Abstract Details

Title Pre-diagnosis Predictors of High Health Care Cost Among Patients with Myasthenia Gravis Using a Combined Machine-learning and Regression Approach

Topic Neuromuscular and Clinical Neurophysiology (EMG)

Presentation(s) P4 - Poster Session 4 (11:45 AM-12:45 PM)

Poster/Presentation
Number 11-011

Objective To identify predictors of high costs among patients with generalized myasthenia gravis (gMG)

Background MG is an autoantibody disorder with high patient and health system burden, reflecting unmet needs. This study used random forest (RF) and Poisson regression methods to identify predictors of high costs in patients newly diagnosed with gMG.

Design/Methods Adults with gMG were identified from the IQVIA PharMetrics® Plus database (01/2017-12/2021) using MG diagnoses and physician specialty. The first MG diagnosis was the index date. Demographic and clinical characteristics were described during the 12-month baseline period, and monthly all-cause healthcare costs (2021USD) were estimated post-index. Those with costs in the top 15^th percentile (≥$9,405) were defined as having high costs. An RF model using baseline characteristics was built to identify the top 50 predictors of high costs based on Shapley values; a modified Poisson regression was used to determine the magnitude and direction of each predictor.

Results 2,739 patients with gMG were selected (mean age: 56.2 years; female: 50.6%). The RF model had good predictive power. The most important predictors were inpatient admissions, MG with exacerbation on the index date, number of outpatient visits, corticosteroid use, diagnosing physician specialty, and comorbidities (diabetes, fluid and electrolyte disorders, DSM-5). Adjusting for all top 50 predictors in the regression model, MG diagnosed by a cardiologist increased the risk of high costs by 177% compared to those diagnosed by a neurologist; index MG exacerbation increased the risk by 78%; metastatic cancer by 74%; weakness or fatigue by 54%; other autoimmune disorder by 42%; diabetes by 36%; every 10 claims of corticosteroids by 33%; inpatient admission by 27% (all p<0.05).

Conclusions This analysis shows the complexity of patients with gMG and the high degree of multimorbidity. By targeting amenable characteristics, healthcare providers can reduce the clinical and economic burden of gMG.

Authors/Disclosures
Jacqueline Pesa (Janssen) PRESENTER	Jacqueline Pesa has received personal compensation for serving as an employee of Johnson and Johnson.
Maryia Zhdanava	Maryia Zhdanava has received personal compensation for serving as an employee of Analysis Group.
Porpong Boonmak (Analysis Group, Inc.)	Porpong Boonmak has received personal compensation for serving as an employee of Analysis Group, Inc..
Samuel Schwartzbein (Analysis Group)	No disclosure on file
Qian Cai	Qian Cai has received personal compensation for serving as an employee of Johnson & Johnson. Qian Cai has stock in Johnson & Johnson.
Dominic Pilon (Analysis Group, Inc.)	Dominic Pilon has received personal compensation for serving as an employee of Analysis Group, Inc..
Zia U. Choudhry, MD, PhD (JOHNSON AND JOHNSON)	Dr. Chaudhry has received personal compensation for serving as an employee of Johnson & Johnson. Dr. Chaudhry has received personal compensation for serving as an employee of Takeda Pharmaceuticals.
Marie-Helene Lafeuille	No disclosure on file
Patrick Lefebvre	No disclosure on file
Nizar Souayah, MD, FAAN (NJMS)	Dr. Souayah has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Takeda. Dr. Souayah has received publishing royalties from a publication relating to health care.

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