Application of a Validated Algorithm to Identify and Describe Contemporary Epidemiology of Privately Insured Myasthenia Gravis Patients in the United States (N6.002)

Abstract

<h3>Objective:</h3> 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. <h3>Background:</h3> 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. <h3>Design/Methods:</h3> 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. <h3>Results:</h3> 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%). <h3>Conclusions:</h3> 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. <b>Disclosure:</b> Miss Hua has nothing to disclose. Dr. Semenov has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Regeneron. Dr. Semenov has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Galderma. Dr. Semenov has received personal compensation in the range of $500-$4,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for Journal of the American Academy of Dermatology. The institution of Dr. Semenov has received research support from Dermatology Foundation. An immediate family member of Dr. Guidon has received personal compensation for serving as an employee of GE Healthcare. Dr. Guidon has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Alexion. Dr. Guidon has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Momenta. Dr. Guidon has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Argenx. The institution of Dr. Guidon has received research support from MGFA. The institution of Dr. Guidon has received research support from Project Data Sphere. The institution of Dr. Guidon has received research support from MGNet. The institution of Dr. Guidon has received research support from MGNet. Dr. Guidon has received publishing royalties from a publication relating to health care.