First-trimester prediction of pre-eclampsia: external validity of algorithms in a prospectively enrolled cohort

Preeclampsia risk prediction from prenatal cell-free DNA screening

Preeclampsia is characterized by placental dysfunction and results in significant morbidity, but reliable early prediction remains challenging. We investigated whether clinically obtained prenatal cell-free DNA (cfDNA) screening (PDNAS) using whole-genome sequencing (WGS) data can be leveraged to predict preeclampsia risk early in pregnancy (≤16 weeks). Using 1,854 routinely collected clinical PDNAS samples (median, 12.1 weeks) with low-coverage (0.5×) WGS data, we developed a framework to quantify maternal and fetal tissue signatures using nucleosome accessibility, revealing early placental and endothelial dysfunction. These signatures informed a prediction model for preeclampsia risk, which achieved a validation performance of 0.85 area under the receiver operating characteristic curve (AUC) (81% sensitivity at 80% specificity) for preterm phenotypes several months prior to disease onset in a separate cohort of 831 consecutively collected samples, and subsequently confirmed in an external cohort of 141 samples (AUC 0.84, 79% sensitivity). We demonstrate that assessment of cfDNA nucleosome accessibility from early-pregnancy cfDNA sequence data enables the detection of early placental and endothelial-tissue aberrations and may aid in the determination of preeclampsia risk.

Exploring new predictors for hypertensive disorders of pregnancy

The best performing predicting Bayesian algorithm for preeclampsia, endorsed by FIGO, identifies high-risk women at first trimester screening who benefits of a closer monitoring and possibly preventive measures. Unfortunately, the most frequent late term and term preeclampsia are less efficiently predicted. This algorithm is based on statistical assumptions at odds with the physiopathology: preeclampsia is a disease and not a syndrome, as we know it is, and the contingent time-based criteria according to which all pregnancies if not terminated by nature should develop this "disease". In addition to this, we know that gestational hypertension might cause in fifty percent of cases severe outcome, comparable to preeclampsia. The very definition of preeclampsia as proteinuric hypertension is now extended to hypertension associated with other end-organ damage, including fetal growth restriction (FGR), this latter condition proceeding, in early onset cases, hypertension. Predicting phenotypes of hypertensive Disorders of pregnancy (HDP) could better help clinical practice. This study reports exploratory observations in women resulted at high and low risk at first trimester screening followed up at second and third trimester, to term. The co-variates interrogated were sFlt1/PlGF ratio, the uterine arteries PI, the systemic vascular resistances (SVR), maternal total body water and visceral fat. Women were classified as HDP-AGA, HDP-FGR, normotensive-FGR and uneventful pregnancies (controls). We performed a longitudinal Bayesian multivariate mixed-effects model corrected both for pre-gestational BMI and trimester of analysis. The sFlt-1/PlGF ratio and SVR confirmed their significant difference in HDP-AGA, in normotensive FGR, and HDP-FGR along the three trimesters from controls, but with different strength along the three trimesters. The bioimpedance analysis of total body water and visceral fat confirmed the association of these co-factors with women who will develop HDP-AGA. The strength of longitudinal changes observed, even on a limited number of cases, provide evidence that Bayesian algorithms applied at screening tests at different gestational ages, should be based on co-variates significantly associated either with HDP-FGR or with HDP-AGA provided that the main causative co-factors involved are adopted by predictive models aimed at these distinct diseases.

Hypertensive Disorders of Pregnancy and the Cardiovascular System: Causes, Consequences, Therapy, and Prevention

Hypertensive disorders of pregnancy continue to be significant contributors to adverse perinatal outcome and maternal mortality, as well as inducing life-long cardiovascular health impacts that are proportional to the severity and frequency of pregnancy complications. The placenta is the interface between the mother and fetus and its failure to undergo vascular maturation in tandem with maternal cardiovascular adaptation by the end of the first trimester predisposes to hypertensive disorders and fetal growth restriction. While primary failure of trophoblastic invasion with incomplete maternal spiral artery remodeling has been considered central to the pathogenesis of preeclampsia, cardiovascular risk factors associated with abnormal first trimester maternal blood pressure and cardiovascular adaptation produce identical placental pathology leading to hypertensive pregnancy disorders. Outside pregnancy blood pressure treatment thresholds are identified with the goal to prevent immediate risks from severe hypertension >160/100 mm Hg and long-term health impacts that arise from elevated blood pressures as low as 120/80 mm Hg. Until recently, the trend for less aggressive blood pressure management during pregnancy was driven by fear of inducing placental malperfusion without a clear clinical benefit. However, placental perfusion is not dependent on maternal perfusion pressure during the first trimester and risk-appropriate blood pressure normalization may provide the opportunity to protect from the placental maldevelopment that predisposes to hypertensive disorders of pregnancy. Recent randomized trials set the stage for more aggressive risk-appropriate blood pressure management that may offer a greater potential for prevention for hypertensive disorders of pregnancy. KEY POINTS: · Optimal management of maternal blood pressure to prevent preeclampsia and its risks is undefined.. · Early gestational rheological damage to the intervillous space predisposes to preeclampsia and FGR.. · First trimester blood pressure management may need to aim for normotension to prevent preeclampsia..

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.

Cell-free DNA methylation-based preeclampsia prediction: A journey to improve maternal health

Presymptomatic prediction of preeclampsia (PE) is crucial to enable early prophylactic treatment. Current screening tools are either complex or lack predictive value. We recently demonstrated that cell-free DNA methylation can be leveraged to predict early-onset PE in 57% at a 10% false positive rate. Importantly, this minimally invasive screening test can be implemented as an add-on to current widespread noninvasive prenatal aneuploidy screening. Here, we highlight the pitfalls and promising prospects of this research.

Cell-Free Nucleic Acids for Early Prediction of Preeclampsia

PURPOSE OF REVIEW

This review summarizes the potential of cell-free nucleic acids for predicting preeclampsia, contrasts them with other methods, and discusses these findings' relevance to preeclampsia's pathogenesis and care.

RECENT FINDINGS

Recent studies have demonstrated the utility of cell-free nucleic acids in early preeclampsia risk prediction. Encouragingly, nucleic acid measurement exhibits similar or better sensitivity as compared to standard screening assays and furthermore sheds light on preeclampsia's underlying placental biology. Over the past decade, liquid biopsies measuring cell-free nucleic acids have found diverse applications, including in prenatal care. Recent advances have extended their utility to predict preeclampsia, a major cause of maternal mortality. These assays assess methylation patterns in cell-free DNA (cfDNA) or gene levels in cell-free RNA (cfRNA). Currently, preeclampsia care focuses on blood pressure control, seizure prevention, and delivery. If validated, early prediction of preeclampsia through liquid biopsies can improve maternal health and deepen our understanding of its causes.

First Trimester Preeclampsia Screening and Prevention: Perspective in Chinese Mainland

Preeclampsia (PE), a multisystem disorder in pregnancy, is one of the leading causes of perinatal morbidity and mortality that poses financial and physical burdens worldwide. Preterm PE with delivery at <37 weeks of gestation is associated with a higher risk of adverse maternal and perinatal outcomes than term PE with delivery at ≥37 weeks of gestation. A myriad of first trimester screening models have been developed to identifying women at risk of preterm PE. In fact, the Fetal Medicine Foundation (FMF) first trimester prediction model has undergone successful internal and external validation. The FMF triple test enables the estimation of patient-specific risks, using Bayes theorem to combine maternal characteristics and medical history together with measurements of mean arterial pressure, uterine artery pulsatility index, and serum placental growth factor. Establishing a quality control process for regular monitoring and to ensure data standardization, reliability, and accuracy is key to maintaining optimal screening performance. The rate of preterm PE can be reduced by 62% by using the FMF prediction model, followed by the administration of low-dose aspirin. Recent evidence has also demonstrated that metformin has the potential for preventing PE in patients at high-risk of the disorder. In this article, we will summarize the existing literature on the different screening methods, different components of risk assessment, therapeutic interventions, and clinical implementation of the first trimester screening and prevention program for PE with specific considerations for Chinese mainland.

Analysis of Predictive Information From Biomarkers Added to Clinical Models of Preeclampsia: Consideration of PAPP-A2, Activin A, and sFlt-1:PlGF Ratio

BACKGROUND

Preeclampsia remains a major cause of maternal and fetal adverse outcomes in pregnancy; however, accurate and universally acceptable predictive tools remain elusive. We investigated whether a panel of biomarkers could improve risk prediction for preeclampsia when measured at various pregnancy time points.

METHODS

In this prospective cohort study, 192 women with first-trimester high-risk singleton pregnancies were consecutively recruited from tertiary obstetrics clinics in Montréal, Canada. Clinical information (height, pre-pregnancy weight, personal and family medical history, medication use) was collected at baseline. Blood pressure was measured and blood samples collected at each trimester to quantify soluble Fms-like tyrosine kinase 1 (sFlt-1), placental growth factor (PlGF), pregnancy-associated plasma protein A2 (PAPP-A2), PAPP-A, activin A, inhibin A, follistatin, and glycosylated fibronectin. A random-effects hierarchic logistic regression model was used to relate change in biomarker levels to incidence of preeclampsia.

RESULTS

When added to a clinical model composed of maternal age, pre-pregnancy body mass index, race, and mean arterial pressure, a positive third-trimester result for both PAPP-A2 and activin A had a better positive predictive value than the sFlt-1:PlGF ratio added to the clinical model (91.67% [95% confidence interval (CI) 78.57%-100%] vs 66.67% [57.14%-100%]), while maintaining a comparable high negative predictive value (97.69% [95% CI 95.34%-100%] vs 96.00% [92.19%-99.21%]).

CONCLUSIONS

Whereas the third-trimester sFlt-1:PlGF ratio can predict short-term absence of preeclampsia, PAPP-A2 and activin A had both high positive and negative predictive values and therefore could serve as biomarkers to predict the occurrence (and absence) of preeclampsia; these findings will be validated in future studies.

Machine-learning predictive model of pregnancy-induced hypertension in the first trimester

In the first trimester of pregnancy, accurately predicting the occurrence of pregnancy-induced hypertension (PIH) is important for both identifying high-risk women and adopting early intervention. In this study, we used four machine-learning models (LASSO logistic regression, random forest, backpropagation neural network, and support vector machines) to predict the occurrence of PIH in a prospective cohort. Candidate features for predicting the occurrence of middle and late PIH were acquired using a LASSO algorithm. The performance of predictive models was assessed using receiver operating characteristic analysis. Finally, a nomogram was established with the model scores, age, and nulliparity. Calibration, clinical usefulness, and internal validation were used to assess the performance of the nomogram. In the training set (2258 pregnant women), eleven candidate factors in the first trimester were significantly associated with the occurrence of PIH (P < 0.001 in the training set). Four models showed AUCs from 0.780 to 0.816 in the training set. For the validation set (939 pregnant women), AUCs varied from 0.516 to 0.795. The nomogram showed good discrimination, with an AUC of 0.847 (95% CI: 0.805-0.889) in the training set and 0.753 (95% CI: 0.653-0.853) in the validation set. Decision curve analysis suggested that the model was clinically useful. The model developed using LASSO logistic regression achieved the best performance in predicting the occurrence of PIH. The derived nomogram, which incorporates the model score and maternal risk factors, can be used to predict PIH in clinical practice. We develop a model with good performance for clinical prediction of PIH in the first trimester.

Low-Dose Aspirin after ASPRE-More Questions Than Answers? Current International Approach after PE Screening in the First Trimester

Preeclampsia (PE) is a multi-factorial disorder of pregnancy, and it continues to be one of the leading causes of fetal and maternal morbidity and mortality worldwide. Aspirin is universally recommended for high-risk women to reduce preeclampsia risk. The purpose of this review is to summarize the recommendations of various scientific societies on predicting preeclampsia and their indications for the inclusion of acetylsalicylic acid (ASA) prophylaxis. Fourteen guidelines were compared. The recommended dose, screening method, and gestational age at the start of the test vary depending on the recommendation. The societies are inclined to recommend using increasingly higher doses (>75 mg) of ASA, with many encouraging doses from 100 mg upward. Most societies indicate that the optimal time for implementing aspirin is prior to 16 weeks' gestation. Following the publication of the Aspirin for Evidence-Based Preeclampsia Prevention (ASPRE) trial results and other papers evaluating the Fetal Medicine Foundation (FMF) screening model, a large number of societies have changed their recommendations from those based on risk factors alone to the ones based on the risk assessment proposed by the FMF. This allows for the detection of a high-risk pregnancy population in whom aspirin will be remarkably effective in preventing preterm PE, thereby decreasing maternal and fetal morbidity.

A mathematical model of vascular and hemodynamics changes in early and late forms of preeclampsia

Preeclampsia-eclampsia syndrome is a leading cause of maternal mortality. The precise etiology of preeclampsia is still not well-defined and different forms exist, including early and late forms or preeclampsia, which may arise via distinctly different mechanisms. Low-dose aspirin administered at the end of the first trimester in women identified as high risk has been shown to reduce the incidence of early, but not late, preeclampsia; however, current risk factors show only fair predictive capability. There is a pressing need to develop accurate descriptions for the different forms of preeclampsia. This paper presents 1D fluid, solid, growth, and remodeling models for pregnancies complicated with early and late forms of preeclampsia. Simulations affirm a broad set of literature results that early forms of preeclampsia are characterized by elevated uterine artery pulsatility index (UA-PI) and total peripheral resistance (TPR) and lower cardiac output (CO), with modestly increased mean arterial blood pressure (MAP) in the first half of pregnancy, with elevation of TPR and MAP beginning at 20 weeks. Conversely, late forms of preeclampsia are characterized by only slightly elevated UA-PI and normal pre-term TPR, and slightly elevated MAP and CO throughout pregnancy, with increased TPR and MAP beginning after 34 weeks. Results suggest that preexisting arterial stiffness may be elevated in women that develop both early forms and late forms of preeclampsia; however, data that verify these results are lacking in the literature. Pulse wave velocity increases in early- and late-preeclampsia, coincident with increases in blood pressure; however, these increases are mainly due to the strain-stiffening response of larger arteries, rather than arterial remodeling-derived changes in material properties. These simulations affirm that early forms of preeclampsia may be associated with abnormal placentation, whereas late forms may be more closely associated with preexisting maternal cardiovascular factors; simulations also highlight several critical gaps in available data.

First-trimester screening for pre-eclampsia and small for gestational age: A comparison of the gaussian and Fetal Medicine Foundation algorithms

OBJECTIVE

Pre-eclampsia (PE) and small for gestational age (SGA) can be predicted from the first trimester. The most widely used algorithm worldwide is the Fetal Medicine Foundation (FMF) algorithm. The recently described Gaussian algorithm has reported excellent results although it is unlikely to be externally validated. Therefore, as an alternative approach, we compared the predictive accuracy for PE and SGA of the Gaussian and FMF algorithms.

METHODS

Secondary analysis of a prospective cohort study was conducted at Vall d'Hebron University Hospital (Barcelona) with 2641 singleton pregnancies. The areas under the curve for the predictive performance for early-onset and preterm PE and early-onset and preterm SGA were calculated with the Gaussian and FMF algorithms and subsequently compared.

RESULTS

The FMF and Gaussian algorithms showed a similar predictive performance for most outcomes and marker combinations. Nevertheless, significant differences for early-onset PE prediction favored the Gaussian algorithm in the following combinations: mean arterial blood pressure (MAP) with pregnancy-associated plasma protein A, MAP with placental growth factor, and MAP alone.

CONCLUSIONS

The first-trimester Gaussian and FMF algorithms have similar performances for PE and SGA prediction when applied with all markers within a routine care setting in a Spanish population, adding evidence to the external validity of the FMF algorithm.

Uterine artery Doppler indices throughout gestation in women with and without previous Cesarean deliveries: a prospective longitudinal case-control study

To determine whether a history of previous Cesarean delivery (CD) impacts uterine artery (UtA) Doppler indices throughout pregnancy. Women with and without CD (NCD) were prospectively enrolled for sequential assessments of the UtA mean/median pulsatility index (UtA-PI), resistance index (UtA-RI), and systolic/diastolic ratio (UtA-S/D) at 11-13 + 6, 14-19 + 6, 30-34 + 6, and 35-37 + 6 weeks' gestation. Data from 269/269, 246/257, 237/254, and 219/242 CD/NCD participants from each gestational period were available for analysis. Multiples of the median (MoMs) of UtA Doppler indices showed biphasic temporal (Δ) pattern; with an initial dropping until the second trimester, then a subsequent elevation until late in pregnancy (p < 0.05). The measurements and Δs of the UtA indices between CD and NCD were not different (p > 0.05). Mixed-effects modelling ruled out effects from nulliparity (n = 0 and 167 for CD and NCD, respectively) (p > 0.05). History of CD neither influenced the measurements nor the temporal changes of the UtA Doppler indices throughout pregnancy. The biphasic Δs of UtA Doppler indices added to the longitudinal data pool, and may aid in future development of a more personalized prediction using sequential/contingent methodologies, which may reduce the false results from the current cross-sectional screening.

Phenotype-Directed Management of Hypertension in Pregnancy

Hypertensive disorders of pregnancy are among the most serious conditions that pregnancy care providers face; however, little attention has been paid to the concept of tailoring clinical care to reduce associated adverse maternal and perinatal outcomes based on the underlying disease pathogenesis. This narrative review discusses the integration of phenotype-based clinical strategies in the management of high-risk pregnant patients that are currently not common clinical practice: real-time placental growth factor testing at Mount Sinai Hospital, Toronto and noninvasive hemodynamic monitoring to guide antihypertensive therapy at the University of Washington Medical Center, Seattle. Future work should focus on promoting more widespread integration of these novel strategies into obstetric care to improve outcomes of pregnancies at high risk of adverse maternal-fetal outcomes from these complications of pregnancy.

A mathematical model of maternal vascular growth and remodeling and changes in maternal hemodynamics in uncomplicated pregnancy

The maternal vasculature undergoes tremendous growth and remodeling (G&R) that enables a > 15-fold increase in blood flow through the uterine vasculature from conception to term. Hemodynamic metrics (e.g., uterine artery pulsatility index, UA-PI) are useful for the prognosis of pregnancy complications; however, improved characterization of the maternal hemodynamics is necessary to improve prognosis. The goal of this paper is to develop a mathematical framework to characterize maternal vascular G&R and hemodynamics in uncomplicated human pregnancies. A validated 1D model of the human vascular tree from the literature was adapted and inlet blood flow waveforms at the ascending aorta at 4 week increments from 0 to 40 weeks of gestation were prescribed. Peripheral resistances of each terminal vessel were adjusted to achieve target flow rates and mean arterial pressure at each gestational age. Vessel growth was governed by wall shear stress (and axial lengthening in uterine vessels), and changes in vessel distensibility were related to vessel growth. Uterine artery velocity waveforms generated from this model closely resembled ultrasound results from the literature. The literature UA-PI values changed significantly across gestation, increasing in the first month of gestation, then dramatically decreasing from 4 to 20 weeks. Our results captured well the time-course of vessel geometry, material properties, and UA-PI. This 1D fluid-G&R model captured the salient hemodynamic features across a broad range of clinical reports and across gestation for uncomplicated human pregnancy. While results capture available data well, this study highlights significant gaps in available data required to better understand vascular remodeling in pregnancy.

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.

Validation and development of models using clinical, biochemical and ultrasound markers for predicting pre-eclampsia: an individual participant data meta-analysis

BACKGROUND

Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk is needed to plan management.

OBJECTIVES

To assess the performance of existing pre-eclampsia prediction models and to develop and validate models for pre-eclampsia using individual participant data meta-analysis. We also estimated the prognostic value of individual markers.

DESIGN

This was an individual participant data meta-analysis of cohort studies.

SETTING

Source data from secondary and tertiary care.

PREDICTORS

We identified predictors from systematic reviews, and prioritised for importance in an international survey.

PRIMARY OUTCOMES

Early-onset (delivery at < 34 weeks' gestation), late-onset (delivery at ≥ 34 weeks' gestation) and any-onset pre-eclampsia.

ANALYSIS

We externally validated existing prediction models in UK cohorts and reported their performance in terms of discrimination and calibration. We developed and validated 12 new models based on clinical characteristics, clinical characteristics and biochemical markers, and clinical characteristics and ultrasound markers in the first and second trimesters. We summarised the data set-specific performance of each model using a random-effects meta-analysis. Discrimination was considered promising for C-statistics of ≥ 0.7, and calibration was considered good if the slope was near 1 and calibration-in-the-large was near 0. Heterogeneity was quantified using I ² and τ². A decision curve analysis was undertaken to determine the clinical utility (net benefit) of the models. We reported the unadjusted prognostic value of individual predictors for pre-eclampsia as odds ratios with 95% confidence and prediction intervals.

RESULTS

The International Prediction of Pregnancy Complications network comprised 78 studies (3,570,993 singleton pregnancies) identified from systematic reviews of tests to predict pre-eclampsia. Twenty-four of the 131 published prediction models could be validated in 11 UK cohorts. Summary C-statistics were between 0.6 and 0.7 for most models, and calibration was generally poor owing to large between-study heterogeneity, suggesting model overfitting. The clinical utility of the models varied between showing net harm to showing minimal or no net benefit. The average discrimination for IPPIC models ranged between 0.68 and 0.83. This was highest for the second-trimester clinical characteristics and biochemical markers model to predict early-onset pre-eclampsia, and lowest for the first-trimester clinical characteristics models to predict any pre-eclampsia. Calibration performance was heterogeneous across studies. Net benefit was observed for International Prediction of Pregnancy Complications first and second-trimester clinical characteristics and clinical characteristics and biochemical markers models predicting any pre-eclampsia, when validated in singleton nulliparous women managed in the UK NHS. History of hypertension, parity, smoking, mode of conception, placental growth factor and uterine artery pulsatility index had the strongest unadjusted associations with pre-eclampsia.

LIMITATIONS

Variations in study population characteristics, type of predictors reported, too few events in some validation cohorts and the type of measurements contributed to heterogeneity in performance of the International Prediction of Pregnancy Complications models. Some published models were not validated because model predictors were unavailable in the individual participant data.

CONCLUSION

For models that could be validated, predictive performance was generally poor across data sets. Although the International Prediction of Pregnancy Complications models show good predictive performance on average, and in the singleton nulliparous population, heterogeneity in calibration performance is likely across settings.

FUTURE WORK

Recalibration of model parameters within populations may improve calibration performance. Additional strong predictors need to be identified to improve model performance and consistency. Validation, including examination of calibration heterogeneity, is required for the models we could not validate.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42015029349.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 24, No. 72. See the NIHR Journals Library website for further project information.

Diagnostic accuracy of first-trimester combined screening for early-onset and preterm pre-eclampsia at 8-10 compared with 11-13 weeks' gestation

OBJECTIVES

To compare the ability of first-trimester combined screening for pre-eclampsia (PE) to predict early-onset and preterm PE when pregnancy-associated plasma protein-A (PAPP-A) and placental growth factor (PlGF) were assessed before vs after 11 weeks' gestation.

METHODS

This was a secondary analysis of a prospective cohort study of singleton pregnancies undergoing routine first-trimester screening conducted at Vall d'Hebron University Hospital, Barcelona, Spain, between October 2015 and September 2017. Demographic characteristics, obstetric history, maternal history and biophysical markers (mean uterine artery pulsatility index and mean arterial blood pressure (MAP)) were recorded at the first-trimester scan (at 11 + 0 to 13 + 6 weeks' gestation). Maternal serum concentrations of PAPP-A and PlGF were assessed from the routine first-trimester blood test (at 8 + 0 to 13 + 6 weeks). Women were classified into two groups depending on whether serum biomarkers were assessed at 8 + 0 to 10 + 6 weeks or at 11 + 0 to 13 + 6 weeks. Probability scores for early-onset and preterm PE were calculated by using two different algorithms: the multivariate Gaussian-distribution model and The Fetal Medicine Foundation (FMF) competing-risks model. Receiver-operating-characteristics (ROC) curves were produced and detection rates at fixed 5% and 10% false-positive rates were computed to compare the performance of these algorithms when PAPP-A and PlGF were assessed before vs after 11 weeks.

RESULTS

Of the 2641 women included, serum biomarkers were assessed before 11 weeks in 1675 (63.4%) and at or after 11 weeks in 966 (36.6%). Of these, 90 (3.4%) women developed PE, including 11 (0.4%) cases of early-onset PE and 30 (1.1%) of preterm PE. Five (45.5%) cases of early-onset and 16 (53.3%) of preterm PE were identified in the group in which serum biomarkers were assessed at 8 + 0 to 10 + 6 weeks and six (54.5%) cases of early-onset and 14 (46.7%) of preterm PE in the group in which serum biomarkers were assessed at 11 + 0 to 13 + 6 weeks. In the prediction of early-onset and preterm PE using the Gaussian algorithm, no differences were observed between the areas under the ROC curves (AUCs) when PAPP-A and PlGF were measured before or after 11 weeks. In the prediction of early-onset and preterm PE using the FMF algorithm, no differences were observed between AUCs for any of the combinations used for risk calculation when the serum biomarkers were obtained before vs after 11 weeks, except for the combination of PAPP-A and MAP, which showed a greater AUC for the prediction of early-onset PE when PAPP-A was measured at or after 11 weeks.

CONCLUSIONS

The prediction of early-onset and preterm PE is similar when serum biomarkers are measured before or after 11 weeks. This allows the use of a two-step approach for PE risk assessment that permits immediate risk calculation at the time of the first-trimester scan. © 2020 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

External validation of prognostic models predicting pre-eclampsia: individual participant data meta-analysis

BACKGROUND

Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk during pregnancy is required to plan management. Although there are many published prediction models for pre-eclampsia, few have been validated in external data. Our objective was to externally validate published prediction models for pre-eclampsia using individual participant data (IPD) from UK studies, to evaluate whether any of the models can accurately predict the condition when used within the UK healthcare setting.

METHODS

IPD from 11 UK cohort studies (217,415 pregnant women) within the International Prediction of Pregnancy Complications (IPPIC) pre-eclampsia network contributed to external validation of published prediction models, identified by systematic review. Cohorts that measured all predictor variables in at least one of the identified models and reported pre-eclampsia as an outcome were included for validation. We reported the model predictive performance as discrimination (C-statistic), calibration (calibration plots, calibration slope, calibration-in-the-large), and net benefit. Performance measures were estimated separately in each available study and then, where possible, combined across studies in a random-effects meta-analysis.

RESULTS

Of 131 published models, 67 provided the full model equation and 24 could be validated in 11 UK cohorts. Most of the models showed modest discrimination with summary C-statistics between 0.6 and 0.7. The calibration of the predicted compared to observed risk was generally poor for most models with observed calibration slopes less than 1, indicating that predictions were generally too extreme, although confidence intervals were wide. There was large between-study heterogeneity in each model's calibration-in-the-large, suggesting poor calibration of the predicted overall risk across populations. In a subset of models, the net benefit of using the models to inform clinical decisions appeared small and limited to probability thresholds between 5 and 7%.

CONCLUSIONS

The evaluated models had modest predictive performance, with key limitations such as poor calibration (likely due to overfitting in the original development datasets), substantial heterogeneity, and small net benefit across settings. The evidence to support the use of these prediction models for pre-eclampsia in clinical decision-making is limited. Any models that we could not validate should be examined in terms of their predictive performance, net benefit, and heterogeneity across multiple UK settings before consideration for use in practice.

TRIAL REGISTRATION

PROSPERO ID: CRD42015029349 .

Current update of first trimester preeclampsia screening in Asia

In consideration of high prevalence of preeclampsia, enormous studies attempted to look for strategies in early gestation. Hence, a powerful screening should be built up in first trimester. Then, Aspirin could be administrated for proper prevention. The objective of this article is reviewing the screening for preeclampsia in first trimester recently. To identify the high-risk group precisely, an effective model should be recommended to Asian population. Articles related to first trimester screening of PE in Asia from databases of PubMed, ScienceDirect and Scopus were searched for this narrative review. The criteria included randomized clinical trials, observational prospective and retrospective cohort studies, case-control studies, systematic review and meta-analysis and professional review articles. Screening models combining maternal factors, biophysical factors, ultrasound studies and biochemical factors achieved high predictive performance of preeclampsia. In Asia, the detection rate of the Fetal Medicine Foundation is superior to those of the American College of Obstetricians and Gynecologists and the National Institute for Health and Care Excellence. Consequently, this effective model from the Fetal Medicine Foundation should be continuously used for screening in first trimester for the Asian.

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.

Gottesfeld-Hohler Memorial Foundation Risk Assessment for Early-Onset Preeclampsia in the United States: Think Tank Summary

Preeclampsia is responsible for significant maternal and neonatal morbidity and is associated with a substantial economic burden. Aspirin has been shown to be effective in decreasing the risk of preterm preeclampsia; however, there is no consensus on the target population for aspirin prophylaxis. In May 2018, the Gottesfeld-Hohler Memorial Foundation organized a working group meeting with the goal of identifying the optimal preeclampsia risk-assessment strategy and consequent intervention in the United States. The meeting brought together experts from the leading professional societies. We discussed available literature and trends in preeclampsia risk assessment, current professional guidelines for identifying women at risk for preeclampsia, prophylactic use of aspirin in the United States and Europe, cost-effectiveness data, and feasibility of implementation of different assessment tools and preventive strategies in the United States. We identified specific knowledge gaps and future research directions in preeclampsia risk assessment and prevention that need to be addressed before practice change.

The impact of periconceptional maternal lifestyle on clinical features and biomarkers of placental development and function: a systematic review

BACKGROUND

Worldwide, placenta-related complications contribute to adverse pregnancy outcomes, such as pre-eclampsia, fetal growth restriction and preterm birth, with implications for the future health of mothers and offspring. The placenta develops in the periconception period and forms the interface between mother and embryo/fetus. An unhealthy periconceptional maternal lifestyle, such as smoking, alcohol and under- and over-nutrition, can detrimentally influence placental development and function.

OBJECTIVE AND RATIONALE

The impact of maternal lifestyle on placental health is largely unknown. Therefore, we aim to summarize the evidence of the impact of periconceptional maternal lifestyle on clinical features and biomarkers of placental development and function throughout pregnancy.

SEARCH METHODS

A comprehensive search in Medline, Embase, Pubmed, The Cochrane Library Web of Science and Google Scholar was conducted. The search strategy included keywords related to the maternal lifestyle, smoking, alcohol, caffeine, nutrition (including folic acid supplement intake) and body weight. For placental markers throughout pregnancy, keywords related to ultrasound imaging, serum biomarkers and histological characteristics were used. We included randomized controlled trials and observational studies published between January 2000 and March 2017 and restricted the analysis to singleton pregnancies and maternal periconceptional lifestyle. Methodological quality was scored using the ErasmusAGE tool. A protocol of this systematic review has been registered in PROSPERO International prospective register of systematic reviews (PROSPERO 2016:CRD42016045596).

OUTCOMES

Of 2593 unique citations found, 82 studies were included. The median quality score was 5 (range: 0-10). The findings revealed that maternal smoking was associated with lower first-trimester placental vascularization flow indices, higher second- and third-trimester resistance of the uterine and umbilical arteries and lower resistance of the middle cerebral artery. Although a negative impact of smoking on placental weight was expected, this was less clear. Alcohol use was associated with a lower placental weight. One study described higher second- and third-trimester placental growth factor (PlGF) levels after periconceptional alcohol use. None of the studies looked at caffeine intake. Adequate nutrition in the first trimester, periconceptional folic acid supplement intake and strong adherence to a Mediterranean diet, were all associated with a lower resistance of the uterine and umbilical arteries in the second and third trimester. A low caloric intake resulted in a lower placental weight, length, breadth, thickness, area and volume. Higher maternal body weight was associated with a larger placenta measured by ultrasound in the second and third trimester of pregnancy or weighed at birth. In addition, higher maternal body weight was associated with decreased PlGF-levels.

WIDER IMPLICATIONS

Evidence of the impact of periconceptional maternal lifestyle on placental health was demonstrated. However, due to poorly defined lifestyle exposures and time windows of investigation, unstandardized measurements of placenta-related outcomes and small sample sizes of the included studies, a cautious interpretation of the effect estimates is indicated. We suggest that future research should focus more on physiological consequences of unhealthy lifestyle during the critical periconception window. Moreover, we foresee that new evidence will support the development of lifestyle interventions to improve the health of mothers and their offspring from the earliest moment in life.

Early prediction of preeclampsia via machine learning

BACKGROUND

Early prediction of preeclampsia is challenging because of poorly understood causes, various risk factors, and likely multiple pathogenic phenotypes of preeclampsia. Statistical learning methods are well-equipped to deal with a large number of variables, such as patients' clinical and laboratory data, and to select the most informative features automatically.

OBJECTIVE

Our objective was to use statistical learning methods to analyze all available clinical and laboratory data that were obtained during routine prenatal visits in early pregnancy and to use them to develop a prediction model for preeclampsia.

STUDY DESIGN

This was a retrospective cohort study that used data from 16,370 births at Lucile Packard Children Hospital at Stanford, CA, from April 2014 to January 2018. Two statistical learning algorithms were used to build a predictive model: (1) elastic net and (2) gradient boosting algorithm. Models for all preeclampsia and early-onset preeclampsia (<34 weeks gestation) were fitted with the use of patient data that were available at <16 weeks gestational age. The 67 variables that were considered in the models included maternal characteristics, medical history, routine prenatal laboratory results, and medication intake. The area under the receiver operator curve, true-positive rate, and false-positive rate were assessed via cross-validation.

RESULTS

Using the elastic net algorithm, we developed a prediction model that contained a subset of the most informative features from all variables. The obtained prediction model for preeclampsia yielded an area under the curve of 0.79 (95% confidence interval, 0.75-0.83), sensitivity of 45.2%, and false-positive rate of 8.1%. The prediction model for early-onset preeclampsia achieved an area under the curve of 0.89 (95% confidence interval, 0.84-0.95), true-positive rate of 72.3%, and false-positive rate of 8.8%.

CONCLUSION

Statistical learning methods in a retrospective cohort study automatically identified a set of significant features for prediction and yielded high prediction performance for preeclampsia risk from routine early pregnancy information.

Prediction of preterm pre-eclampsia at midpregnancy using a multivariable screening algorithm

BACKGROUND

Competing risk models used for midpregnancy prediction of preterm pre-eclampsia have shown detection rates (DR) of 85%, at fixed false-positive rate (FPR) of 10%. The full algorithm used between 19⁺⁰ and 24⁺⁶  weeks includes maternal factors, mean arterial pressure (MAP), mean uterine artery pulsatility index (UtAPI), serum placental growth factor (PlGF) level in multiples of the median (MoM), and soluble Fms-like tyrosine kinase-1 (sFlt-1) level in MoM.

AIMS

To assess performance of the Fetal Medicine Foundation (FMF) algorithm at midpregnancy to screen for preterm (<37 weeks) pre-eclampsia. The outcome measured was preterm pre-eclampsia.

MATERIALS AND METHODS

This is a prospective study including singleton pregnancies at 19-22 weeks gestation. Maternal bloods were collected and analysed using three different immunoassay platforms. Maternal characteristics, medical history, MAP, mean UtAPI, serum PlGF MoM and serum sFlt-1 MoM were used for risk assessment. DR and FPR were calculated, and receiver operating characteristic curves produced.

RESULTS

Five hundred and twelve patients were included. Incidence of preterm pre-eclampsia was 1.6%. Using predicted risk of pre-eclampsia of one in 60 or more and one in 100 or higher, as given by the FMF predictive algorithm, the combination with the best predictive performance for preterm pre-eclampsia included maternal factors, MAP, UtAPI and PlGF MoM, giving DRs of 100% and 100%, respectively, and FPRs of 9.3 for all platforms and 12.9-13.5, respectively. Addition of sFlt-1 to the algorithm did not appear to improve performance. sFlt-1 MoM and PlGF MoM values obtained on the different platforms performed very similarly.

CONCLUSIONS

Second trimester combined screening for preterm pre-eclampsia by maternal history, MAP, mean UtAPI and PlGF MoM using the FMF algorithm performed very well in this patient population.

Clinical risk assessment in early pregnancy for preeclampsia in nulliparous women: A population based cohort study

Objective

To evaluate the capacity of multivariable prediction of preeclampsia during pregnancy, based on detailed routinely collected early pregnancy data in nulliparous women.

Design and setting

A population-based cohort study of 62 562 pregnancies of nulliparous women with deliveries 2008–13 in the Stockholm-Gotland Counties in Sweden.

Methods

Maternal social, reproductive and medical history and medical examinations (including mean arterial pressure, proteinuria, hemoglobin and capillary glucose levels) routinely collected at the first visit in antenatal care, constitute the predictive variables. Predictive models for preeclampsia were created by three methods; logistic regression models using 1) pre-specified variables (similar to the Fetal Medicine Foundation model including maternal factors and mean arterial pressure), 2) backward selection starting from the full suite of variables, and 3) a Random forest model using the same candidate variables. The performance of the British National Institute for Health and Care Excellence (NICE) binary risk classification guidelines for preeclampsia was also evaluated. The outcome measures were diagnosis of preeclampsia with delivery <34, <37, and ≥37 weeks’ gestation.

Results

A total of 2 773 (4.4%) nulliparous women subsequently developed preeclampsia. The pre-specified variables model was superior the other two models, regarding prediction of preeclampsia with delivery <34 and <37 weeks, both with areas under the curve of 0.68, and sensitivity of 30.6% (95% CI 24.5–37.2) and 29.2% (95% CI 25.2–33.4) at a 10% false positive rate, respectively. The performance of these customizable multivariable models at the chosen false positive rate, was significantly better than the binary NICE-guidelines for preeclampsia with delivery <37 and ≥37 weeks’ gestation.

Conclusion

Multivariable models in early pregnancy had a modest performance, although providing advantages over the NICE-guidelines, in predicting preeclampsia in nulliparous women. Use of a machine learning algorithm (Random forest) did not result in superior prediction.

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.

Screening for preeclampsia in the first trimester of pregnancy in routine clinical practice in Hungary

We aimed to evaluate the contribution of different factors in the Fetal Medicine Foundation algorithms for preeclampsia (PE) risk calculation during first-trimester screening in Hungary. We selected subjects for the nested case-control study from a prospective cohort of 2545 low-risk pregnancies. Eighty-two patients with PE and 82 gestational age-matched controls were included. Individual PE risk was calculated using two risk-assessing softwares. Using Astraia 2.3.1, considering maternal characteristics and biophysical parameters only, detection rates (DR) were 63.6% for early-PE and 67.6% for late-PE. When we added placenta associated plasma protein A (PAPP-A) to the risk calculation, DRs decreased to 54.5% and 64.8% respectively. Using Astraia 2.8.2 with maternal characteristics and biophysical parameters resulted in the DRs of 63.6% (early-PE) and 56.3% (late-PE). If we added PAPP-A to the risk calculation, DRs improved to 72.7% and 54.9%. The addition of placental growth factor (PlGF) did not increase detection rates in either calculation. In conclusion, using maternal characteristics, biophysical parameters, and PAPP-A, an acceptable screening efficacy could be achieved for early-PE during first-trimester screening. Since PlGF did not improve efficacy in our study, we suggest setting new standard curves for PlGF in Eastern European pregnant women, and the evaluation of novel biochemical markers.

The International Federation of Gynecology and Obstetrics (FIGO) initiative on pre-eclampsia: A pragmatic guide for first-trimester screening and prevention

Pre‐eclampsia (PE) is a multisystem disorder that typically affects 2%–5% of pregnant women and is one of the leading causes of maternal and perinatal morbidity and mortality, especially when the condition is of early onset. Globally, 76 000 women and 500 000 babies die each year from this disorder. Furthermore, women in low‐resource countries are at a higher risk of developing PE compared with those in high‐resource countries. Although a complete understanding of the pathogenesis of PE remains unclear, the current theory suggests a two‐stage process. The first stage is caused by shallow invasion of the trophoblast, resulting in inadequate remodeling of the spiral arteries. This is presumed to lead to the second stage, which involves the maternal response to endothelial dysfunction and imbalance between angiogenic and antiangiogenic factors, resulting in the clinical features of the disorder. Accurate prediction and uniform prevention continue to elude us. The quest to effectively predict PE in the first trimester of pregnancy is fueled by the desire to identify women who are at high risk of developing PE, so that necessary measures can be initiated early enough to improve placentation and thus prevent or at least reduce the frequency of its occurrence. Furthermore, identification of an “at risk” group will allow tailored prenatal surveillance to anticipate and recognize the onset of the clinical syndrome and manage it promptly. PE has been previously defined as the onset of hypertension accompanied by significant proteinuria after 20 weeks of gestation. Recently, the definition of PE has been broadened. Now the internationally agreed definition of PE is the one proposed by the International Society for the Study of Hypertension in Pregnancy (ISSHP). According to the ISSHP, PE is defined as systolic blood pressure at ≥140 mm Hg and/or diastolic blood pressure at ≥90 mm Hg on at least two occasions measured 4 hours apart in previously normotensive women and is accompanied by one or more of the following new‐onset conditions at or after 20 weeks of gestation: 1.Proteinuria (i.e. ≥30 mg/mol protein:creatinine ratio; ≥300 mg/24 hour; or ≥2 + dipstick); 2.Evidence of other maternal organ dysfunction, including: acute kidney injury (creatinine ≥90 μmol/L; 1 mg/dL); liver involvement (elevated transaminases, e.g. alanine aminotransferase or aspartate aminotransferase >40 IU/L) with or without right upper quadrant or epigastric abdominal pain; neurological complications (e.g. eclampsia, altered mental status, blindness, stroke, clonus, severe headaches, and persistent visual scotomata); or hematological complications (thrombocytopenia–platelet count <150 000/μL, disseminated intravascular coagulation, hemolysis); or 3.Uteroplacental dysfunction (such as fetal growth restriction, abnormal umbilical artery Doppler waveform analysis, or stillbirth). It is well established that a number of maternal risk factors are associated with the development of PE: advanced maternal age; nulliparity; previous history of PE; short and long interpregnancy interval; use of assisted reproductive technologies; family history of PE; obesity; Afro‐Caribbean and South Asian racial origin; co‐morbid medical conditions including hyperglycemia in pregnancy; pre‐existing chronic hypertension; renal disease; and autoimmune diseases, such as systemic lupus erythematosus and antiphospholipid syndrome. These risk factors have been described by various professional organizations for the identification of women at risk of PE; however, this approach to screening is inadequate for effective prediction of PE. PE can be subclassified into: 1.Early‐onset PE (with delivery at <34+0 weeks of gestation); 2.Preterm PE (with delivery at <37+0 weeks of gestation); 3.Late‐onset PE (with delivery at ≥34+0 weeks of gestation); 4.Term PE (with delivery at ≥37+0 weeks of gestation). These subclassifications are not mutually exclusive. Early‐onset PE is associated with a much higher risk of short‐ and long‐term maternal and perinatal morbidity and mortality. Obstetricians managing women with preterm PE are faced with the challenge of balancing the need to achieve fetal maturation in utero with the risks to the mother and fetus of continuing the pregnancy longer. These risks include progression to eclampsia, development of placental abruption and HELLP (hemolysis, elevated liver enzyme, low platelet) syndrome. On the other hand, preterm delivery is associated with higher infant mortality rates and increased morbidity resulting from small for gestational age (SGA), thrombocytopenia, bronchopulmonary dysplasia, cerebral palsy, and an increased risk of various chronic diseases in adult life, particularly type 2 diabetes, cardiovascular disease, and obesity. Women who have experienced PE may also face additional health problems in later life, as the condition is associated with an increased risk of death from future cardiovascular disease, hypertension, stroke, renal impairment, metabolic syndrome, and diabetes. The life expectancy of women who developed preterm PE is reduced on average by 10 years. There is also significant impact on the infants in the long term, such as increased risks of insulin resistance, diabetes mellitus, coronary artery disease, and hypertension in infants born to pre‐eclamptic women. The International Federation of Gynecology and Obstetrics (FIGO) brought together international experts to discuss and evaluate current knowledge on PE and develop a document to frame the issues and suggest key actions to address the health burden posed by PE. FIGO's objectives, as outlined in this document, are: (1) To raise awareness of the links between PE and poor maternal and perinatal outcomes, as well as to the future health risks to mother and offspring, and demand a clearly defined global health agenda to tackle this issue; and (2) To create a consensus document that provides guidance for the first‐trimester screening and prevention of preterm PE, and to disseminate and encourage its use. Based on high‐quality evidence, the document outlines current global standards for the first‐trimester screening and prevention of preterm PE, which is in line with FIGO good clinical practice advice on first trimester screening and prevention of pre‐eclampsia in singleton pregnancy.1 It provides both the best and the most pragmatic recommendations according to the level of acceptability, feasibility, and ease of implementation that have the potential to produce the most significant impact in different resource settings. Suggestions are provided for a variety of different regional and resource settings based on their financial, human, and infrastructure resources, as well as for research priorities to bridge the current knowledge and evidence gap. To deal with the issue of PE, FIGO recommends the following: Public health focus: There should be greater international attention given to PE and to the links between maternal health and noncommunicable diseases (NCDs) on the Sustainable Developmental Goals agenda. Public health measures to increase awareness, access, affordability, and acceptance of preconception counselling, and prenatal and postnatal services for women of reproductive age should be prioritized. Greater efforts are required to raise awareness of the benefits of early prenatal visits targeted at reproductive‐aged women, particularly in low‐resource countries. Universal screening: All pregnant women should be screened for preterm PE during early pregnancy by the first‐trimester combined test with maternal risk factors and biomarkers as a one‐step procedure. The risk calculator is available free of charge at https://fetalmedicine.org/research/assess/preeclampsia. FIGO encourages all countries and its member associations to adopt and promote strategies to ensure this. The best combined test is one that includes maternal risk factors, measurements of mean arterial pressure (MAP), serum placental growth factor (PLGF), and uterine artery pulsatility index (UTPI). Where it is not possible to measure PLGF and/or UTPI, the baseline screening test should be a combination of maternal risk factors with MAP, and not maternal risk factors alone. If maternal serum pregnancy‐associated plasma protein A (PAPP‐A) is measured for routine first‐trimester screening for fetal aneuploidies, the result can be included for PE risk assessment. Variations to the full combined test would lead to a reduction in the performance screening. A woman is considered high risk when the risk is 1 in 100 or more based on the first‐trimester combined test with maternal risk factors, MAP, PLGF, and UTPI. Contingent screening: Where resources are limited, routine screening for preterm PE by maternal factors and MAP in all pregnancies and reserving measurements of PLGF and UTPI for a subgroup of the population (selected on the basis of the risk derived from screening by maternal factors and MAP) can be considered. Prophylactic measures: Following first‐trimester screening for preterm PE, women identified at high risk should receive aspirin prophylaxis commencing at 11–14+6 weeks of gestation at a dose of ~150 mg to be taken every night until 36 weeks of gestation, when delivery occurs, or when PE is diagnosed. Low‐dose aspirin should not be prescribed to all pregnant women. In women with low calcium intake (<800 mg/d), either calcium replacement (≤1 g elemental calcium/d) or calcium supplementation (1.5–2 g elemental calcium/d) may reduce the burden of both early‐ and late‐onset PE.

Comparison of two "a priori" risk assessment algorithms for preeclampsia in Italy: a prospective multicenter study

PURPOSE

To compare the performance of the algorithms proposed by the Fetal Medicine Foundation in 2012 and BCNatal in 2013 in an Italian population.

METHODS

A multicentric prospective study was carried out which included pregnancies at 11-13 weeks' gestation from Jan 2014 through May 2017. Two previously published algorithms were used for the calculation of the "a priori" risk of preeclampsia (based on risk factors from medical history) in each individual.

RESULTS

In a study population of 11,632 cases, 67 (0.6%) developed early preeclampsia and 211 (1.8%) developed late preeclampsia. The detection rates (95% CI) for early and late preeclampsia were 58.2% (45.5-70.2) vs. 41.8% (29.6-54.5) (p value < 0.05) and 44.1% (37.3-51.1) vs. 38% (31.3-44.8) (p value < 0.05) for the Fetal Medicine Foundation and BCNatal, respectively (at a 10% false positive rate). The associated risk was 1:226 and 1:198 (p value ns) for early PE, and 1:17 and 1:24 (p value ns) for late PE for the Fetal Medicine Foundation and BCNatal, respectively.

CONCLUSIONS

The Fetal Medicine Foundation screening for preeclampsia at 11-13 weeks' gestation scored the highest detection rate for both early and late PE. At a fixed 10% false positive rate, the estimated "a priori" risks of both the Fetal Medicine Foundation and the BCNatal algorithms in an Italian population were quite similar, and both were reliable and consistent.

External validation of prognostic models for preeclampsia in a Dutch multicenter prospective cohort

OBJECTIVE

To perform an external validation of all published prognostic models for first-trimester prediction of the risk of developing preeclampsia (PE).

METHODS

Women <14 weeks of pregnancy were recruited in the Netherlands. All systematically identified prognostic models for PE that contained predictors commonly available were eligible for external validation.

RESULTS

3,736 women were included; 87 (2.3%) developed PE. Calibration was poor due to overestimation. Discrimination of 9 models for LO-PE ranged from 0.58 to 0.71 and of 9 models for all PE from 0.55 to 0.75.

CONCLUSION

Only a few easily applicable prognostic models for all PE showed discrimination above 0.70, which is considered an acceptable performance.

Biochemical Markers for Prediction of Hypertensive Disorders of Pregnancy

Background

Gestational hypertension (GH) and pre eclampsia (PE) are the most common gestational complications. Several placental biochemical markers are used to predict GH/PE, but with conflicting results.

Methods

The study aim was to estimate the biochemical markers’ ability to predict hypertensive disorders in pregnancy. On the first ultrasonographic examination, 104 healthy pregnant women were recruited. At the regular pregnancy check-ups, BMI, blood pressure, occurrence of gestational hypertension (early or late onset), preeclampsia, eclampsia and other complications were recorded. Serum concentrations (in multiples of median – MoM) of human chorionic gonadotropin (HCG) and pregnancyassociated plasma protein A (PAPPA) were measured from the 11th to 14th gestational week, while HCG, alpha feto protein (AFP), estriol and inhibin were determined between the 16th and 19th gestational week.

Results

Hypertensive disorders throughout pregnancy were diagnosed in 20.2% women. Early-onset GH was registered in 7 and PE in 6 patients, while 14 had late-onset GH and 10 additional women PE. There were no significant differences (p≥0.05) in biochemical markers concentrations between women with and without GH/PE. PAPPA levels in the first and HCG in the second trimester correlated with early and late GH/PE. Moreover, higher AFP concentrations were registered in women with preeclampsia signs/symptoms. According to ROC analysis, AFP>1.05 MoM properly identified 80% of GH/PE cases. Obtained models imply that HCG, PAPPA and AFP should be used for GH/PE prediction.

Conclusions

Biochemical markers HCG, PAPPA and AFP could be useful in predicting gestational hypertension and preeclampsia. However, different markers should be used for early and late onset GH/PE.

Predictive performance of the competing risk model in screening for preeclampsia

BACKGROUND

The established method of screening for preeclampsia is to identify risk factors from maternal demographic characteristics and medical history; in the presence of such factors the patient is classified as high risk and in their absence as low risk. However, the performance of such an approach is poor. We developed a competing risks model, which allows combination of maternal factors (age, weight, height, race, parity, personal and family history of preeclampsia, chronic hypertension, diabetes mellitus, systemic lupus erythematosus or antiphospholipid syndrome, method of conception and interpregnancy interval), with biomarkers to estimate the individual patient-specific risks of preeclampsia requiring delivery before any specified gestation. The performance of this approach is by far superior to that of the risk scoring systems.

OBJECTIVE

The objective of the study was to examine the predictive performance of the competing risks model in screening for preeclampsia by a combination of maternal factors, mean arterial pressure, uterine artery pulsatility index, and serum placental growth factor, referred to as the triple test, in a training data set for the development of the model and 2 validation studies.

STUDY DESIGN

The data for this study were derived from 3 previously reported prospective, nonintervention, multicenter screening studies for preeclampsia in singleton pregnancies at 11⁺⁰ to 13⁺⁶ weeks' gestation. In all 3 studies, there was recording of maternal factors and biomarkers and ascertainment of outcome by appropriately trained personnel. The first study of 35,948 women, which was carried out between February 2010 and July 2014, was used to develop the competing risks model for prediction of preeclampsia and is therefore considered to be the training set. The 2 validation studies were comprised of 8775 and 16,451 women, respectively, and they were carried out between February and September 2015 and between April and December 2016, respectively. Patient-specific risks of delivery with preeclampsia at <34, <37, and <41⁺³ weeks' gestation were calculated using the competing risks model and the performance of screening for preeclampsia by maternal factors alone and the triple test in each of the 3 data sets was assessed. We examined the predictive performance of the model by first, the ability of the model to discriminate between the preeclampsia and no-preeclampsia groups using the area under the receiver operating characteristic curve and the detection rate at fixed screen-positive rate of 10%, and second, calibration by measurements of calibration slope and calibration in the large.

RESULTS

The detection rate at the screen-positive rate of 10% of early-preeclampsia, preterm-preeclampsia, and all-preeclampsia was about 90%, 75%, and 50%, respectively, and the results were consistent between the training and 2 validation data sets. The area under the receiver operating characteristic curve was >0.95, >0.90, and >0.80, respectively, demonstrating a very high discrimination between affected and unaffected pregnancies. Similarly, the calibration slopes were very close to 1.0, demonstrating a good agreement between the predicted risks and observed incidence of preeclampsia. In the prediction of early-preeclampsia and preterm-preeclampsia, the observed incidence in the training set and 1 of the validation data sets was consistent with the predicted one. In the other validation data set, which was specifically designed for evaluation of the model, the incidence was higher than predicted, presumably because of better ascertainment of outcome. The incidence of all-preeclampsia was lower than predicted in all 3 data sets because at term many pregnancies deliver for reasons other than preeclampsia, and therefore, pregnancies considered to be at high risk for preeclampsia that deliver for other reasons before they develop preeclampsia can be wrongly considered to be false positives.

CONCLUSION

The competing risks model provides an effective and reproducible method for first-trimester prediction of early preeclampsia and preterm preeclampsia as long as the various components of screening are carried out by appropriately trained and audited practitioners. Early prediction of preterm preeclampsia is beneficial because treatment of the high-risk group with aspirin is highly effective in the prevention of the disease.

Risk assessment for preterm preeclampsia in first trimester: Comparison of three calculation algorithms

OBJECTIVE

To better adjust the risk for preeclampsia, multifactorial models in first trimester of pregnancy have found the way in clinical practice. This study compares the available test algorithms.

STUDY DESIGN

In a cross-sectional study between November 2013 and April 2016 we compared the tests results of three first trimester testing algorithms for preeclampsia in 413 women. Risk for preterm preeclampsia was calculated with three different algorithms: Preeclampsia Predictor™ Software by PerkinElmer (PERK), ViewPoint® Software by GE Healthcare (VP) and the online calculator of the Fetal Medicine Foundation (FMF).We analyzed the data descriptively and determined Cohen's Kappa to assess the agreement among the algorithms.

RESULTS

VP classified 89(21.5%) women, PERK 43(10.4%) women and FMF 90 (21.8%) women as having high risk for preterm preeclampsia (<34 weeks of gestation for VP and PERK and <37 weeks of gestation for FMF). Agreement between tests ranged from moderate to substantial (PERK/VP: κ = 0.56, PERK/ FMF: κ = 0.50, and VP/ FMF: κ = 0.72).

CONCLUSION

The three algorithms are similar but not equal. This may depend on chosen cut off, but also on test properties. This study cannot decide which algorithm is the best, but differences in results and cut offs should be taken into account.

Ophthalmic artery Doppler for prediction of pre-eclampsia: systematic review and meta-analysis

OBJECTIVE

To determine the accuracy of ophthalmic artery Doppler in pregnancy for the prediction of pre-eclampsia (PE).

METHODS

MEDLINE, EMBASE, CINAHL and The Cochrane Library were searched for relevant citations without language restrictions. Two reviewers independently selected studies that evaluated the accuracy of ophthalmic artery Doppler to predict the development of PE and extracted data to construct 2 × 2 tables. Individual patient data were obtained from the authors if available. A bivariate random-effects model was used for the quantitative synthesis of data. Logistic regression analysis was employed to generate receiver-operating characteristics (ROC) curves and obtain optimal cut-offs for each investigated parameter, and a bivariate analysis was employed using predetermined cut-offs to obtain sensitivity and specificity values and generate summary ROC curves.

RESULTS

A total of 87 citations matched the search criteria of which three studies, involving 1119 pregnancies, were included in the analysis. All included studies had clear description of the index and reference tests, avoidance of verification bias and adequate follow-up. Individual patient data were obtained for all three included studies. First diastolic peak velocity of ophthalmic artery Doppler at a cut-off of 23.3 cm/s showed modest sensitivity (61.0%; 95% CI, 44.2-76.1%) and specificity (73.2%; 95% CI, 66.9-78.7%) for the prediction of early-onset PE (area under the ROC curve (AUC), 0.68; 95% CI, 0.61-0.76). The first diastolic peak velocity had a much lower sensitivity (39.0%; 95% CI, 20.6-61.0%), a similar specificity (73.2%; 95% CI, 66.9-78.7%) and a lower AUC (0.58; CI, 0.52-0.65) for the prediction of late-onset PE. The pulsatility index of the ophthalmic artery did not show a clinically useful sensitivity or specificity at any cut-off for early- or late-onset PE. Peak ratio above 0.65 showed a similar diagnostic accuracy to that of the first diastolic peak velocity with an AUC of 0.67 (95% CI, 0.58-0.77) for early-onset PE and 0.57 (95% CI, 0.51-0.63) for late-onset disease.

CONCLUSIONS

Ophthalmic artery Doppler is a simple, accurate and objective technique with a standalone predictive value for the development of early-onset PE equivalent to that of uterine artery Doppler evaluation. The relationship between ophthalmic Doppler indices and PE cannot be a consequence of trophoblast invasion and may be related to maternal hemodynamic adaptation to pregnancy. The findings of this review justify efforts to elucidate the effectiveness and underlying mechanism whereby two seemingly unrelated maternal vessels can be used for the prediction of a disease considered a 'placental disorder'. Copyright © 2018 ISUOG. Published by John Wiley & Sons Ltd.

Creating biobanks in low and middle-income countries to improve knowledge - The PREPARE initiative

•

Low and middle-income (LMIC) countries still suffer with pre-eclampsia.

•

Risk assessment of pre-eclampsia has evolved in high-income countries.

•

Biobanks can provide a unique opportunity to understand preeclampsia in LMIC.

•

Databanks can provide a unique opportunity to understand preeclampsia in LMIC.

The Millennium Development Goal 5, a project signed in 2000, intended to improve maternal health and reduce maternal mortality by 75% by 2015. Despite all efforts, little progress has been achieved in low and middle-income countries (LMIC) and 99% of all maternal deaths related to pre-eclampsia (PE) still occur in these settings. It is important to determine whether women in LMIC, where PE carries a greater risk than in high-income countries (HIC), have unique risk factors. Some variances may alter the risk, severity and pertinent pathophysiology of PE. We posit based upon this, that women from LMIC may have biomarkers specific to this population. Discovering such specific biomarkers and testing the relevance of biomarkers developed in high-income populations could increase the clinical usefulness of these analyses without increasing cost-effective approaches for prediction of PE.

Here we briefly describe our platform to develop the PREPARE – Biobank in tertiary hospitals or basic units for antenatal care from 6 different cities in Brazil.

The PREPARE – Biobank has been developed with two arms. The first arm is a cross-sectional study that will collect clinical information and biosamples from more than 1000 women who developed preterm PE. The second arm is a cohort study of 7000 women. It will collect clinical information and longitudinal biosamples from women at three times during pregnancy, <16 weeks, between 28 and 32 weeks and at delivery or diagnosis of adverse outcomes.

The biobank will be supported and complemented by a Brazilian database using the CoLab COLLECT Database.

Maternal allergy as an isolated risk factor for early-onset preeclampsia: An epidemiological study

Immunological mechanisms underlying the development of preeclampsia are well known, but no association to allergy has yet been demonstrated. The aim of this study was to assess the correlation between maternal pre-gestational allergy, and early-onset and late-onset preeclampsia, respectively. It was a retrospective cohort study including all women giving birth in the Norwegian cities of Stavanger (1996-2014) and Bergen (2009-2014). Pre-gestational asthma, allergy, other known risk factors for preeclampsia, maternal age and parity were obtained from the electronic medical record system. The main outcome variables were early-onset and late-onset preeclampsia (before and after 34 completed weeks of gestation, respectively). We used multinomial logistic regression to estimate odds ratios (OR) with 95% confidence intervals (95% CI) for early and late-onset preeclampsia in women with pre-gestational allergy when compared to women without allergy, adjusting for covariates. Predicted probabilities for the outcomes were also calculated. Of the 110 064 included pregnancies, 2 799 developed late-onset preeclampsia (2.5%) and 348 developed early-onset preeclampsia (0.3%). Pre-gestational allergy increased the risk of early-onset preeclampsia (OR 1.7, 95% CI 1.3-2.4), and reduced the risk of late-onset preeclampsia (OR 0.8, 95% CI 0.7-0.9). These findings add valuable information on preeclampsia as an immunological complication of pregnancy and corroborate the understanding of early- and late-onset preeclampsia as two different entities.

Prediction and prevention of pre-eclampsia in Asian subpopulation

The benefit of the early administration of aspirin to reduce preterm pre-eclampsia among screened positive European women from multivariate algorithmic approach (ASPRE trial) has opened an intense debate on the feasibility of universal screening. This review aims to assess the new perspectives in the combined screening of pre-eclampsia in the first trimester of pregnancy and the chances for prevention using low-dose aspirin with special emphasis on the particularities of the Asian population. PubMed, CENTRAL and Embase databases were searched from inception until 15 November 2017 using combinations of the search terms: preeclampsia, Asian, prenatal screening, early prediction, ultrasonography, pregnancy, biomarker, mean arterial pressure, soluble fms-like tyrosine kinase-1, placental growth factor, pregnancy-associated plasma protein-A and pulsatility index. This is not a systematic review or meta-analysis, so the risk of bias of the selected published articles and heterogeneity among the studies need to be considered. The prevalence of pre-eclampsia and serum levels of biochemical markers in Asian are different from Caucasian women; hence, Asian ethnicity needs to be corrected for in the algorithmic assessment of multiple variables to improve the screening performance. Aspirin prophylaxis may still be viable in Asian women, but resource implication needs to be considered. Asian ethnicity should be taken into account before implementing pre-eclampsia screening strategies in the region. The variables included can be mixed and matched to achieve an optimal performance that is appropriate for economical restriction in individual countries.

First-trimester screening for early and late preeclampsia using maternal characteristics, biomarkers, and estimated placental volume

BACKGROUND

Preeclampsia is a major cause of perinatal morbidity and mortality. First-trimester screening has been shown to be effective in selecting patients at an increased risk for preeclampsia in some studies.

OBJECTIVE

We sought to evaluate the feasibility of screening for preeclampsia in the first trimester based on maternal characteristics, medical history, biomarkers, and placental volume.

STUDY DESIGN

This is a prospective observational nonintervention cohort study in an unselected US population. Patients who presented for an ultrasound examination between 11-13+6 weeks' gestation were included. The following parameters were assessed and were used to calculate the risk of preeclampsia: maternal characteristics (demographic, anthropometric, and medical history), maternal biomarkers (mean arterial pressure, uterine artery pulsatility index, placental growth factor, pregnancy-associated plasma protein A, and maternal serum alpha-fetoprotein), and estimated placental volume. After delivery, medical records were searched for the diagnosis of preeclampsia. Detection rates for early-onset preeclampsia (<34 weeks' gestation) and later-onset preeclampsia (≥34 weeks' gestation) for 5% and 10% false-positive rates using various combinations of markers were calculated.

RESULTS

We screened 1288 patients of whom 1068 (82.99%) were available for analysis. In all, 46 (4.3%) developed preeclampsia, with 13 (1.22%) having early-onset preeclampsia and 33 (3.09%) having late-onset preeclampsia. Using maternal characteristics, serum biomarkers, and uterine artery pulsatility index, the detection rate of early-onset preeclampsia for either 5% or 10% false-positive rate was 85%. With the same protocol, the detection rates for preeclampsia with delivery <37 weeks were 52% and 60% for 5% and 10% false-positive rates, respectively. Based on maternal characteristics, the detection rates for late-onset preeclampsia were 15% and 48% for 5% and 10%, while for preeclampsia at ≥37 weeks' gestation the detection rates were 24% and 43%, respectively. The detection rates for late-onset preeclampsia and preeclampsia with delivery at >37 weeks' gestation were not improved by the addition of biomarkers.

CONCLUSION

Screening for preeclampsia at 11-13+6 weeks' gestation using maternal characteristics and biomarkers is associated with a high detection rate for a low false-positive rate. Screening for late-onset preeclampsia yields a much poorer performance. In this study the utility of estimated placental volume and mean arterial pressure was limited but larger studies are needed to ultimately determine the effectiveness of these markers.

Aspirin for Prevention of Preeclampsia

Aspirin is currently the most widely prescribed treatment in the prevention of cardiovascular complications. The indications for the use of aspirin during pregnancy are, however, the subject of much controversy. Since the first evidence of the obstetric efficacy of aspirin in 1985, numerous studies have tried to determine the effect of low-dose aspirin on the incidence of preeclampsia, with very controversial results. Large meta-analyses including individual patient data have demonstrated that aspirin is effective in preventing preeclampsia in high-risk patients, mainly those with a history of preeclampsia. However, guidelines regarding the usage of aspirin to prevent preeclampsia differ considerably from one country to another. Screening modalities, target population, and aspirin dosage are still a matter of debate. In this review, we report the pharmacodynamics of aspirin, its main effects according to dosage and gestational age, and the evidence-based indications for primary and secondary prevention of preeclampsia.

Simple approach based on maternal characteristics and mean arterial pressure for the prediction of preeclampsia in the first trimester of pregnancy

AIM

To propose a simple model for predicting preeclampsia (PE) in the 1st trimester of pregnancy on the basis of maternal characteristics (MC) and mean arterial pressure (MAP).

METHODS

A prospective cohort was performed to predict PE between 11 and 13+6 weeks of gestation. The MC evaluated were maternal age, skin color, parity, previous PE, smoking, family history of PE, hypertension, diabetes mellitus and body mass index (BMI). Mean arterial blood pressure (MAP) was measured at the time of the 1st trimester ultrasound. The outcome measures were the incidences of total PE, preterm PE (delivery <37 weeks) and term PE (delivery ≥37 weeks). We performed logistic regression analysis to determine which factors made significant contributions for the prediction of the three outcomes.

RESULTS

We analyzed 733 pregnant women; 55 developed PE, 21 of those developed preterm PE and 34 term PE. For total PE, the best model was MC+MAP, which had an area under the receiver operating characteristic curve (AUC ROC) of 0.79 [95% confidence interval (CI)=0.76-0.82]. For preterm PE, the best model was MC+MAP, with an AUC ROC of 0.84 (95% CI=0.81-0.87). For term PE, the best model was MC, with an AUC ROC of 0.75 (0.72-0.79). The MC+MAP model demonstrated a detection rate of 67% cases of preterm PE, with a false-positive rate of 10%, positive predictive value of 17% and negative predictive value of 99%.

CONCLUSION

The MC+MAP model showed good accuracy in predicting preterm PE in the 1st trimester of gestation.

External validation of preexisting first trimester preeclampsia prediction models

OBJECTIVE

To validate the increasing number of prognostic models being developed for preeclampsia using our own prospective study.

STUDY DESIGN

A systematic review of literature that assessed biomarkers, uterine artery Doppler and maternal characteristics in the first trimester for the prediction of preeclampsia was performed and models selected based on predefined criteria. Validation was performed by applying the regression coefficients that were published in the different derivation studies to our cohort. We assessed the models discrimination ability and calibration.

RESULTS

Twenty models were identified for validation. The discrimination ability observed in derivation studies (Area Under the Curves) ranged from 0.70 to 0.96 when these models were validated against the validation cohort, these AUC varied importantly, ranging from 0.504 to 0.833. Comparing Area Under the Curves obtained in the derivation study to those in the validation cohort we found statistically significant differences in several studies.

CONCLUSION

There currently isn't a definitive prediction model with adequate ability to discriminate for preeclampsia, which performs as well when applied to a different population and can differentiate well between the highest and lowest risk groups within the tested population. The pre-existing large number of models limits the value of further model development and future research should be focussed on further attempts to validate existing models and assessing whether implementation of these improves patient care.

Relevance of Haemodynamics in Treating Pre-eclampsia

Blood pressure is a way of describing the end result of changes in cardiac output, intravascular volume and peripheral resistance. It has certain advantages in that it is a reproducible and easily measured parameter, but in itself, it offers only a limited understanding of the underlying haemodynamics. In pregnancy, profound haemodynamic changes occur and in hypertensive diseases of pregnancy defining a condition by blood pressure alone risks missing the underlying cause. Partly, this has been a problem of ascribing the cause of hypertensive syndromes to the placenta which has inhibited rigorous research into other possible causes of haemodynamic dysfunction. It is becoming increasingly evident that hypertension in pregnancy may be associated with primarily high cardiac output or high peripheral resistance. A knowledge of the underlying type of hypertension may allow more rational treatment of these conditions in pregnancy rather than therapeutic attempts at controlling blood pressure by any means possible as an end in itself.