Antibiotic therapy in patients with amniotic fluid sludge and risk of preterm birth: a meta-analysis

Abstract

Abstract Purpose Amniotic Fluid Sludge (AFS) has been theorized to be sonographic evidence of an underlying infection/inflammation and studies have concluded that approximately 10% of the patients who show signs of preterm labor with intact membranes have an underlying intraamniotic infection, mostly subclinical, carrying an increased risk for preterm birth with its subsequent neonatal and maternal complications. The purpose of the present systematic review is to evaluate the impact of antibiotic therapy on preterm birth rates of women diagnosed with AFS. Methods We searched Medline, Scopus, the Cochrane Central Register of Controlled Trials CENTRAL, Google Scholar, and Clinicaltrials.gov databases for relevant articles published until the 30th of September 2022. Observational studies (prospective and retrospective) that evaluated the impact of antibiotics on preterm delivery rates of patients with AFS were considered eligible for inclusion. Statistical meta-analysis was performed with RStudio and we calculated pooled risk ratios (OR) and 95% confidence intervals (CI). To evaluate the information size, we performed trial sequential analysis (TSA) and the methodological quality of the included studies was assessed using RoBINS tools. Results Overall, four retrospective cohort studies were included in the present systematic review and 369 women were enrolled. We demonstrated that preterm delivery prior to 34, 32 and 28 weeks of gestational age was comparable among the groups of women that had antibiotics and those that did not (OR: 0.34, 95% CI 0.05, 2.14, 0.40 [0.09, 1.66], 0.35 [0.08, 1.58], respectively) but the statistical heterogenicity of the studies included was high for every gestational period that was examined. Conclusions According to our study, we cannot conclude that the use of antibiotics in women with amniotic fluid sludge benefit the prognostic risk to deliver prematurely. It is quite clear that data from larger sample sizes and more well adjusted and designed studies are needed.