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.