Biophysical and Biochemical prediction of preeclampsia at 20-24 weeks' gestation

Abstract

Preeclampsia (PE) affects 2-3% of all pregnancies and is a major cause of maternal and perinatal morbidity and mortality1,2. The traditional approach to screening for PE is to identify risk factors from maternal demographic characteristics and medical history (maternal factors).3,4 According to ACOG, taking a medical history to evaluate for risk factors is currently the best and only recommended screening approach for PE.3 In the UK, the National Institute for Health and Clinical Excellence (NICE) has issued guidelines recommending that women should be considered to be at high-risk of developing PE if they have any one high-risk factor or any two moderate-risk factors.4 However, the performance of such approach, which essentially treats each risk factor as a separate screening test with additive detection rate (DR) and screen positive rate, is poor with DR of only 35% of all-PE and 40% of preterm-PE requiring delivery at <37 weeks’ gestation, at false positive rate (FPR) of about 10%.5

An alternative approach to screening, which allows estimation of individual patient-specific risks of PE requiring delivery before a specified gestation, is to use Bayes theorem to combine the a priori risk from maternal factors, derived by a multivariable logistic model, with the results of various combinations of biophysical and biochemical measurements made at different times during pregnancy5-8. We have previously reported that first-trimester screening by a combination of maternal factors with mean arterial pressure (MAP), uterine artery pulsatility index (UTPI) and serum placental growth factor (PLGF) can predict 75% of preterm-PE and 47% of term-PE, at 10% FPR 8.