Patterns of Use and Discontinuation for Tumor Necrosis Factor Inhibitors in Pregnant Women: Insights From a Real-World Sample
Bibliographic record
Abstract
Objective We aimed to evaluate tumor necrosis factor inhibitor (TNFi) usage patterns in pregnant women with rheumatoid arthritis (RA), radiographic axial spondyloarthritis (r-axSpA), psoriatic arthritis (PsA), psoriasis (PsO), and inflammatory bowel diseases (IBD), and to compare corticosteroid use in those discontinuing TNFi at specific gestational timepoints vs those continuing throughout gestation. Methods We analyzed pregnancies resulting in a live birth among women with RA, r-axSpA, PsA, PsO, and/or IBD, aged 15-45 years, who were hospitalized for delivery between January 2011 and December 2021, using the MarketScan commercial claims database. TNFi exposure was defined as ≥ 1 filled prescription or infusion procedure claim for a TNFi. Timing of exposure was categorized by the gestational period and specific trimesters, with a grace period of 5 half-lives added to each prescription to account for ongoing biological activity. Results We identified 3711 pregnancies exposed to TNFi among 49,925 women with RA, r-axSpA, PsA, PsO, and/or IBD. Of the 3711 pregnancies, 64% had continuous TNFi use throughout all 3 trimesters. Most (89%) of TNFi-exposed pregnancies had preconception exposure, and 68% of the women continued TNFi postpartum. The proportion of pregnancies with TNFi use throughout all trimesters increased from 55% in 2011-2013 to 73% in 2020-2021 ( P for trend < 0.001). Corticosteroid use during pregnancy/postpartum was less frequent in pregnancies exposed to TNFi throughout gestation vs those exposed in the first and/or second trimester only. Conclusion We observed trends toward increased continuous TNFi use throughout gestation (and fewer corticosteroids in this group), suggesting growing confidence in the safety and effectiveness of TNFi use in pregnancy.
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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 | 0.006 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".