Objective:

1) To derive a cohort of patients with myasthenia gravis (MG) from a large United States (US) claims database using an algorithm previously validated in a Medicare population; 2) To describe epidemiology of this patient cohort.

Background:

Epidemiologic studies of MG using reliable case-ascertainment are lacking. A previously validated algorithm identified “definite” MG cases with sensitivity and positive predictive value of 80% among Medicare patients. To our knowledge, it has not been applied to other datasets. Analysis of contemporary care utilization for MG depends on accurate patient identification.

Design/Methods:

We performed a retrospective cohort study using years 2017-20 of Truven Marketscan, an administrative claims database. Adult patients with autoimmune MG were identified by presence of at least 2 MG-related ICD10 codes (G70.0, G70.00 and G70.01) associated with outpatient visits, separated by ≥ 4 weeks; OR ≥ 1 hospitalization and 1 outpatient visit using MG-related ICD codes, separated by ≥ 4 weeks, within 2 years. Exclusion criteria included other neuromuscular junction disorders. Annual incidence was calculated for 2019-20, using a 2 year wash-out period. We derived a non-MG comparison group from a random 1% sample of the total population. 

Results:

Among 47,803,384 patients, we identified 12,121 (0.025%) with MG. MG patients were more likely female (56% vs 51%), age 40-64 (58% vs 46%) and with history of thymoma (2% vs 0.01%). Like the non-MG group, at least 11% lived in rural areas, but a lower proportion of MG patients lived in Western states (12% vs 17%). Total of 1,556/21,935,502 continuously enrolled patients had incident diagnosis of MG 2019-20 (0.007%). 

Conclusions:

An algorithm validated in Medicare patients for MG identification was applied to a private insurer claims database. Incidence and prevalence of MG were calculated and are comparable to previous estimates. The cohort’s healthcare utilization data will be presented.