Hydroxychloroquine Levels throughout Pregnancies Complicated by Rheumatic Disease: Implications for Maternal and Neonatal Outcomes
Bibliographic record
Abstract
OBJECTIVE: Pregnancies in women with active rheumatic disease often result in poor neonatal outcomes. Hydroxychloroquine (HCQ) reduces disease activity and flares; however, pregnancy causes significant physiologic changes that may alter HCQ levels and lead to therapeutic failure. Therefore, our objective was to evaluate HCQ concentrations during pregnancy and relate levels to outcomes. METHODS: We performed an observational study of pregnant patients with rheumatic disease who were taking HCQ from a single center during 2013-2016. Serum samples were analyzed using high-performance liquid chromatography/mass spectrometry. Primary HCQ exposure was categorized as nontherapeutic (≤ 100 ng/ml) or therapeutic (> 100 ng/ml). Categorical outcomes were analyzed using Fisher's exact test and continuous outcomes using linear regression models, Wilcoxon signed-rank test, Kruskal-Wallis test, t test, and ANOVA. RESULTS: We analyzed 145 samples from 50 patients with rheumatic disease, 56% of whom had systemic lupus erythematosus (SLE). HCQ concentration varied widely among individuals at each trimester. Mean physician's global assessment scores in patients with SLE were significantly higher in those with average drug levels ≤ 100 ng/ml compared to > 100 ng/ml (0.93 vs 0.32, p = 0.01). Of patients with SLE, 83% with average drug levels ≤ 100 ng/ml delivered prematurely (n = 6), compared to only 21% with average levels > 100 ng/ml (n = 19; p = 0.01). HCQ levels were not associated with prematurity or disease activity in non-SLE patients. CONCLUSION: With both high and low HCQ levels associated with preterm birth and disease activity in SLE, further study is necessary to understand HCQ disposition throughout pregnancy and to clarify the relationship between drug levels and outcomes.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".