Anti-hypertensive therapy for preeclampsia: a network meta-analysis and systematic review

ABSTRACT

Background

Preeclampsia (PE) is a pregnancy disorder that represents a major cause of maternal and perinatal morbidity and mortality.

Methods

This network meta-analysis was registered with PROSPERO. We searched the PubMed, ClinicalTrials.gov. and Embase databases for studies published from inception to the 31^st of March 2023. RevMan5.3 software provided by the Cochrane Collaboration was used for direct meta-analysis (DMA) statistical analysis. Funnel maps, network meta-analysis (NMA), the surface under the cumulative ranking curve (SUCRA) to rank the different interventions and publication bias were generated by STATA 17.0 software.

Results

We included eight randomized controlled trials (RCTs) involving a total of 1192 women with PE; two studies were of high quality and six were of moderate quality. Eight interventions were addressed in the NMA. In the DMA, we found that blood pressure in the Ketanserin group were significantly higher than those in the Nicardipine group. NMA showed that blood pressure in the Dihydralazine group was significantly higher than that in the Methyldopa, Labetalol, Nicardipine and Diltiazem groups. And the blood pressure in the Labetalol group was significantly lower than that in the Nicardipine group. SUCRA values showed that Diltiazem was more effective in lowering blood pressure than other drugs looked at in this study.

Conclusion

According to the eight RCTs included in this study, Diltiazem was the most effective in reducing blood pressure in PE patients; Labetalol and Nicardipine also had good effects. Diltiazem is preferred for the treatment of patients with severe PE and high blood pressure.

KEYWORDS:

• Preeclampsia
• anti-hypertensive therapy
• network meta-analysis

Disclosure statement

No potential conflict of interest was reported by the author(s).

Authors’ contributions

Conception and design: Ting Wang, Ruoan Jiang, Yingsha Yao

Acquisition and data: Ruoan Jiang, Yingsha Yao, Ting Xu

Analysis and interpretation of data: Ruoan Jiang, Yingsha Yao, Ting Xu

Drafting of the manuscript: Ruoan Jiang, Yingsha Yao, Na Li

Critical revision of the manuscript for important intellectual content: Ting Wang

Statistical analysis: Ting Wang, Yingsha Yao

Supervision: Ting Wang

Consent for publication

The authors declare that they agree with the publication of the article.

Data availability statement

Further inquiries can be directed to the corresponding author/s.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.