Healthcare Costs and Resource Utilization in Patients with Multiple Sclerosis Relapses Treated with H.P. Acthar Gel®
Bibliographic record
Abstract
INTRODUCTION: Multiple sclerosis (MS) is an autoimmune disorder with large annual costs. This study evaluated utilization and costs for the management of MS relapses with H.P. Acthar(®) Gel (repository corticotropin injection; Acthar; Mallinckrodt) compared to receipt of plasmapheresis (PMP) or intravenous immunoglobulin (IVIG) among patients with MS who experienced multiple relapses. METHODS: We identified patients with MS diagnoses who had relapses treated with intravenous methylprednisolone (IVMP), the first-line treatment for MS relapse. Patients who were treated for the subsequent relapses were eligible for the study. We analyzed 12- and 24-month healthcare utilization and costs among patients who received Acthar prescriptions compared to patients who were treated with PMP/IVIG using generalized linear and logistic regression models to calculate unadjusted and adjusted means and 95% confidence intervals. RESULTS: For the 12-month analysis, a total of 213 patients received Acthar prescriptions and 226 were treated with PMP or IVIG. Patients who received Acthar prescriptions were similar to those who received other treatments in terms of most demographic variables. Acthar recipients had fewer hospitalizations (0.2 vs. 0.4; P = 0.01) and received fewer outpatient services (29 vs. 43; P < 0.0001) but received more prescription medications (36 vs. 30; P < 0.0001) compared to recipients of PMP/IVIG. Patients who received Acthar prescriptions had lower inpatient and outpatient costs ($15,000 lower; P = 0.001; and $54,000 lower; P < 0.0001, respectively) but similar total costs. Similar results were seen in the cohort with 24 months of outcome data. CONCLUSION: Acthar may be a useful treatment option compared to PMP/IVIG for patients with MS experiencing multiple relapses. FUNDING: This study was funded by a grant to the University of Washington from Mallinckrodt Pharmaceuticals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".