Scholarlink

Background <p>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.</p> Methods <p>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).</p> Results <p>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).</p> Conclusion <p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月3日

查看全文
  • Almaha Alfakhri,
  • Ohoud Almadani,
  • Adel Alrwisan,
  • Omar Albalawi,
  • Talal Alshihayb,
  • Yasser Albogami,
  • Shymaa Alkahtani,
  • Renad Alkhalifah,
  • Yaser Al Malik,
  • Ahmad Abulaban,
  • Turki Althunian

摘要

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.