Antimicrobial Use and Serious Infections Among Patients With Psoriatic Arthritis After Initiating Tumor Necrosis Factor Inhibitors: A Nationwide Matched Cohort Study
Bibliographic record
Abstract
Objective To analyze antimicrobial use and serious infections (SI) among patients with psoriatic arthritis (PsA) before and after initiating tumor necrosis factor inhibitor (TNFi) treatment. Methods In this nationwide matched cohort study, we extracted data on patients with PsA initiating TNFi from 2005 to 2018 from the ICEBIO registry and matched them by age, sex, and calendar time to 5 randomly selected comparators from the general population. All filled prescriptions for antimicrobials, glucocorticoids, and methotrexate 2 years before and after initiating TNFi treatment were extracted from the Icelandic Prescription Medicines Register. All infection-related hospitalizations, a proxy for SI, were extracted from the Icelandic Hospital Discharge Register. Results The study included 399 PsA patients and 1986 matched comparators. Patients received more antimicrobial prescriptions before TNFi treatment, with a mean number of prescriptions per year of 1.26 vs 0.60 ( P < 0.001). The mean number of prescriptions increased to 1.64 ( P < 0.001) in the 12 months following TNFi initiation but returned to baseline thereafter. No statistically significant increase in SI was found. Adjusted for covariates, patients with PsA had a hazard ratio of 1.43 (95% CI 1.18-1.72, P < 0.001) for receiving a prescription for antimicrobials following TNFi treatment initiation compared to before TNFi treatment. The risk was increased in the first year after treatment initiation but not in the second year, suggesting a transient effect. Conclusion Following TNFi initiation, antimicrobial use rose temporarily, with no significant increase in SI. These results support the safety of TNFi in PsA management, and the risk of SI should not weigh heavily in treatment decisions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".