OBJECTIVES

: Neuromyelitis optica spectrum disorder (NMO) is a rare auto-immune disease affecting the central nervous system and has clinical characteristics similar to multiple sclerosis (MS), making clinical diagnosis challenging. This study aims to improve the identification of NMO patients by leveraging nferX, a natural language processing platform, to enable encoding of patient claims that are specific to NMO versus MS.

METHODS

: Disease phenotypes associated with NMO or MS were identified from the biomedical literature using the nferX platform. Patients with at least 1 NMO or MS diagnosis code were identified in the IQVIA Pharmetrics Plus database. Accompanying diagnosis, procedure, and drug codes present in greater than 0.1% of patients and at least 2-fold more prevalent in either cohort were mapped to nferX-identified phenotypes. These phenotypes’ relative association with NMO versus MS were quantified using nferX’s adaptation of the pointwise mutual information metric. Associations were aggregated into a summary score for each patient from the number of corresponding claims in the year preceding the initial NMO or MS diagnosis claim. Separation of scores between cohorts of NMO and MS patients identified with existing standard algorithms was assessed.

RESULTS

: A total of 307,166 patients with at least 1 NMO or MS claim between 2006-2018 were identified. 3,132 NMO patients and 102,611 MS patients were selected using previously published algorithms. The Cohen’s d separation of summary scores between these two cohorts was 1.50, suggesting significant separation. By applying a cutoff of one standard deviation above the mean summary score, 14,822 NMO patients were identified, including 13,150 patients without any claims for NMO.

CONCLUSIONS

: Use of the nferX platform has the potential to improve identification of NMO patients from claims data. Further validation of the approach is needed to support its future use.