Prediction of stillbirth: an umbrella review of evaluation of prognostic variables

BACKGROUND: Stillbirth accounts for over 2 million deaths a year worldwide and rates remains stubbornly high. Multivariable prediction models may be key to individualised monitoring, intervention or early birth in pregnancy to prevent stillbirth.

OBJECTIVES: To collate and evaluate systematic reviews of factors associated with stillbirth in order to identify variables relevant to prediction model development.

SEARCH STRATEGY: MEDLINE, Embase, DARE and Cochrane Library databases and reference lists were searched up to November 2019.

SELECTION CRITERIA: We included systematic reviews of association of individual variables with stillbirth without language restriction.

DATA COLLECTION AND ANALYSIS: Abstract screening and data extraction were conducted in duplicate. Methodological quality was assessed using AMSTAR and QUIPS criteria. The evidence supporting association with each variable was graded.

RESULTS: The search identified 1198 citations. Sixty-nine systematic reviews reporting 64 variables were included. The most frequently reported were maternal age (n = 5), body mass index (n = 6) and maternal diabetes (n = 5). Uterine artery Doppler appeared to have the best performance of any single test for stillbirth. The strongest evidence of association was for nulliparity and pre-existing hypertension.

CONCLUSION: We have identified variables relevant to the development of prediction models for stillbirth. Age, parity and prior adverse pregnancy outcomes had a more convincing association than the best performing tests, which were PAPP-A, PlGF and UtAD. The evidence was limited by high heterogeneity and lack of data on intervention bias.

TWEETABLE ABSTRACT: Review shows key predictors for use in developing models predicting stillbirth include age, prior pregnancy outcome and PAPP-A, PLGF and Uterine artery Doppler.