Impact of Antihypertensive Treatment on Maternal and Perinatal Outcomes in Pregnancy Complicated by Chronic Hypertension: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
Background Chronic hypertension complicates around 3% of all pregnancies. There is evidence that treating severe hypertension reduces maternal morbidity. This study aimed to systematically review randomized controlled trials of antihypertensive agents treating chronic hypertension in pregnancy to determine the effect of this intervention. Methods and Results Medline (via OVID ), Embase (via OVID ) and the Cochrane Trials Register were searched from their earliest entries until November 30, 2016. All randomized controlled trials evaluating antihypertensive treatments for chronic hypertension in pregnancy were included. Data were extracted and analyzed in Stata (version 14.1). Fifteen randomized controlled trials (1166 women) were identified for meta‐analysis. A clinically important reduction in the incidence of severe hypertension was seen with antihypertensive treatment versus no antihypertensive treatment/placebo (5 studies, 446 women; risk ratio 0.33, 95% CI 0.19‐0.56; I 2 0.0%). There was no difference in the incidence of superimposed pre‐eclampsia (7 studies, 727 women; risk ratio 0.74, 95% CI 0.49‐1.11; I 2 28.1%), stillbirth/neonatal death (4 studies, 667 women; risk ratio 0.37, 95% CI 0.11‐1.26; I 2 0.0%), birth weight (7 studies, 802 women; weighted mean difference −60 g, 95% CI −200 to 80 g; I 2 0.0%), or small for gestational age (4 studies, 369 women; risk ratio 1.01, 95% CI 0.53‐1.94; I 2 0.0%) with antihypertensive treatment versus no treatment/placebo. Conclusions Antihypertensive treatment reduces the risk of severe hypertension in pregnant women with chronic hypertension. A considerable paucity of data exists to guide choice of antihypertensive agent. Adequately powered head‐to‐head randomized controlled trials of commonly used antihypertensive agents are required to inform prescribing.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".