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

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

PURPOSE OF REVIEW

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

RECENT FINDINGS

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

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

The role of short-chain fatty acids produced by gut microbiota in the regulation of pre-eclampsia onset

Background

Preeclampsia (PE) is a common pregnancy-related disorder characterized by disrupted maternal-fetal immune tolerance, involving diffuse inflammatory responses and vascular endothelial damage. Alterations in the gut microbiota (GM) during pregnancy can affect intestinal barrier function and immune balance.

Aims and purpose

This comprehensive review aims to investigate the potential role of short-chain fatty acids (SCFAs), essential metabolites produced by the GM, in the development of PE. The purpose is to examine their impact on colonic peripheral regulatory T (Treg) cells, the pathogenic potential of antigen-specific helper T (Th) cells, and the inflammatory pathways associated with immune homeostasis.

Key insights

An increasing body of evidence suggests that dysbiosis in the GM can lead to alterations in SCFA levels, which may significantly contribute to the development of PE. SCFAs enhance the number and function of colonic Treg cells, mitigate the pathogenic potential of GM-specific Th cells, and inhibit inflammatory progression, thereby maintaining immune homeostasis. These insights highlight the potential significance of GM dysregulation and SCFAs produced by GM in the pathogenesis of PE. While the exact causes of PE remain elusive, and definitive clinical treatments are lacking, the GM and SCFAs present promising avenues for future clinical applications related to PE, offering a novel approach for prophylaxis and therapy.

Systematic review and meta-analysis protocol for development and validation of a prediction model for gestational hypertension in Africa

Objective

Examining the development and validation of predictive models for gestational hypertension, evaluating the validity of the methodology, and investigating predictors typically employed in such models.

Design

Systematic review and meta-analysis protocol.

Methods

The Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) guideline will be used to carry out the study procedure. Using the key phrases "Gestational hypertension," "prediction, risk prediction," and "validation," a full systematic search will be conducted in PubMed/MEDLINE, Hinari, Cochrane Library, and Google Scholar. The methodological quality of the included studies will be evaluated using the prediction model risk of bias assessment tool. The CHARMS (checklist for critical evaluation and data extraction for systematic reviews of prediction modeling research) will be used to extract the data, and STATA 16 will be used to analyze it. The degree of study heterogeneity will be assessed using Cochrane I² statistics.

Discussion

A subgroup analysis will be performed to reduce the variance between primary studies. To examine the impact of individual studies on the pooled estimates, a sensitivity analysis will be performed. The funnel plot test and Egger's statistical test will be used to assess the small study effect. The presence of a modest study effect is shown by Egger's test (p-value 0.05), which will be handled by nonparametric trim and fill analysis using the random-effects model. The protocol has been registered in the PROSPERO-International Prospective Register of systematic reviews, with the registration number CRD42022314601.

Predictive Performance of Machine Learning-Based Methods for the Prediction of Preeclampsia-A Prospective Study

(1) Background: Preeclampsia (PE) prediction in the first trimester of pregnancy is a challenge for clinicians. The aim of this study was to evaluate and compare the predictive performances of machine learning-based models for the prediction of preeclampsia and its subtypes. (2) Methods: This prospective case-control study evaluated pregnancies that occurred in women who attended a tertiary maternity hospital in Romania between November 2019 and September 2022. The patients' clinical and paraclinical characteristics were evaluated in the first trimester and were included in four machine learning-based models: decision tree (DT), naïve Bayes (NB), support vector machine (SVM), and random forest (RF), and their predictive performance was assessed. (3) Results: Early-onset PE was best predicted by DT (accuracy: 94.1%) and SVM (accuracy: 91.2%) models, while NB (accuracy: 98.6%) and RF (accuracy: 92.8%) models had the highest performance when used to predict all types of PE. The predictive performance of these models was modest for moderate and severe types of PE, with accuracies ranging from 70.6% and 82.4%. (4) Conclusions: The machine learning-based models could be useful tools for EO-PE prediction and could differentiate patients who will develop PE as early as the first trimester of 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.

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

OBJECTIVES

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

STUDY DESIGN

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

MAIN OUTCOME MEASURES

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

RESULTS

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

CONCLUSIONS

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

Development and validation of a nomogram for the early prediction of preeclampsia in pregnant Chinese women

To make early predictions of preeclampsia before diagnosis, we developed and validated a new nomogram for the early prediction of preeclampsia in pregnant Chinese women. A stepwise regression model was used for feature selection. Multivariable logistic regression analysis was used to develop the prediction model. We incorporated BMI, blood pressure, uterine artery ultrasound parameters, and serological indicator risk factors, and this was presented with a nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Internal validation was assessed. The signature, which consisted of 11 selected features, was associated with preeclampsia status (P < 0.1) for the development dataset. Predictors contained in the individualized prediction nomogram included BMI, blood pressure, uterine artery ultrasound parameters, and serological indicator levels. The model showed good discrimination, with an area under the ROC curve of 0.8563 (95% CI: 0.8364-0.8761) and good calibration. The nomogram still had good discrimination and good calibration when applied to the validation dataset (area under ROC curve of 0.8324, 95% CI: 0.7873-0.8775). Decision curve analysis demonstrated that the nomogram was clinically useful. The nomogram presented in this study incorporates BMI, blood pressure, uterine artery ultrasound parameters, and serological indicators and can be conveniently used to facilitate the individualized prediction of preeclampsia.

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.

A risk factor-based predictive model for new-onset hypertension during pregnancy in Chinese Han women

BACKGROUND

Hypertensive disorders of pregnancy (HDP) is one of the leading causes of maternal and neonatal mortality, increasing the long-term incidence of cardiovascular diseases. Preeclampsia and gestational hypertension are the major components of HDP. The aim of our study is to establish a prediction model for pregnant women with new-onset hypertension during pregnancy (increased blood pressure after gestational age > 20 weeks), thus to guide the clinical prediction and treatment of de novo hypertension.

METHODS

A total of 117 pregnant women with de novo hypertension who were admitted to our hospital's obstetrics department were selected as the case group and 199 healthy pregnant women were selected as the control group from January 2017 to June 2018. Maternal clinical parameters such as age, family history and the biomarkers such as homocysteine, cystatin C, uric acid, total bile acid and glomerular filtration rate were collected at a mean gestational age in 16 to 20 weeks. The prediction model was established by logistic regression.

RESULTS

Eleven indicators have statistically significant difference between two groups (P < 0.05). These 11 factors were substituted into the logistic regression equation and 7 independent predictors were obtained. The equation expressed including 7 factors. The calculated area under the curve was 0.884(95% confidence interval: 0.848-0.921), the sensitivity and specificity were 88.0 and 75.0%. A scoring system was established to classify pregnant women with scores ≤15.5 as low-risk pregnancy group and those with scores > 15.5 as high-risk pregnancy group.

CONCLUSIONS

Our regression equation provides a feasible and reliable means of predicting de novo hypertension after pregnancy. Risk stratification of new-onset hypertension was performed to early treatment interventions in high-risk populations.

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.

Pre-pregnancy or first-trimester risk scoring to identify women at high risk of preterm birth

Objective To develop a pre-pregnancy or first-trimester risk score to identify women at high risk of preterm birth. Study design In this retrospective cohort analysis, the sample was drawn from California singleton livebirths from 2007 to 2012 with linked birth certificate and hospital discharge records. The dataset was divided into a training (2/3 of sample) and a testing (1/3 of sample) set for discovery and validation. Predictive models for preterm birth using pre-pregnancy or first-trimester maternal factors were developed using backward stepwise logistic regression on a training dataset. A risk score for preterm birth was created for each pregnancy using beta-coefficients for each maternal factor remaining in the final multivariable model. Risk score utility was replicated in a testing dataset and by race/ethnicity and payer for prenatal care. Results The sample included 2,339,696 pregnancies divided into training and testing datasets. Twenty-three maternal risk factors were identified including several that were associated with a two or more increased odds of preterm birth (preexisting diabetes, preexisting hypertension, sickle cell anemia, and previous preterm birth). Approximately 40% of women with a risk score ≥ 3.0 in the training and testing samples delivered preterm (40.6% and 40.8%, respectively) compared to 3.1-3.3% of women with a risk score of 0.0 [odds ratio (OR) 13.0, 95% confidence interval (CI) 10.7-15.8, training; OR 12.2, 95% CI 9.4-15.9, testing). Additionally, over 18% of women with a risk score ≥ 3.0 had an adverse outcome other than preterm birth. Conclusion Maternal factors that are identifiable prior to pregnancy or during the first-trimester can be used create a cumulative risk score to identify women at the lowest and highest risk for preterm birth regardless of race/ethnicity or socioeconomic status. Further, we found that this cumulative risk score could also identify women at risk for other adverse outcomes who did not have a preterm birth. The risk score is not an effective screening test, but does identify women at very high risk of a preterm birth.

Discriminatory Accuracy of Preeclampsia Risk Factors in Primary Care

BACKGROUND

Although it is common to use risk factors in the screening for preeclampsia, they do not always accurately identify patients who truly have this condition.

AIM OF THE STUDY

To determine the discriminatory accuracy of known preeclampsia risk factors, both individually and in combination.

METHODS

We studied patients undergoing prenatal care who were diagnosed with preeclampsia or eclampsia (n = 160 cases) in primary care and those who were not (n = 430 controls). Data on history of preeclampsia, type 2 diabetes, chronic hypertension, multiple gestation, first pregnancy, pregnancy interval ≥10 years, overweight/obesity, mean arterial pressure (MAP) ≥80 mmHg, and age (<20 years and ≥40 years) were obtained using a dichotomous scale. Discriminatory accuracy indicators were true-positive (TP) and false-positive (FP) rates, positive and negative likelihood ratios (LR+ and LR-), diagnostic odds ratio (DOR), and the area under the receiver-operating characteristic (AUROC) curve; stratified by parity. The case-control status was the reference standard.

RESULTS

Certain combinations performed better than individual factors, independent of parity status. Among multiparous women, MAP ≥80 mmHg together with previous preeclampsia and overweight/obesity accumulated the greatest number of discriminatory accuracy indicators, with acceptable values: TP, 72.2%; FP, 1.5%; LR+, 48.4; LR-, 0.3; DOR, 171.6; and AUROC, 0.85.

CONCLUSIONS

Discriminatory accuracy was low for almost all individual preeclampsia risk factors. However, the accuracy improved after some factors were combined. To the best of our knowledge, this is the first study to examine the discriminatory accuracy of preeclampsia risk factors used for screening high-risk pregnancies in primary care in Mexico.

Revisiting the discriminatory accuracy of traditional risk factors in preeclampsia screening

BACKGROUND

Preeclampsia (PE) is associated with a high risk of perinatal morbidity and mortality. However, there is no consensus in the definition of high-risk women.

AIM

To question current definition of high PE risk and propose a definition that considers individual heterogeneity to improves risk classification.

METHODS

A stratified analysis by parity was conducted using the Swedish Birth Register between 2002-2010 including 626.600 pregnancies. The discriminatory accuracy (DA) of traditional definitions of high-risk women was compared with a new definition based on 1) specific combinations of individual variables and 2) a centile cut-off of the probability of PE predicted by a multiple logistic regression model.

RESULTS

None of the classical risk-factors alone reached an acceptable DA. In multiparous, any combination of a risk-factor with previous PE or HBP reached a +LR>10. The combination of obesity and multiple pregnancy reached a good DA particularly in the presence of previous preeclampsia (positive likelihood ratio (LR+) = 26.5 or chronic hypertension (HBP) LR+ = 40.5. In primiparous, a LR+>15 was observed in multiple pregnancies with the simultaneous presence of obesity and diabetes mellitus or with HBP. Predicted probabilities above 97 centile in multiparous and 99 centile in primiparous provided high (LR+ = 12.5), and moderate (LR+ = 5.85), respectively. No one risk factor alone or in combination provided a LR- sufficiently low to rule-out the disease.

CONCLUSIONS

In preeclampsia prediction the combination of specific risk factors provided a better discriminatory accuracy than traditional single risk approach. Our results contribute to a more personalized risk estimation of preeclampsia.

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.

First trimester serum placental growth factor and hyperglycosylated human chorionic gonadotropin are associated with pre-eclampsia: a case control study

Background

To study whether maternal serum hyperglycosylated human chorionic gonadotropin (hCG-h) improves first trimester prediction of pre-eclampsia when combined with placental growth factor (PlGF), pregnancy-associated plasma protein-A (PAPP-A) and maternal risk factors.

Methods

Gestational-age-adjusted concentrations of hCG, hCG-h, PlGF and PAPP-A were analysed in serum samples by time-resolved immunofluorometric assays at 8–13 weeks of gestation. The case–control study included 98 women who developed pre-eclampsia, 25 who developed gestational hypertension, 41 normotensive women with small-for-gestational-age (SGA) infants and 177 controls.

Results

Of 98 women with pre-eclampsia, 24 women developed preterm pre-eclampsia (diagnosis < 37 weeks of gestation) and 13 of them had early-onset pre-eclampsia (diagnosis < 34 weeks of gestation). They had lower concentrations of PlGF, PAPP-A and proportion of hCG-h to hCG (%hCG-h) than controls. In receiver-operating characteristics (ROC) curve analysis, the area under the curve (AUC) for the combination of PlGF, PAPP-A, %hCG-h, nulliparity and mean arterial blood pressure was 0.805 (95% confidence interval, CI, 0.699–0.912) for preterm pre-eclampsia and 0.870 (95% CI 0.750–0.988) for early-onset pre-eclampsia. Without %hCG-h the AUC values were 0.756 (95% CI 0.651–0.861) and 0.810 (95% CI 0.682–0.938) respectively. For prediction of gestational hypertension, the AUC for %hCG-h was 0.708 (95% CI 0.608–0.808), but for other markers the AUC values were not significant. None of the AUC values were significant for the prediction of SGA infants in normotensive women.

Conclusions

First trimester maternal serum %hCG-h tended to improve prediction of preterm and early-onset pre-eclampsia when combined with PlGF, PAPP-A and maternal risk factors.

Reliability of First-Trimester Ultrasonic Biopsy for the Evaluation of Placental and Myometrial Blood Perfusion and the Prediction of Preeclampsia

OBJECTIVE

Low placental vascularization measured by three-dimensional (3-D) ultrasound with power Doppler can predict preeclampsia. We evaluated the reliability and reproducibility of the ultrasonic sphere biopsy (USSB) technique to evaluate placental and subplacental myometrium vascularization in the first trimester.

METHODS

We performed a secondary analysis of a case-control study nested in a prospective cohort of women with a singleton pregnancy undergoing ultrasound at 11 to 14 weeks' gestation. Women who developed preeclampsia (n = 20) and randomly selected controls (n = 60) were compared. Other controls (n = 60) were also randomly selected to evaluate intra- and inter-observer reproducibility. Using 3-D power Doppler, the vascularization index (VI), flow index (FI), and vascularization flow index (VFI) were measured from the volume of the whole placenta and the subplacental myometrium and from their respective USSB. Pearson's correlation coefficients (cc) with their P-values were calculated.

RESULTS

We observed that USSB is reliable in estimating the vascularization of the whole placenta in the first trimester (cc of VI 0.83; of FI 0.62; and of VFI 0.78; P < 0.001 for all) but was not as reliable for estimating subplacental myometrium vascularization (cc of VI 0.71; of FI 0.35; and of VFI 0.73). Measurement of placental vascularization using USSB showed good to excellent intra- and inter-observer reproducibility (cc of VI 0.86 and 0.85, respectively; of FI 0.75 and 0.75, respectively; and of VFI 0.83 and 0.83, respectively; P < 0.001 for all). Finally, we observed that women who subsequently developed preeclampsia had lower placental USSB VI (2.1 vs 4.8, P = 0.02), FI (32.4 vs. 42.5, P = 0.002), and VFI (0.8 vs. 2.1, P = 0.01) than controls.

CONCLUSION

First-trimester USSB of the placenta using 3-D power Doppler is a reliable and reproducible procedure for estimating placental vascularization and could be used to predict preeclampsia.

Early prediction and aspirin for prevention of pre-eclampsia (EPAPP) study: a randomized controlled trial

OBJECTIVE

To estimate the effect of early administration of aspirin on the prevention of pre-eclampsia in high-risk women.

METHODS

This was planned as a randomized, double-blind, placebo-controlled trial of aspirin for women with risk factors for pre-eclampsia. Participants were randomized to start either aspirin (81 mg/day) or placebo at 11 + 0 to 13 + 6 weeks of gestation. The primary outcome was pre-eclampsia and secondary outcomes included gestational hypertension and small-for-gestational age at birth.

RESULTS

The trial suffered from slow recruitment, leading to a protocol change to broaden the inclusion criteria (from a minimum score of multiple risk factors to at least one risk factor for pre-eclampsia). The trial was then terminated prematurely due to continuing slow recruitment and a lack of equipoise given a change in national guidelines to administer aspirin to high-risk women. From the 53 women who were randomized, 30 were included in the final analysis. There was no evidence that the primary outcome of pre-eclampsia was prevented by low-dose aspirin (relative risk (RR) 0.88, 95% CI 0.21-3.66). Gestational hypertension was seen in two women, both in the aspirin group. There was no evidence that the occurrence of small-for-gestational age was reduced by aspirin (RR 0.88, 95% CI 0.06-12.72).

CONCLUSIONS

Although this study was underpowered to show effectiveness of aspirin compared to placebo due to the premature termination and difficulties encountered, it highlights practical issues to inform future studies.