Validation of Fetal Medicine Foundation algorithm for prediction of pre-eclampsia in the first trimester in an unselected Brazilian population

• Published in: The journal of maternal-fetal & neonatal medicine Vol. 32; no. 2; pp. 286 - 292
• Main Authors: Lobo, Guilherme Antonio Rago, Nowak, Paulo Martin, Panigassi, Ana Paula, Lima, Angélia Iara Felipe, Araujo Júnior, Edward, Nardozza, Luciano Marcondes Machado, Pares, David Baptista Silva
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 17-01-2019
• Subjects: Adult; Algorithms; Brazil; Brazilian population; Female; fetal medicine foundation; first trimester; Gestational Age; Humans; Perinatology - organization & administration; Perinatology - standards; Pre-Eclampsia - diagnosis; prediction; preeclampsia; Pregnancy; Pregnancy Trimester, First; Prenatal Diagnosis - methods; Prenatal Diagnosis - standards; Societies, Medical - standards; Young Adult; Brazil; Brazilian population; prediction; preeclampsia; first trimester; fetal medicine foundation
• ISSN: 1476-7058; 1476-4954
• DOI: 10.1080/14767058.2017.1378332

Objective: The objective of this study is to evaluate the predictive performance of the Fetal Medicine Foundation (FMF) algorithm for prediction of preeclampsia (PE) between 11 and 14 weeks of gestation in an unselected Brazilian population.
Methods: We conducted a prospective cohort study with 617 singleton pregnancies of unselected risk. Biophysical markers (mean pulsatility index, mean arterial pressure) and biochemical markers (placental growth factor (PLGF) and PAPP-A) were inserted into the FMF software and converted into multiples of the median (MoM). The subjects were divided into five groups: early-onset PE, parturition <34 weeks' gestation; preterm PE, parturition <37 weeks; PE, parturition at any gestational age; gestational hypertension (GH); and control group. Areas under the receiver operating characteristics curve (AUC) were calculated for the outcomes.
Results: Among 617 patients, seven developed early-onset PE, 18 developed preterm PE (seven early PE plus 11 delivered between 34 and 36 + 6 weeks gestation), 34 developed PE (18 preterm PE plus 16 delivered after 37-week gestation), 12 pregnant women developed GH, and 517 women comprised the control group. The best predictive performance using the FMF algorithm occurred in the early-onset PE group, with AUC = 0.946 (95% CI 0.919-0.973) and the detection rate of 28.6% and 85.7% for 5% and 10% false-positive (FP), respectively.
Conclusions: The FMF algorithm to predict PE was effective in a Brazilian population, mainly in the early-onset form of the disease at 10% FP.