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Updated: Jun 5, 2026
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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.
Background:
Identifying relapse in electronic health records (EHRs) is challenging in patients with multiple sclerosis (MS). This study aimed to validate rule-based detection methods for relapses using a Saudi structured EHR data.
Methods:
Two rule-based detection methods were developed using MS patient data from a large multi-regional Saudi healthcare institution. Detection Method I required high-dose corticosteroid use and hospitalization of at least one day, whereas Detection Method II required either a single hospitalization lasting at least three days or multiple consecutive neurology admissions totaling three or more days. These methods were applied to a cohort of 1,812 MS patients. Relapse episodes were adjudicated by neurologists, and validation metrics-including sensitivity, specificity, positive predictive value [PPV], and negative predictive value [NPV]-were calculated with their respective 95% confidence intervals (CIs).
Results:
The final sample included 174 cases (n[Detection Method I] = 157; n[Detection Method II] = 17) and 226 controls. The performance of these methods showed a sensitivity of 0.98 (95% CI, 0.92-0.99) and NPV of 0.99 (95% CI, 0.97-1.00), whereas specificity was 0.72 (95% CI, 0.67-0.77) and PPV was 0.50 (95% CI, 0.43-0.57).
Conclusion:
The observed diagnostic performance metrics indicate that the study's detection methods are effective in identifying relapse episodes in real-world settings; however, further confirmatory procedures are necessary to ensure that the detected cases represent true relapse episodes.
