Princic, Nicole

Abstract Disclosure: L. Miller-Wilson: Employee of Immunovant, Inc. N. Princic: Employee of Merative. K. Evans: Employee of Merative. N. Atreja: Employee of Immunovant, Inc. K. Tollefsen: Employee of Immunovant, Inc.. D. Toro-Tobón: None. Objectives: Graves’ disease (GD) is an autoimmune condition responsible for most cases of hyperthyroidism in the United States. An estimated 20%-50% of patients with GD will also develop thyroid eye disease (TED). Current treatments for GD focus on managing hyperthyroidism, but frequent monitoring, dose adjustments, and the high rate of relapse after discontinuation of antithyroid drugs can increase treatment costs and complicate long-term management. This study describes healthcare resource utilization (HCRU) and costs among patients with GD, with and without TED. Methods: This retrospective analysis utilized claims data from the Merative™ MarketScan® Research Databases. Individuals aged ≥18 years with ≥2 claims for a GD diagnosis from January 1, 2017-December 31, 2023 were included (first claim = index). To ensure a newly diagnosed cohort, patients with a history of GD-specific treatment or a GD diagnosis in the 12 months preceding the index were excluded. The final GD cohort was stratified into patients who had evidence of TED at any time during the 12-month pre-index period through 90 days post-index, and those who did not. Propensity score matching was used to balance patients with GD (with and without TED) with non-GD controls (1:3) on demographic and baseline clinical characteristics. Both GD cases (with and without TED) and controls were required to have ≥12 months of post-index follow-up. HCRU and costs were compared between cases and controls during a fixed 12-month post-index period. Categorical outcomes were compared using chi-squared tests, while continuous endpoints were compared using paired t-tests. Results: The analysis included 1,767 GD cases with TED matched with 5,301 controls (mean age, 53.1 years [standardized mean difference (SMD), 0.00]; 77% female [SMD, 0.00]; mean Charlson comorbidity score, 0.9 [SMD, 0.02]) and 43,120 GD cases without TED matched with 129,360 controls (mean age, 49.8 years [SMD, 0.00]; 78% female [SMD, 0.00]; mean Charlson comorbidity score, 0.7 [SMD, 0.01]). Compared with matched controls, patients with GD and TED had 78% more inpatient visits (0.16 vs 0.09), 104% more outpatient visits (65.06 vs 31.86), and 43% more prescriptions (30.21 vs 21.13) during the 12-month post-index period, while patients with GD without TED had 75% more inpatient visits (0.14 vs 0.08), 83% more outpatient visits (52.15 vs 28.48), and 38% more prescriptions (24.27 vs 17.56) (p<0.001 for all). Annual mean healthcare costs were 305% higher ($56,584 vs. $13,981) among GD patients with TED and 65% higher ($19,913 vs $12,076) in those without TED compared with matched controls. Conclusion: Patients with GD had significantly higher HCRU and healthcare costs than matched controls in the 12 months following diagnosis, and this difference was notably greater among GD patients who also had TED. More effective treatments are needed to address the health and financial burden of GD. Presentation: Monday, July 14, 2025

e16286 Background: GEP-NET is a rare disease with variable tumor biology. An indolent disease course with subtle symptoms renders timely diagnosis difficult; 2/3 of patients (pts) are diagnosed at locally advanced or metastasized stage. In clinical decision-making, disease-specific factors, patient (pt) age and long-term disease control must be balanced against the aggressiveness of therapies. NCCN, NANETS and ENETS guidelines recognize the diversity of mGEP-NET by recommending individualized treatment approaches. We aimed to describe treatment (Tx) utilization patterns in mGEP-NET. Methods: Retrospective cohort study using MarketScan® Commercial and Medicare Databases spanning 07/01/2017-01/31/2023. Eligible were GEP-NET pts with ≥1 inpatient or ≥2 outpatient claims, a subsequent new diagnosis of metastatic disease (index), no secondary malignancy, and follow-up time of ≥12 months (m) after index. Results: Of 16218 GEP-NET pts, 2002 (mean age 60y; 50% female) met inclusion criteria. Mean follow-up time after index was 17m, with 49%, 27% and 15% followed up for ≥12, 24 and 36m, resp. Medical history (12m baseline before index) included mean Charlson Comorbidity Index of 2.62, with hypertension (HTN), type 2 diabetes (T2DM), chronic liver disease (CLD), asthma/COPD, cardiovascular disease (CVD), and chronic kidney disease (CKD) present in 56, 26, 22, 18, 17 and 10% of pts, resp.; 5% had carcinoid syndrome. In total, 949 pts (47.4%) received first-line (1L) systemic Tx: somatostatin analogs (SSA; 61.6%) of either very short or long duration (median 116d vs 775d), chemotherapy (38.8%), targeted therapies (3.4%) and PRRT (0.5%). Relative to NCCN cancer surveillance guidelines, 49.5% received more and 36.8% received fewer than 4 scans in the first year of treatment. Conversely, 1053 (52.6%) did not receive any 1L systemic Tx. With the exception of CLD, pts with vs without 1L Tx had similar proportions of medical conditions during the 12m baseline and during follow-up (HTN 72.4 vs 66.5%; T2DM 34.0 vs 30.3%; asthma/COPD 16.5 vs 19.3%; CVD 27.3 vs 36.9%; CKD 18.9 vs 17.9%; however, CLD 54.0 vs 36.7%). See Table comparing both cohorts on insurance, treatments, and health care utilization during follow-up. Conclusions: This claims analysis reveals treatment differences among mGEP-NET pts that cannot be explained by the parameters assessed herein. Further research should investigate factors influencing diagnosis and treatment decision-making, applying also pt-level health record data; and analyze economic barriers to diagnosis and treatment. [Table: see text]

Aim To compare healthcare resource use and costs between newly diagnosed multiple myeloma (NDMM) patients with and without skeletal-related events (SREs). Methods Adults newly diagnosed with MM (1 January 2006 and 30 June 2017) with at least 12 months continuous health coverage prior to diagnosis were identified using the IBM MarketScan administrative claims. To control for baseline differences, NDMM patients with SREs were propensity score matched to NDMM patients without SREs. Outcomes included annual HRU and costs during follow-up along with number and type of SREs (SRE cohort only). Patients with SREs were stratified by number of SREs, and annual SRE-related costs were reported. Student's t test and Chi-squared test were used to compare outcomes. Results Before matching, the 6648 patients in the SRE cohort had more comorbidities, were more likely to have MM treatment, and had higher pre-index healthcare costs than the 7458 patients in the non-SRE cohort. After matching, cohorts of 3432 patients were well balanced on baseline characteristics. Patients with SREs (vs. without SREs) had significantly higher inpatient, outpatient, and pharmacy HRU. Patients with SREs had significantly higher mean annual all-cause healthcare costs ($213,361 vs. $94,896, p < 0.001) with hospitalization being the leading driver of increased costs (38.7% of total). Among 6648 patients with SREs, the mean annual SRE-related healthcare costs were $39,603, $45,463, and $50,111 for patients with one, two, and three or more events, respectively. Conclusions NDMM patients with SREs have more than twice the all-cause healthcare costs than matched patients without SREs. Costs increase with the number of SRE events.