Validation of a first-trimester screening model for pre-eclampsia in an unselected population

Screening for pre-eclampsia using pregnancy-associated plasma protein-A or placental growth factor measurements in blood samples collected at 8-14 weeks' gestation

OBJECTIVES

To assess the value of pregnancy-associated plasma protein-A (PAPP-A) in screening for preterm pre-eclampsia (PE) (delivery < 37 weeks' gestation) measured in maternal blood samples collected before 11 weeks, and to compare the screening performance of PAPP-A with that of placental growth factor (PlGF) from blood samples collected at 8-14 weeks.

METHODS

This study analyzed data from women who participated in the PRESIDE (Pre-eclampsia Screening in Denmark) study, a prospective, non-interventional multicenter study investigating the predictive performance of the Fetal Medicine Foundation first-trimester screening algorithm for PE in a Danish population. As part of combined first-trimester screening, a routine blood sample was collected at 8-14 weeks' gestation and PAPP-A was measured. Excess serum was stored at -80°C and analyzed for PlGF in batches after delivery. Most women in the PRESIDE study had an extra blood sample collected at the time of the first-trimester scan at 11-14 weeks, which was also analyzed for PlGF and PAPP-A in batches after all the participants had delivered. Screening performance was assessed in terms of the detection rate at a 10% screen-positive rate (SPR) for a combination of PAPP-A or PlGF with maternal factors alone and for a combination of each of these biomarkers with maternal factors, mean arterial pressure (MAP) and uterine artery pulsatility index (UtA-PI).

RESULTS

The study population comprised 8386 women who had a routine combined first-trimester aneuploidy screening blood sample collected at 8-14 weeks' gestation. In pregnancies that developed preterm PE, the median PAPP-A multiples of the median from routine blood samples were 0.78 (95% CI, 0.67-0.90) before 10 weeks, 0.80 (95% CI, 0.58-1.10) at 10 weeks and 0.64 (95% CI, 0.53-0.78) at 11-14 weeks. In women with samples collected before 10 weeks, there was no significant improvement in the detection rate of preterm PE when PAPP-A or PlGF was combined with maternal factors alone or when combined with maternal factors, MAP and UtA-PI. In routine samples collected at or after 10 weeks, PAPP-A only increased the detection rate of preterm PE slightly. However, PlGF in samples collected at or after 10 weeks increased the detection rate from 31.3% (95% CI, 16.1-50.0%) to 56.3% (95% CI, 37.7-73.6%) at a 10% SPR, i.e. an increase in the detection rate of 25.0% (95% CI, 4.3-44.4%), when combined with maternal factors alone. When PlGF collected from the PRESIDE sample at 11-14 weeks was combined with maternal factors, MAP and UtA-PI, there was an increase in the detection rate from 50.9% (95% CI, 37.1-64.6%) to 67.3% (95% CI, 53.3-79.3%), i.e. an increase of 16.4% (95% CI, 5.6-29.0%) at a 10% SPR.

CONCLUSIONS

PAPP-A has limited value in first-trimester screening for PE, whereas PlGF adds significantly to the detection rate of preterm PE at 10-14 weeks' gestation. © 2025 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Machine Learning Algorithms Versus Classical Regression Models in Pre-Eclampsia Prediction: A Systematic Review

PURPOSE OF REVIEW

Machine learning (ML) approaches are an emerging alternative for healthcare risk prediction. We aimed to synthesise the literature on ML and classical regression studies exploring potential prognostic factors and to compare prediction performance for pre-eclampsia.

RECENT FINDINGS

From 9382 studies retrieved, 82 were included. Sixty-six publications exclusively reported eighty-four classical regression models to predict variable timing of onset of pre-eclampsia. Another six publications reported purely ML algorithms, whilst another 10 publications reported ML algorithms and classical regression models in the same sample with 8 of 10 findings that ML algorithms outperformed classical regression models. The most frequent prognostic factors were age, pre-pregnancy body mass index, chronic medical conditions, parity, prior history of pre-eclampsia, mean arterial pressure, uterine artery pulsatility index, placental growth factor, and pregnancy-associated plasma protein A. Top performing ML algorithms were random forest (area under the curve (AUC) = 0.94, 95% confidence interval (CI) 0.91-0.96) and extreme gradient boosting (AUC = 0.92, 95% CI 0.90-0.94). The competing risk model had similar performance (AUC = 0.92, 95% CI 0.91-0.92) compared with a neural network. Calibration performance was not reported in the majority of publications. ML algorithms had better performance compared to classical regression models in pre-eclampsia prediction. Random forest and boosting-type algorithms had the best prediction performance. Further research should focus on comparing ML algorithms to classical regression models using the same samples and evaluation metrics to gain insight into their performance. External validation of ML algorithms is warranted to gain insights into their generalisability.

Externally validated prediction models for pre-eclampsia: systematic review and meta-analysis

OBJECTIVE

This systematic review and meta-analysis aimed to evaluate the performance of existing externally validated prediction models for pre-eclampsia (PE) (specifically, any-onset, early-onset, late-onset and preterm PE).

METHODS

A systematic search was conducted in five databases (MEDLINE, EMBASE, Emcare, CINAHL and Maternity & Infant Care Database) and using Google Scholar/reference search to identify studies based on the Population, Index prediction model, Comparator, Outcome, Timing and Setting (PICOTS) approach until 20 May 2023. We extracted data using the CHARMS checklist and appraised the risk of bias using the PROBAST tool. A meta-analysis of discrimination and calibration performance was conducted when appropriate.

RESULTS

Twenty-three studies reported 52 externally validated prediction models for PE (one preterm, 20 any-onset, 17 early-onset and 14 late-onset PE models). No model had the same set of predictors. Fifteen any-onset PE models were validated externally once, two were validated twice and three were validated three times, while the Fetal Medicine Foundation (FMF) competing-risks model for preterm PE prediction was validated widely in 16 different settings. The most common predictors were maternal characteristics (prepregnancy body mass index, prior PE, family history of PE, chronic medical conditions and ethnicity) and biomarkers (uterine artery pulsatility index and pregnancy-associated plasma protein-A). The FMF model for preterm PE (triple test plus maternal factors) had the best performance, with a pooled area under the receiver-operating-characteristics curve (AUC) of 0.90 (95% prediction interval (PI), 0.76-0.96), and was well calibrated. The other models generally had poor-to-good discrimination performance (median AUC, 0.66 (range, 0.53-0.77)) and were overfitted on external validation. Apart from the FMF model, only two models that were validated multiple times for any-onset PE prediction, which were based on maternal characteristics only, produced reasonable pooled AUCs of 0.71 (95% PI, 0.66-0.76) and 0.73 (95% PI, 0.55-0.86).

CONCLUSIONS

Existing externally validated prediction models for any-, early- and late-onset PE have limited discrimination and calibration performance, and include inconsistent input variables. The triple-test FMF model had outstanding discrimination performance in predicting preterm PE in numerous settings, but the inclusion of specialized biomarkers may limit feasibility and implementation outside of high-resource settings. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Performance of first-trimester combined screening for preterm pre-eclampsia: findings from cohort of 10 110 pregnancies in Spain

OBJECTIVE

To evaluate the diagnostic accuracy of the Fetal Medicine Foundation (FMF) competing-risks model, incorporating maternal characteristics, mean arterial pressure (MAP), uterine artery pulsatility index (UtA-PI) and placental growth factor (PlGF) (the 'triple test'), for the prediction at 11-13 weeks' gestation of preterm pre-eclampsia (PE) in a Spanish population.

METHODS

This was a prospective cohort study performed in eight fetal medicine units in five different regions of Spain between September 2017 and December 2019. All pregnant women with a singleton pregnancy and a non-malformed live fetus attending a routine ultrasound examination at 11 + 0 to 13 + 6 weeks' gestation were invited to participate. Maternal demographic characteristics and medical history were recorded and MAP, UtA-PI, serum PlGF and pregnancy-associated plasma protein-A (PAPP-A) were measured following standardized protocols. Treatment with aspirin during pregnancy was also recorded. Raw values of biomarkers were converted into multiples of the median (MoM), and audits were performed periodically to provide regular feedback to operators and laboratories. Patient-specific risks for term and preterm PE were calculated according to the FMF competing-risks model, blinded to pregnancy outcome. The performance of screening for PE, taking into account aspirin use, was assessed by calculating the area under the receiver-operating-characteristics curve (AUC) and detection rate (DR) at a 10% fixed screen-positive rate (SPR). Risk calibration of the model was assessed.

RESULTS

The study population comprised 10 110 singleton pregnancies, including 72 (0.7%) that developed preterm PE. In the preterm PE group, compared to those without PE, median MAP MoM and UtA-PI MoM were significantly higher, and median serum PlGF MoM and PAPP-A MoM were significantly lower. In women with PE, the deviation from normal in all biomarkers was inversely related to gestational age at delivery. Screening for preterm PE by a combination of maternal characteristics and medical history with MAP, UtA-PI and PlGF had a DR, at 10% SPR, of 72.7% (95% CI, 62.9-82.6%). An alternative strategy of replacing PlGF with PAPP-A in the triple test was associated with poorer screening performance for preterm PE, giving a DR of 66.5% (95% CI, 55.8-77.2%). The calibration plot showed good agreement between predicted risk and observed incidence of preterm PE, with a slope of 0.983 (95% CI, 0.846-1.120) and an intercept of 0.154 (95% CI, -0.091 to 0.397).

CONCLUSIONS

The FMF model is effective in predicting preterm PE in the Spanish population at 11-13 weeks' gestation. This method of screening is feasible to implement in routine clinical practice, but it should be accompanied by a robust audit and monitoring system, in order to maintain high-quality screening. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

Combined maternal risk factors and the Quadruple test to predict late-onset preeclampsia in pregnant Thai women

BACKGROUND

This study aimed to evaluate the predictive power of a model combining maternal risk factors and the Quadruple screen test for late-onset preeclampsia (PE).

METHODS

All pregnant women that received the Quadruple test for Down syndrome at 15^+ 0-20^+ 6 weeks' gestation were recruited. Maternal serum α-fetoprotein, β-human chorionic gonadotropin, unconjugated estriol, and inhibin A were measured as multiples of the median. A logistic regression model was used to identify predictors associated with late-onset PE with severe features. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to assess the model's predictive ability.

RESULTS

Fifty-five of the 2,000 pregnant women had PE, and 31 of 55 women had late-onset PE. Multivariate analysis identified maternal age ≥ 35 years, inhibin A, history of previous PE, history of infertile, cardiac disease, chronic hypertension, and thyroid disease as significant risk factors. The area under the curve of the receiver operating characteristic curve was 0.78. The likelihood ratio to predict late-onset PE was 49.4 (total score > 60).

CONCLUSIONS

Our model combining serum inhibin A with maternal risk factors was useful in predicting late-onset PE. Close monitoring of these patients is recommended.

Potential biomarkers for late-onset and term preeclampsia: A scoping review

Preeclampsia is a progressive, multisystem pregnancy disorder. According to the time of onset or delivery, preeclampsia has been subclassified into early-onset (<34 weeks) and late-onset (≥34 weeks), or preterm (<37 weeks) and term (≥37 weeks). Preterm preeclampsia can be effectively predicted at 11-13 weeks well before onset, and its incidence can be reduced by preventively using low-dose aspirin. However, late-onset and term preeclampsia are more prevalent than early forms and still lack effective predictive and preventive measures. This scoping review aims to systematically identify the evidence of predictive biomarkers reported in late-onset and term preeclampsia. This study was conducted based on the guidance of the Joanna Briggs Institute (JBI) methodology for scoping reviews. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for scoping reviews (PRISMA-ScR) was used to guide the study. The following databases were searched for related studies: PubMed, Web of Science, Scopus, and ProQuest. Search terms contain "preeclampsia," "late-onset," "term," "biomarker," or "marker," and other synonyms combined as appropriate using the Boolean operators "AND" and "OR." The search was restricted to articles published in English from 2012 to August 2022. Publications were selected if study participants were pregnant women and biomarkers were detected in maternal blood or urine samples before late-onset or term preeclampsia diagnosis. The search retrieved 4,257 records, of which 125 studies were included in the final assessment. The results demonstrate that no single molecular biomarker presents sufficient clinical sensitivity and specificity for screening late-onset and term preeclampsia. Multivariable models combining maternal risk factors with biochemical and/or biophysical markers generate higher detection rates, but they need more effective biomarkers and validation data for clinical utility. This review proposes that further research into novel biomarkers for late-onset and term preeclampsia is warranted and important to find strategies to predict this complication. Other critical factors to help identify candidate markers should be considered, such as a consensus on defining preeclampsia subtypes, optimal testing time, and sample types.

Cardiac remodeling from the fetus to adulthood

Prenatal cardiac remodeling refers to in utero changes in the fetal heart that occur as a response to an adverse intrauterine environment. In this article, we will review the main mechanisms leading to cardiac remodeling and dysfunction, summarizing and describing the major pathological conditions that have been reported to be related to this in utero plastic adaptive process. We will also recap the current evidence regarding the persistence of fetal cardiac remodeling and dysfunction, both in infancy and later in adult life. Moreover, we will discuss primary, secondary, and tertiary preventive measures and future clinical and research aspects.

Effects of maternal characteristics and medical history on first trimester biomarkers for preeclampsia

Objective

To identify and quantify the effects of maternal characteristics and medical history on the distribution of Placental Growth Factor (PlGF), mean arterial pressure (MAP), and Uterine Artery Mean Pulsatility Index (UtA-PI); and to standardize the expected values for these biomarkers in the first trimester to create unique multiples of the median (MoMs) for Latin-American population.

Methods

This is a prospective cohort built exclusively for research purposes of consecutive pregnant women attending their first-trimester screening ultrasound at a primary care center for the general population in Mexico City between April 2019 and October 2021. We excluded fetuses with chromosomal abnormalities, major fetal malformations, and women delivering in another care center. Linear regression was used on log-transformed biomarkers to assess the influence of maternal characteristics on non-preeclamptic women to create MoM.

Results

Of a total of 2,820 pregnant women included in the final analysis, 118 (4.18%) developed PE, of which 22 (0.78%) delivered before 34 weeks of gestation, 74 (2.62%) before 37 weeks, and 44 (1.56%) from 37 weeks gestation. Characteristics that significantly influenced PLGF were fetal crown rump length (CRL), maternal age, nulliparity, body mass index (BMI), chronic hypertension, Lupus, spontaneous pregnancy, polycystic ovary syndrome (PCOS), hypothyroidism, preeclampsia (PE) in a previous pregnancy, and mother with PE. MAP had significant influence from CRL, maternal age, PE in a previous pregnancy, induction of ovulation, a mother with PE, chronic hypertension, BMI, and hypothyroidism. UtA-PI was influenced by CRL, maternal age, a mother with PE, chronic hypertension, and gestational diabetes mellitus (GDM) in a previous pregnancy.

Conclusion

Population-specific multiples of the median (MoMs) for PlGF, MAP, and UtA-PI in the first trimester adequately discriminate among women developing preeclampsia later in pregnancy.

Predictors of Preeclampsia in the First Trimester in Normotensive and Chronic Hypertensive Pregnant Women

Preeclampsia (PE) is characterized by the new onset of hypertension (HT) and proteinuria beyond the 20th week of gestation. We aimed to find the best predictor of PE and find out if it is different in women with or without HT. Consecutively attended pregnant women were recruited in the first trimester of pregnancy and followed-up. Laboratory and office and 24 h-ambulatory blood pressure (BP) data were collected. PE occurred in 6.25% of normotensives (n = 124). Both office mean BP and 24 h-systolic BP in the first trimester were higher in women with versus those without PE (p ≤ 0.001). In women with chronic hypertension (cHT), PE occurred in 55%; office SBP (p = 0.769) and 24 h-SBP (p = 0.589) were similar between those with and those without PE. Regarding biochemistry, in cHT, plasma urea and creatinine were higher in PE women than in those without cHT (p = 0.001 and p = 0.004 for the differences in both parameters). These differences were not observed in normotensives. In normotensives, mean BP was the best predictor of PE [ROC curve = 0.91 (95%CI 0.82-0.99)], best cut-off = 80.3 mmHg. In cHT, plasma urea and creatinine were the best predictors of PE, with ROC curves of 0.94 (95%CI 0.84-1.00) and 0.93 (95%CI 0.83-1.00), respectively. In the first trimester of pregnancy, the strongest predictor of PE in normotensive women is office mean BP, while in cHT, renal parameters are the strongest predictors. Otherwise, office BP is non-inferior to 24 h ambulatory BP to predict PE.

The Role of Cluster C19MC in Pre-Eclampsia Development

Pre-eclampsia is a placenta-related complication occurring in 2-10% of all pregnancies. miRNAs are a group of non-coding RNAs regulating gene expression. There is evidence that C19MC miRNAs are involved in the development of the placenta. Deregulation of chromosome 19 microRNA cluster (C19MC) miRNAs expression leads to impaired cell differentiation, abnormal trophoblast invasion and pathological angiogenesis, which can lead to the development of pre-eclampsia. Information was obtained through a review of articles available in PubMed Medline. Articles on the role of the C19MC miRNA in the development of pre-eclampsia published in 2009-2022 were analyzed. This review article summarizes the current data on the role of the C19MC miRNA in the development of pre-eclampsia. They indicate a significant increase in the expression of most C19MC miRNAs in placental tissue and a high level of circulating fractions in serum and plasma, both in the first and/or third trimester in women with PE. Only for miR-525-5p, low levels of plasma expression were noted in the first trimester, and in the placenta in the third trimester. The search for molecular factors indicating the development of pre-eclampsia before the onset of clinical symptoms seems to be a promising diagnostic route. Identifying women at risk of developing pre-eclampsia at the pre-symptomatic stage would avoid serious complications in both mothers and fetuses. We believe that miRNAs belonging to cluster C19MC could be promising biomarkers of pre-eclampsia development.

First trimester preeclampsia screening and prediction

Preeclampsia is a major cause of maternal and perinatal morbidity and mortality. Early-onset disease requiring preterm delivery is associated with a higher risk of complications in both mothers and babies. Evidence suggests that the administration of low-dose aspirin initiated before 16 weeks' gestation significantly reduces the rate of preterm preeclampsia. Therefore, it is important to identify pregnant women at risk of developing preeclampsia during the first trimester of pregnancy, thus allowing timely therapeutic intervention. Several professional organizations such as the American College of Obstetricians and Gynecologists (ACOG) and National Institute for Health and Care Excellence (NICE) have proposed screening for preeclampsia based on maternal risk factors. The approach recommended by ACOG and NICE essentially treats each risk factor as a separate screening test with additive detection rate and screen-positive rate. Evidence has shown that preeclampsia screening based on the NICE and ACOG approach has suboptimal performance, as the NICE recommendation only achieves detection rates of 41% and 34%, with a 10% false-positive rate, for preterm and term preeclampsia, respectively. Screening based on the 2013 ACOG recommendation can only achieve detection rates of 5% and 2% for preterm and term preeclampsia, respectively, with a 0.2% false-positive rate. Various first trimester prediction models have been developed. Most of them have not undergone or failed external validation. However, it is worthy of note that the Fetal Medicine Foundation (FMF) first trimester prediction model (namely the triple test), which consists of a combination of maternal factors and measurements of mean arterial pressure, uterine artery pulsatility index, and serum placental growth factor, has undergone successful internal and external validation. The FMF triple test has detection rates of 90% and 75% for the prediction of early and preterm preeclampsia, respectively, with a 10% false-positive rate. Such performance of screening is superior to that of the traditional method by maternal risk factors alone. The use of the FMF prediction model, followed by the administration of low-dose aspirin, has been shown to reduce the rate of preterm preeclampsia by 62%. The number needed to screen to prevent 1 case of preterm preeclampsia by the FMF triple test is 250. The key to maintaining optimal screening performance is to establish standardized protocols for biomarker measurements and regular biomarker quality assessment, as inaccurate measurement can affect screening performance. Tools frequently used to assess quality control include the cumulative sum and target plot. Cumulative sum is a sensitive method to detect small shifts over time, and point of shift can be easily identified. Target plot is a tool to evaluate deviation from the expected multiple of median and the expected median of standard deviation. Target plot is easy to interpret and visualize. However, it is insensitive to detecting small deviations. Adherence to well-defined protocols for the measurements of mean arterial pressure, uterine artery pulsatility index, and placental growth factor is required. This article summarizes the existing literature on the different methods, recommendations by professional organizations, quality assessment of different components of risk assessment, and clinical implementation of the first trimester screening for preeclampsia.

Clinical implementation of pre-eclampsia screening in the first trimester of pregnancy

OBJECTIVES

Early identification of preeclampia in the first trimester of pregnancy represents one of the major challenges of modern fetal medicine. The primary aim of our study was to evaluate the effectiveness of implementation of preeclampsia screening in Tuscany, Italy. The secondary aim was to evaluate pregnancy/neonatal outcome in the positive screening group compared with the negative screening group.

STUDY DESIGN

Retrospective study including singleton pregnancies undergoing screening for preeclampsia. The screening test was a multiparametric algorithm based on maternal history, biochemical and biophysical parameters (Fetal Medicine Foundation algorithm).

MAIN OUTCOME MEASURES

The overall performance of the test was calculated, in terms of sensitivity, specificity, positive and negative predictive value and in relation to gestational age at onset (primary aim). Pregnancy and neonatal outcomes were then compared between the positive and negative population at preeclampsia screening test (secondary aim).

RESULTS

Of the 5719 patients enrolled, 4797 were included in the analysis. The sensitivity for early onset of preeclampsia (≤34 weeks) was 0.75 (CI:0.41-0.93) and specificity 0.93 (CI:0.92-0.94) for a false positive rate of 7%. The population that tested positive for preeclampsia screening showed a higher incidence of deliveries at lower gestational ages (p < 0.001), preeclampsia onset despite prophylaxis with aspirin (p < 0.001), emergency caesarean section (p < 0.001), low fetal birth weight (p < 0.001) and neonatal admission in intensive care unit (p < 0.001).

CONCLUSIONS

Our data confirm the validity of first trimester screening test in identifying a category of patients at greatest risk for preeclampsia even in the presence of a post-test pharmacological prophylaxis.

Cut-off values for Gaussian first-trimester screening for early-onset preeclampsia with maternal history, biochemical markers and uterine artery Doppler

INTRODUCTION

Several algorithms for first-trimester screening for preeclampsia are available; however, the Gaussian model algorithm is more likely to match the characteristics of different populations. It is recommended to validate a screening strategy before being implemented in clinical practice; unfortunately, the validation process might not be feasible in all settings. Thus, the aim of this study was to provide cut-off values for the Gaussian model for its use in clinical practice.

MATERIAL AND METHODS

This prospective cohort study was conducted at Vall d'Hebron University Hospital (Barcelona) from October 2015 to September 2017. A total of 2641 women with singleton pregnancies were recruited. Recorded at the first-trimester scan were demographic characteristics, maternal obstetric history, maternal history, uterine artery Doppler and arterial blood pressure. Serum concentrations of pregnancy-associated plasma protein-A and placental growth factor were assessed from the first-trimester blood test. Detection rates and cut-off values for fixed 5%, 10 %, 15 %, 20 %, 25 % and 30 % false-positive rates were calculated for all combinations of markers.

RESULTS

Ninety (3.41 %) of the 2641 women developed preeclampsia, which was early-onset in 11 (0.42 %). The cut-off values and their respective detection rates, for the screening of early-onset PE by all possible combinations of markers involved in this model, are provided.

DISCUSSION

When external validation of first-trimester screening for preeclampsia before its clinical implementation is not feasible, the cut-off values from the Gaussian model algorithm provided in this study could be used and median values corrected prospectively if necessary.

A new model for screening for early-onset preeclampsia

BACKGROUND

Early identification of women with an increased risk for preeclampsia is of utmost importance to minimize adverse perinatal events. Models developed until now (mainly multiparametric algorithms) are thought to be overfitted to the derivation population, which may affect their reliability when applied to other populations. Options allowing adaptation to a variety of populations are needed.

OBJECTIVE

The objective of the study was to assess the performance of a first-trimester multivariate Gaussian distribution model including maternal characteristics and biophysical/biochemical parameters for screening of early-onset preeclampsia (delivery <34 weeks of gestation) in a routine care low-risk setting.

STUDY DESIGN

Early-onset preeclampsia screening was undertaken in a prospective cohort of singleton pregnancies undergoing routine first-trimester screening (8 weeks 0/7 days to 13 weeks 6/7 days of gestation), mainly using a 2-step scheme, at 2 hospitals from March 2014 to September 2017. A multivariate Gaussian distribution model including maternal characteristics (a priori risk), serum pregnancy-associated plasma protein-A and placental growth factor assessed at 8 weeks 0/7 days to 13 weeks 6/7 days and mean arterial pressure and uterine artery pulsatility index measured at 11.0-13.6 weeks was used.

RESULTS

A total of 7908 pregnancies underwent examination, of which 6893 were included in the analysis. Incidence of global preeclampsia was 2.3% (n = 161), while of early-onset preeclampsia was 0.2% (n = 17). The combination of maternal characteristics, biophysical parameters, and placental growth factor showed the best detection rate, which was 59% for a 5% false-positive rate and 94% for a 10% false-positive rate (area under the curve, 0.96, 95% confidence interval, 0.94-0.98). The addition of placental growth factor to biophysical markers significantly improved the detection rate from 59% to 94%.

CONCLUSION

The multivariate Gaussian distribution model including maternal factors, early placental growth factor determination (at 8 weeks 0/7 days to 13 weeks 6/7 days), and biophysical variables (mean arterial pressure and uterine artery pulsatility index) at 11 weeks 0/7 days to 13 weeks 6/7 days is a feasible tool for early-onset preeclampsia screening in the routine care setting. Performance of this model should be compared with predicting models based on regression analysis.

Systematic review of prediction models for gestational hypertension and preeclampsia

INTRODUCTION

Prediction models for gestational hypertension and preeclampsia have been developed with data and assumptions from developed countries. Their suitability and application for low resource settings have not been tested. This review aimed to identify and assess the methodological quality of prediction models for gestational hypertension and pre-eclampsia with reference to their application in low resource settings.

METHODS

Using combinations of keywords for gestational hypertension, preeclampsia and prediction models seven databases were searched to identify prediction models developed with maternal data obtained before 20 weeks of pregnancy and including at least three predictors (Prospero registration CRD 42017078786). Prediction model characteristics and performance measures were extracted using the CHARMS, STROBE and TRIPOD checklists. The National Institute of Health quality assessment tools for observational cohort and cross-sectional studies were used for study quality appraisal.

RESULTS

We retrieved 8,309 articles out of which 40 articles were eligible for review. Seventy-seven percent of all the prediction models combined biomarkers with maternal clinical characteristics. Biomarkers used as predictors in most models were pregnancy associated plasma protein-A (PAPP-A) and placental growth factor (PlGF). Only five studies were conducted in a low-and middle income country.

CONCLUSIONS

Most of the studies evaluated did not completely follow the CHARMS, TRIPOD and STROBE guidelines in prediction model development and reporting. Adherence to these guidelines will improve prediction modelling studies and subsequent application of prediction models in clinical practice. Prediction models using maternal characteristics, with good discrimination and calibration, should be externally validated for use in low and middle income countries where biomarker assays are not routinely available.

The prediction of early preeclampsia: Results from a longitudinal proteomics study

OBJECTIVES

To identify maternal plasma protein markers for early preeclampsia (delivery <34 weeks of gestation) and to determine whether the prediction performance is affected by disease severity and presence of placental lesions consistent with maternal vascular malperfusion (MVM) among cases.

STUDY DESIGN

This longitudinal case-control study included 90 patients with a normal pregnancy and 33 patients with early preeclampsia. Two to six maternal plasma samples were collected throughout gestation from each woman. The abundance of 1,125 proteins was measured using high-affinity aptamer-based proteomic assays, and data were modeled using linear mixed-effects models. After data transformation into multiples of the mean values for gestational age, parsimonious linear discriminant analysis risk models were fit for each gestational-age interval (8-16, 16.1-22, 22.1-28, 28.1-32 weeks). Proteomic profiles of early preeclampsia cases were also compared to those of a combined set of controls and late preeclampsia cases (n = 76) reported previously. Prediction performance was estimated via bootstrap.

RESULTS

We found that 1) multi-protein models at 16.1-22 weeks of gestation predicted early preeclampsia with a sensitivity of 71% at a false-positive rate (FPR) of 10%. High abundance of matrix metalloproteinase-7 and glycoprotein IIbIIIa complex were the most reliable predictors at this gestational age; 2) at 22.1-28 weeks of gestation, lower abundance of placental growth factor (PlGF) and vascular endothelial growth factor A, isoform 121 (VEGF-121), as well as elevated sialic acid binding immunoglobulin-like lectin 6 (siglec-6) and activin-A, were the best predictors of the subsequent development of early preeclampsia (81% sensitivity, FPR = 10%); 3) at 28.1-32 weeks of gestation, the sensitivity of multi-protein models was 85% (FPR = 10%) with the best predictors being activated leukocyte cell adhesion molecule, siglec-6, and VEGF-121; 4) the increase in siglec-6, activin-A, and VEGF-121 at 22.1-28 weeks of gestation differentiated women who subsequently developed early preeclampsia from those who had a normal pregnancy or developed late preeclampsia (sensitivity 77%, FPR = 10%); 5) the sensitivity of risk models was higher for early preeclampsia with placental MVM lesions than for the entire early preeclampsia group (90% versus 71% at 16.1-22 weeks; 87% versus 81% at 22.1-28 weeks; and 90% versus 85% at 28.1-32 weeks, all FPR = 10%); and 6) the sensitivity of prediction models was higher for severe early preeclampsia than for the entire early preeclampsia group (84% versus 71% at 16.1-22 weeks).

CONCLUSION

We have presented herein a catalogue of proteome changes in maternal plasma proteome that precede the diagnosis of preeclampsia and can distinguish among early and late phenotypes. The sensitivity of maternal plasma protein models for early preeclampsia is higher in women with underlying vascular placental disease and in those with a severe phenotype.

Prenatal screening for pre-eclampsia: Frequently asked questions

The current approach to screening for pre-eclampsia is based on guidelines that rely on medical and obstetric history in early pregnancy to select a high-risk group that might benefit from low-dose aspirin. However, combined screening tests with the addition of biophysical and biochemical measurements have shown significantly better detection rates for preterm pre-eclampsia. Furthermore, the administration of aspirin for the 10% screen-positive group can lead to a significant reduction in severe and preterm forms of pre-eclampsia. This review aims to answer frequently asked questions related to the clinical implementation of screening and the management of screening results.

Quality assessment of uterine artery Doppler measurement in first-trimester combined screening for pre-eclampsia

OBJECTIVE

To assess the quality of mean uterine artery (UtA) pulsatility index (PI) measurement in a first-trimester pre-eclampsia screening program.

METHODS

Consecutive women with a singleton pregnancy attending first-trimester screening for fetal chromosomal abnormalities also had combined screening for pre-eclampsia based on the Fetal Medicine Foundation (FMF) algorithm, at a large practice in Sydney, Australia, from May 2014 to February 2017. Distributions of mean UtA-PI multiples of the median (MoM) on a logarithmic scale were plotted in relation to the normal median with 95% CI for each operator and for each month. Central tendency and dispersion and cumulative sum charts were produced. Mean UtA-PI MoM values between 0.95 and 1.05 were considered ideal and those between 0.90 and 1.10 were considered acceptable. The screen-positive rates for preterm pre-eclampsia in different groups of sonographers according to their mean log₁₀ UtA-PI MoM were calculated and compared using the chi-square test.

RESULTS

A total of 21 010 women attended for first-trimester ultrasound and had screening for pre-eclampsia. The overall median UtA-PI MoM was 1.042 (interquartile range (IQR), 0.85-1.26). Of 46 sonographers, 42 (91.3%) performed more than 50 examinations and, of those, 41 (97.6%) measured UtA-PI within the acceptable range. Sonographers measuring UtA-PI MoM on average below 0.95 and those measuring it above 1.05 had, respectively, lower and higher screen-positive rates when compared with those with measurements within the 0.95-1.05 UtA-PI MoM interval (7.2% and 13.2% vs 11.2%, respectively, P < 0.001).

CONCLUSION

UtA Doppler is measured well among trained operators when following an established protocol. While slight variations are expected, systematic error in this measurement impacts on the screen-positive rate. Therefore, a quality control process should be in place and retraining of staff may be required. Copyright © 2018 ISUOG. Published by John Wiley & Sons Ltd.

Performance of first trimester biochemical markers and mean arterial pressure in prediction of early-onset pre-eclampsia

OBJECTIVE

To develop a predictive risk model for early-onset pre-eclampsia (EO-PE) using maternal characteristics, combined screening markers, previously reported biomarkers for PE and mean arterial pressure (MAP).

METHODS

This retrospective study was conducted at Oulu University hospital between 2006 and 2010. Maternal serum from first trimester combined screening was further analyzed for alpha fetoprotein (AFP), placental growth factor (PlGF), soluble tumor necrosis factor receptor-1 (sTNFR1), retinol binding protein-4 (RBP4), a disintegrin and metalloprotease-12 (ADAM12), soluble P-selectin (sP-selectin), follistatin like-3 (FSTL3), adiponectin, angiopoietin-2 (Ang-2) and sex hormone binding globulin (SHBG). First, the training sample set with 29 cases of EO-PE and 652 controls was developed to study whether these biomarkers separately or in combination with prior risk (maternal characteristics, first trimester pregnancy associated plasma protein-A (PAPP-A) and free beta human chorionic gonadotrophin (fβ-hCG)) could be used to predict the development of EO-PE. Second, the developed risk models were validated with a test sample set of 42 EO-PE and 141 control subjects. For the test set MAP data was also available.

RESULTS

Single marker statistically significant (ANOVA p<0.05) changes between control and EO-PE pregnancies were observed with AFP, RBP4 and sTNFR1 with both training and test sample sets. Based on the test sample set performances, the best detection rate, 47% for a 10% false positive rate, was achieved with PlGF and sTNFR1 added with prior risk and MAP.

CONCLUSION

Based on our results, the best first trimester biomarkers to predict the subsequent EO-PE were AFP, PlGF, RBP4 and sTNFR1. The risk models that performed best for the prediction of EO-PE included prior risk, MAP, sTNFR1 and AFP or PlGF or RBP4.

First trimester screening of circulating C19MC microRNAs and the evaluation of their potential to predict the onset of preeclampsia and IUGR

Objectives

A nested case control study of a longitudinal cohort comparing pregnant women enrolled at 10 to 13 gestational weeks was carried out to evaluate risk assessment for preeclampsia and IUGR based on circulating placental specific C19MC microRNAs in early pregnancy.

Methods

The expression of placental specific C19MC microRNAs (miR-516b-5p, miR-517-5p, miR-518b, miR-520a-5p, miR-520h, and miR-525-5p) was determined in plasma samples from pregnancies that subsequently developed preeclampsia (n = 21), IUGR (n = 18), and 58 normal pregnancies using real-time PCR and comparative Ct method relative to synthetic Caenorhabditis elegans microRNA (cel-miR-39).

Results

Circulating C19MC microRNAs were up-regulated (miR-517-5p, p = 0.005; miR-518b, p = 0.013; miR-520h, p = 0.021) or showed a trend toward up-regulation in patients destined to develop preeclampsia (miR-520a-5p, p = 0.067; miR-525-5p, p = 0.073). MiR-517-5p had the best predictive performance for preeclampsia with a sensitivity of 42.9%, a specificity of 86.2%, a PPV of 52.9% and a NPV of 80.6%. The combination of all examined circulating C19MC microRNAs had no advantage over using only the miR-517-5p biomarker to predict the occurrence of preeclampsia (a sensitivity of 20.6%, a specificity of 90.8%, a PPV of 44.8%, and a NPV of 76.0%).

Conclusions

Up-regulation of miR-517-5p, miR-518b and miR-520h was associated with a risk of later development of preeclampsia. First trimester screening of extracellular miR-517-5p identified a proportion of women with subsequent preeclampsia. No circulating C19MC microRNA biomarkers were identified that could predict later occurrence of IUGR.