SSRI and SNRI use during pregnancy and the risk of persistent pulmonary hypertension of the newborn
Bibliographic record
Abstract
AIM: The use of selective serotonin reuptake inhibitors (SSRIs) in late pregnancy may be associated with an increased risk of persistent pulmonary hypertension of the newborn (PPHN). Limited data are available on the risk of PPHN associated with serotonin norepinephrine reuptake inhibitors (SNRIs). We aimed to quantify both associations. METHODS: Using data from the Quebec Pregnancy Cohort between 1998 and 2009, we included women covered by the provincial drug plan who had a singleton live birth. Exposure categories were SSRI, SNRI and other antidepressant use; non-users were considered as the reference category. Generalized estimating equation models were used to obtain risk estimates and 95% confidence intervals (CIs). Confounding by indication was minimized by adjusting for history of maternal depression/anxiety before pregnancy. RESULTS: Overall, 143 281 pregnancies were included; PPHN was identified in 0.2% of newborns. Adjusting for maternal depression, and other potential confounders, SSRI use during the second half of pregnancy was associated with an increased risk of PPHN [adjusted odds ratio (aOR) 4.29, 95% CI 1.34, 13.77] compared with non-use of antidepressants; SNRI use during the same time window was not statistically associated with the risk of PPHN (aOR 0.59, 95% CI 0.06, 5.62). Use of SSRIs and SNRIs before the 20th week of gestation was not associated with the risk of PPHN. CONCLUSIONS: Use of SSRIs in the second half of pregnancy was associated with the risk of PPHN. Given our results on SNRIs and the lack of statistical power for these analyses, it is unclear whether SNRI use during pregnancy also increases the risk of PPHN.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".