PS4:80 Hydroxychloroquine in lupus pregnancy: a meta-analysis of individual participant data
Bibliographic record
Abstract
Purpose Our current knowledge about how to treat lupus in pregnancy derives from small prospective or retrospective cohorts. The goal of this individual participant meta-analysis was to pool data from multiple prospective cohorts to answer the clinical question of whether hydroxychloroquine (HCQ) treatment affects pregnancy outcomes Methods The literature was searched for prospective cohorts of pregnancies among women with lupus. HCQ use was defined as use any time during pregnancy. Outcomes of interest included fetal loss, preterm birth, high disease, and preeclampsia. Data from each cohort were collected and analysed individually. Pooled ORs were calculated by random-effect models in Review Manager. Due to multiple pregnancies per patient, one pregnancy was randomly selected per patient. Primary analysis included only women with first trimester visits (6 cohorts). Subgroup analyses were stratified by a history of nephritis, APS, and disease activity at first clinic visit. Results The current analysis included 591 pregnancies from six cohorts, of which 73% were exposed to HCQ during pregnancy. Fetal loss: Overall, there was a 51% decrease in the risk of fetal loss among patients taking HCQ during pregnancy (OR: 0.49; 95% CI: 0.24 to 1.00). Among patients with a history of lupus nephritis, taking HCQ during pregnancy reduced the risk of fetal loss by 76% (OR: 0.24; 95% CI: 0.07 to 0.83; table 1). Preterm birth: There was no evidence that HCQ decreased the risk of preterm birth. Disease activity: Although not significant, among patients with a history of lupus nephritis, HCQ use during pregnancy may reduce the risk of having high disease activity during pregnancy (OR: 0.47; 95% CI: 0.21 to 1.09). Preeclampsia: Overall, there was no evidence that HCQ decreased the risk of. Among patients with APS, there may be a protective effect of HCQ, but the precision of the estimate was limited (OR: 0.55; 95% CI: 0.12 to 2.45). Conclusion Our results suggest that among patients with lupus nephritis, HCQ use may decrease the risk of fetal loss and decrease high disease activity during pregnancy. The heterogeneity of data collection suggests the need for a unified approach to identify larger cohorts of lupus pregnancies.
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.055 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".