Clinical and research implications
Townsend et al. have proposed that in the future development of a robust
risk prediction tool for stillbirth the following candidate variables
should be incorporated: maternal age, BMI, parity, pre-existing
hypertension, diabetes, previous stillbirth, nicotine consumption,
uterine artery Doppler, pregnancy-associated plasma protein PAPP-A and
placental growth factor
PlGF.4 The merit of such
clinical model would be twofold: primarily, the accurate discrimination
of high- from low risk pregnant women, and secondarily, recognizing the
variables that may require early enough alteration if they are
modifiable. Whilst maternal age, parity, previous stillbirths and
biomarkers cannot be adapted by intervention, maternal weight,
hypertension, type 2 diabetes and nicotine consumption can be improved
through life style modifications. To extrapolate this concept to the
demographic model of the FMF Stillbirth Risk Calculator, only
four variables may be potential subjects to change and therefore
possible risk reduction (weight, smoking, diabetes, hypertension). As
with many other risk-adjustment models, however, social and behavioural
variables, such as domestic abuse, stress, employment and deprivation
are hard to capture and should be further considered within a
population-based conceptual
framework.20 Additional
research into prediction models may objectify the true preventability of
stillbirth by adaption of modifiable risk factors in the future.