Accuracy of the FMF Bayes theorem-based model for predicting preeclampsia at 11–13 weeks of gestation in a Japanese population

Abstract

This study aimed to investigate the diagnostic accuracy of the Fetal Medicine Foundation (FMF) Bayes theorem-based model for the prediction of preeclampsia (PE) at 11–13 weeks of gestation in the Japanese population. In this prospective cohort study, we invited 2655 Japanese women with singleton pregnancies at 11–13 weeks of gestation to participate, of whom 1036 women provided written consent. Finally, we included 913 women for whom all measurements and perinatal outcomes were available. Data on maternal characteristics and medical history were recorded. Mean arterial pressure (MAP), uterine artery pulsatility index, and maternal serum placental growth factor (PlGF) were measured. The patients delivered their babies at Showa University Hospital between June 2017 and December 2019. Participants were classified into high- and low-risk groups according to the FMF Bayes theorem-based model. Frequencies of PE were compared between groups. The screening performance of the model was validated using the area under receiver operating characteristic (AUROC) curve. A total of 26 patients (2.8%) developed PE, including 11 patients (1.2%) with preterm PE (delivery at <37 weeks). The frequency of preterm PE was significantly higher in the high-risk group than in the low-risk group (3.8% vs. 0.2%, p < 0.05). This population model achieved a 91% detection rate for the prediction of preterm PE at a screen-positive rate of 10% by a combination of maternal characteristics, MAP, and PlGF. The AUROC curve for the prediction of preterm PE was 0.962 (0.927–0.981). In conclusion, the prediction of preterm PE using the FMF Bayes theorem-based model is feasible in the Japanese population.