Application of a first-trimester prediction model for pre-eclampsia based on uterine arteries and maternal history in high-risk pregnancies

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.

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 .

Systematic review of prediction models for gestational hypertension and preeclampsia

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

FIRST TRIMESTER SCREENING FOR PREECLAMPSIA - A SYSTEMATIC REVIEW

Objective: To increase the detection rate of preterm preeclampsia (PE) first trimester combined screening tests are being developed. The aim of this review is to create an overview of the currently investigated screening markers, algorithms and their validations.Methods: Comprehensive review of the literature concerning first trimester screening for PEResults and conclusions: Studies investigating a total of 160 biochemical, 6 biophysical and 14 ultrasound markers could be identified. Of the 21 algorithms published, mainly the algorithm published by the Fetal Medicine Foundation London has been validated. This algorithm performes significantly better than screening by anamnestic risk factors only.

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.

External Validation and Clinical Usefulness of First Trimester Prediction Models for the Risk of Preeclampsia: A Prospective Cohort Study

INTRODUCTION

This study assessed the external validity of all published first trimester prediction models for the risk of preeclampsia (PE) based on routinely collected maternal predictors. Moreover, the potential utility of the best-performing models in clinical practice was evaluated.

MATERIAL AND METHODS

Ten prediction models were systematically selected from the literature. We performed a multicenter prospective cohort study in the Netherlands between July 1, 2013, and December 31, 2015. Eligible pregnant women completed a web-based questionnaire before 16 weeks' gestation. The outcome PE was established using postpartum questionnaires and medical records. Predictive performance of each model was assessed by means of discrimination (c-statistic) and a calibration plot. Clinical usefulness was evaluated by means of decision curve analysis and by calculating the potential impact at different risk thresholds.

RESULTS

The validation cohort contained 2,614 women of whom 76 developed PE (2.9%). Five models showed moderate discriminative performance with c-statistics ranging from 0.73 to 0.77. Adequate calibration was obtained after refitting. The best models were clinically useful over a small range of predicted probabilities.

DISCUSSION

Five of the ten included first trimester prediction models for PE showed moderate predictive performance. The best models may provide more benefit compared to risk selection as used in current guidelines.

Prediction of pre-eclampsia and its subtypes in high-risk cohort: hyperglycosylated human chorionic gonadotropin in multivariate models

Background

The proportion of hyperglycosylated human chorionic gonadotropin (hCG-h) to total human chorionic gonadotropin (%hCG-h) during the first trimester is a promising biomarker for prediction of early-onset pre-eclampsia. We wanted to evaluate the performance of clinical risk factors, mean arterial pressure (MAP), %hCG-h, hCGβ, pregnancy-associated plasma protein A (PAPP-A), placental growth factor (PlGF) and mean pulsatility index of the uterine artery (Uta-PI) in the first trimester in predicting pre-eclampsia (PE) and its subtypes early-onset, late-onset, severe and non-severe PE in a high-risk cohort.

Methods

We studied a subcohort of 257 high-risk women in the prospectively collected Prediction and Prevention of Pre-eclampsia and Intrauterine Growth Restriction (PREDO) cohort. Multivariate logistic regression was used to construct the prediction models. The first model included background variables and MAP. Additionally, biomarkers were included in the second model and mean Uta-PI was included in the third model. All variables that improved the model fit were included at each step. The area under the curve (AUC) was determined for all models.

Results

We found that lower levels of serum PlGF concentration were associated with early-onset PE, whereas lower %hCG-h was associated with the late-onset PE. Serum PlGF was lower and hCGβ higher in severe PE, while %hCG-h and serum PAPP-A were lower in non-severe PE. By using multivariate regression analyses the best prediction for all PE was achieved with the third model: AUC was 0.66, and sensitivity 36% at 90% specificity. Third model also gave the highest prediction accuracy for late-onset, severe and non-severe PE: AUC 0.66 with 32% sensitivity, AUC 0.65, 24% sensitivity and AUC 0.60, 22% sensitivity at 90% specificity, respectively. The best prediction for early-onset PE was achieved using the second model: AUC 0.68 and 20% sensitivity at 90% specificity.

Conclusions

Although the multivariate models did not meet the requirements to be clinically useful screening tools, our results indicate that the biomarker profile in women with risk factors for PE is different according to the subtype of PE. The heterogeneous nature of PE results in difficulty to find new, clinically useful biomarkers for prediction of PE in early pregnancy in high-risk cohorts.

Trial registration

International Standard Randomised Controlled Trial number ISRCTN14030412, Date of registration 6/09/2007, retrospectively registered.

Electronic supplementary material

The online version of this article (10.1186/s12884-018-1908-9) contains supplementary material, which is available to authorized users.

External Validation Study of First Trimester Obstetric Prediction Models (Expect Study I): Research Protocol and Population Characteristics

BACKGROUND

A number of first-trimester prediction models addressing important obstetric outcomes have been published. However, most models have not been externally validated. External validation is essential before implementing a prediction model in clinical practice.

OBJECTIVE

The objective of this paper is to describe the design of a study to externally validate existing first trimester obstetric prediction models, based upon maternal characteristics and standard measurements (eg, blood pressure), for the risk of pre-eclampsia (PE), gestational diabetes mellitus (GDM), spontaneous preterm birth (PTB), small-for-gestational-age (SGA) infants, and large-for-gestational-age (LGA) infants among Dutch pregnant women (Expect Study I). The results of a pilot study on the feasibility and acceptability of the recruitment process and the comprehensibility of the Pregnancy Questionnaire 1 are also reported.

METHODS

A multicenter prospective cohort study was performed in The Netherlands between July 1, 2013 and December 31, 2015. First trimester obstetric prediction models were systematically selected from the literature. Predictor variables were measured by the Web-based Pregnancy Questionnaire 1 and pregnancy outcomes were established using the Postpartum Questionnaire 1 and medical records. Information about maternal health-related quality of life, costs, and satisfaction with Dutch obstetric care was collected from a subsample of women. A pilot study was carried out before the official start of inclusion. External validity of the models will be evaluated by assessing discrimination and calibration.

RESULTS

Based on the pilot study, minor improvements were made to the recruitment process and online Pregnancy Questionnaire 1. The validation cohort consists of 2614 women. Data analysis of the external validation study is in progress.

CONCLUSIONS

This study will offer insight into the generalizability of existing, non-invasive first trimester prediction models for various obstetric outcomes in a Dutch obstetric population. An impact study for the evaluation of the best obstetric prediction models in the Dutch setting with respect to their effect on clinical outcomes, costs, and quality of life-Expect Study II-is being planned.

TRIAL REGISTRATION

Netherlands Trial Registry (NTR): NTR4143; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=4143 (Archived by WebCite at http://www.webcitation.org/6t8ijtpd9).

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

OBJECTIVE

To validate the performance of a previously constructed first-trimester predictive model for pre-eclampsia (PE) in routine care of an unselected population.

METHODS

A validation cohort of 4621 consecutive women attending their routine first-trimester ultrasound examination was used to test a prediction model for PE that had been developed previously in 5170 women. The prediction model included maternal factors, uterine artery Doppler, blood pressure and pregnancy-associated plasma protein-A. Model performance was evaluated using receiver-operating characteristics (ROC) curve analysis and ROC curves from both cohorts were compared unpaired.

RESULTS

Among the 4203 women included in the final analysis, 169 (4.0%) developed PE, including 141 (3.4%) cases of late-onset PE and 28 (0.7%) cases of early-onset PE. For early-onset PE, the model showed an area under the ROC curve of 0.94 (95% CI, 0.88-0.99), which did not differ significantly (P = 0.37) from that obtained in the construction cohort (0.88 (95% CI, 0.78-0.99)). For late-onset PE, the final model showed an area under the ROC curve of 0.72 (95% CI, 0.66-0.77), which did not differ significantly (P = 0.49) from that obtained in the construction cohort (0.75 (95% CI, 0.67-0.82)).

CONCLUSION

The prediction model for PE achieved a similar performance to that obtained in the construction cohort when tested on a subsequent cohort of women, confirming its validity as a predictive model for PE. Copyright © 2016 ISUOG. Published by John Wiley & Sons Ltd.

The role of second trimester uterine artery Doppler in pregnancies with systemic lupus erythematosus

OBJECTIVE

The aim of this article is to assess the predictive value of second trimester mean uterine artery Doppler pulsatility index (mUtA PI) for pregnancy complications in women with systemic lupus erythematosus (SLE).

METHODS

Cohort study of consecutive pregnancies complicated with SLE during a period of 12 years is used. SLE diagnosis was made before pregnancy. mUtA PI was measured between 23 + 0 and 26 + 6 weeks' gestation. Pregnancy and neonatal outcomes were collected. Small for gestational age (SGA) was defined as birth weight <10th percentile. Adverse pregnancy outcome (APO) was defined as one of the following: pre-eclampsia (PE), SGA, placental abruption, stillbirth, or neonatal death. Differential diagnosis between PE and renal flare was made according to SLE-disease activity index.

RESULTS

There are 70 pregnancies in 64 women. PE was observed in four cases (6%), SGA in five cases (7%), and APO in seven cases (10%). mUtA PI showed a sensitivity and a specificity of 1.0 (95% CI 0.5-1.0) and 1.0 (95% CI 0.95-1.0) for PE, 0.40 (95% CI 0.12-0.77) and 0.97 (95% CI 0.89-0.99) for SGA, and 0.57 (95% CI 0.25-0.84) and 1.0 (95% CI 0.94-1.0) for APO, respectively.

CONCLUSION

Our findings suggest that uterine artery Doppler is confirmed to be a high sensitivity and a high specificity test for predicting PE even in SLE patients.

Prognostic models in obstetrics: available, but far from applicable

Health care provision is increasingly focused on the prediction of patients' individual risk for developing a particular health outcome in planning further tests and treatments. There has been a steady increase in the development and publication of prognostic models for various maternal and fetal outcomes in obstetrics. We undertook a systematic review to give an overview of the current status of available prognostic models in obstetrics in the context of their potential advantages and the process of developing and validating models. Important aspects to consider when assessing a prognostic model are discussed and recommendations on how to proceed on this within the obstetric domain are given. We searched MEDLINE (up to July 2012) for articles developing prognostic models in obstetrics. We identified 177 papers that reported the development of 263 prognostic models for 40 different outcomes. The most frequently predicted outcomes were preeclampsia (n = 69), preterm delivery (n = 63), mode of delivery (n = 22), gestational hypertension (n = 11), and small-for-gestational-age infants (n = 10). The performance of newer models was generally not better than that of older models predicting the same outcome. The most important measures of predictive accuracy (ie, a model's discrimination and calibration) were often (82.9%, 218/263) not both assessed. Very few developed models were validated in data other than the development data (8.7%, 23/263). Only two-thirds of the papers (62.4%, 164/263) presented the model such that validation in other populations was possible, and the clinical applicability was discussed in only 11.0% (29/263). The impact of developed models on clinical practice was unknown. We identified a large number of prognostic models in obstetrics, but there is relatively little evidence about their performance, impact, and usefulness in clinical practice so that at this point, clinical implementation cannot be recommended. New efforts should be directed toward evaluating the performance and impact of the existing models.

Number of episodes of reduced fetal movement at term: association with adverse perinatal outcome

OBJECTIVE

The aims of this study were evaluation of the association of reduced fetal movements (RFM) and small-for-gestational-age (SGA) birth at term and to explore if fetal and maternal outcomes are different with single vs repeated episodes of RFM and normal fetal assessment test results.

STUDY DESIGN

This was a retrospective cohort study of all singleton pregnancies referred for RFMs at a tertiary fetal medicine unit from January 2008 through September 2014. Ultrasound and Doppler indices were obtained from a computerized ultrasound database and pregnancy outcome was collected from hospital records.

RESULTS

Of the 21,944 women with a singleton pregnancy booked for maternity care during the study period, 1234 women (5.62%) reported RFMs >36+0 weeks. Of these, 1029 women (83.4%) reported a single episode of RFM and 205 (16.6%) had ≥2 presentations for RFM. Women with repeated RFMs had a significantly higher mean uterine artery pulsatility index in the second trimester. The prevalence of SGA baby at birth in women presenting with a single episode as compared to repeated episodes of RFM was 9.8% and 44.2%, respectively (odds ratio, 7.3; 95% confidence interval, 5.1-10.4; P < .05).

CONCLUSION

Repeated episodes of RFMs at term are more likely to occur in women with high second-trimester uterine artery Doppler resistance indices and are strongly associated with the birth of SGA infants. Women presenting with repeated episodes of RFM should be treated as being at high risk of placental dysfunction irrespective of the results of prenatal ultrasound and Doppler assessment.

Development and validation of a risk factor scoring system for first-trimester prediction of preeclampsia

OBJECTIVE

The aim of this study was to develop a multiparameter risk-based scoring system for first-trimester prediction of preeclampsia and to validate this scoring system in our patient population.

STUDY DESIGN

Secondary analysis of a prospective cohort of 1,200 patients presenting for first-trimester aneuploidy screening. Maternal serum pregnancy-associated plasma protein A (PAPP-A) levels were measured and bilateral uterine artery (UA) Doppler studies performed. Using the first half of the study population, a prediction model for preeclampsia was created. Test performance characteristics were used to determine the optimal score for predicting preeclampsia. This model was then validated in the second half of the population.

RESULTS

Significant risk factors and their weighted scores derived from the prediction model were chronic hypertension (4), history of preeclampsia (3), pregestational diabetes (2), body mass index ≥ 30 kg/m(2) (2), bilateral UA notching (1), and PAPP-A MoM < 10 th percentile (1). The area under the curve (AUC) for the risk scoring system was 0.76 (95% confidence interval [CI], 0.69-0.83), and the optimal threshold for predicting preeclampsia was a total score of ≥ 6. This AUC did not differ significantly from the AUC observed in our validation cohort (AUC, 0.78 [95% CI, 0.69-0.86]; p = 0.75].

CONCLUSION

Our proposed risk factor scoring system demonstrates modest accuracy but excellent reproducibility for first-trimester prediction of preeclampsia.

Prediction of preeclampsia utilizing the first trimester screening examination

OBJECTIVE

To derive a prediction rule for preeclampsia and early onset preeclampsia requiring delivery <34 weeks using first trimester maternal, ultrasound, and serum markers.

STUDY DESIGN

Prospective cohort study of women enrolled at first trimester screening. Maternal history, demographics, anthropometry, ultrasound parameters, and serum analytes were compared between women with preeclampsia and normal outcome. The prediction rule was derived by Lasso logistic regression analysis.

RESULTS

In 2441 women, 108 (4.4%) women developed preeclampsia, and 18 (0.7%) early preeclampsia. Nulliparity, prior hypertension, diabetes, prior preeclampsia, mean arterial pressure, and the log pregnancy-associate pregnancy protein-A multiples of the median were primary risk factors. Prediction rules for preeclampsia/early preeclampsia had an area under the curve of 0.82/0.83 respectively. Preeclampsia was predicted with 49% sensitivity and early preeclampsia with 55% sensitivity for a 10% false positive rate.

CONCLUSION

First trimester prediction rules using parameters currently available at first trimester screening identify a significant proportion of women with subsequent preeclampsia.

Pre-eclampsia part 2: prediction, prevention and management

An antiangiogenic state might constitute a terminal pathway for the multiple aetiologies of pre-eclampsia, especially those resulting from placental abnormalities. The levels of angiogenic and antiangiogenic proteins in maternal blood change prior to a diagnosis of pre-eclampsia, correlate with disease severity and have prognostic value in identifying women who will develop maternal and/or perinatal complications. Potential interventions exist to ameliorate the imbalance of angiogenesis and, hence, might provide opportunities to improve maternal and/or perinatal outcomes in pre-eclampsia. Current strategies for managing pre-eclampsia consist of controlling hypertension, preventing seizures and timely delivery of the fetus. Prediction of pre-eclampsia in the first trimester is of great interest, as early administration of aspirin might reduce the risk of pre-eclampsia, albeit modestly. Combinations of biomarkers typically predict pre-eclampsia better than single biomarkers; however, the encouraging initial results of biomarker studies require external validation in other populations before they can be used to facilitate intervention in patients identified as at increased risk. Angiogenic and antiangiogenic factors might also be useful in triage of symptomatic patients with suspected pre-eclampsia, differentiating pre-eclampsia from exacerbations of pre-existing medical conditions and performing risk assessment in asymptomatic women. This Review article discusses the performance of predictive and prognostic biomarkers for pre-eclampsia, current strategies for preventing and managing the condition and its long-term consequences.

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

OBJECTIVE

To evaluate the performance of published first-trimester prediction algorithms for pre-eclampsia (PE) in a prospectively enrolled cohort of women.

METHOD

A MEDLINE search identified first-trimester screening-prediction algorithms for early-onset (requiring delivery < 34 weeks) and late-onset (requiring delivery ≥ 34 weeks) PE. Maternal variables, ultrasound parameters and biomarkers were determined prospectively in singleton pregnancies enrolled between 9 and 14 weeks. Prediction algorithms were applied to this population to calculate predicted probabilities for PE. The performance of the prediction algorithms was compared with that in the original publication and evaluated for factors explaining differences in prediction.

RESULTS

Six early and two late PE prediction algorithms were applicable to 871-2962 women, depending on the variables required. The prevalence of early PE was 1.0-1.2% and of late PE was 4.1-5.0% in these patient subsets. One early PE prediction algorithm performed better than in the original publication (80% detection rate (DR) of early PE for 10% false-positive rate (FPR)); the remaining five prediction algorithms underperformed (29-53% DR). Prediction algorithms for late PE also underperformed (18-31% DR, 10% FPR). Applying the screening cut-offs based on the highest Youden index probability scores correctly detected 40-80% of women developing early PE and 71-82% who developed late PE. Exclusion of patients on first-trimester aspirin resulted in DRs of 40-83% and 65-82% for early and late PE, respectively.

CONCLUSION

First-trimester prediction algorithms for PE share a high negative predictive value if applied to an external population but underperform in their ability to correctly identify women who develop PE. Further research is required to determine the factors responsible for the suboptimal external validity.

External validity of first-trimester algorithms in the prediction of pre-eclampsia disease severity

OBJECTIVE

To compare disease features in women with pre-eclampsia between those who are correctly identified (true positive) and those who are missed (false negative) when applying first-trimester prediction algorithms for pre-eclampsia to a prospectively enrolled population.

METHOD

Six first-trimester early (requiring delivery < 34 weeks' gestation) pre-eclampsia algorithms were applied to a prospective cohort of singleton pregnancies enrolled at first-trimester screening. Maternal outcomes, neonatal outcomes and severity parameters for pre-eclampsia were compared between true-positive and false-negative predictions.

RESULTS

Twenty of 2446 (0.8%) women developed early pre-eclampsia, with 65% of these developing severe features and 20% HELLP syndrome. At enrollment, true-positive cases were more likely to be African-American and chronically hypertensive, while false-negative cases were more likely to be Caucasian. At delivery, true-positive cases were more likely to have pre-eclampsia superimposed on hypertension, severely elevated blood pressure and creatinine level > 1.1 mg/dL. False-negative cases were more likely to have HELLP syndrome (all P < 0.05).

CONCLUSION

In an urban population with a high prevalence of chronic hypertension, patients who are correctly identified by first-trimester screening models are more likely to develop pre-eclampsia superimposed on chronic hypertension with severely elevated blood pressure and evidence of renal failure. In contrast, patients who are missed by these algorithms are more likely to have HELLP syndrome. Further research is needed to confirm these findings and the algorithm adjustments that may be necessary to better predict pre-eclampsia phenotypes.

Added value of angiogenic factors for the prediction of early and late preeclampsia in the first trimester of pregnancy

OBJECTIVE

To explore the predictive role of angiogenic factors for the prediction of early and late preeclampsia (PE) in the first trimester.

METHODS

A nested case-control study, within a cohort of 5,759 pregnancies, including 28 cases of early, 84 of late PE (cut-off 34 weeks) and 84 controls. Maternal characteristics, mean blood pressure (MAP), uterine artery (UtA) Doppler (11-13 weeks), vascular endothelial growth factor, placental growth factor (PlGF), soluble Fms-like tyrosine kinase-1 (sFlt-1) and soluble endoglin (8-11 weeks) were measured/recorded. All parameters were normalized by logarithmic transformation; logistic regression analysis was used to predict PE.

RESULTS

For early PE, significant contributions were chronic hypertension, previous PE, MAP, UtA Doppler, PlGF and sFlt-1. A model including these predictors achieved detection rates (DR) of 77.8 and 88.9% for 5 and 10% false-positive rates (FPR), respectively (AUC 0.958; 95% CI 0.920-0.996). For late PE, significant contributions were provided by body mass index, previous PE, UtA Doppler, PlGF and sFlt-1. The model including these factors achieved DR of 51.2 and 69% at 5 and 10% FPR, respectively (AUC 0.888; 95% CI 0.840-0.936).

CONCLUSIONS

Among angiogenic factors, not only PlGF but also sFlt-1 substantially improve the prediction for early and late PE. The data need confirmation in larger studies.

Predictive value of sequential models of uterine artery Doppler in pregnancies at high risk for pre-eclampsia

OBJECTIVE

To evaluate the performance of models described previously for the prediction of pre-eclampsia (PE), based on the sequential evaluation of uterine artery resistance at 11-13 weeks and 19-22 weeks, in a high-risk population.

METHODS

This was a prospective study in 135 women with singleton pregnancies and at least one of the following high-risk conditions: PE and/or intrauterine growth restriction in a previous pregnancy, chronic hypertension, pregestational diabetes, renal disease, body mass index > 30 kg/m(2) , autoimmune disease (systemic lupus erythematosus, antiphospholipid syndrome or rheumatoid arthritis) and thrombophilia. Mean uterine artery pulsatility index (mUtA-PI) at 11-13 and at 19-22 weeks' gestation was measured and analyzed according to quantitative and semi-quantitative models, to predict late PE (resulting in delivery ≥ 34 weeks) and early PE (delivery < 34 weeks).

RESULTS

Late PE developed in 21 (15.6%) pregnancies and early PE in six (4.4%). Using mUtA-PI, the detection rates of late and early PE for a false-positive rate of 10% were 14.3% and 33.3%, respectively, at 11-13 weeks, and 19.0% and 66.7%, respectively, at 19-22 weeks. Using a semi-quantitative approach, the group of pregnant women with mUtA-PI ≥ 90(th) percentile at both 11-13 and 19-22 weeks had a greater risk for early PE (odds ratio, 21.4 (95% CI, 2.5-184.7)) compared with the group with mUtA-PI < 90(th) percentile at both periods. Using a quantitative approach, there was relative worsening in the mUtA-PI (multiples of the median) from the first to the second trimester in all cases of early PE.

CONCLUSION

The application of semi-quantitative and especially quantitative models to evaluate sequential changes in uterine artery Doppler findings between the first and second trimesters could be of additional value in assessing high-risk women regarding their true risk of developing early PE.

Screening for pre-eclampsia: a systematic review of tests combining uterine artery Doppler with other markers

AIMS

To perform a systematic review of screening for pre-eclampsia (PE) with the combination of uterine artery Doppler (UAD), maternal history, mean arterial pressure and/or maternal serum markers.

METHODS

We identified eligible studies through a search of Medline, and, for each included study, we assessed the risk of bias and extracted relevant data. We reported the performance of screening tests according to the target population (low- or high-risk), the trimester of screening (first and/or second) and the subset of PE screened for (early and late).

RESULTS

Several tests provided moderate or convincing prediction of early PE, but screening for late PE was poor. Although UAD is more accurate in the second trimester, we found encouraging results for first-trimester screening when it was combined with other markers. Performance of screening was consistently lower in populations with risk factors for PE in the maternal history.

CONCLUSIONS

We present encouraging results for the prediction of early PE, even in the first trimester of pregnancy. The different performance of tests in screening for early vs. late PE, and of low- vs. high-risk populations, supports the concept that PE is a heterogeneous disease.

Screening for pre-eclampsia--lessons from aneuploidy screening

BACKGROUND

Antenatal screening for aneuploidy is an established routine clinical practice worldwide. The same statistical methodology, developed and refined over three decades, might be adapted to screening for pre-eclampsia.

METHODS

The published literature is reviewed for evidence that the methodology is valid for pre-eclampsia using first trimester maternal serum PP13, PAPP-A, PlGF, ADAM12 and inhibin A, together with MAP and uterine artery Doppler PI. Risk is estimated for both early onset pre-eclampsia, requiring delivery before 34 weeks, or late onset disease. Prior risk from the background prevalence multiplied by likelihood ratios (LRs) for ethnicity, parity, adiposity and family history is multiplied by an LR from the screening marker profile. Markers are expressed in multiples of the gestation-specific median and adjusted for body mass, ethnicity and smoking status as appropriate. A standardized population with a fixed distribution of risk factors and a multi-variate Gaussian model of marker profiles is used to predict performance.

RESULTS

There is sufficient published data to estimate individual risks reasonably well. Modeling predicts that using PAPP-A and one other serum marker, together with the physical markers more than two-thirds of early and one-third of late onset cases can be detected by classifying less than 2% of pregnancies as high risk; three-quarters of early case could be detected with a 5% high risk rate.

CONCLUSION

Whilst more data on some markers is still required modeling so far suggests that extending first trimester aneuploidy screening programs to include pre-eclampsia screening would yield a high detection. However, prospective studies are needed to verify the model predictions.

Clinical risk prediction for pre-eclampsia in nulliparous women: development of model in international prospective cohort

OBJECTIVES

To develop a predictive model for pre-eclampsia based on clinical risk factors for nulliparous women and to identify a subgroup at increased risk, in whom specialist referral might be indicated.

DESIGN

Prospective multicentre cohort.

SETTING

Five centres in Auckland, New Zealand; Adelaide, Australia; Manchester and London, United Kingdom; and Cork, Republic of Ireland.

PARTICIPANTS

3572 "healthy" nulliparous women with a singleton pregnancy from a large international study; data on pregnancy outcome were available for 3529 (99%).

MAIN OUTCOME MEASURE

Pre-eclampsia defined as ≥ 140 mm Hg or diastolic blood pressure ≥ 90 mm Hg, or both, on at least two occasions four hours apart after 20 weeks' gestation but before the onset of labour, or postpartum, with either proteinuria or any multisystem complication. Preterm pre-eclampsia was defined as women with pre-eclampsia delivered before 37(+0) weeks' gestation. In the stepwise logistic regression the comparison group was women without pre-eclampsia.

RESULTS

Of the 3529 women, 186 (5.3%) developed pre-eclampsia, including 47 (1.3%) with preterm pre-eclampsia. Clinical risk factors at 14-16 weeks' gestation were age, mean arterial blood pressure, body mass index (BMI), family history of pre-eclampsia, family history of coronary heart disease, maternal birth weight, and vaginal bleeding for at least five days. Factors associated with reduced risk were a previous single miscarriage with the same partner, taking at least 12 months to conceive, high intake of fruit, cigarette smoking, and alcohol use in the first trimester. The area under the receiver operating characteristics curve (AUC), under internal validation, was 0.71. Addition of uterine artery Doppler indices did not improve performance (internal validation AUC 0.71). A framework for specialist referral was developed based on a probability of pre-eclampsia generated by the model of at least 15% or an abnormal uterine artery Doppler waveform in a subset of women with single risk factors. Nine per cent of nulliparous women would be referred for a specialist opinion, of whom 21% would develop pre-eclampsia. The relative risk for developing pre-eclampsia and preterm pre-eclampsia in women referred to a specialist compared with standard care was 5.5 and 12.2, respectively.

CONCLUSIONS

The ability to predict pre-eclampsia in healthy nulliparous women using clinical phenotype is modest and requires external validation in other populations. If validated, it could provide a personalised clinical risk profile for nulliparous women to which biomarkers could be added. Trial registration ACTRN12607000551493.

Sonographic evaluation and the pregnancy complicated by diabetes

Sonography is a fundamental tool in the management of pregnancies affected by maternal diabetes. Purposeful use of ultrasound in each trimester provides an invaluable amount of information about the developing fetus including gestational age and growth patterns, anatomical structure and function, assessment of fetal well-being, and prediction of adverse outcome. There are great ongoing research efforts in this field of prenatal diagnosis and management, yet even more are needed.

How long do preconception risk prediction models hold? Influence of selective fertility on model performance

Risk estimates derived from preconception prediction models can be used to counsel women with regard to any future pregnancies. Women with a high predicted risk of an adverse pregnancy outcome may decide more frequently not to try for another pregnancy than women with a low predicted risk. This prediction-guided selective fertility can cause a change in the composition of the pregnant population with respect to those parameters that are included in the prediction model. The question is whether such a change in composition could influence the performance parameters of the prediction model, such as sensitivity, specificity, positive and negative predictive values as well as the discriminative ability, when evaluating risks in the new population and whether it could compromise the longevity of the model. Using a hypothetical example, we show that the original sensitivity and specificity estimates of a preconception prediction model for an adverse pregnancy outcome no longer hold when the model is applied to a population affected by model-based selective fertility: sensitivity decreases, while specificity increases. However, individual patient risk estimates remain unbiased and discriminative ability, expressed as the area under the receiver operating characteristic curve, remains unaffected.