Impact of Disease Factors of Patients with Psoriasis and Psoriatic Arthritis on Biologic Therapy Switching: Real-World Evidence from the CorEvitas Psoriasis Registry
Bibliographic record
Abstract
Patients with psoriasis (PSO) and psoriatic arthritis (PsA) may frequently switch biologic therapies over the course of treatment because of symptom variability and individual responses. Real-world studies analyzing patient characteristics and clinical factors associated with biologic switching are limited. This longitudinal cohort study used real-world data from the CorEvitas Psoriasis Registry to evaluate the relationship between associated disease factors and biologic switching among patients with PSO and PsA in the United States (US) and Canada following initiation of a biologic. Patients were evaluated between April 2015–August 2022. Combinations of disease severity (as measured by Psoriasis Area Severity Index [PASI]) and Dermatology Life Quality Index (DLQI) as a measure of health-related quality of life (HRQoL) were assessed, and the association with time to switching was calculated using Cox proportional hazards regression modeling. Among 2580 patient-initiations (instances of patients initiating a biologic), 504 (19.5%) switched biologics within 30 months of initiation. Switching was more frequent when either PASI > 10 or DLQI > 5 compared with PASI ≤ 10 or DLQI ≤ 5 at follow-up. Patients with higher skin involvement (PASI > 10) and impact on HRQoL (DLQI > 5) were 14 times more likely to switch (hazard ratio = 14.2, 95% confidence interval: 10.7, 18.9) than those with lower skin involvement (PASI ≤ 10) and HRQoL (DLQI ≤ 5). Patients with PSO and PsA treated in a real-world dermatology setting with substantial disease factors following biologic initiation were more likely to switch therapies. Those with PASI > 10 and DLQI > 5 switched more frequently than those with PASI ≤ 10 and DLQI ≤ 5. Many patients with psoriasis may also have a related condition called psoriatic arthritis. Biologic medications work by helping to reduce inflammation and are commonly used to treat the symptoms of psoriasis and psoriatic arthritis. Patients might not all respond the same way to treatment and may need to change their medications over time. It is important we understand the reasons for switching medications to help patients better manage their symptoms. This study used information from a database on patients with both psoriasis and psoriatic arthritis. The database includes information on patients’ medical history, including when they start and change their medication. We looked at data from patients who switched medications and patients who did not switch medications and examined differences in both how serious a doctor found their disease and the patients’ own opinions of their overall health. We found that patients were more likely to change their biologic medication if they had more difficult psoriasis and psoriatic arthritis symptoms that caused worse skin problems, joint pain, and effects on their overall health compared with patients who had not changed their medication. These results suggest that it is important to consider both how serious a doctor finds their disease and patients’ opinions of how much their symptoms affect their overall health. Understanding the reasons why patients switch medications will help to develop better ways of managing psoriasis and psoriatic arthritis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".