Association and prediction of amniotic fluid measurements for adverse pregnancy outcome: systematic review and meta-analysis.

Background: Measurements of amniotic fluid volume are used for pregnancy surveillance despite a lack of evidence for their predictive ability.

Objective: To evaluate the association and predictive value of ultrasound measurements of amniotic fluid volume for adverse pregnancy outcome.

Search Strategy: Electronic databases (inception to October 2011), reference lists, hand searching of journals, contact with experts.

Selection Criteria: Studies comparing measurements of amniotic fluid volume with adverse outcome, excluding pre-labour ruptured membranes or congenital/structural anomalies.

Data Collection: Data on study characteristics, design, quality. Random effects meta-analysis to estimate summary odds ratios (prognostic association) and summary sensitivity, specificity and likelihood ratios (predictive ability).

Main Results: Forty-three studies (244,493 fetuses) were included demonstrating a strong association between oligohydramnios (varying definitions) and birthweight <10th centile (summary odds ratio [OR] 6.31, 95% confidence interval [95% CI] 4.15-9.58; high-risk population [author definition] n = 6 studies, 28,510 fetuses), and mortality (neonatal death any population summary OR 8.72, 95% CI 2.43-31.26; n = 6 studies, 55,735 fetuses; and perinatal mortality high-risk population summary OR 11.54, 95% CI 4.05-32.9; n = 2 studies, 27;891 fetuses). There was a strong association between polyhydramnios (maximum pool depth >8 cm or amniotic fluid index ≥25 cm) and birthweight >90th centile (OR 11.41, 95% CI 7.09-18.36; n = 1 study, 3960 fetuses). Despite strong associations, predictive accuracy for perinatal outcome was poor.

Author's Conclusion: Current evidence suggests that oligohydramnios is strongly associated with being small for gestational age and mortality, and polyhydramnios with birthweight >90th centile. Despite strong associations with poor outcome, they do not accurately predict outcome risk for individuals.