Identifying Patients With Relapsing-Remitting Multiple Sclerosis Using Algorithms Applied to US Integrated Delivery Network Healthcare Data

• Published in: Value in health 2019-01, Vol.22 (1), p.77-84
• Main Authors: Van Le, Hoa, Le Truong, Chi Thi, Kamauu, Aaron W.C., Holmén, John, Fillmore, Christopher, Kobayashi, Monica G., Martin, Canter, Sabidó, Meritxell, Wong, Schiffon L.
• Format: Article
• Language: English
• Subjects: Administrative Claims, Healthcare; Adult; Aged; algorithm; Algorithms; claims; Data Mining - methods; Databases, Factual; Delivery of Health Care, Integrated; Electronic Health Records; Female; Humans; Immunologic Factors - therapeutic use; International Classification of Diseases; Male; Middle Aged; multiple sclerosis; Multiple Sclerosis, Relapsing-Remitting - classification; Multiple Sclerosis, Relapsing-Remitting - diagnosis; Multiple Sclerosis, Relapsing-Remitting - drug therapy; relapsing-remitting multiple sclerosis; Reproducibility of Results; Retrospective Studies; United States
• ISSN: 1098-3015; 1524-4733
• DOI: 10.1016/j.jval.2018.06.014

Relapsing-remitting multiple sclerosis (RRMS) has a major impact on affected patients; therefore, improved understanding of RRMS is important, particularly in the context of real-world evidence. To develop and validate algorithms for identifying patients with RRMS in both unstructured clinical notes found in electronic health records (EHRs) and structured/coded health care claims data. US Integrated Delivery Network data (2010–2014) were queried for study inclusion criteria (possible multiple sclerosis [MS] base cohort): one or more MS diagnosis code, patients aged 18 years or older, 1 year or more baseline history, and no other demyelinating diseases. Sets of algorithms were developed to search narrative text of unstructured clinical notes (EHR clinical notes–based algorithms) and structured/coded data (claims-based algorithms) to identify adult patients with RRMS, excluding patients with evidence of progressive MS. Medical records were reviewed manually for algorithm validation. Positive predictive value was calculated for both EHR clinical notes–based and claims-based algorithms. From a sample of 5308 patients with possible MS, 837 patients with RRMS were identified using only the EHR clinical notes–based algorithms and 2271 patients were identified using only the claims-based algorithms; 779 patients were identified using both algorithms. The positive predictive value was 99.1% (95% confidence interval [CI], 94.2%–100%) for the EHR clinical notes–based algorithms and 94.6% (95% CI, 89.1%–97.8%) to 94.9% (95% CI, 89.8%–97.9%) for the claims-based algorithms. The algorithms evaluated in this study identified a real-world cohort of patients with RRMS without evidence of progressive MS that can be studied in clinical research with confidence.