Prevalence and predictors of tumour necrosis factor inhibitor persistence in psoriatic arthritis
Bibliographic record
Abstract
Objectives: To evaluate TNF-α inhibitor (TNFi) persistence when used as first- or second-line biologic therapy for the management of PsA, and to determine baseline clinical and laboratory parameters associated with TNFi persistence. Methods: A retrospective single-centre cohort study was performed on all patients with PsA initiated on TNFi therapy between 2003 and 2015. Demographic, clinical and laboratory characteristics were compared with TNFi persistence, using Kaplan-Meier survival and Cox proportional hazards models. Results: One hundred and eighty-eight patients with PsA were prescribed TNFi therapy as first-line biologic therapy over a period of 635 person-years [46% male, mean (s.d.) age 47.3 (11.4) years; median (interquartile range) disease duration 11 (7-16) years]. At 12 months of follow-up 79% of patients persisted with TNFi therapy, and 73% at 24 months. Of those discontinuing TNFi, 35% stopped due to primary inefficacy, 22% secondary inefficacy and 43% adverse events. Multivariable analysis identified female sex (hazard ratio (HR) 2.57; 95% CI: 1.26, 5.24; P = 0.01) and the presence of metabolic syndrome-related co-morbidities (HR = 2.65, 95% CI: 1.24, 5.69; P = 0.01) as predictors of lower persistence. Of 32 cases treated with a second TNFi, persistence at 12 months was 56%. TNFi persistence was 2-fold less likely in these 32 cases compared with first-line TNFi users (HR = 2.02, 95% CI: 1.20, 3.42; P = 0.01). Conclusion: Patients with PsA who are female and have metabolic syndrome-related co-morbidities have lower TNFi persistence. Although persistence was lower in patients who had switched to a second TNFi, a substantial proportion of these cases responded, advocating switching to a second TNFi as a valid therapeutic strategy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".