Pregnancy outcomes of non-tumour necrosis factor inhibitor biologic disease-modifying antirheumatic drugs among individuals with autoimmune diseases: a scoping review and proposed framework for reporting outcomes
Bibliographic record
Abstract
Objectives: Biologic disease-modifying antirheumatic drugs (bDMARDs) have transformed autoimmune disease treatment. Although the perinatal impacts of tumour necrosis factor inhibitors (TNFis) have been systematically synthesised, comparable evidence syntheses for non-TNFi bDMARDs are lacking. We conducted a scoping review to synthesise evidence on the impact of non-TNFi bDMARDs on pregnancy outcomes. Methods: We searched Embase, MEDLINE, and CENTRAL databases in November 2023. We included studies among individuals with chronic autoimmune disease that examined non-TNFi bDMARD exposure in mothers during pregnancy, fathers prior to conception, and/or foetuses/neonates in utero. We extracted data on sample size, study design, drug exposure (dose and duration), pregnancy outcomes, and synthesised patterns in methodologic reporting. Results: Of 6712 studies screened, 135 were included (38 case reports, 25 case series, 40 cross-sectional, and 32 analytical) among patients with inflammatory bowel disease, psoriasis, and rheumatoid arthritis. Ustekinumab, vedolizumab, and tocilizumab were the most studied drugs. The analytical studies assessed 41 pregnancy outcomes, showing significant associations between non-TNFis and preterm birth, congenital anomalies, and miscarriage. For methodologic gaps, we identified substantial heterogeneity across reporting of sample size units, drug exposure (timing and duration), and pregnancy outcomes. In response, we developed a Reproductive Health Outcomes Reporting Framework to standardise reporting according to maternal, foetal/neonatal, and foetal/neonatal-maternal outcomes. Conclusions: Our scoping review shows that the evidence base for the perinatal impacts of non-TNFi DMARDs is growing. Analytic findings identified evidence gaps that limit informed decision-making for patients and providers around pregnancy. Methodologic findings informed our recommendations for improving reporting practices, thereby enhance the comparability and interpretability of results in future perinatal pharmacoepidemiologic research.
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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 | 0.119 | 0.359 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.041 | 0.040 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| 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".