First trimester screening for early and late preeclampsia based on maternal characteristics, biophysical parameters, and angiogenic factors.

OBJECTIVE The aim of this article is to develop the best first-trimester screening model for preeclampsia (PE) based on maternal characteristics, biophysical parameters, and angiogenic factors in a low-risk population. METHODS A prospective cohort of 9462 pregnancies undergoing first-trimester screening is used. Logistic regression predictive models were developed for early and late PE (cut-off of 34 weeks' gestation at delivery). Data included the a priori risk (maternal characteristics), mean arterial pressure (MAP), and uterine artery (UtA) Doppler (11-13 weeks) in all cases. Plasma levels (8-11 weeks) of human chorionic gonadotrophin, pregnancy-associated plasma protein A, placental growth factor (PlGF), and soluble Fms-like tyrosine kinase-1 (sFlt-1) were analyzed using a nested case-control study design. RESULTS The best model for early PE (n = 57, 0.6%) included a priori risk, MAP, UtA Doppler, PlGF, and sFlt-1 achieving detection rates of 87.7% and 91.2% for 5% and 10% false-positive rates, respectively (AUC: 0.98 [95% CI: 0.97-0.99]). For late PE (n = 246, 2.6%), the best model included the a priori risk, MAP, UtA Doppler, PlGF, and sFlt-1 achieving detection rates of 68.3% and 76.4% at 5% and 10% of false-positive rates, respectively (AUC: 0.87 [95% CI: 0.84-0.90]). CONCLUSION Preeclampsia can be predicted with high accuracy in general obstetric populations with a low risk for PE, by combined algorithms. Angiogenic factors substantially improved the prediction.