RESUMEN
BACKGROUND: The current version of the Fetal Medicine Foundation competing risks model for preeclampsia prediction has not been previously validated in Brazil. OBJECTIVE: This study aimed (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: This was a cohort study, including consecutive singleton pregnancies undergoing preeclampsia screening at 11 to 14 weeks of gestation, 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 a cutoff of 1/100 to evaluate predictive performance. Expected and observed cases with preeclampsia according to the Fetal Medicine Foundation-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 Combined Multimarker Screening and Randomized Patient Treatment with Aspirin for Evidence-Based Preeclampsia Prevention 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 2749 pregnancies, preterm preeclampsia occurred in 84 (3.1%). With a risk cutoff 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, 0.773-0.863). In the risk range ≥1/10, there is an agreement between the number of expected cases and the number of 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 estimated that 16 of these cases of preterm preeclampsia would have been avoided if this group had received prophylaxis. CONCLUSION: 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.
RESUMEN
OBJECTIVE: To validate a combined algorithm for early prediction of pre-eclampsia (PE) in the Brazilian population. STUDY DESIGN: This is an unplanned secondary analysis of a cohort study. Consecutive singleton pregnancies undergoing first-trimester screening for PE involving examination of maternal characteristics, medical history, and biophysical markers were considered eligible. Women were classified as low-or high-risk using a cutoff of 1/200, but the individual risk was not used to dictate management, as aspirin prophylaxis was given to women based solely on clinical risk factors. Receiver-operating characteristics (ROC) curves for PE, preterm PE(PE < 37) and early 34(PE < 34) were constructed and detection rates(DR) and false-positive rates(FPR) were calculated, adjusting for the effect of aspirin. Propensity score analysis was utilized to account for possible confounding by indication. MAIN OUTCOME MEASURES: Screening performance and PE rates. RESULTS: Among 1695 women, 323(19.1%) were classified as high-risk for PE and 1372(80.9%) were considered low-risk. Aspirin use was registered in 62(3.7%) in the high-risk group and 33(1.9%) in the low-risk group. There were 164(9.7%) women who developed PE, including 41(2.4%) with PE < 37 and 18(1.1%) PE < 34.Subgroups with aspirin had higher incidence of PE, suggest confounding by indication. The algorithm had an AUC of 0.87, DR of 72% for PE < 34; an AUC of 0.8, DR of 59% for PE < 37, both with FPR of 18%. Accounting for effect of aspirin, we observed an improvement in DR of PE < 37 to 67%. CONCLUSION: Using combined predictive algorithm for preterm PE prediction is feasible in clinical practice in low/middle-income countries. Aspirin use needs to be accounted for when evaluating the performance of screening.
