Meta-Analysis of Physical Interventions for Perineal Trauma Prevention

A systematic review and Bayesian network meta-analysis compares the effectiveness of physical interventions for preventing perineal trauma during vaginal delivery. The analysis synthesizes data from 31 randomized controlled trials involving 10,745 participants across 15 countries. The study was authored by Linli Xu and published on figshare in 2026.

Use Cases

• Rank intervention efficacy using SUCRA values, such as comparing antenatal pelvic floor exercise (SUCRA 86.58%) to massage combined with warm compresses (SUCRA 90.08%) for episiotomy prevention.
• Estimate risk reduction for specific outcomes using reported relative risks, like the 50% risk reduction (RR=0.50) associated with antenatal pelvic floor exercise for overall laceration.
• Compare safety profiles of interventions, such as evaluating the neonatal safety data reported across all included physical interventions.
• Assess the null effect of education-only interventions for perineal protection using the reported relative risk (RR=1.03).

Strengths

• Evidence synthesis from 31 randomized controlled trials, a substantial number for a meta-analysis.
• Large pooled sample size of 10,745 participants from 15 countries.
• Uses advanced Bayesian network meta-analysis and SUCRA ranking for comparative effectiveness.

Limitations

• Limited generalizability as the majority of participants were primiparous women from high-resource settings.
• Exploratory findings for severe perineal pain outcomes due to sparse network structures and wide credible intervals.
• Dataset is a 17.2 KB document containing summary results, not the underlying patient-level trial data.

Provenance

Source

Systematic review of trials from PubMed, Web of Science, Embase, and Cochrane CENTRAL.

Collection Method

Bayesian network meta-analysis performed using R Studio on extracted trial data.

Time Range

null

Freshness

Last updated in April 2026, indicating recent analysis.

Geography

Trials from 15 countries, primarily in high-resource settings.