Can risk prediction models help us individualise stillbirth prevention? A systematic review and critical appraisal of published risk models

Abstract

Background Stillbirth prevention is an international priorityrisk prediction models could individualise care and reduce unnecessary intervention, but their use requires evaluation. Objectives To identify risk prediction models for stillbirth, and assess their potential accuracy and clinical benefit in practice. Search strategy MEDLINE, Embase, DH-DATA and AMED databases were searched from inception to June 2019 using terms relevant to stillbirth, perinatal mortality and prediction models. The search was compliant with Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. Selection criteria Studies developing and/or validating prediction models for risk of stillbirth developed for application during pregnancy. Data collection and analysis Study screening and data extraction were conducted in duplicate, using the CHARMS checklist. Risk of bias was appraised using the PROBAST tool. Results The search identified 2751 citations. Fourteen studies reporting development of 69 models were included. Variables consistently included were: ethnicity, body mass index, uterine artery Doppler, pregnancy-associated plasma protein and placental growth factor. For almost all models there were significant concerns about risk of bias. Apparent model performance (i.e. in the development dataset) was highest in models developed for use later in pregnancy and including maternal characteristics, and ultrasound and biochemical variables, but few were internally validated and none were externally validated. Conclusions Almost all models identified were at high risk of bias. There are first-trimester models of possible clinical benefit in early risk stratification; these require validation and clinical evaluation. There were few later pregnancy models but, if validated, these could be most relevant to individualised discussions around timing of birth.

Predicting singleton antepartum stillbirth by the demographic Fetal Medicine Foundation Risk Calculator—A retrospective case-control study

“…Also, we considered all IUFD cases from gestational week 21 +0 onwards. One reason for this is the international heterogeneity in definitions of stillbirth by gestational age and to address those who define fetal death as of week 20 +0 [ 15 ].…”

“…Prediction models for stillbirths are commonly defined as “models, scores or clinical decision tools” which estimate the risk of adverse perinatal outcome upon certain variables [ 15 ]. To date, 69 prediction models for stillbirth have been described in the literature.…”

Automated development of clinical prediction models enables real-time risk stratification with exemplar application to hypoxic-ischaemic encephalopathy

“…Predictions based on physicians’ behaviour are unlikely to provide improvements in individual-level care beyond the average action taken for an average patient 31 . These challenges also apply in perinatal prediction, for example women with risk factors for stillbirth are delivered earlier and therefore prediction of stillbirth after 37 weeks will be ineffective 32 . To ensure models are unaffected by these biases, extensive external validation in different clinical environments where policies and procedures differ is needed.…”

Fetal monitoring from 39 weeks’ gestation to identify South Asian-born women at risk of perinatal compromise: a retrospective cohort study

“…These findings were not surprising given the poor sensitivity and predictive values of amniotic liquor volume (AFI) to assess fetal wellbeing 21 , 22 . The addition of other components, including fetoplacental Dopplers, and measurement of biomarkers in maternal blood, such as placental growth factor (PlGF) and soluble fms-like tyrosine kinase-1 (sFLT-1) may improve the ability of surveillance to identify at-risk fetuses 23 with further research needed.…”