Prediction of preterm pre-eclampsia at midpregnancy using a multivariable screening algorithm

Background Competing risk models used for midpregnancy prediction of preterm pre-eclampsia have shown detection rates (DR) of 85%, at fixed false-positive rate (FPR) of 10%. The full algorithm used between 19(+0) and 24(+6) weeks includes maternal factors, mean arterial pressure (MAP), mean uterine artery pulsatility index (UtAPI), serum placental growth factor (PlGF) level in multiples of the median (MoM), and soluble Fms-like tyrosine kinase-1 (sFlt-1) level in MoM. Aims To assess performance of the Fetal Medicine Foundation (FMF) algorithm at midpregnancy to screen for preterm (<37 weeks) pre-eclampsia. The outcome measured was preterm pre-eclampsia. Materials and Methods This is a prospective study including singleton pregnancies at 19-22 weeks gestation. Maternal bloods were collected and analysed using three different immunoassay platforms. Maternal characteristics, medical history, MAP, mean UtAPI, serum PlGF MoM and serum sFlt-1 MoM were used for risk assessment. DR and FPR were calculated, and receiver operating characteristic curves produced. Results Five hundred and twelve patients were included. Incidence of preterm pre-eclampsia was 1.6%. Using predicted risk of pre-eclampsia of one in 60 or more and one in 100 or higher, as given by the FMF predictive algorithm, the combination with the best predictive performance for preterm pre-eclampsia included maternal factors, MAP, UtAPI and PlGF MoM, giving DRs of 100% and 100%, respectively, and FPRs of 9.3 for all platforms and 12.9-13.5, respectively. Addition of sFlt-1 to the algorithm did not appear to improve performance. sFlt-1 MoM and PlGF MoM values obtained on the different platforms performed very similarly. Conclusions Second trimester combined screening for preterm pre-eclampsia by maternal history, MAP, mean UtAPI and PlGF MoM using the FMF algorithm performed very well in this patient population.