Improved stillbirth risk stratification, an urgent global need
Jessica Page
Intermountain Health Care Inc
Salt Lake City
Utah
United States
Stillbirth is among the most devastating pregnancy complications and is
also one of the hardest complications to predict. Traditionally,
stillbirth risk stratification has incorporated maternal demographic
characteristics such as age and ethnicity as well as medical and
pregnancy conditions including multiple gestation, chronic hypertension
and pregestational diabetes. While these are clear risk factors for
stillbirth, they are nonspecific and are often present in live births.
Thus, tailoring an antenatal surveillance strategy to this poorly
defined and heterogenous population at increased risk of stillbirth is
difficult and can result in unnecessary obstetric intervention and
health system costs. Townsend et al have synthesized existing stillbirth
risk prediction models to address the need for a better method by which
to identify those pregnancies at highest risk for stillbirth (BJOG 2020
xxxx).
In this report, the variables most used in stillbirth risk prediction
systems included maternal ethnicity, body mass index (BMI), uterine
artery Doppler, pregnancy-associated plasma protein (PAPP-A) and
placental growth factor (PlGF). Biomarkers that can be assessed
prospectively at earlier gestational ages are attractive candidates for
stillbirth risk prediction as this may facilitate recognition of an
at-risk pregnancy that would otherwise not be identified. Individually
these biomarkers have poor positive predictive values for pregnancies
ultimately ending in stillbirth. (Dugoff et al. Am J Obstet Gynecol
2004;191:1446e51; Heazell AEP et al. Cochrane Database Syst Rev
2015;11:CD011202.) To address this, maternal demographic and medical
characteristics have been combined with ultrasound and biochemical
markers into multivariable models. However, as Townsend and colleagues
show, these models are prone to bias and lack external validation which
limits their clinical utility.
Important steps in improving stillbirth risk prediction and
identification of those pregnancies which would benefit from obstetric
intervention include novel biomarker discovery and high- quality
stillbirth evaluation and data collection. A large proportion of
stillbirths are associated with placental dysfunction and research into
biomarkers of placental insufficiency is ongoing. (Cleaton et al. Nat
Genet 2016 Dec;48(12):1473-1480; Chaiworapongsa T, et al. Am J Obstet
Gynecol 2013;208(4):287.) This research is an important step toward
better understanding the mechanisms of placentally-mediated stillbirths
and developing better risk identification tools.
Large variation in stillbirth definitions and evaluation exist due to
differences in resources, as well as practice variation within the same
resource setting, resulting in discordant datasets and incompletely
characterized stillbirth cases. This heterogeneity in practice is
problematic, as even with complete evaluation, up to a quarter of
stillbirths remain unexplained. (Stillbirth Collaborative Research
Network Writing Group. JAMA 2011;306:2459e68.) As pointed out by
Townsend et al (BJOG 2020 xxxx), this variation in practice makes
external validation of a stillbirth risk prediction model exceedingly
difficult due to heterogeneous populations and datasets. Given the
global need to reduce stillbirth, collaboration among groups with
ongoing collection of uniform and standardized stillbirth data is an
imperative step forward to improving care for those at highest risk and
reducing unnecessary obstetric intervention.
No disclosures: A completed disclosure of interest form is
available to view online as supporting information.