ArticleBMC medical informatics and decision making2026
Validation of rule-based detection methods for relapse in multiple sclerosis.
Article in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundIdentifying 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.
methodsTwo 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).
resultsThe 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).
conclusionThe 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.
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