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Record W4414135421 · doi:10.2147/oarrr.s538839

Treatment Patterns and Outcomes of Acthar Gel in Ankylosing Spondylitis and Psoriatic Arthritis: A Physician-Reported Chart Review

2025· article· en· W4414135421 on OpenAlexfundno aff
Amit Patel, Priyanka P Shanbhag, Kyle Hayes, Mary Panaccio, George J. Wan

Bibliographic record

VenueOpen Access Rheumatology Research and Reviews · 2025
Typearticle
Languageen
FieldMedicine
TopicSpondyloarthritis Studies and Treatments
Canadian institutionsnot available
FundersMallinckrodt Pharmaceuticals
KeywordsAnkylosing spondylitisChartPsoriasisPsoriatic arthritisCorticosteroidMEDLINE

Abstract

fetched live from OpenAlex

Purpose: To describe the characteristics of patients with ankylosing spondylitis (AS) or psoriatic arthritis (PsA) treated with Acthar Gel, medication utilization, and physicians’ assessments of the effects of Acthar Gel on patients’ health status. Patients and Methods: A prospectively designed, cross-sectional, medical chart review study with a predefined protocol and analysis plan was conducted in November 2024, with data abstracted from patient records between April 2022 and November 2024. Eligible patients were aged ≥ 18 years, had AS or PsA, and had received Acthar Gel within ≤ 24 months. Results: On average, patients with AS were 44 years, and those with PsA were 51 years; patients were primarily Caucasian/non-Hispanic. Most patients with AS were male (67%, 42/63), whereas PsA had a similar gender distribution (49% [38/77] each). Common comorbidities included arthritis/osteoarthritis, chronic joint disease, and hypertension. Before receiving Acthar Gel, physicians reported 41% (26/63) of patients with AS and 44% (34/77) with PsA had fair-to-poor health status. Frequent symptoms in AS were back pain, lower back/hip stiffness, and fatigue, and in PsA were joint swelling and pain, reduced range of motion, and fatigue. Based on physician assessment, 95% (60/63) with AS and 88% (68/77) with PsA had improved health after Acthar Gel treatment. Improvements included reduction in overall symptoms (AS: 70% [42/60]; PsA: 63% [43/68]), decreased pain (AS: 68% [41/60]; PsA: 62% [42/68]), improved physical function (AS: 53% [32/60]; PsA: 54% [37/68], improved fatigue (AS: 35% [21/60]; PsA: 32% [22/68]), and reduced corticosteroid use (AS: 30% [18/60]; PsA: 31% [21/68]). Conclusion: Based on chart review, Acthar Gel may represent a potential treatment option for appropriate patients with AS or PsA. In this study, among patients with AS or PsA treated with Acthar Gel, physicians documented a reduction in overall symptoms, decreased pain, improved physical function, reduced corticosteroid use, improved strength, and improved fatigue using prespecified assessments. Plain Language Summary: People living with ankylosing spondylitis (AS) or psoriatic arthritis (PsA) can still have flares or symptoms even after trying several medicines. Acthar Gel is a prescription medicine that may be used for short periods when other options are insufficient. We wanted to understand how physicians use Acthar Gel in everyday practice and how their patients were doing around that time. We asked rheumatologists to review recent charts for adults with AS or PsA who received Acthar Gel. The physicians reported whether patients’ overall health was better at a set follow-up point and whether common goals, such as less pain or fatigue, better physical function or strength, and lowering steroid use, were met. Physicians reported that many patients experienced overall improvement after starting Acthar Gel, with notable improvements in pain, fatigue, physical function, and strength for some, as well as reduced steroid use for others. These observations describe what physicians observed in their routine practice after prescribing Acthar Gel to their patients. The findings suggest that physicians use Acthar Gel selectively for short-term needs, highlighting areas where future studies could provide more evidence. Keywords: Acthar Gel, ankylosing spondylitis, outcomes, psoriatic arthritis, real-world study

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.317
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.143
GPT teacher head0.499
Teacher spread0.355 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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