Performance of the first-trimester Fetal Medicine Foundation competing risks model for preeclampsia prediction: An external validation study in Brazil

Background: The current version of the Fetal Medicine Foundation competing risks model for preeclampsia prediction has not been previously validated in Brazil. Objectives: (1) To validate the Fetal Medicine Foundation combined algorithm for the prediction of preterm preeclampsia in the Brazilian population and (2) to describe the accuracy and calibration of the Fetal Medicine Foundation algorithm when considering the prophylactic use of aspirin, by clinical criteria. Study design: Cohort study, including consecutive singleton pregnancies undergoing preeclampsia screening at 11-14 weeks, examining maternal characteristics, medical history, and biophysical markers between October 2010 and December 2018 in a university hospital in Brazil. Risks were calculated using the 2018 version of the algorithm available on the Fetal Medicine Foundation website, and cases were classified as low- or high-risk using cut-off of 1/100 to evaluate predictive performance. Expected and observed cases with PE according to the FMF estimated risk range (≥1 in 10; 1 in 11 to 1 in 50; 1 in 51 to 1 in 100; 1 in 101 to 1 in 150; and <1 in 150) were compared. After identifying high-risk pregnant women who used aspirin, the treatment effect of 62% reduction in preterm preeclampsia identified in the ASPRE trial was used to evaluate the predictive performance adjusted for the effect of aspirin. The number of potentially unpreventable cases in the group without aspirin use was estimated. Results: Among 2,749 pregnancies, preterm preeclampsia occurred in 84 (3.1%). With a risk cut-off of 1/100, the screen-positive rate was 25.8%. The detection rate was 71.4%, with a false positive rate of 24.4%. The area under the curve was 0.818 (95% confidence interval 0.773 to 0.863). In the risk range ≥1/10, there is an agreement between the number of expected and observed cases, and in the other ranges, the predicted risk was lower than the observed rates. Accounting for the effect of aspirin resulted in an increase in detection rate and positive predictive values and a slight decrease in the false positive rate. With 27 cases of preterm preeclampsia in the high-risk group without aspirin use, we estimate that 16 of these cases of preterm preeclampsia would have been avoided if this group had received prophylaxis. Conclusions: In a high prevalence setting, the Fetal Medicine Foundation algorithm can identify women who are more likely to develop preterm preeclampsia. Not accounting for the effect of aspirin underestimates the screening performance.