Guselkumab and Il-17 Inhibitors Improve Patient-Perceived Impact of Psoriatic Arthritis Similarly: 6 Month Interim Results of the PsABIOnd Observational Cohort Study
Bibliographic record
Abstract
Objectives Targeted drugs in PsA have demonstrated efficacy in randomized controlled trials, including aspects of patient-reported impact. However, comparison data from observational studies are scarce, particularly for IL-23 and IL-17 inhibitors (i). As part of the 6 month (M) interim analysis of the first ≥600 participants (pts) enrolled in the PsABIOnd observational study, we assessed changes from baseline (BL) in PsA Impact of Disease-12 (PsAID12) following biologic treatment initiation. Methods PsABIOnd ( NCT05049798 ) is an ongoing international, prospective study in 1300 planned PsA pts starting guselkumab (GUS) or IL-17i as first- to fourth-line biologic therapy (monotherapy or in combination with other agents) per standard clinical practice.[1] All enrolled pts with available PsAID-12 data at BL and the 6M visit (± 3M) were analyzed according to their treatment group (regardless of later switches). Impact of PsA was assessed with PsAID-12 comprising 12 items (including pain, fatigue, skin problems, etc.) scored 010, with higher values indicating a worse state. Mean change from BL in PsAID-12 subdomain and total scores, and proportions of pts achieving minimal clinically important improvement (MCII, ≥1.4) at the 6M visit were determined. Propensity score (PS) analysis evaluated treatment effect for the change in PsAID-12 total score and MCII (using nonresponder imputation), adjusting for BL imbalances across cohorts. Subdomain analyses were descriptive. Results At the Jan 2024 cutoff date, 323 and 296 pts receiving GUS or IL-17i, respectively, with PsAID-12 data available at BL and the 6M visit were analyzed. In both cohorts, PsAID-12 subdomains with highest impact at BL were pain, fatigue, and discomfort. At the 6M visit, mean (95% confidence interval [CI]) changes from BL in PsAID-12 total score were similar in the GUS (−1.5 [−1.7; −1.3]) and IL-17i (1.6 [1.8; 1.3]) cohorts. PS-adjusted treatment effect (regression coefficient [95% CI]) for GUS vs IL17i in change from BL in PsAID-12 total score was not significant (0.2 [−0.3; 0.6]). Proportions of pts achieving MCII in PsAID-12 total score at the 6M visit were 53% and 48% in the GUS and IL-17i cohorts, respectively, with a non-significant PS-adjusted treatment effect (odds ratio [95% CI]: 1.2 [0.8; 1.8]). Mean changes from BL in subdomain scores were similar across cohorts (Figure 1). Figure 1: Mean change from baseline in PsAID-12 subdomain scores with guselkumab and IL-17i at the 6M visit Last observation carried forward was imputed for participants with no 6M visit. Propensity score-adjusted treatment effect (regression coefficient [95% CI]) for GUS vs IL-17i for pain; fatigue; skin problems; work/leisure activities; functional capacity; discomfort; sleep disturbance; coping; anxiety, fear and uncertainty; embarrassment/shame; social participation; and depression were: 0.4 (−0.2,0.9); 0.1 (−0.5, 0.6); −0.1 (−0.8, 0.6); 0.1 (−0.6, 0.7); 0.1 (−0.5, 0.7); 0.3 (−0.3,1.0); 0.2 (−0.4, 0.9); 0.4 (−0.3,1.0); 0.1 (−0.5, 0.7); −0.01 (−0.7, 0.6); 0.1 (−0.6, 0.8); and −0.1 (−0.6, 0.5), respectively. For participants who switched/stopped initial treatment, data occurring after the switch or stop was excluded from the analysis. Conclusion By 6M of treatment, clinically meaningful improvements in PsAID-12 total score were seen in around half of pts treated with GUS or IL-17i, with similar magnitudes of effect across subdomains in both cohorts. These results may be useful in shared treatment decision-making. [1.] Siebert S. Rheumatol Ther 2023;10:489.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".