Comparison of different methods of screening for preterm pre‐eclampsia: cohort study

ABSTRACT Objective To compare the predictive performance for pre‐eclampsia (PE) of three different first‐trimester mathematical models of screening, 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, based on NICE and ACOG recommendations. Methods This 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 pregnancies and non‐malformed live fetuses 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, 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 Serra et al ., Gaussian model. Patient classification based on NICE and ACOG guidelines was also performed. We estimated detection rates (DR) with their 95% confidence intervals (CIs) at a fixed 10% screen positive rate (SPR), as well as the area under the receiver operating characteristic curve (AUROC) 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. Results The study population comprised of 10,110 singleton pregnancies, including 32 (0.3%) that developed early‐PE, 72 (0.7%) that developed preterm‐PE and 230 (2.3%) of any PE. At fixed 10% SPR, the FMF, Crovetto et al ., and Serra et al. , detected 82.7% (95% CI, 69.6 to 95.8%), 73.8% (95% CI, 58.7 to 88.9%), and 79.8% (95% CI, 66.1 to 93.5%) of early‐PE; 72.7% (95% CI, 62.9 to 82.6%), 69.2% (95% CI, 58.8 to 79.6%), and 74.1% (95% CI, 64.2 to 83.9%) of preterm‐PE and 55.1% (95% CI, 48.8 to 61.4%), 47.1% (95% CI, 40.6 to 53.5%), and 53.9% (95% CI, 47.4 to 60.4%) of all PE, respectively. The best correlation between predicted and observed cases was achieved by the FMF model, with an AUROC of 0.911 (95% CI, 0.879 to 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 to 58.0%) of preterm‐PE at 11% SPR and ACOG criteria identified 65.9% (95% CI, 55.4 to 76.4%) of preterm‐PE at 33.8% SPR. Conclusions The 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 like NICE or ACOG criteria. While all three algorithms show similar results in terms of overall prediction, the FMF model showed the best performance at the individual level. This article is protected by copyright. All rights reserved.