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Prediction of preeclampsia throughout gestation with maternal characteristics and biophysical and biochemical markers: a longitudinal study

  • Adi L. Tarca
    Correspondence
    Corresponding author: Adi L. Tarca, PhD.
    Affiliations
    Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, United States Department of Health and Human Services, Bethesda, MD, and Detroit, MI

    Departments of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI

    Department of Computer Science, Wayne State University College of Engineering, Detroit, MI
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  • Andreea Taran
    Affiliations
    Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, United States Department of Health and Human Services, Bethesda, MD, and Detroit, MI

    Departments of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI
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  • Roberto Romero
    Affiliations
    Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, United States Department of Health and Human Services, Bethesda, MD, and Detroit, MI

    Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, MI

    Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI

    Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI

    Detroit Medical Center, Detroit, MI

    Department of Obstetrics and Gynecology, Florida International University, Miami, FL
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  • Eunjung Jung
    Affiliations
    Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, United States Department of Health and Human Services, Bethesda, MD, and Detroit, MI

    Departments of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI
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  • Carmen Paredes
    Affiliations
    Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, United States Department of Health and Human Services, Bethesda, MD, and Detroit, MI

    Departments of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI
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  • Gaurav Bhatti
    Affiliations
    Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, United States Department of Health and Human Services, Bethesda, MD, and Detroit, MI
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  • Corina Ghita
    Affiliations
    Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, United States Department of Health and Human Services, Bethesda, MD, and Detroit, MI
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  • Tinnakorn Chaiworapongsa
    Affiliations
    Departments of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI
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  • Nandor Gabor Than
    Affiliations
    Systems Biology of Reproduction Lendulet Research Group, Institute of Enzymology, Research Centre for Natural Sciences, Budapest, Hungary

    Maternity Clinic, Budapest, Hungary
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  • Chaur-Dong Hsu
    Affiliations
    Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, United States Department of Health and Human Services, Bethesda, MD, and Detroit, MI

    Departments of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI

    Physiology, Wayne State University School of Medicine, Detroit, MI
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Published:April 15, 2021DOI:https://doi.org/10.1016/j.ajog.2021.01.020

      Background

      The current approach to predict preeclampsia combines maternal risk factors and 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). Such models require the transformation of biomarker data into multiples of the mean values by using population- and site-specific models. Previous studies have focused on a narrow window in gestation and have not included the maternal blood concentration of soluble endoglin, an important antiangiogenic factor up-regulated in preeclampsia.

      Objective

      This study aimed (1) to develop models for the calculation of multiples of the mean values for mean arterial pressure and biochemical markers; (2) to build and assess 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 collected throughout pregnancy; and (3) to evaluate how prediction accuracy is affected by the presence of chronic hypertension and gestational age.

      Study Design

      This longitudinal case-cohort study included 1150 pregnant women: women without preeclampsia with (n=49) and without chronic hypertension (n=871) and those who developed preeclampsia (n=166) or superimposed preeclampsia (n=64). Mean arterial pressure and immunoassay-based maternal plasma placental growth factor, soluble vascular endothelial growth factor receptor-1, and soluble endoglin concentrations were available throughout pregnancy (median of 5 observations per patient). A prior-risk model for preeclampsia was established by using Poisson regression based on maternal characteristics and obstetrical history. Next, multiple regression was used to fit biophysical and biochemical marker data as a function of maternal characteristics by using data collected 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 observed values were converted into multiples of the mean values. Then, multivariable prediction models for preeclampsia were fit based on the biomarker multiples of the mean data and prior-risk estimates. Separate models were derived for overall, preterm, and term preeclampsia, which were evaluated by receiver operating characteristic curves and sensitivity at fixed false-positive rates.

      Results

      (1) The inclusion of soluble endoglin in prediction models for all preeclampsia, together with the prior-risk estimates, mean arterial pressure, placental growth factor, and soluble vascular endothelial growth factor receptor-1, increased the sensitivity (at a fixed false-positive rate of 10%) for early prediction of superimposed preeclampsia, with the largest increase (from 44% to 54%) noted at 20 to 23+6 weeks (McNemar test, P<.05); (2) combined evidence from prior-risk estimates and biomarkers predicted preterm preeclampsia with a sensitivity (false-positive rate, 10%) of 55%, 48%, 62%, 72%, and 84% at 8 to 15+6, 16 to 19+6, 20 to 23+6, 24 to 27+6, and 28 to 31+6 week intervals, respectively; (3) the sensitivity for term preeclampsia (false-positive rate, 10%) was 36%, 36%, 41%, 43%, 39%, and 51% 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, respectively; (4) the detection rate for superimposed preeclampsia among women with chronic hypertension was similar to that in women without chronic hypertension, especially earlier in pregnancy, reaching at most 54% at 20 to 23+6 weeks (false-positive rate, 10%); and (5) prediction models performed comparably to the Fetal Medicine Foundation calculators when the same maternal risk factors and biomarkers (mean arterial pressure, placental growth factor, and soluble vascular endothelial growth factor receptor-1 multiples of the mean values) were used as input.

      Conclusion

      We introduced prediction models for preeclampsia throughout pregnancy. These models can be useful to identify 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, there was a substantial improvement in the detection rate for preterm and term preeclampsia by using data collected after 20 and 32 weeks’ gestation, respectively. The inclusion of plasma soluble endoglin improves the early prediction of superimposed preeclampsia, which may be valuable when Doppler velocimetry of the uterine arteries is not available.

      Key words

      Introduction

      Preeclampsia is a complex obstetrical syndrome responsible for maternal and infant morbidity and mortality worldwide.
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       Why was this study conducted?

      This study aimed to generate models and calculators for the prediction of preeclampsia based on maternal risk factors and multiples of the mean of biophysical and biochemical markers collected longitudinally.

       Key findings

      Relative to the results based on data collected at 8 to 15+6 weeks, the sensitivity for preterm and term preeclampsia improved after 20 and 32 weeks, respectively. The inclusion of plasma soluble endoglin (sEng) improved early prediction of disease. Models performed similarly to the Fetal Medicine Foundation calculators when the same biomarker data were used as input, suggesting a modest impact of differences in the analytical approaches.

       What does this add to what is known?

      Models and calculators to predict preeclampsia throughout gestation were developed, and prediction performance increased with advancing gestational age. The inclusion of sEng increased accuracy early in gestation for preterm and superimposed preeclampsia, which may be valuable when Doppler velocimetry of the uterine arteries is unavailable.
      State-of-the-art prediction models for preeclampsia were proposed in a series of papers published by the Fetal Medicine Foundation (FMF). These models involve a combination of maternal risk factors and multiples of the mean (MoM) values of mean arterial pressure (MAP), uterine artery pulsatility index (UtA-PI), and blood concentrations of PlGF, sVEGFR-1 (also known as sFLT-1), and pregnancy-associated plasma protein A (PAPP-A).
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      The use of such prediction models in practice requires not only the availability of biomarker measurements but also standards to define their expected values, given maternal characteristics, gestational age at measurement, and the type of assay used to measure biochemical markers. Such customized biomarker standards (referred to herein as MoM models) are then used to determine how abnormal the observed biomarker values are by calculating the MoM values. Additional customization of risk cutoff values may be required depending on ethnicity and clinical site. For example, a pooled analysis of 3 prospective nonintervention screening studies (61,174 women with a singleton pregnancy; 1770 cases of preeclampsia) found that first-trimester screening in white women detected 70% of preterm preeclampsia (<37 weeks’ gestation) and 40% of term preeclampsia (≥37 weeks’ gestation) at a 10% false-positive rate (FPR).
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      The same risk cutoff values applied in women of Afro-Caribbean racial origin led to detection rates of 92% for preterm preeclampsia and 75% for term preeclampsia, yet the FPR was 3-fold higher than that of white women.
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      The International Federation of Gynecology and Obstetrics (FIGO) initiative on pre-eclampsia: a pragmatic guide for first-trimester screening and prevention.
      Therefore, based on a previously described retrospective longitudinal study design, we aimed first to develop MoM models for biophysical (MAP) and biochemical (PlGF, sVEGFR-1, and sEng) markers in our majority African American population attending the Detroit Medical Center that could determine MoM values to be used in existing or novel prediction models for preeclampsia. A second goal was to assess the predictive performance of risk models based on maternal characteristics and obstetrical history and biophysical and biochemical marker MoM values throughout pregnancy. Finally, we evaluated how the prediction performance of the novel models was affected by the presence of chronic hypertension, the timing of delivery, and the method used to combine prior-risk and biomarker-based evidence. This latter aspect is important because state-of-the-art approaches for the prediction of preeclampsia give equal weight to both sources of evidence.

      Materials and Methods

       Study design

      This was a retrospective analysis of data from 1150 pregnancies, previously described as part of a case-cohort of 1499 pregnancies on which we reported the prediction of early delivery of placentas presenting lesions of maternal vascular underperfusion.
      • Korzeniewski S.J.
      • Romero R.
      • Chaiworapongsa T.
      • et al.
      Maternal plasma angiogenic index-1 (placental growth factor/soluble vascular endothelial growth factor receptor-1) is a biomarker for the burden of placental lesions consistent with uteroplacental underperfusion: a longitudinal case-cohort study.
      The original multiple disease case-cohort (n=1499), from which this preeclampsia case-cohort study (n=1150) was drawn, was designed in 2 stages to include 1000 randomly selected women and all remaining major obstetrical complications (ie, preeclampsia, preterm labor, preterm prelabor rupture of the membranes, and small-for-gestational-age gestation [<5th percentile]) from a cohort of 4006 women with a singleton pregnancy, enrolled at 6 to 22 weeks’ gestation, in a longitudinal biomarker study.
      • Korzeniewski S.J.
      • Romero R.
      • Chaiworapongsa T.
      • et al.
      Maternal plasma angiogenic index-1 (placental growth factor/soluble vascular endothelial growth factor receptor-1) is a biomarker for the burden of placental lesions consistent with uteroplacental underperfusion: a longitudinal case-cohort study.
      The preeclampsia case-cohort retained for this study included all women from the random sample of 1000 pregnancies and all additional preeclampsia cases from the cohort of 4006. Therefore, the resulting preeclampsia case-cohort included women without preeclampsia or chronic hypertension (normotensive controls, n=871), women with chronic hypertension without preeclampsia (hypertensive controls, n=49), women who developed preeclampsia (n=166), or women with preeclampsia superimposed on chronic hypertension (n=64). Of the 230 cases of preeclampsia, 83 were preterm preeclampsia (delivery at <37 weeks’ gestation).
      Blood samples in the original case-cohort were collected in 3 to 5 of 6 predefined intervals of gestation (8–15+6, 16–19+6, 20–23+6, 24–27+6, 28–31+6, and 32–36+6 weeks). Maternal plasma PlGF, sEng, and sVEGFR-1 were determined by enzyme-linked immunosorbent assays. In total, 6035 samples across multiple gestational intervals were included in the current analysis (median number of samples, 5; interquartile range [IQR], 4–6).
      The use of clinical data and biologic specimens was approved by the Institutional Review Boards of Wayne State University and the Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, United States Department of Health and Human Services. All patients provided written informed consent before the collection of samples.

       Sample collection and immunoassays

      Samples were collected by venipuncture into tubes containing ethylenediaminetetraacetic acid, centrifuged, and stored at −70°C. The most centrally located venipuncture sample within each of the 6 intervals of gestational age for each patient was used for analysis. The inter- and intra-assay coefficients of variation of the assays were 1.4% and 3.9% for sVEGFR-1, 2.3% and 4.6% for sEng, and 6.02% and 4.8% for PlGF, respectively. The sensitivity of each assay was 16.97 pg/mL for sVEGFR-1, 0.08 ng/mL for sEng, and 9.52 pg/mL for PlGF. Sample collection methods, biospecimen processing, and validation of the assays used were previously reported in greater detail.
      • Oggè G.
      • Romero R.
      • Kusanovic J.P.
      • et al.
      Serum and plasma determination of angiogenic and antiangiogenic factors yield different results: the need for standardization in clinical practice.

       Clinical definitions and outcomes

      The primary outcome was the development of preeclampsia. Secondary outcomes were preterm (<37 weeks’ gestation) preeclampsia, term preeclampsia, and superimposed preeclampsia. We defined uncomplicated pregnancy as a pregnancy with no major obstetrical, medical, or surgical complications, where women delivered a term neonate. Preeclampsia was defined as new-onset proteinuria and hypertension—blood pressure of ≥140/90 mm Hg on 2 occasions at least 4 hours apart or ≥160/110 mm Hg within a shorter interval (minutes)—at ≥20 weeks’ gestation.
      • Chaiworapongsa T.
      • Chaemsaithong P.
      • Yeo L.
      • Romero R.
      Pre-eclampsia part 1: current understanding of its pathophysiology.
      Proteinuria was defined as a urine protein of ≥300 mg in a 24-hour urine collection, or 2 random urine specimens, obtained 4 hours to 1 week apart, showing ≥1+by dipstick.
      ACOG Committee on Practice Bulletins--Obstetrics
      ACOG practice bulletin. Diagnosis and management of preeclampsia and eclampsia. Number 33, January 2002.
      Hypertension was defined as a systolic blood pressure of ≥140 and/or a diastolic blood pressure of ≥90 mm Hg, measured at least on 2 occasions, 4 hours to 1 week apart.
      ACOG Committee on Practice Bulletins--Obstetrics
      ACOG practice bulletin. Diagnosis and management of preeclampsia and eclampsia. Number 33, January 2002.
      Chronic hypertension was defined in women with hypertension at <20 weeks’ gestation or in those who reported a history of hypertension. For women with chronic hypertension, superimposed preeclampsia was diagnosed by a new onset of proteinuria (either 300 mg/24 hours or ≥2+by dipstick) or thrombocytopenia (platelet count of <100×103/mm3), elevated liver enzymes (aspartate aminotransferase or alanine aminotransferase of >70 IU/L), or pulmonary edema.

       Statistical analysis

       Demographic data analysis

      Demographic and clinical characteristics were compared between the groups by using Fisher exact tests for categorical data and 2-tailed t tests for continuous variables, respectively. P<.05 was considered a statistically significant result.

       Calculation of the multiples of the mean for biomarkers

      The plasma concentrations of PlGF, sEng, and sVEGFR-1 and MAP were first logarithmically transformed to improve the normality of distribution and to stabilize variance.
      • Royston P.
      • Altman D.G.
      Regression using fractional polynomials of continuous covariates: parsimonious parametric modelling.
      The expected values of these variables among controls (with and without chronic hypertension) were estimated as a function of maternal characteristics and gestational age in each interval of gestation by using linear regression. The following maternal characteristics were considered for inclusion in the regression models and retained if they contributed to decreasing the Akaike information criterion by using stepwise backward elimination: gestational age at sample collection, maternal age, nulliparity, history of preeclampsia, weight, smoking status, and interaction terms between these covariates and chronic hypertension. MoM values of biomarkers were then calculated as the ratio between the observed biomarker value and expected values for gestational age and maternal characteristics for all samples. MoM values were further log10 transformed. Box-and-whisker plots (median, IQR, and range) of the biomarker log10 MoM values for each outcome group were created. Two-tailed t tests were used to determine the significance of differences in the MoM values between groups.

       Prior-risk model for preeclampsia by using maternal risk factors and obstetrical history

      Poisson regression with sandwich estimation of variance was used to estimate the prior (anterior) risk of preeclampsia based on maternal characteristics and obstetrical history. Model selection was based on the findings from a logistic regression model with backward stepwise elimination of variables, starting with maternal age, weight, height, nulliparity, smoking status, history of preeclampsia, and interaction terms between these covariates and chronic hypertension. Once variables were selected, the Poisson regression model was fit, setting the weight of cases to 1.0, whereas the weights of the noncases were set so that the ratio of the total weight of cases to controls was the same as in the parent cohort of 4006 pregnancies.

       Developing prediction models for preeclampsia by using prior risk and multiples of the mean data of biophysical and biochemical markers

      Multivariable Poisson regression models were fit by using data collected in each interval of gestation (8–15+6, 16–19+6, 20–23+6, 24–27+6, 28–31+6, and 32–36+6 weeks). The models included as predictors the log10 MoM of MAP, PlGF, sVEGFR-1, sEng, and all pairwise interaction terms among these variables. The predicted risk (log thereof) based on the prior-risk model was included as an input in the biomarker-based model; hence, the 2 types of evidence were automatically weighted to maximize model fit. As in the prior-risk model, the weight of cases was set to 1.0 whereas the weights of controls were set so that the ratio of the total weight of cases to controls was the same as in the parent cohort of 4006 pregnancies. Cutoff values on the predicted combined-risk scores were identified so that the FPRs were either 10% or 20%. Performance indices (area under the receiver operating characteristic [ROC] curve [AUC], sensitivity, specificity, and positive [+] and negative [−] predictive likelihood ratios) were calculated for each interval of gestation. A risk assessment calculator is available from the authors’ website (https://bioinformaticsprb.med.wayne.edu/software/). Analysis was performed using caret, ROCR, and pROC packages for the R statistical language and environment (www.r-project.org).

      Results

       Maternal characteristics, obstetrical history, and the prior risk of preeclampsia

      Demographic and clinical characteristics of the study population according to pregnancy outcome are presented in Table 1. The Poisson regression model used to calculate the prior risk of preeclampsia based on maternal characteristics and obstetrical history is presented in Supplemental Table 1. Risk factors included in the model were chronic hypertension, maternal weight, nulliparity, and history of preeclampsia, and all were associated with an increased risk of disease. Based on these variables, the prior-risk model predicted overall preeclampsia, preterm preeclampsia, and term preeclampsia with an AUC of 0.7 (0.66–0.74), 0.67 (0.6–0.74), and 0.71 (0.67–0.76), respectively (Figure 1).
      Table 1Demographic characteristics of the study population
      Clinical featuresControls without chronic hypertension n=871
      Indicates the number of cases part of the random samples of 1000 pregnancies
      Controls with chronic hypertension n=49
      Indicates the number of cases part of the random samples of 1000 pregnancies
      Preeclampsia n=166 (56
      Indicates the number of cases part of the random samples of 1000 pregnancies
      )
      Superimposed preeclampsia n=64 (24
      Indicates the number of cases part of the random samples of 1000 pregnancies
      )
      Age in y, median (IQR)23 (20–27)26 (22–31)
      P<.001
      21 (19–26)27 (22–32.2)
      P<.001
      Racial origin
       African American, n (%)850 (92.4)47 (95.9)159 (95.8)
      P<.05
      60 (93.8)
      P<.001
       White, n (%)23 (2.5)0 (0)3 (1.8)
      P<.001
      1 (1.6)
      P<.001
       Other
      Includes women who identified as Hispanic, Asian, or “Other.” Maternal height and weight were recorded in inches and pounds and then converted into cm and kg, respectively, before analysis. Statistically significant differences are reported vs normotensive controls (Fisher exact for categorical variables and 2-sided t tests for continuous variables).
      , n (%)
      47 (5.1)2 (4.1)
      P<.05
      4 (2.4)
      P<.001
      3 (4.7)
      P<.001
      Nulliparity, n (%)354 (38.5)15 (30.6)
      P<.001
      87 (52.4)19 (29.7)
      P<.001
      History of preeclampsia, n (%)29 (3.2)4 (8.2)11 (6.6)10 (15.6)
      Smoking, n (%)188 (20.4)12 (24.5)
      P<.001
      28 (16.9)22 (34.4)
      P<.001
      Weight (kg), median (IQR)70 (59–86)99 (73–115)
      P<.001
      73 (61–87.8)91 (78–109)
      P<.001
      Height (cm), median (IQR)162.6 (157.5–167.6)165.1 (157.5–170.2)162.6 (157.5–167.6)165.1 (159.4–170.2)
      Birthweight (g), median (IQR)3185 (2820–3485)3060 (2637–3265)
      P<.05
      2820 (2171.2–3268.8)
      P<.001
      2725 (2087.5–3270)
      P<.001
      Gestational age at delivery in wk, median (IQR)39.3 (38.1–40.3)38.4 (37.6–39.3)37.7 (36.1–39.1)
      P<.001
      37.2 (35.6–38.2)
      P<.001
      Data are expressed as median (IQR) or number (percentage).
      IQR, interquartile range.
      Tarca et al. Prediction of preeclampsia throughout pregnancy. Am J Obstet Gynecol 2022.
      a Indicates the number of cases part of the random samples of 1000 pregnancies
      b P<.001
      c P<.05
      d Includes women who identified as Hispanic, Asian, or “Other.” Maternal height and weight were recorded in inches and pounds and then converted into cm and kg, respectively, before analysis. Statistically significant differences are reported vs normotensive controls (Fisher exact for categorical variables and 2-sided t tests for continuous variables).
      Figure thumbnail gr1
      Figure 1Prediction performance of the preeclampsia prior-risk model and the combined-risk models
      ROC curve for prediction of (A) overall preeclampsia (B) preterm preeclampsia, and (C) term preeclampsia based on the prior-risk model and the combined evidence (prior risk and biomarkers).
      ROC, receiver operating characteristic.
      Tarca et al. Prediction of preeclampsia throughout pregnancy. Am J Obstet Gynecol 2022.

       Factors affecting the biophysical and biochemical marker data in women without preeclampsia

      Concentrations of PlGF, sVEGFR-1, and sEng were measured in 6035 maternal plasma samples (median number of samples, 5; IQR, 4–6) collected throughout gestation from the 1150 women in the study case-cohort. The MAP data were also available at the date of blood sample collection. To create standards for MoM calculation, the biomarker data among women without preeclampsia were fit as a function of maternal characteristics and obstetrical history by using linear regression. The MoM models (Supplemental Table 2) show that biochemical and biophysical markers in women without preeclampsia change with maternal weight, height, parity, smoking status, and history of preeclampsia and that the effect of these covariates is modified by the presence of chronic hypertension.

       Distribution of biomarker multiples of the mean data by the presence or absence of preeclampsia, chronic hypertension status, and gestational age at blood sample collection

      The biomarker MoM values (log10 thereof) were displayed and compared among groups based on disease status (ie, preeclampsia) and the presence of chronic hypertension (Supplemental Figure 1). The angiogenic profiles differed by preeclampsia and chronic hypertension status throughout gestation (Supplemental Figure 1). The PlGF MoM values were significantly lower and sEng MoM values were higher in the preeclampsia and/or superimposed preeclampsia groups than in the normotensive control group from the 20 to 23+6 week interval onward (all P<.05). The sVEGFR-1 MoM values were higher in the preeclampsia and/or superimposed preeclampsia group than in the normotensive control group from the 24 to 27+6 week interval onward (all P<.05). Of note, after 20 weeks onward, the PlGF MoM values were more abnormal in the preeclampsia group than in the superimposed preeclampsia group, relative to normotensive controls (Supplemental Figure 1). The MAP MoM values were significantly increased, starting with the 8 to 15+6 week interval in the preeclampsia and/or superimposed preeclampsia group compared to the control group, and the magnitude of the difference increased with advancing gestational age.
      At 8 to 15+6 weeks, the log10 PlGF MoM values showed a significant correlation with gestational age at delivery with preeclampsia, yet this was not the case for MAP (Supplemental Figure 2). This finding suggests that although abnormally high MAP MoM values at this gestational age are predictive of preeclampsia, they do not predict gestational age at delivery with preeclampsia, which was the case in other reports that used a standardized approach to blood pressure measurement.
      • Wright D.
      • Wright A.
      • Nicolaides K.H.
      The competing risk approach for prediction of preeclampsia.

       Prediction performance for preeclampsia based on combined prior risk and evidence from biomarkers

      Prediction models for preeclampsia, which are based on combined prior risk and evidence from biophysical and biochemical marker values throughout gestation, are presented in Supplemental Table 3. Of note, the combined-risk models displayed different weights for the prior-risk component (coefficient for prior-risk variable in Supplemental Table 3) depending on the gestational age at measurements. The weight of the prior-risk component diminished with advancing gestational age at measurement as the biomarker data became more informative. Assigning a fixed weight to the prior-risk component would have resulted in significantly lower sensitivity (FPR, 10%) than allowing it to vary as shown in Supplemental Table 3 (McNemar test, P<.05 at 20–23+6, 24–27+6, and 28–31+6 weeks when combining prior risk with PlGF-derived evidence for predicting all preeclampsia).
      The ROC curves for the prediction of preeclampsia by the prior-risk model and the combination of prior risk with biomarker-based evidence collected throughout gestation are presented in Figure 1. The AUC for the prediction of all preeclampsia slightly improved from 0.75 (0.69–0.8), when only prior-risk evidence was considered, to 0.76 (0.71–0.81) when the prior risk was combined with evidence from the biomarker-based model by using data from patients with available biomarker data collected at 8 to 15+6 weeks (Table 2). As gestational age increased, hence approaching diagnosis, the AUC increased to 0.86 (0.83–0.89) at 32 to 36+6 weeks. The detection rates (FPR, 10%) for preeclampsia (all cases) were 44%, 36%, 45%, 52%, 55%, and 61% 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, respectively.
      Table 2Screening performance for preeclampsia using the prior-risk model and the combined-risk model (prior risk and biomarkers)
      Gestational age (wk)Prior-risk AUCCombined-risk AUCCombined-risk FPR of 10%Combined-risk FPR of 20%
      CutoffSensitivityLR+LR−CutoffSensitivityLR+LR−
      All preeclampsia
      8–15+60.75 (0.69–0.8)0.76 (0.71–0.81)0.0840.44 (0.35–0.54)4.43 (3.14–6.24)0.62 (0.52–0.73)0.0550.55 (0.45–0.64)2.72 (2.12–3.49)0.57 (0.46–0.7)
      16–19+60.68 (0.63–0.73)0.71 (0.66–0.76)0.0840.36 (0.28–0.45)3.62 (2.61–5.01)0.71 (0.63–0.81)0.0580.53 (0.44-0.61)2.62 (2.09–3.28)0.59 (0.5–0.71)
      20–23+60.67 (0.62–0.72)0.77 (0.72–0.81)0.0870.45 (0.37–0.53)4.51 (3.4–5.98)0.61 (0.53–0.7)0.0610.6 (0.52–0.67)3.00 (2.46–3.65)0.50 (0.41–0.61)
      24–27+60.70 (0.65–0.74)0.80 (0.76–0.83)0.0810.52 (0.44–0.59)5.13 (3.97–6.63)0.54 (0.46–0.63)0.0550.65 (0.57–0.72)3.23 (2.7–3.86)0.44 (0.36–0.54)
      28–31+60.69 (0.64–0.73)0.80 (0.76–0.84)0.0750.55 (0.48–0.63)5.58 (4.35–7.15)0.49 (0.42–0.58)0.0510.66 (0.59–0.73)3.32 (2.79–3.95)0.42 (0.34–0.52)
      32–36+60.69 (0.65–0.74)0.86 (0.83–0.89)0.0840.61 (0.54–0.69)6.15 (4.86–7.79)0.43 (0.36–0.52)0.050.74 (0.67–0.8)3.69 (3.14–4.34)0.33 (0.25–0.42)
      Preterm preeclampsia
      8–15+60.70 (0.6–0.8)0.78 (0.7–0.86)0.0450.55 (0.39–0.7)5.45 (3.71–8.02)0.50 (0.36–0.7)0.0240.62 (0.46–0.76)3.08 (2.29–4.15)0.48 (0.32–0.7)
      16–19+60.65 (0.56–0.74)0.80 (0.74–0.86)0.040.48 (0.34–0.62)4.84 (3.35–6.98)0.58 (0.44–0.75)0.0260.63 (0.49–0.76)3.14 (2.42–4.07)0.46 (0.33–0.66)
      20–23+60.63 (0.54–0.72)0.88 (0.83–0.92)0.0340.62 (0.48–0.75)6.15 (4.53–8.36)0.42 (0.3–0.59)0.0180.76 (0.63–0.87)3.83 (3.1–4.73)0.30 (0.18–0.48)
      24–27+60.65 (0.56–0.73)0.91 (0.88–0.95)0.0270.72 (0.6–0.83)7.18 (5.53–9.32)0.31 (0.21–0.46)0.0160.82 (0.7–0.9)4.08 (3.39–4.9)0.23 (0.14–0.39)
      28–31+60.64 (0.55–0.73)0.94 (0.91–0.98)0.0160.84 (0.71–0.92)8.41 (6.61–10.68)0.18 (0.1–0.33)0.0090.87 (0.76–0.95)4.36 (3.67–5.17)0.16 (0.08–0.32)
      32–36+60.65 (0.56–0.73)0.97 (0.95–0.99)0.0160.92 (0.81–0.98)9.26 (7.44–11.53)0.09 (0.03–0.22)0.0060.96 (0.87–1)4.8 (4.14–5.55)0.05 (0.01–0.19)
      Term preeclampsia
      8–15+60.78 (0.72–0.84)0.78 (0.72–0.84)0.0560.36 (0.25–0.49)3.62 (2.38–5.5)0.71 (0.59–0.85)0.0340.64 (0.51–0.75)3.17 (2.45–4.1)0.46 (0.33–0.63)
      16–19+60.70 (0.64–0.76)0.71 (0.65–0.77)0.0470.36 (0.26–0.48)3.67 (2.53–5.3)0.71 (0.6–0.83)0.0360.49 (0.38–0.6)2.46 (1.88–3.22)0.63 (0.51–0.78)
      20–23+60.69 (0.64–0.75)0.73 (0.68–0.79)0.060.41 (0.32–0.51)4.12 (2.99–5.68)0.65 (0.55–0.77)0.0410.54 (0.44–0.64)2.70 (2.14–3.41)0.58 (0.47–0.71)
      24–27+60.73 (0.68–0.78)0.77 (0.72–0.82)0.0530.43 (0.34–0.53)4.31 (3.19–5.81)0.63 (0.53–0.74)0.0380.58 (0.48–0.67)2.88 (2.32–3.56)0.53 (0.43–0.66)
      28–31+60.71 (0.66–0.76)0.75 (0.7–0.8)0.0540.39 (0.3–0.48)3.92 (2.88–5.33)0.68 (0.59–0.78)0.0360.59 (0.5–0.68)2.96 (2.42–3.63)0.51 (0.41–0.63)
      32–36+60.71 (0.66–0.76)0.82 (0.78–0.86)0.0610.51 (0.42–0.6)5.14 (3.93–6.72)0.54 (0.45–0.65)0.040.68 (0.59–0.76)3.38 (2.82–4.06)0.40 (0.31–0.52)
      The sensitivity at fixed 10% and 20% FPR for the gestational-age interval–specific models predicting preeclampsia. Cutoffs were chosen so that FPR is 10% or 20%. AUC CIs were calculated using DeLong method. Note that slight variations in prediction performance of the prior-risk model are caused by differences in the sets of cases and controls with an available sample in each interval.
      AUC, area under the receiver operating characteristic curve; CI, confidence interval; FPR, false-positive rate; LR, likelihood ratio.
      Tarca et al. Prediction of preeclampsia throughout pregnancy. Am J Obstet Gynecol 2022.
      The AUC for prediction of term preeclampsia by prior-risk and biomarkers was 0.78 (0.72–0.84) at 8 to 15+6 weeks and did not improve with advancing gestational age at measurements until the 32 to 36+6 week interval when it reached 0.82 (0.78–0.86). The detection rates (FPR, 10%) for term preeclampsia were 36%, 36%, 41%, 43%, 39%, and 51% 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, respectively.
      In agreement with previous reports, the prediction of preterm preeclampsia was more accurate than that of term preeclampsia. The AUC for preterm preeclampsia improved from 0.7 (0.6–0.8), when only prior-risk evidence was considered, to 0.78 (0.7–0.86) when the prior-risk evidence was combined with evidence derived from biomarkers at 8 to 15+6 weeks, and it reached 0.94 (0.91–0.98) at the 28 to 31+6 week interval. The detection rates (FPR, 10%) for preterm preeclampsia were 55%, 48%, 62%, 72%, and 84% at 8 to 15+6, 16 to 19+6, 20 to 23+6, 24 to 27+6, and 28 to 31+6 week intervals, respectively.
      When the prediction models for all preeclampsia were evaluated in the subset of women with chronic hypertension (comparison of superimposed preeclampsia with chronic hypertension without superimposed preeclampsia), the detection rates (FPR, 10%) were 40%, 38% 54%, 48%, 38%, and 43% 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, respectively (Figure 2, Table 3). Of note, the risk cutoff values required to reach the same FPR when screening patients with chronic hypertension (Table 3) were higher than the cutoff values used when screening the entire study population (Table 2). This finding can be explained by the higher prior risk of disease among women with chronic hypertension than in the overall population.
      Figure thumbnail gr2
      Figure 2Prediction performance of the preeclampsia combined-risk models for patients with and without chronic hypertension
      ROC curve for prediction of preeclampsia are based on the all preeclampsia risk models in . Separate ROC curves are drawn for patients with and without chronic hypertension.
      ROC, receiver operating characteristic.
      Tarca et al. Prediction of preeclampsia throughout pregnancy. Am J Obstet Gynecol 2022.
      Table 3Prediction of preeclampsia in women with chronic hypertension
      GA (wk)Prior-risk AUCCombined-risk AUCCombined-risk FPR of 10%Combined-risk FPR of 20%
      CutoffSensitivityLR+LR−CutoffSensitivityLR+LR−
      All preeclampsia
      8–15+60.49 (0.34–0.63)0.67 (0.54–0.8)0.3250.40 (0.24–0.58)4.13 (1.31–13.05)0.66 (0.5–0.89)0.3070.40 (0.24–0.58)2.07 (0.91–4.72)0.74 (0.54–1.03)
      16–19+60.52 (0.39–0.66)0.64 (0.51–0.77)0.2630.38 (0.22–0.55)3.50 (1.27–9.64)0.70 (0.53–0.92)0.2360.43 (0.27–0.61)2.29 (1.07–4.9)0.70 (0.51–0.97)
      20–23+60.56 (0.44–0.68)0.69 (0.59–0.8)0.2840.54 (0.39–0.69)4.98 (2.09–11.86)0.51 (0.37–0.71)0.2140.60 (0.45–0.74)3.09 (1.65–5.79)0.49 (0.34–0.72)
      24–27+60.51 (0.39–0.63)0.65 (0.53–0.76)0.2430.48 (0.34–0.62)4.81 (1.82–12.7)0.58 (0.44–0.76)0.2240.52 (0.38–0.66)2.60 (1.32–5.09)0.60 (0.44–0.83)
      28–31+60.55 (0.43–0.67)0.70 (0.59–0.81)0.3120.38 (0.25–0.54)3.93 (1.45–10.66)0.68 (0.53–0.88)0.2020.55 (0.4–0.7)2.84 (1.45–5.56)0.56 (0.39–0.79)
      32–36+60.48 (0.36–0.6)0.75 (0.65–0.85)0.2880.43 (0.29–0.59)4.09 (1.68–9.97)0.63 (0.48–0.83)0.2190.59 (0.43–0.73)3.07 (1.62–5.79)0.51 (0.35–0.74)
      The sensitivity at fixed 10% and 20% FPR for the GA interval–specific models predicting preeclampsia. Cutoffs were chosen so that FPR is 10% or 20%. AUC CIs were calculated using DeLong method. Note that slight variations in prediction performance of the prior-risk model are caused by differences in the sets of cases and controls with a sample in each interval.
      AUC, area under the receiver operating characteristic curve; CI, confidence interval; FPR, false-positive rate; GA, gestational age; LR, likelihood ratio.
      Tarca et al. Prediction of preeclampsia throughout pregnancy. Am J Obstet Gynecol 2022.
      To assess the added value of sEng, we compared the prediction performance for preterm preeclampsia and separately for superimposed preeclampsia based on models with and without sEng and found that the inclusion of sEng increased the prediction performance in the first 3 intervals of gestational age (Table 4). The addition of sEng data in the prediction models that included the prior-risk estimate, MAP, PlGF, and sVEGFR-1 increased the sensitivity (FPR, 10%) for the prediction of superimposed preeclampsia from 31% to 40%, from 32% to 38%, and from 44% to 54% at 8 to 15+6, 16 to 19+6, and 20 to 23+6 week intervals, respectively (McNemar test for paired sensitivity, P<.05 for changes at 20–23+6 weeks). Point estimates of AUC for the prediction of preterm preeclampsia were also increased up to 0.03 units when sEng was included in the 3 models based on data collected at 24 weeks (Table 4).
      Table 4Screening performance for preeclampsia based on combined-risk models with and without sEng


      Gestational age (wk)
      Prior risk+MAP+PlGF+sVEGFR-1Prior risk+MAP+PlGF+sVEGFR-1+sEng
      AUCSensitivity (FPR=10%)AUCSensitivity (FPR=10%)
      Prediction of preeclampsia in women with chronic hypertension based on all preeclampsia risk models
      8–15+60.67 (0.54–0.80)0.31 (0.17–0.49)0.67 (0.54–0.8)0.40 (0.24–0.58)
      16–19+60.63 (0.51–0.76)0.32 (0.18–0.50)0.64 (0.51–0.77)0.38 (0.22–0.55)
      20–23+60.67 (0.55–0.78)0.44 (0.29–0.59)0.69 (0.59–0.80)0.54 (0.39–0.69)
      P<.05 McNemar test for paired sensitivities indicating that the inclusion of sEng improves sensitivity at 10% FPR
      24–27+60.62 (0.50–0.73)0.42 (0.29–0.57)0.65 (0.53–0.76)0.48 (0.34–0.62)
      28–31+60.70 (0.59–0.81)0.38 (0.25–0.54)0.70 (0.59–0.81)0.38 (0.25–0.54)
      32–36+60.75 (0.64–0.85)0.43 (0.29–0.59)0.75 (0.65–0.85)0.43 (0.29–0.59)
      Prediction of preterm preeclampsia based on preterm preeclampsia risk models
      8–15+60.77 (0.69–0.85)0.50 (0.34–0.66)0.78 (0.70–0.86)0.55 (0.39–0.70)
      16–19+60.77 (0.70–0.84)0.48 (0.34–0.62)0.80 (0.74–0.86)0.48 (0.34–0.62)
      20–23+60.86 (0.80–0.91)0.60 (0.46–0.73)0.88 (0.83–0.92)0.62 (0.48–0.75)
      24–27+60.91 (0.86–0.95)0.75 (0.63–0.85)0.91 (0.88–0.95)0.72 (0.60–0.83)
      28–31+60.94 (0.91–0.97)0.82 (0.69–0.91)0.94 (0.91–0.98)0.84 (0.71–0.92)
      32–36+60.97 (0.95–0.99)0.92 (0.81–0.98)0.97 (0.95–0.99)0.92 (0.81–0.98)
      AUC, area under the ROC curve; FPR, false-positive rate; MAP, mean arterial pressure; P1GF, placental growth factor; ROC, receiver operating characteristic; sEng, soluble endoglin; sVEGFR-1, soluble vascular endothelial growth factor receptor-1.
      Tarca et al. Prediction of preeclampsia throughout pregnancy. Am J Obstet Gynecol 2022.
      a P<.05 McNemar test for paired sensitivities indicating that the inclusion of sEng improves sensitivity at 10% FPR
      Finally, to put into perspective the results herein with those previously published and to assess the potential for the overfitting of our models to the current cohort, we applied the FMF’s preeclampsia risk models and compared prediction performance on our study population. In this analysis, the same maternal risk factors used in our prior-risk model (Supplemental Table 1) and the MoM data for MAP, PlGF, and sVEGFR-1 were used to fit combined-risk models as described earlier, and models were evaluated based on samples collected in the gestational-age windows defined by the FMF’s risk calculators. The AUC of the prior and combined-risk models developed herein performed similarly with the existing preeclampsia risk models when applied to patients with available data collected at 11 to 14+1 and 35 to 37+6 week intervals (Figure 3, Supplemental Table 4). However, the combined-risk models developed herein had modestly higher AUC (up to 0.07 difference) when applied to patients with available data collected at 19 to 24+6 and 30 to 34+6 week intervals (Figure 3, Supplemental Table 4). The sensitivity for overall preeclampsia (FPR, 10%) was 48% vs 36%, 43% vs 36%, 53% vs 46%, and 66% vs 53% for the models herein vs those of the FMF at 11 to 14+1, 19 to 24+6, 30 to 34+6, and 35 to 37+6 weeks, respectively. This finding suggests (1) minimal overfitting to the current cohort of the models developed in this work and (2) differences in prediction performance reported herein and those reported by the FMF are most likely caused by the way biomarkers are measured and to the patient populations, as opposed to differences in analytical approaches.
      Figure thumbnail gr3
      Figure 3Prediction performance for preeclampsia by models developed herein and the FMF models
      ROC curve for prediction of all preeclampsia based on data collected in gestational-age intervals specified in FMF calculators. Separate curves are drawn for prior-risk models and combined-risk models. For this analysis, the models in were modified to exclude the contribution of sEng, which is not used in FMF models. The FMF models utilized a combination of maternal factors and clinical history (), MAP, and PlGF. For analyses based on data collected after 25 weeks’ gestation, sVEGFR-1 was also included.
      FMF, Fetal Medicine Foundation; MAP, mean arterial pressure; PlGF, placental growth factor; ROC, receiver operating characteristic; sEng, soluble endoglin; sVEGFR-1, soluble vascular endothelial growth factor receptor-1.
      Tarca et al. Prediction of preeclampsia throughout pregnancy. Am J Obstet Gynecol 2022.

      Comment

       Principal findings

      The longitudinal study herein focused on the development of MoM models for MAP- and blood-based biomarkers and risk prediction models for preeclampsia that can be used in similar populations. Our findings are as follows: (1) inclusion of sEng in the prediction models, together with the currently used MAP, PlGF, and sVEGFR-1 biomarkers and prior-risk evidence (from maternal characteristics and obstetrical history), increased the sensitivity for the prediction of superimposed preeclampsia early in gestation; (2) the prediction of overall preeclampsia was achieved with a sensitivity ranging from 44% to 61% (FPR, 10%) as gestational age at measurement increased from 8 to 15+6 to 32 to 36+6 week intervals, respectively; (3) the prediction of preterm preeclampsia was achieved with a sensitivity ranging from 55% to 84% (FPR, 10%) as gestational age at measurement increased from 8 to 15+6 to 28 to 31+6 week intervals, respectively; (4) the sensitivity for term preeclampsia (FPR, 10%) ranged from 36% to 51% (FPR, 10%) as gestational age at measurement increased from 8 to 15+6 to 32 to 36+6 week intervals, respectively; and (5) the accuracy of prediction models for superimposed preeclampsia among women with chronic hypertension was similar to that among women without chronic hypertension, especially earlier in pregnancy, reaching at most 54% at 20 to 23+6 weeks (FPR, 10%).

       Results in the context of what is known

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      Personalized assessment of cervical length improves prediction of spontaneous preterm birth: a standard and a percentile calculator.
      After removing the effect of gestational age and maternal characteristics on biomarker levels, any remaining abnormality in MoM values in cases relative to controls is considered independent evidence that can be used to improve accuracy of predictions.
      However, our approach differs from the FMF in several aspects. Although the FMF treats the outcome as a continuous variable (gestational age at delivery with preeclampsia), we modeled preeclampsia as a binary outcome. A key assumption of the FMF approach is that the more abnormal the biomarker in any given measurement interval (eg, 11–14 weeks’ gestation), the earlier the delivery with preeclampsia. Our data support this concept for PlGF because more abnormal values (lower log10 MoM values at 8–16 weeks’ gestation) are observed in patients who deliver earlier with preeclampsia (Supplemental Figure 2); this is consistent with the higher frequency of lesions of maternal vascular underperfusion and the higher magnitude of angiogenic imbalance in preterm preeclampsia. For MAP, higher MoM values were noted herein at 8 to 16 weeks’ gestation for women who developed preeclampsia compared to the controls, yet there was no correlation between the gestational age at delivery with preeclampsia and log MAP MoM values—likely owing to differences in the protocol used for blood pressure measurement among studies. Despite these methodological differences, the prediction performance on the data generated in this study resulted only in an up to 7% higher AUC for the combined-risk models developed herein than the FMF calculators given the same input data, depending on the gestational-age interval as assessment. Therefore, we conclude that the analytical aspects alone have modest effects on the prediction performance (Figure 3 and Supplemental Table 4).
      The sensitivity of the FMF approach based on prior risk, MAP, PlGF, UtA-PI, and PAPP-A at 11 to 14 weeks’ gestation in a mixed white and Afro-Caribbean patient population was reported to be 75% and 41% for preterm and term preeclampsia, respectively (FPR, 10%). Our results, using prior risk, MAP, PlGF, sVEGFR-1, and sEng at 8 to 16 weeks’ gestation, were 55% and 36% for preterm and term preeclampsia, respectively (FPR, 10%) (Table 3). First-trimester prediction of term preeclampsia was suboptimal for both studies based on the report herein and that of Tan et al
      • Tan M.Y.
      • Syngelaki A.
      • Poon L.C.
      • et al.
      Screening for pre-eclampsia by maternal factors and biomarkers at 11–13 weeks’ gestation.
      ; a similar gap in predictive performance for late-onset vs early-onset disease was noted.
      • Chaiworapongsa T.
      • Romero R.
      • Espinoza J.
      • et al.
      Evidence supporting a role for blockade of the vascular endothelial growth factor system in the pathophysiology of preeclampsia. Young Investigator Award.
      ,
      • Chaiworapongsa T.
      • Romero R.
      • Kim Y.M.
      • et al.
      Plasma soluble vascular endothelial growth factor receptor-1 concentration is elevated prior to the clinical diagnosis of pre-eclampsia.
      ,
      • Kusanovic J.P.
      • Romero R.
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      • et al.
      A prospective cohort study of the value of maternal plasma concentrations of angiogenic and anti-angiogenic factors in early pregnancy and midtrimester in the identification of patients destined to develop preeclampsia.
      ,
      • Erez O.
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      • Maymon E.
      • et al.
      The prediction of late-onset preeclampsia: results from a longitudinal proteomics study.
      ,
      • Akolekar R.
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      • Sarquis R.
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      Prediction of early, intermediate and late pre-eclampsia from maternal factors, biophysical and biochemical markers at 11-13 weeks.
      ,
      • Tsiakkas A.
      • Saiid Y.
      • Wright A.
      • Wright D.
      • Nicolaides K.H.
      Competing risks model in screening for preeclampsia by maternal factors and biomarkers at 30-34 weeks’ gestation.
      ,
      • Crispi F.
      • Llurba E.
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      • Martín-Gallán P.
      • Cabero L.
      • Gratacós E.
      Predictive value of angiogenic factors and uterine artery Doppler for early- versus late-onset pre-eclampsia and intrauterine growth restriction.
      ,
      • Stepan H.
      • Hund M.
      • Andraczek T.
      Combining biomarkers to predict pregnancy complications and redefine preeclampsia: the angiogenic-placental syndrome.
      • Crispi F.
      • Domínguez C.
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      • Cabero L.
      • Gratacós E.
      Placental angiogenic growth factors and uterine artery Doppler findings for characterization of different subsets in preeclampsia and in isolated intrauterine growth restriction.
      • Vatten L.J.
      • Eskild A.
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      • Jenum P.A.
      • Staff A.C.
      Changes in circulating level of angiogenic factors from the first to second trimester as predictors of preeclampsia.
      • Akolekar R.
      • Zaragoza E.
      • Poon L.C.
      • Pepes S.
      • Nicolaides K.H.
      Maternal serum placental growth factor at 11 +0 to 13 +6 weeks of gestation in the prediction of pre-eclampsia.
      To improve the prediction of preeclampsia beyond what is possible by using MAP, Doppler velocimetry of the uterine arteries, and the maternal blood proteins PlGF and PAPP-A during the first trimester, researchers have explored the value of additional biomarkers
      • Than N.G.
      • Romero R.
      • Tarca A.L.
      • et al.
      Integrated systems biology approach identifies novel maternal and placental pathways of preeclampsia.
      ,
      • Erez O.
      • Romero R.
      • Maymon E.
      • et al.
      The prediction of late-onset preeclampsia: results from a longitudinal proteomics study.
      • Docheva N.
      • Romero R.
      • Chaemsaithong P.
      • et al.
      The profiles of soluble adhesion molecules in the “great obstetrical syndromes”.
      • Tarca A.L.
      • Romero R.
      • Benshalom-Tirosh N.
      • et al.
      The prediction of early preeclampsia: results from a longitudinal proteomics study.
      ,
      • Tarca A.L.
      • Romero R.
      • Erez O.
      • et al.
      Maternal whole blood mRNA signatures identify women at risk of early preeclampsia: a longitudinal study.
      and whether their measurement at subsequent gestational-age intervals closer to delivery, or both, would be helpful.
      • Erez O.
      • Romero R.
      • Maymon E.
      • et al.
      The prediction of late-onset preeclampsia: results from a longitudinal proteomics study.
      ,
      • Tarca A.L.
      • Romero R.
      • Benshalom-Tirosh N.
      • et al.
      The prediction of early preeclampsia: results from a longitudinal proteomics study.
      ,
      • Tarca A.L.
      • Romero R.
      • Erez O.
      • et al.
      Maternal whole blood mRNA signatures identify women at risk of early preeclampsia: a longitudinal study.
      Screening for preeclampsia after the first trimester, at 19 to 24+6 weeks,
      • Gallo D.M.
      • Wright D.
      • Casanova C.
      • Campanero M.
      • Nicolaides K.H.
      Competing risks model in screening for preeclampsia by maternal factors and biomarkers at 19-24 weeks’ gestation.
      30 to 34 weeks,
      • Tsiakkas A.
      • Saiid Y.
      • Wright A.
      • Wright D.
      • Nicolaides K.H.
      Competing risks model in screening for preeclampsia by maternal factors and biomarkers at 30-34 weeks’ gestation.
      and 35 to 37 weeks,
      • Andrietti S.
      • Silva M.
      • Wright A.
      • Wright D.
      • Nicolaides K.H.
      Competing-risks model in screening for pre-eclampsia by maternal factors and biomarkers at 35-37 weeks’ gestation.
      has shown that the prediction of both term and preterm preeclampsia increased with advancing gestation, which is in agreement with the data presented herein (Table 3). The current study was especially well suited to assess the effect of gestational age at measurement on prediction performance, given that longitudinal data were available. Screening at 19 to 24+6 weeks based on MAP, PlGF, sVEGFR-1, and UtA-PI by the FMF approach indicated a sensitivity (FPR, 10%) of 85% and 46% for the prediction of preterm and term preeclampsia, respectively.
      • Gallo D.M.
      • Wright D.
      • Casanova C.
      • Campanero M.
      • Nicolaides K.H.
      Competing risks model in screening for preeclampsia by maternal factors and biomarkers at 19-24 weeks’ gestation.
      Our approach using sEng instead of UtA-PI at 20 to 23+6 weeks achieved a sensitivity of 62% and 41% for preterm and term preeclampsia, respectively (Table 3). Moreover, screening at 30 to 34 weeks based on MAP, PlGF, sVEGFR-1, and UtA-PI by the FMF approach was reported to increase the detection rate (FPR, 10%) of preterm and term preeclampsia to 98% and 65%, respectively.
      • Tsiakkas A.
      • Saiid Y.
      • Wright A.
      • Wright D.
      • Nicolaides K.H.
      Competing risks model in screening for preeclampsia by maternal factors and biomarkers at 30-34 weeks’ gestation.
      Our approach using sEng instead of UtA-PI at 28 to 31+6 achieved a sensitivity of 84% for preterm preeclampsia and 39% for term preeclampsia (Table 3).

       Clinical implications

      Given that the models developed herein produce similar results compared to FMF models when the same data are used as input, this study suggests that the superior prediction reported in FMF studies especially for preterm preeclampsia is caused by additional maternal risk factors considered in the FMF prior-risk model (eg, diabetes mellitus, autoimmune disease), which did not reach significance to be included here and, more importantly, to the more accurate determination of MAP and the inclusion of UtA-PI data in the FMF reports. For clinical setups where standardized MAP determinations and UtA-PI data are not available, the inclusion of maternal plasma sEng in the prediction models could be used to increase prediction performance of superimposed preeclampsia and, to some extent, preterm preeclampsia early in pregnancy (Table 4). However, future prospective studies would be needed to determine whether the additional preterm preeclampsia cases identified as being at risk can also benefit from aspirin treatment.
      In agreement with previous reports, the prediction performance for preeclampsia increased with advancing gestational age at data collection, suggesting utility for patient management. Furthermore, the prediction of preeclampsia in women with chronic hypertension was achieved by the model derived for overall preeclampsia with detection rates (FPR, 10%) comparable to those in women without chronic hypertension. Of all risk factors determining the prior risk of preeclampsia listed in Supplemental Table 1, the presence of chronic hypertension is the most powerful predictor; hence, when prediction of preeclampsia is assessed only in the subset of women with chronic hypertension, the prediction by prior risk alone is very low (AUC at most 0.56); hence, adding evidence from biomarkers is important (Table 4 and Supplemental Figure 1). However, to maintain the same FPR when screening among women with chronic hypertension, the combined-risk cutoff values need to be higher given that this subset of women have higher prior risk. For example, to ensure an FPR of 10% when screening at 8 to 16 weeks for overall preeclampsia, a cutoff value of 1/12 on the combined risk is required. However, for women with chronic hypertension, a cutoff value of 1/3 is necessary to maintain the same FPR. Women with chronic hypertension may not benefit from low-dose aspirin to prevent preterm preeclampsia
      • Poon L.C.
      • Wright D.
      • Rolnik D.L.
      • et al.
      Aspirin for Evidence-Based preeclampsia Prevention trial: effect of aspirin in prevention of preterm preeclampsia in subgroups of women according to their characteristics and medical and obstetrical history.
      ,
      • Wertaschnigg D.
      • Reddy M.
      • Mol B.W.J.
      • da Silva Costa F.
      • Rolnik D.L.
      Evidence-based prevention of preeclampsia: commonly asked questions in clinical practice.
      ; however, accurately assessing the risk of preeclampsia in chronic hypertensive women is important for patient management. Because baseline levels of biochemical markers and their time-varying profiles depend on chronic hypertension status, we accommodated for such chronic hypertension-specific effects when developing the MoM models (Supplemental Table 2).

       Research implications

      Another observation in this study is that the prior-risk evidence should not be treated as equal to the biomarker-based evidence. Indeed, when, for example, equal weight is assigned to the prior-risk evidence and evidence from 1 biomarker (PlGF) in a combined-risk model, the prediction performance was lower than when the prior risk was allowed to be automatically up- or downweighted depending on the gestational-age interval at screening. Early in gestation, when biomarker data are less informative of the future onset of preeclampsia, the prior-risk contribution in the models was higher (eg, coefficient of 1.25 for early preeclampsia at 8–15+6 weeks), yet later in gestation when biomarker data become more predictive of disease, the weight of the prior-risk component was lower than 1.0 (Supplemental Table 3). This finding indicates the possibility that the competing risk approach,
      • Wright D.
      • Wright A.
      • Nicolaides K.H.
      The competing risk approach for prediction of preeclampsia.
      in which the prior-risk and biomarker-risk data are treated as equal and simply multiplied according to Bayes’ theorem, can also benefit by considering different weights depending on the gestational age at screening; however, additional research is needed to test this hypothesis.

       Strengths and limitations

      This study focuses on the development of prediction models for preeclampsia based on a longitudinal case-cohort design that comprises a combination of plasma angiogenic and antiangiogenic factors (PlGF, sVEGFR-1, and sEng), maternal characteristics, and MAP. Although previous longitudinal biomarker studies included women with superimposed preeclampsia, a few included both chronic hypertensive and normotensive control groups.
      • Caruso A.
      • Caforio L.
      • Testa A.C.
      • Ferrazzani S.
      • Mastromarino C.
      • Mancuso S.
      Chronic hypertension in pregnancy: color Doppler investigation of uterine arteries as a predictive test for superimposed preeclampsia and adverse perinatal outcome.
      • Perni U.
      • Sison C.
      • Sharma V.
      • et al.
      Angiogenic factors in superimposed preeclampsia: a longitudinal study of women with chronic hypertension during pregnancy.
      • Roncaglia N.
      • Crippa I.
      • Locatelli A.
      • et al.
      Prediction of superimposed preeclampsia using uterine artery Doppler velocimetry in women with chronic hypertension.
      • Zeeman G.G.
      • McIntire D.D.
      • Twickler D.M.
      Maternal and fetal artery Doppler findings in women with chronic hypertension who subsequently develop superimposed pre-eclampsia.
      • Becker D.A.
      • Machemehl H.C.
      • Biggio J.R.
      • Siegel A.M.
      • Tita A.T.
      • Harper L.M.
      Pregnancy outcomes of exacerbated chronic hypertension compared with superimposed preeclampsia.
      An additional strength of this work is the implementation of the MoM-based models and preeclampsia risk models as online calculators that can be evaluated by other researchers.
      Although an independent test set was not used to assess prediction performance, we have provided evidence for minimal overfitting to the current cohort because the prediction performance we report is similar to the one of FMF calculators when the same input data were used for 2 of the 4 gestational-age intervals considered. Other limitations are the fact that Doppler measurements were not available to integrate with the other biomarkers and that cases of preeclampsia were not subclassified by preterm or term acquiescence or feature severity. Although not within the scope of the current manuscript, future work may assess the value of multiple within-subject measurements for improving prediction performance relative to a single evaluation.

       Conclusions

      We introduced models and calculators to determine MoM values of biophysical and biochemical biomarkers and risk of preeclampsia based on data collected throughout pregnancy. These models can be used to identify women who may benefit from low-dose aspirin treatment or, later in pregnancy, to inform patient management. This study suggests that the inclusion of sEng in combination with MAP, PlGF, and sVEGFR-1 improves the prediction of women at risk of preterm and superimposed preeclampsia, which is important especially when Doppler velocimetry of the uterine arteries is not available.

      Acknowledgment

      The authors acknowledge Maureen McGerty (Wayne State University) for proofreading and copyediting this manuscript.

      Appendix

      Figure thumbnail fx1ab
      Supplemental Figure 1Distribution of biophysical and biochemical marker MoM values among groups
      Box-and-whisker plots (median, interquartile) of (A) PlGF, (B) sEng, (C) sVEGFR-1, and (D) MoM values in normotensive controls (gray) and controls with CHTN (yellow), PE (blue), and sPE (red). MoMs were derived by using the models shown in . Single asterisk denotes P<.001 in comparison with normotensive controls, unless otherwise noted in brackets.
      CHTN, women without preeclampsia with chronic hypertension; MoM, multiples of the mean; PE, preeclampsia; sPE, superimposed preeclampsia.
      Tarca et al. Prediction of preeclampsia throughout pregnancy. Am J Obstet Gynecol 2022.
      Figure thumbnail fx1cd
      Supplemental Figure 1Distribution of biophysical and biochemical marker MoM values among groups
      Box-and-whisker plots (median, interquartile) of (A) PlGF, (B) sEng, (C) sVEGFR-1, and (D) MoM values in normotensive controls (gray) and controls with CHTN (yellow), PE (blue), and sPE (red). MoMs were derived by using the models shown in . Single asterisk denotes P<.001 in comparison with normotensive controls, unless otherwise noted in brackets.
      CHTN, women without preeclampsia with chronic hypertension; MoM, multiples of the mean; PE, preeclampsia; sPE, superimposed preeclampsia.
      Tarca et al. Prediction of preeclampsia throughout pregnancy. Am J Obstet Gynecol 2022.
      Figure thumbnail fx2
      Supplemental Figure 2Biomarker MoM values at 8–15+6 weeks’ gestation and gestational age at delivery with preeclampsia
      The figure shows the log10 MoM values as a function of gestational age at delivery with preeclampsia at 8–15+6 weeks’ gestation. The log10 PlGF MoM values were positively correlated with gestational ages at delivery with preeclampsia.
      MoM, multiple of the mean; PlGF, placental growth factor.
      Tarca et al. Prediction of preeclampsia throughout pregnancy. Am J Obstet Gynecol 2022.
      Supplemental Table 1Model coefficients for calculation of prior risk of preeclampsia
      VariableEstimateStandard errorP value
      (Intercept)−3.39400.1102<.001
      Chronic hypertension1.60340.1838<.001
      Maternal weight, 75 kg0.00490.0031.105
      Nulliparity0.50070.1499.001
      History of preeclampsia0.89860.2715.001
      Nulliparity and history of preeclampsia are binary variables. As an example, to calculate risk for a multiparous woman who weighs 76 kg and has a history of preeclampsia and chronic hypertension in the current pregnancy, we use the following formula: risk=exp[−3.394+1×1.603+(76−75)×0.0049 + 0×0.5007+1×0.898]=0.41.
      Tarca et al. Prediction of preeclampsia throughout pregnancy. Am J Obstet Gynecol 2022.
      Supplemental Table 2MoM models coefficients for plasma PlGF, sEng, sVEGFR-1, and MAP
      AnalyteIntervalInterceptGA-startCHTNAgeWeightHeightSmokingNulliparityCHTN×weightCHTN×heightCHTN×smokingCHTN×sgeHistPECHTN×nulliparityCHTN×HistPE
      MAP8–15+61.9349−0.00270.04440.00060.0005−0.0002.0.0091.−0.0018.....
      MAP16–19+61.9180.0.0413.0.0004.−0.00740.0073.......
      MAP20–23+61.9157.0.02620.00030.00050.0004−0.00960.0099...0.0019...
      MAP24–27+61.9165.0.0328.0.0007..0.0089....−0.0232−0.02480.0479
      MAP28–31+61.91260.00290.0199−0.00040.0005..0.0094...0.00250.0176..
      MAP32–36+61.91910.00250.02150.00090.00030.0004.0.0106.......
      PlGF8–15+61.17490.11400.0842−0.0011−0.0034.0.1166−0.0479...−0.0172...
      PlGF16–19+62.10110.0724−0.0903.−0.0036.0.09980.03530.0035...−0.0055.0.4072
      PlGF20–23+62.42100.05410.0053−0.0001−0.0043−0.00080.1306..−0.0083.−0.0138...
      PlGF24–27+62.62880.0389−0.0965.−0.0048−0.00130.12820.03300.0033−0.0096.....
      PlGF28–31+62.81570.0182−0.0917.−0.0047−0.00280.0811.0.0032...−0.0966..
      PlGF32–36+62.8649−0.0351−0.1140.−0.0041.0.0147−0.03630.0043.0.2036..−0.1789.
      sEng8–15+60.8387−0.00460.0435.−0.0012−0.0001−0.00390.0411−0.00140.0044−0.1117....
      sEng16–19+60.7887−0.0137−0.0205.−0.00130.0008.0.0315.0.0042.....
      sEng20–23+60.7425.0.0147.−0.00130.0009.0.0177.0.0035..−0.0409..
      sEng24–27+60.73940.00590.0331−0.0015−0.00110.0004.0.0155−0.00120.0046..0.0218.−0.0987
      sEng28–31+60.76730.02440.02210.0020−0.0009−0.0002−0.00460.0184−0.00140.0061−0.0837..0.0888.
      sEng32–36+60.88180.0221.0.0023−0.0013..0.0506.......
      sVEGFR-18–15+62.9574.−0.0143−0.0036−0.0017.−0.02330.0836..−0.1293....
      sVEGFR-116–19+62.9475−0.0194−0.0569−0.0044−0.0023..0.0790.......
      sVEGFR-120–23+62.9166..−0.0048−0.0027..0.0808.......
      sVEGFR-124–27+62.9265..−0.0061−0.0031..0.0584.......
      sVEGFR-128–31+62.95790.0154−0.0217.−0.0027−0.0006.0.0692.0.0066.....
      sVEGFR-132–36+63.06200.0485−0.02830.0026−0.0019−0.0032.0.1147.0.0100.....
      The following variables were centered by subtracting the calculated means: age, 24 years; weight, 75 kg; and height, 163 cm. GA was offset by subtracting the value of the start of the GA interval (eg, start=20 weeks for the 20–23+6 week interval). “.” indicates a null coefficient for the corresponding variable (column). As an example, to calculate the expected log10sEngMoM value for a patient at 10 weeks’ gestation who is 25 years old, weighs 76 kg, is 170 cm tall, had 2 previous deliveries (nulliparity, 0), has CHTN, does smoke, and has a history of PE, we use the equation above for sEng for the 8–15+6 week interval as follows: expected log10sEng=0.8387 −0.0046×(10−8)+0.0435×1 − 0.0012×(76−75) –(170−163)×0.0001 – 1×0.0039 − 0.0014×(76−75)+0.0044×1×(170−163) −0.1117×1×1=0.785. If the observed value of sEng at 10 weeks was 10 ng/mL, then the MoM can be calculated as observed/expected=10/100.856=1.64.
      CHTN, chronic hypertension status; GA, gestational age; HistPE, history of preeclampsia; MAP, mean arterial pressure; MoM, multiple of the mean; P1GF, placental growth factor; sEng, soluble endoglin; sVEGFR-1, soluble vascular endothelial growth factor receptor-1.
      Tarca et al. Prediction of preeclampsia throughout pregnancy. Am J Obstet Gynecol 2022.
      Supplemental Table 3PE risk model coefficients based on prior risk and biomarkers
      OutcomeIntervalInterceptPrior riskMAPPlGFsVEGFR-1sEngsVEGFR-1×sEngsEng×PlGFPlGF×MAPsVEGFR-1×PlGFsVEGFR-1×MAPsEng×MAP
      ALL PE8–15+6−0.12021.06867.8718−1.0559−1.07971.28268.22885.140619.5762−0.8956−13.347416.4718
      ALL PE16−19+6−0.61080.84304.6875−0.8972−0.36112.6166−1.23312.617510.0090−1.6311−13.308141.6410
      ALL PE20−23+6−0.70490.873810.6076−1.8162−0.00632.5699−0.62893.471516.5989−2.02014.80945.9228
      ALL PE24−27+6−0.69710.892011.2481−0.97220.83442.1170−0.31601.13051.2041−0.5118−3.3517−7.9670
      ALL PE28−31+6−0.82690.87218.9072−1.30340.99851.2981−1.46530.565111.5049−0.89952.59523.9396
      ALL PE32−36+6−1.18150.902020.4648−0.62881.21170.3282−0.7268−0.665311.6130−0.4909−7.611610.0733
      Preterm PE8−15+6−0.77141.25376.1118−2.8421−1.83314.57599.26697.911828.1042−3.6625−30.827169.5482
      Preterm PE16−19+6−1.64520.92756.0976−3.1082−0.61816.1603−2.76829.557225.9851−4.2670−21.363663.1223
      Preterm PE20−23+6−2.34490.903116.2719−4.53520.56094.38940.07397.191628.0664−1.6351−2.609521.4030
      Preterm PE24−27+6−3.19960.702021.1821−3.54611.37023.2879−0.30282.949117.56350.0151−2.94283.6563
      Preterm PE28−31+6−3.90110.736323.3031−4.24322.47772.3102−0.95981.332225.63741.21970.99564.9074
      Preterm PE32−36+6−3.89921.031739.1053−2.40141.05992.69690.57192.824413.6525−1.2452−20.77119.7698
      Term PE8−15+6−0.47911.08839.4549−0.1044−1.0406−0.67823.65745.544011.4307−0.7475−6.0523−8.2593
      Term PE16−19+6−0.95240.86415.33630.3179−0.34410.92650.69880.7248−0.4748−1.6705−9.492228.9287
      Term PE20−23+6−0.67930.985011.3022−0.2998−0.60770.7387−8.10105.1591−9.57790.244626.244241.9978
      Term PE24−27+6−0.34261.10089.99020.01360.52451.0688−0.34920.8826−9.9709−0.3422−1.8160−0.3052
      Term PE28−31+6−0.47371.04546.4453−0.63360.58310.2541−2.6886−1.50207.4780−1.755513.91649.3967
      Term PE32−36+6−1.06960.967617.4767−0.63020.64270.1030−1.6573−1.041926.5962−1.27278.500119.3735
      Concentrations of chemical biomarkers and MAP are first converted into MoM values using models in Supplemental Table 2 and then log10 transformed before use as inputs in the models above. Using the same example as in legend of Supplemental Table 1 (prior risk=0.41) for which MAP and biochemical markers are determined at 28 weeks of gestation, with PlGF MoM=0.6, whereas MoM values of MAP, sVEGFR-1 and sEng are all 1.0 (ie, log10 MoM=0.0 for these 3 predictors), the combined-risk of any PE: risk=exp[−0.8269+0.872×log(0.41)−1.303×log10(0.6)]=0.268. Note this risk value is greater than the cutoff (0.075) shown in Table 2 for all PE at 28–31+6 weeks, so the patient will be considered at risk.
      MAP, mean arterial pressure; MoM, multiple of the mean; PE, preeclampsia; P1GF, placental growth factor; sEng, soluble endoglin; sVEGFR-1, soluble vascular endothelial growth factor receptor-1.
      Tarca et al. Prediction of preeclampsia throughout pregnancy. Am J Obstet Gynecol 2022.
      Supplemental Table 4Comparison of the FMF preeclampsia risk models to those derived in this study


      GA (wk)

      FMFThis study
      AUCSensitivityAUCSensitivityP value for difference for combined-risk models
      Prior riskCombined riskFPR 10%FPR 20%Prior riskCombined riskFPR 10%FPR 20%AUCFPR 10%FPR 20%
      11–14+10.69 (0.59–0.79)0.69 (0.6–0.79)0.36 (0.22–0.52)0.55 (0.39–0.7)0.71 (0.61–0.8)0.73 (0.64–0.81)0.48 (0.32–0.63)0.55 (0.39–0.7)0.240.0591
      19–250.67 (0.62–0.73)0.72 (0.67–0.77)0.36 (0.28–0.45)0.48 (0.39–0.57)0.68 (0.62–0.73)0.76 (0.71–0.81)0.43 (0.34–0.52)0.56 (0.47–0.65)0.0020.0450.025
      30–350.66 (0.61–0.72)0.74 (0.68–0.79)0.46 (0.37–0.55)0.52 (0.43–0.62)0.66 (0.61–0.72)0.81 (0.76–0.85)0.53 (0.44–0.62)0.65 (0.56–0.73)0.00010.080.0027
      35–380.70 (0.59–0.81)0.83 (0.76–0.91)0.53 (0.35–0.71)0.62 (0.44–0.79)0.74 (0.65–0.83)0.87 (0.81–0.94)0.66 (0.47–0.81)0.75 (0.57–0.89)0.1010.0450.0455
      AUC and sensitivities at 10% and 20% FPR obtained with the bulk calculator from https://fetalmedicine.org/ and those obtained in this study. For this analysis, the models in Supplemental Table 3 were modified to exclude the contribution of sEng which is not used in FMF models. FMF models utilized a combination of maternal factors and clinical history (same as in Supplemental Table 1), MAP, and PlGF. For analyses based on data collected after 25 weeks, sVEGFR-1 was also included. P values represent 2-tailed McNemar tests for differences in paired sensitivities.
      AUC, area under the receiver operating characteristic curve; FMF, Fetal Medicine Foundation; GA, gestational age; MAP, mean arterial pressure; PlGF, placental growth factor; sEng, soluble endoglin; sVEGFR-1, soluble vascular endothelial growth factor receptor-1.
      Tarca et al. Prediction of preeclampsia throughout pregnancy. Am J Obstet Gynecol 2022.

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