Tarca AL, Taran A, Romero R, et al. - For prediction of preeclampsia, maternal risk factors are combined with evidence from biophysical markers (mean arterial pressure, Doppler velocimetry of the uterine arteries) and maternal blood proteins (placental growth factor, soluble vascular endothelial growth factor receptor-1, pregnancy-associated plasma protein A). In such models, there is necessity for the transformation of biomarker data into multiples of the mean values by utilizing population- and site-specific models. Via performing this study, researchers sought (1) to create models for the estimation of multiples of the mean values for mean arterial pressure and biochemical markers; (2) to develop and determine the predictive models for preeclampsia based on maternal risk factors, the biophysical (mean arterial pressure) and biochemical (placental growth factor, soluble vascular endothelial growth factor receptor-1, and soluble endoglin) markers obtained throughout pregnancy; and (3) to ascertain the effect on prediction accuracy by the presence of chronic hypertension and gestational age. In this longitudinal case-cohort study, a total of 1,150 pregnant women were included: women without preeclampsia with (n = 49) and without chronic hypertension (n = 871) and those who developed preeclampsia (n = 166) or superimposed preeclampsia (n = 64). There was availability of mean arterial pressure and immunoassay-based maternal plasma placental growth factor, soluble vascular endothelial growth factor receptor-1, and soluble endoglin concentrations throughout pregnancy (median of 5 observations per patient). Based on maternal characteristics and obstetrical history, they established a prior-risk model for preeclampsia by using Poisson regression. Then, they used multiple regression to fit biophysical and biochemical marker data as a function of maternal characteristics by utilizing data obtained at 8 to 15⁺⁶, 16 to 19⁺⁶, 20 to 23⁺⁶, 24 to 27⁺⁶, 28 to 31⁺⁶, and 32 to 36⁺⁶ week intervals, and converted observed values into multiples of the mean values. Findings of this study support the utility of these models to detect women at risk during the first trimester who could benefit from aspirin treatment or later in pregnancy to inform patient management. Relative to prediction performance at 8 to 15⁺⁶ weeks, data obtained after 20 and 32 weeks’ gestation led to a substantial improvement in the detection rate for preterm and term preeclampsia, respectively. The early prediction of superimposed preeclampsia may improve with the inclusion of plasma soluble endoglin, which may be valuable when there is no availability of Doppler velocimetry of the uterine arteries.