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ASPRE trial: incidence of preterm preeclampsia in patients fulfilling ACOG and NICE criteria according to risk by the FMF algorithm Liona Poon The Chinese University of Hong Kong, The Chinese University of Hong Kong, Shatin, Hong Kong SAR Objectives To report the incidence of preterm preeclampsia (PE) in women that fulfilled the screening criteria of the National Institute for Health and Clinical Excellence (NICE) and American Congress of Obstetricians and Gynecologists (ACOG) and compare the incidence in those that were screen positive and screen negative by the Fetal Medicine Foundation (FMF) algorithm. Methods This was a secondary analysis of data from the ASPRE study. The study population consisted of women with singleton pregnancies who had prospective screening for preterm-PE by means of the FMF algorithm that combines maternal factors and biomarkers at 11-13 weeks’ gestation. We estimated the incidence of preterm-PE in those fulfilling the NICE and ACOG criteria; in these patients we then calculated the incidence of preterm-PE in those that were screen negative relative to those that were screen positive by the FMF algorithm. Results 34,573 women with singleton pregnancies were included. There were 239 (0.7%) cases of preterm-PE. At least one ACOG-criteria was fulfilled in 22,287 (64.5%) of pregnancies and the incidence of preterm-PE was 0.97%; in those that were FMF-screen-positive the incidence was 4.80%, in those that were screen-negative it was 0.25% and the relative incidence was 0.051. In 1,392 (4.0%) pregnancies at least one NICE high-risk criteria was fulfilled and the incidence of preterm-PE was 5.17%; in the subgroups of screen-positive and screen-negative by the FMF algorithm the incidence of preterm-PE was 8.71% and 0.65%, respectively, and the relative incidence was 0.075. Conclusions In ACOG or NICE screen positive women that are screen negative by the FMF algorithm the risk of preterm-PE is reduced to within or below background levels. The results provide further evidence to support risk based screening using biomarkers.
