Chloroquine and Hydroxychloroquine Use During Pregnancy and the Risk of Adverse Pregnancy Outcomes Using Real-World Evidence
Bibliographic record
Abstract
Introduction: Chloroquine (CQ) and hydroxychloroquine (HCQ) are currently used for the prevention/treatment of malaria, and treatment of systemic lupus erythematosus (SLE), and rheumatoid arthritis (RA). Although present data do not show their efficacy to treat COVID-19, they have been used as potential treatments for COVID-19. Given that pregnant women are excluded from randomized controlled trials, and present evidence are inconsistent and inconclusive, we aimed to investigate the safety of CQ or HCQ use in a large pregnancy cohort using real-world evidence. Methods: Using Quebec Pregnancy Cohort, we identified women who delivered a singleton liveborn, 1998–2015, ( n = 233,748). The exposure time window for analyses on prematurity and low birth weight (LBW) was the second/third trimesters; was any time during pregnancy; only first trimester exposure was considered for analyses on major congenital malformations (MCM). The risk of prematurity, LBW, and MCM (overall and organ-specific) were quantified using generalized estimation equations. Results: We identified 288 pregnancies (0.12%) exposed to CQ (183, 63.5%) or HCQ (105, 36.5%) that resulted in liveborn singletons; CQ/HCQ was used for RA (17.4%), SLE (16.3%) or malaria (0.7%). CQ/HCQ was used for 71.8 days on average [standard-deviation (SD) 70.5], at a dose of 204.3 mg/d (SD, 155.6). We did not observe any increased risk related to CQ/HCQ exposure for prematurity (adjusted odds ratio [aOR] 1.39, 95%CI 0.84–2.30), LBW (aOR 1.11, 95%CI 0.59–2.06), or MCM (aOR 1.01, 95%CI 0.67–1.52). Conclusion: in this large CQ/HCQ exposed pregnancy cohort, we saw no clear increased risk of prematurity, LBW, or MCM, although number of exposed cases remained low.
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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 | 0.001 |
| 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 | 0.001 |
| 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".