Risk of Treatment Discontinuation among Patients with Psoriasis Initiated on Ustekinumab and Other Biologics in the USA
Bibliographic record
Abstract
INTRODUCTION: Biologics are a standard therapy for patients with moderate-to-severe psoriasis, yet treatment persistence is essential to achieve disease control. Compared with other biologics, ustekinumab has been associated with lower rates of discontinuation and better adherence among patients with psoriasis, but prior studies have included limited data from the period after approval of self-administration for ustekinumab. This study was conducted to assess discontinuation risk among patients with plaque psoriasis initiating ustekinumab or other biologics. METHODS: Adults with psoriasis and one or more claim for ustekinumab, secukinumab, adalimumab, or ixekizumab were identified in Optum's de-identified Clinformatics Data Mart Database (1 January 2010 to 30 June 2019). Treatment discontinuation was defined as a gap in days of therapy supply based on (1) each drug's per-label frequency of administration (main analysis) or (2) > 90 days (sensitivity analysis). Differences in baseline characteristics between the ustekinumab and other cohorts were adjusted with entropy balancing. Risk of discontinuation was compared with Cox proportional hazard models. RESULTS: Overall, 2230 patients were included in the ustekinumab cohort, with 1807 in the secukinumab, 4483 in the adalimumab, and 535 in the ixekizumab cohorts (mean age 49.0 years, 49.3% female for all cohorts). In the main analysis, risk of discontinuation for the ustekinumab cohort was 62.2% lower than for adalimumab, 46.4% lower than for secukinumab, and 43.8% lower than for ixekizumab cohorts (all p < 0.001). Sensitivity analyses revealed no significant differences between the ustekinumab and other cohorts. CONCLUSIONS: Patients with psoriasis initiating ustekinumab had lower risk of treatment discontinuation compared with other biologics when discontinuation was based on each drug's per-label frequency of administration. This finding may help inform choice of biologic based on compliance.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".