Development and validation of a claims-based algorithm to identify patients with Neuromyelitis Optica Spectrum disorder.
Publication Title
Journal of the neurological sciences
Document Type
Article
Publication Date
8-15-2024
Keywords
Humans; Neuromyelitis Optica; Algorithms; Female; Male; Middle Aged; Adult; Aged; Sensitivity and Specificity; Databases, Factual; Young Adult; United States; Multiple Sclerosis; Acute myelitis; Administrative claims analysis; Medical record review; Multiple sclerosis; Optic neuritis.: oregon; portland
Abstract
INTRODUCTION: No validated algorithm exists to identify patients with neuromyelitis optica spectrum disorder (NMOSD) in healthcare claims data. We developed and tested the performance of a healthcare claims-based algorithm to identify patients with NMOSD.
METHODS: Using medical record data of 101 adults with NMOSD, multiple sclerosis (MS), or myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), we tested the sensitivity and specificity of claims-based algorithms developed through interviews with neurologists. We tested the best-performing algorithm's face validity using 2016-2019 data from IBM MarketScan Commercial and Medicare Supplemental databases. Demographics and clinical characteristics were reported.
RESULTS: Algorithm inclusion criteria were age ≥ 18 years and (≥1 NMO diagnosis [or ≥ 1 transverse myelitis (TM) and ≥ 1 optic neuritis (ON) diagnosis] and ≥ 1 NMOSD drug) or (≥2 NMO diagnoses ≥90 days apart). Exclusion criteria were MS diagnosis or use of MS-specific drug after last NMO diagnosis or NMOSD drug; sarcoidosis diagnosis after last NMO diagnosis; or use of ≥1 immune checkpoint inhibitor. In medical record billing data of 50 patients with NMOSD, 30 with MS, and 21 with MOGAD, the algorithm had 82.0% sensitivity and 70.6% specificity. When applied to healthcare claims data, demographic and clinical features of the identified cohort were similar to known demographics of NMOSD.
CONCLUSIONS: This clinically derived algorithm performed well in medical records. When tested in healthcare claims, demographics and clinical characteristics were consistent with previous clinical findings. This algorithm will enable a more accurate estimation of NMOSD disease burden using insurance claims datasets.
Area of Special Interest
Neurosciences (Brain & Spine)
Specialty/Research Institute
Neurosciences
Specialty/Research Institute
Ophthalmology
DOI
10.1016/j.jns.2024.123110
