Competing risks model in screening for preeclampsia by maternal factors and biomarkers at 19–24 weeks’ gestation

Background Preeclampsia (PE) affects 2–3% of all pregnancies and is a major cause of maternal and perinatal morbidity and mortality. The traditional approach to screening for PE is to use a risk-scoring system based on maternal demographic characteristics and medical history (maternal factors), but the performance of such an approach is very poor. Objective To develop a model for PE based on a combination of maternal factors with second-trimester biomarkers. Study Design The data for this study were derived from prospective screening for adverse obstetric outcomes in women attending their routine hospital visit at 19–24 weeks’ gestation in 3 maternity hospitals in England between January 2006 and July 2014. We had data from maternal factors, uterine artery pulsatility index (UTPI), mean arterial pressure (MAP), serum placental growth factor (PLGF), and serum soluble fms-like tyrosine kinase-1 (SFLT) from 123,406, 67,605, 31,120, 10,828, and 8079 pregnancies, respectively. Bayes’ theorem was used to combine the a priori risk from maternal factors with various combinations of biomarker multiple of the median (MoM) values. The modeled performance of screening for PE requiring delivery at