Incidence and Predictors of Secondary Failure to Biologic Therapy in Patients With Psoriatic Arthritis
Bibliographic record
Abstract
OBJECTIVE: Secondary failure to biologic disease-modifying antirheumatic drugs (bDMARDs) is challenging and contributes to the complexity of managing psoriatic arthritis (PsA). We aimed to define the frequency and incidence of this phenomenon in PsA and identify the risk factors for its occurrence. METHODS: We retrieved data on patients with PsA from our single-center, specialized-care, prospective observational cohort who initiated and remained on bDMARDs for ≥ 1 year after clinic enrollment between 2000 and 2023. We defined response to therapy at the 1-year visit (baseline) as achievement of ≥ 40% reduction in the swollen joint count (SJC) and either ≥ 50% reduction in Psoriasis Area and Severity Index (PASI) or PASI ≤ 2. We defined secondary failure as the inability to maintain response criteria or as the clinician's judgment of loss of effectiveness. To examine factors associated with secondary failure, we fitted Cox regression models. RESULTS: Of 482 patients included in the study, 264 (54.8%) were responders at 1 year. Of these, 94 (35.6%) developed secondary failure at a median of 1.6 (IQR 0.7-3.8) years from response. In the multivariable model, higher SJC (hazard ratio [HR] 1.39, 95% CI 1.05-1.84) and PASI (HR 1.14, 95% CI 1.01-1.29) at baseline were associated with secondary failure. Tumor necrosis factor inhibitors (TNFi) vs other bDMARD use (HR 0.39, 95% CI 0.18-0.88), initiation as first-line bDMARD (HR 0.48, 95% CI 0.25-0.91), and treatment initiation during more recent calendar years (HR 0.34, 95% CI 0.12-0.98) were associated with less secondary failure. CONCLUSION: Secondary failure to bDMARD is common in PsA and may be influenced by both disease- and therapy-related factors.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".