Tumor Necrosis Factor Inhibitors and Risk of Serious Infections in Pregnant Women With Chronic Inflammatory Diseases
Bibliographic record
Abstract
OBJECTIVE: Tumor necrosis factor inhibitors (TNFi) are used by over 20% of pregnant women with chronic inflammatory diseases, which could further impede immune function and increase the risk of infections that require hospitalization. We assessed the risk of serious infections during pregnancy and postpartum between women exposed and unexposed to TNFi with chronic inflammatory diseases. METHODS: Using MarketScan, we identified pregnant women with chronic inflammatory diseases and modeled TNFi exposure during pregnancy and postpartum as a time-varying variable. Cox proportional hazards models estimated adjusted hazard ratios (HRs) for TNFi and the risk of hospitalized infection. RESULTS: We followed a total of 62,813 women who had 70,529 pregnancies and 69,412 births. Among these, 4,485 (7.1%) women were exposed to at least one TNFi prescription during pregnancy and 3,559 women during postpartum. Overall, 449 women were hospitalized for infection during pregnancy, including 31 pregnant women who were exposed to TNFi. During postpartum, 205 women had hospitalized infection, of which 17 women were TNFi-exposed. Compared with no TNFi exposure, TNFi treatment during pregnancy was associated with a HR of 1.39 (95% confidence interval [CI], 0.95-2.05) for serious infections, whereas the HR during postpartum was 1.22 (95% CI, 0.72-2.06). CONCLUSION: In this population-based study, we found no statistically significant association between TNFi exposure during pregnancy and serious infection risk. Although point estimates were higher for pregnant women exposed to TNFi, CIs were wide and included the null, indicating that an increased risk cannot be ruled out. Given the frequency of TNFi treatment for pregnant women, these results support continued investigation and may inform counseling regarding TNFi treatment during pregnancy and postpartum.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".