Comparison of different methods of first‐trimester screening for preterm pre‐eclampsia: cohort study
Authors: Cuenca‐Gómez D. , De Paco Matallana C. , Rolle V. , Mendoza M. , Valiño N. , Revello R. , Adiego B. , Casanova M. C. , Molina F. S. , Delgado J. L. , Wright A. , Figueras F. , Nicolaides K. H. , Santacruz B. , Gil M. M. 
Published: February 2024

ABSTRACTObjectiveTo compare the predictive performance of three different mathematical models for first‐trimester screening of pre‐eclampsia (PE), which combine maternal risk factors with mean arterial pressure (MAP), uterine artery pulsatility index (UtA‐PI) and serum placental growth factor (PlGF), and two risk‐scoring systems.MethodsThis was a prospective cohort study performed in eight fetal medicine units in five different regions of Spain between September 2017 and December 2019. All pregnant women with singleton pregnancy and a non‐malformed live fetus attending their routine ultrasound examination at 11 + 0 to 13 + 6 weeks' gestation were invited to participate in the study. Maternal characteristics and medical history were recorded and measurements of MAP, UtA‐PI, serum PlGF and pregnancy‐associated plasma protein‐A (PAPP‐A) were converted into multiples of the median (MoM). Risks for term PE, preterm PE (< 37 weeks' gestation) and early PE (< 34 weeks' gestation) were calculated according to the FMF competing‐risks model, the Crovetto et al. logistic regression model and the Serra et al. Gaussian model. PE classification was also performed based on the recommendations of the National Institute for Health and Care Excellence (NICE) and the American College of Obstetricians and Gynecologists (ACOG). We estimated detection rates (DR) with their 95% CIs at a fixed 10% screen‐positive rate (SPR), as well as the area under the receiver‐operating‐characteristics curve (AUC) for preterm PE, early PE and all PE for the three mathematical models. For the scoring systems, we calculated DR and SPR. Risk calibration was also assessed.ResultsThe study population comprised 10 110 singleton pregnancies, including 32 (0.3%) that developed early PE, 72 (0.7%) that developed preterm PE and 230 (2.3%) with any PE. At a fixed 10% SPR, the FMF, Crovetto et al. and Serra et al. models detected 82.7% (95% CI, 69.6–95.8%), 73.8% (95% CI, 58.7–88.9%) and 79.8% (95% CI, 66.1–93.5%) of early PE; 72.7% (95% CI, 62.9–82.6%), 69.2% (95% CI, 58.8–79.6%) and 74.1% (95% CI, 64.2–83.9%) of preterm PE; and 55.1% (95% CI, 48.8–61.4%), 47.1% (95% CI, 40.6–53.5%) and 53.9% (95% CI, 47.4–60.4%) of all PE, respectively. The best correlation between predicted and observed cases was achieved by the FMF model, with an AUC of 0.911 (95% CI, 0.879–0.943), a slope of 0.983 (95% CI, 0.846–1.120) and an intercept of 0.154 (95% CI, –0.091 to 0.397). The NICE criteria identified 46.7% (95% CI, 35.3–58.0%) of preterm PE at 11% SPR and ACOG criteria identified 65.9% (95% CI, 55.4–76.4%) of preterm PE at 33.8% SPR.ConclusionsThe best performance of screening for preterm PE is achieved by mathematical models that combine maternal factors with MAP, UtA‐PI and PlGF, as compared to risk‐scoring systems such as those of NICE and ACOG. While all three algorithms show similar results in terms of overall prediction, the FMF model showed the best performance at an individual level. © 2024 International Society of Ultrasound in Obstetrics and Gynecology.