Antihypertensive therapy for pregnancy hypertension and implications for fetal and neonatal heart rate monitoring: A systematic review of randomized trials and observational studies
Bibliographic record
Abstract
Abstract Introduction Our objective was to evaluate whether antihypertensives affect fetal (FHR) or neonatal (neoHR) heart rate. Material and methods Electronic databases and clinical trial registers were searched to August 31, 2024. Eligibility included randomized (RCTs) or observational studies evaluating antihypertensives for pregnancy hypertension. Two reviewers independently assessed studies for inclusion and extracted data. Random effects meta‐analysis was used to determine risk ratios (RRs) and 95% confidence intervals (CIs). Network meta‐analysis was undertaken in a sensitivity analysis. Results Fifty‐four RCTs (n = 5736 pregnancies) and 28 observational studies (n = 2 283 855) reported FHR (usually visually‐interpreted) or neoHR (usually clinically‐assessed). FHR: Non‐Severe Hypertension Antihypertensives did not increase adverse FHR effects in RCTs of antihypertensives versus placebo/no therapy (RR = 1.08, 95% CI [0.62–1.89]; I2 = 43%; N = 10, n = 1567 pregnancies), antihypertensives versus methyldopa (RR = 1.40 [0.97–2.04]; I2 = 0%; N = 6, n = 515), or labetalol or pure beta‐blockers versus other antihypertensives (RR = 1.70 [0.96–2.99]; I2 = 30%; N = 5, n = 501). In observational studies, adverse FHR effects were more common with: labetalol versus methyldopa, nifedipine or Chinese herbal medication (RR = 2.17 [1.15–4.08]; I2 = 47%; N = 4, n = 664), and bendroflumethiazide versus metoprolol (but not hydralazine), but 95% CIs were wide. FHR: Severe Hypertension Antihypertensives had no FHR effects in RCTs of antihypertensives versus either: placebo/no therapy (RR = 0.43 [0.16–1.20]; I2 = 0%; N = 3, n = 242), hydralazine (RR = 0.71 [0.29–1.72]; I2 = 13%; N = 11, n = 727), or CCBs (RR = 0.52 [0.12–2.16]; I2 = 0%, N = 9, n = 1675). In observational studies, there was no difference for labetalol versus other antihypertensives (RR = 0.34 [0.10–1.14], I2 = 87%; N = 4, n = 590), with heterogeneity due to a lower‐quality labetalol versus hydralazine study. There were fewer adverse FHR effects for nifedipine versus hydralazine study (RR = 0.09 [0.01–0.68]; n = 49). NeoHR: Severe Hypertension RCTs of antihypertensives versus placebo/no therapy were not associated with adverse neoHR effects (RR = 1.26 [0.31–5.19]; I2 = 66%; N = 4, n = 406), with heterogeneity attributed to more neoHR effects with continuously monitored neoHR. Observational studies revealed no effect on neoHR of antihypertensives versus no therapy (RR = 1.06 [0.67–1.67]; I2 = 54%; N = 4, n = 37 359), but labetalol was associated with more adverse effects and metoprolol with fewer. In RCTs of antihypertensives versus other antihypertensives, there was no difference in adverse neoHR (RR = 3.0 [0.13–71.74]; N = 3, n = 162). Observational studies showed adverse neoHR effects in labetalol versus pure beta‐blockers (RR = 1.99 [1.36–2.91]; I2 = 0%; N = 3, n = 16 204). No severe hypertension RCTs reported neoHR. Observational studies were limited. Network meta‐analysis showed no significant relationships between antihypertensives and FHR or neoHR; 95% CIs were very wide. Conclusions Evidence is inadequate to draw reliable conclusions about the impact of antihypertensives on FHR or neoHR. At present, adverse FHR or neoHR effects should be attributed to evolving placental dysfunction.
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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 | 0.022 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.015 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".