MS relapse episodes.
  • To validate these methods against expert neurologist adjudication using Saudi EHR data.

Main Methods:

  • Two rule-based detection methods were developed and applied to a cohort of 1,812 MS patients.
  • Method I: High-dose corticosteroid use + hospitalization (≥1 day). Method II: Hospitalization (≥3 days) or consecutive neurology admissions (≥3 days).
  • Performance metrics (sensitivity, specificity, PPV, NPV) were calculated with 95% confidence intervals.

Main Results:

  • The study analyzed 174 cases and 226 controls.
  • Detection methods achieved a sensitivity of 0.98 (95% CI, 0.92-0.99) and a negative predictive value (NPV) of 0.99 (95% CI, 0.97-1.00).
  • Specificity was 0.72 (95% CI, 0.67-0.77) and positive predictive value (PPV) was 0.50 (95% CI, 0.43-0.57).

Conclusions:

  • The developed rule-based methods demonstrate effectiveness in identifying potential MS relapse episodes in real-world EHR data.
  • While effective, further confirmatory steps are recommended to ensure accuracy in identifying true relapse cases.