Critical Determinants of the Risk-benefit Assessment of Antidepressants in Pregnancy: Pharmacokinetic, Safety and Economic Considerations
Bibliographic record
Abstract
Untreated depression in pregnancy may result in adverse health outcomes to both the mother and her unborn child. Pharmacotherapy with antidepressants is the most common treatment option for depression; however, the decision to treat with medication becomes complicated by pregnancy. Risk benefit assessments are critical tools to guide the treatment decision. Factors that should be included in these analyses include the pharmacokinetics and pharmacodynamics of antidepressants in pregnancy and their maternal and fetal safety. The economic cost of untreated maternal depression is also important to keep in mind. When the pharmacokinetics of the antidepressants venlafaxine and bupropion were studied in pregnancy it was found that the apparent oral clearance rate of bupropion was increased in late pregnancy when compared to early pregnancy (p = 0.03). There was a trend for lower area under the curve for these medications when the third trimester was compared to the first trimester. When the metabolism of antidepressants was investigated using hair analysis it was found that there was increased metabolism in pregnancy when compared to the postpartum period for citalopram (p = 0.02) but not venlafaxine (p = 0.77). Follow up of depressive symptoms throughout pregnancy identified that depression scores were highest in the first trimester of pregnancy, which may be due to concurrent nausea and vomiting of pregnancy. A meta-analysis of paroxetine use in early pregnancy demonstrated that there was no increased risk for cardiac malformations; case-control studies had an odds ratio of 1.18 (CI95: 0.88 – 1.59) while a weighted average difference of 0.3% was found in case-control studies (CI95: -0.1 – 0.7%, p = 0.19) The direct medical costs incurred by the Ontario government due to discontinuation of antidepressant medications in pregnancy was estimated to exceed $20,000,000 CAD. The management of depression in pregnancy with pharmacotherapy is an important and complex issue. My study documents the advantages of conducting risk benefit assessments for vulnerable populations such as pregnant women with depression.
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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 | 0.068 | 0.297 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| 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".