Identifying Patients With Relapsing-Remitting Multiple Sclerosis Using Algorithms Applied to US Integrated Delivery...

Area of Science:

• Clinical Informatics
• Epidemiology
• Neurology

Background:

• Relapsing-remitting multiple sclerosis (RRMS) significantly impacts patients, necessitating improved understanding through real-world evidence.
• Accurate identification of RRMS patients in real-world data is crucial for clinical research and understanding disease burden.

Purpose of the Study:

• To develop and validate algorithms for identifying patients with RRMS.
• To utilize both unstructured clinical notes from electronic health records (EHRs) and structured healthcare claims data for patient identification.

Main Methods:

• Algorithms were developed to search EHR clinical notes and claims data for adult patients meeting RRMS criteria, excluding progressive MS.
• A cohort of possible multiple sclerosis (MS) patients was established from US Integrated Delivery Network data (2010-2014).
• Manual medical record review was performed for algorithm validation, calculating positive predictive values.

Main Results:

• EHR clinical notes-based algorithms identified 837 RRMS patients with a positive predictive value of 99.1%.
• Claims-based algorithms identified 2271 RRMS patients with positive predictive values ranging from 94.6% to 94.9%.
• 779 patients were identified by both algorithm types.

Conclusions:

• The developed algorithms reliably identify real-world cohorts of RRMS patients.
• These validated algorithms enable confident clinical research on RRMS populations, excluding those with progressive MS.