Predictors of High Healthcare Cost Among Patients with Generalized Myasthenia Gravis: A Combined Machine Learning and Regression Approach from a US Payer Perspective

Background High healthcare costs could arise from unmet needs. This study used random forest (RF) and regression methods to identify predictors of high costs from a US payer perspective in patients newly diagnosed with generalized myasthenia gravis (gMG). Methods Adults with gMG (first diagnosis = index) were selected from the IQVIA PharMetrics® Plus database (2017–2021). Predictors of high healthcare costs were measured 12 months pre-index (main cohort) and during both the 12 months pre- and post-index (subgroup). Top 50 predictors of high costs [≥ $9404 (main cohort) and ≥ $9159 (subgroup) per-patient-per-month] were identified with RF models; the magnitude and direction of association were estimated with multivariable modified Poisson regression models. Results The main cohort and subgroup included 2739 and 1638 patients, respectively. In RF analysis, the most important predictors of high costs before/on the index date were index MG exacerbation, all-cause inpatient admission, and number of days with corticosteroids. After the index date, these were immunoglobulin and monoclonal antibody use and number of all-cause outpatient visits and MG-related encounters. Adjusting for the top 50 predictors, post-index immunoglobulin use increased the risk of high costs by 261%, monoclonal antibody use by 135%, index MG exacerbation by 78%, and pre-index all-cause inpatient admission by 27% (all p