Hydroxychloroquine in the Pregnancies of Women with Lupus: A Meta-Analysis of Individual Participant Data
Bibliographic record
Abstract
Abstract Objective Multiple guidelines recommend continuing hydroxychloroquine (HCQ) for systemic lupus erythematosus (lupus) during pregnancy based on observational data. The goal of this individual patient data meta-analysis was to combine multiple datasets to compare pregnancy outcomes in women with lupus on and off HCQ. Methods Eligible studies included prospectively-collected pregnancies in women with lupus. After a manuscript search, 7 datasets were obtained. Pregnancy outcomes and lupus activity were compared for pregnancies with a visit in the first trimester in women who did or did not take HCQ throughout pregnancy. Birth defects were not systematically collected. This analysis was conducted in each dataset and results were aggregated to provide a pooled odds ratio. Results Seven cohorts provided 938 pregnancies in 804 women. After selecting one pregnancy per patient with a 1 st trimester visit, 668 pregnancies were included; 63% took HCQ throughout pregnancy. Compared to pregnancies without HCQ, those with HCQ had lower rates of highly active lupus, but did not have different rates of fetal loss, preterm birth, or preeclampsia. Among women with low lupus activity, HCQ reduced the risk for preterm delivery. Conclusion This large study of prospectively-collected lupus pregnancies demonstrates a decrease in SLE activity among woman who continue HCQ through pregnancy and no harm to pregnancy outcomes. Like all studies of HCQ in lupus pregnancy, this study is confounded by indication and non-adherence. As this study confirms the safety of HCQ and diminished SLE activity with use, it is consistent with current recommendations to continue HCQ throughout 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 | 0.028 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.061 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".