Prospective evaluation of ultrasound and biochemical-based multivariable models for the prediction of late pre-eclampsia: MULTIVARIABLE PREDICTION OF PRE-ECLAMPSIA

Prediction of preeclampsia in asymptomatic women

Preeclampsia is a major cause of maternal and perinatal morbidity and mortality. It is important to identify women who are at high risk of developing this disorder in their first trimester of pregnancy to allow timely therapeutic intervention. The use of low-dose aspirin initiated before 16 weeks of gestation can significantly reduce the rate of preterm preeclampsia by 62 %. Effective screening recommended by the Fetal Medicine Foundation (FMF) consists of a combination of maternal risk factors, mean arterial pressure, uterine artery pulsatility index (UtA-PI) and placental growth factor (PLGF). The current model has detection rates of 90 %, 75 %, and 41 % for early, preterm, and term preeclampsia, respectively at 10 % false-positive rate. Similar risk assessment can be performed during the second trimester in all pregnant women irrespective of first trimester screening results. The use of PLGF, UtA-PI, sFlt-1 combined with other investigative tools are part of risk assessment.

The association between human chorionic gonadotropin and adverse pregnancy outcomes: a systematic review and meta-analysis

OBJECTIVE

This study aimed to evaluate the association between human chorionic gonadotropin and adverse pregnancy outcomes.

DATA SOURCES

Medline, Embase, PubMed, and Cochrane were searched in November 2021 using Medical Subject Headings (MeSH) and relevant key words.

STUDY ELIGIBILITY CRITERIA

This analysis included published full-text studies of pregnant women with serum human chorionic gonadotropin testing between 8 and 28 weeks of gestation, investigating fetal outcomes (fetal death in utero, small for gestational age, preterm birth) or maternal factors (hypertension in pregnancy: preeclampsia, pregnancy-induced hypertension, placental abruption, HELLP syndrome, gestational diabetes mellitus).

METHODS

Studies were extracted using REDCap software. The Newcastle-Ottawa scale was used to assess for risk of bias. Final meta-analyses underwent further quality assessment using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) method.

RESULTS

A total of 185 studies were included in the final review, including the outcomes of fetal death in utero (45), small for gestational age (79), preterm delivery (62), hypertension in pregnancy (107), gestational diabetes mellitus (29), placental abruption (17), and HELLP syndrome (2). Data were analyzed separately on the basis of categorical measurement of human chorionic gonadotropin and human chorionic gonadotropin measured on a continuous scale. Eligible studies underwent meta-analysis to generate a pooled odds ratio (categorical human chorionic gonadotropin level) or difference in medians (human chorionic gonadotropin continuous scale) between outcome groups. First-trimester low human chorionic gonadotropin levels were associated with preeclampsia and fetal death in utero, whereas high human chorionic gonadotropin levels were associated with preeclampsia. Second-trimester high human chorionic gonadotropin levels were associated with fetal death in utero and preeclampsia.

CONCLUSION

Human chorionic gonadotropin levels are associated with placenta-mediated adverse pregnancy outcomes. Both high and low human chorionic gonadotropin levels in the first trimester of pregnancy can be early warning signs of adverse outcomes. Further analysis of human chorionic gonadotropin subtypes and pregnancy outcomes is required to determine the diagnostic utility of these findings in reference to specific cutoff values.

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.

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.

A retrospective cohort study on the effects of Down's screening markers and maternal characteristics on pregnancy outcomes in preeclampsia

BACKGROUND

To investigate the effects of down's screening markers and maternal characteristics on preeclampsia (PE) pregnancy outcome during early and middle pregnancy.

METHODS

A retrospective study of a cohort of 246 PE and 18,709 No-PE pregnant women who participated in Down's screening during early and middle pregnancy was performed. Clinical data of pregnancy-related were collected. Multivariate binary logistic regression was used to analyze the adjusted odds ratio (aOR) and 95% confidence interval (CI) of Down's screening markers, maternal characteristics, pregnancy outcome, and other related variables, and to evaluate the influencing factors of each indicator on PE. P < .05 was considered to be statistically significant.

RESULTS

Compared with the non-PE group, the concentration and median multiple (MoM) of pregnancy-associated plasma protein-A (PAPP-A) and free beta subunit of human chorionic gonadotropin (free β-hCG) in PE group were both lower (P < .001). Multivariate binary logistic regression analysis showed that low birth weight, hydronephrosis, premature delivery, fetal growth retardation, cesarean section, live birth, hyperlipemia, infection, decreased free β-hCG and first trimester maternal weight were risk factors for PE (aOR were: 7.552, 6.684, 4.154, 3.762, 3.612, 2.454, 1.757, 1.562, 1.270, and 1.077, respectively), while uterine scar, premature rupture of membranes and elevated PAPP-A were protective factors of PE (aOR were: 0.222, 0.328 and 0.612, respectively).

CONCLUSION

Decreased maternal serum PAPP-A level, increased free β-hCG, hyperlipemia, premature delivery, cesarean section, live birth, hydronephrosis, fetal growth retardation, low birth weight, and infection are risk factors for PE, while uterine scar and premature rupture of membrane are protective factors for PE.

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.

Impaction of factors associated with oxidative stress on the pathogenesis of gestational hypertension and preeclampsia: A Chinese patients based study

We aimed to investigate the effect of Kelch-like ECH-associated protein 1/NF-E2 p45-related factor 2 (Keap1/Nrf2) pathway on the biological function of trophoblast cells in oxidative stress model at the cellular level, and analyzed the expression level and clinical significance of Keap1/Nrf2 pathway and related antioxidant factors in placental tissues of Preeclampsia (PE) patients at clinical level. In present study, we found that under hypoxia/reoxygenation conditions, the activity of oxidative stress-related enzymes (CAT, GSH-Px, SOD) in HTR8/SVneo cells was significantly lower than that before treatment (P < .01). The activities of CAT, GSH-Px and SOD in HTR8/SVneo cells in SiRNA+H/R group decreased significantly (P < .01), indicating the important defense effect of Keap1/Nrf2 signaling pathway in oxidative stress. As a control group of Nrf2 SiRNA+H/R group, Si-NC+H/R group had CAT, GSH-Px and SOD activities decreasing, which was similar to that in H/R group. Moreover, the activities of oxidative stress-related active enzymes in patients with PE were further confirmed by detecting and comparing the activities of CAT, GSH-Px and SOD in placental tissues. The results showed that the activity of SOD (P < .001), GSH-Px (P < .01) and CAT (P < .01) in placental tissues of patients with PE were significant different from those of normal placental tissues. The expression level of Keap1 in placenta of patients with PE was slightly lower than that of normal placenta. While the expression of Nrf2 in placenta of patients with PE was significantly higher than that of normal placenta. HO-1 expression in placenta of patients with PE was significantly higher than that of normal placenta. These results implicate the importance of Keap-1/Nrf2 pathway in PE.

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 .

Diagnostic Performance of First Trimester Screening of Preeclampsia Based on Uterine Artery Pulsatility Index and Maternal Risk Factors in Routine Clinical Use

Preeclampsia is a pregnancy-specific disorder defined by new onset of hypertension and proteinuria after 20 weeks of gestation. The early detection of patients at risk of developing preeclampsia is crucial, however, predictive models are still controversial. We aim to evaluate the diagnostic performance of a predictive algorithm in the first trimester of pregnancy, in order to identify patients that will subsequently develop preeclampsia, and to study the effect of aspirin on reducing the rate of this complication in patients classified as high risk by this algorithm. A retrospective cohort including 1132 patients attending prenatal care at Clínica Dávila in Santiago, Chile, was conceived. The risk of developing preeclampsia (early and late onset) was calculated using algorithms previously described by Plasencia et al. Patients classified as high risk, in the first trimester of pregnancy, by these algorithms, were candidates to receive 100 mg/daily aspirin as prophylaxis at the discretion of the attending physician. The overall incidence of preeclampsia in this cohort was 3.5% (40/1132), and the model for early onset preeclampsia prediction detected 33% of patients with early onset preeclampsia. Among the 105 patients considered at high risk of developing preeclampsia, 56 received aspirin and 49 patients did not. Among those who received aspirin, 12% (7/56) developed preeclampsia, which is equal to the rate of preeclampsia (12% (6/49)) of those who did not receive this medication. Therefore, the diagnostic performance of an algorithm combining uterine artery Doppler and maternal factors in the first trimester predicted only one third of patients that developed preeclampsia. Among those considered at high risk for developing the disease using this algorithm, aspirin did not change the incidence of preeclampsia, however, this could be due either to the small study sample size or the type of the study, a retrospective, non-interventional cohort study.

Prospective evaluation of screening performance of first-trimester prediction models for preterm preeclampsia in an Asian population

BACKGROUND

The administration of aspirin <16 weeks gestation to women who are at high risk for preeclampsia has been shown to reduce the rate of preterm preeclampsia by 65%. The traditional approach to identify such women who are at risk is based on risk factors from maternal characteristics, obstetrics, and medical history as recommended by the American College of Obstetricians and Gynecologists and the National Institute for Health and Care Excellence. An alternative approach to screening for preeclampsia has been developed by the Fetal Medicine Foundation. This approach allows the estimation of patient-specific risks of preeclampsia that requires delivery before a specified gestational age with the use of Bayes theorem-based model.

OBJECTIVE

The purpose of this study was to examine the diagnostic accuracy of the Fetal Medicine Foundation Bayes theorem-based model, the American College of Obstetricians and Gynecologists, and the National Institute for Health and Care Excellence recommendations for the prediction of preterm preeclampsia at 11-13⁺⁶ weeks gestation in a large Asian population STUDY DESIGN: This was a prospective, nonintervention, multicenter study in 10,935 singleton pregnancies at 11-13⁺⁶ weeks gestation in 11 recruiting centers across 7 regions in Asia between December 2016 and June 2018. Maternal characteristics and medical, obstetric, and drug history were recorded. Mean arterial pressure and uterine artery pulsatility indices were measured according to standardized protocols. Maternal serum placental growth factor concentrations were measured by automated analyzers. The measured values of mean arterial pressure, uterine artery pulsatility index, and placental growth factor were converted into multiples of the median. The Fetal Medicine Foundation Bayes theorem-based model was used for the calculation of patient-specific risk of preeclampsia at <37 weeks gestation (preterm preeclampsia) and at any gestation (all preeclampsia) in each participant. The performance of screening for preterm preeclampsia and all preeclampsia by a combination of maternal factors, mean arterial pressure, uterine artery pulsatility index, and placental growth factor (triple test) was evaluated with the adjustment of aspirin use. We examined the predictive performance of the model by the use of receiver operating characteristic curve and calibration by measurements of calibration slope and calibration in the large. The detection rate of screening by the Fetal Medicine Foundation Bayes theorem-based model was compared with the model that was derived from the application of American College of Obstetricians and Gynecologists and National Institute for Health and Care Excellence recommendations.

RESULTS

There were 224 women (2.05%) who experienced preeclampsia, which included 73 cases (0.67%) of preterm preeclampsia. In pregnancies with preterm preeclampsia, the mean multiples of the median values of mean arterial pressure and uterine artery pulsatility index were significantly higher (mean arterial pressure, 1.099 vs 1.008 [P<.001]; uterine artery pulsatility index, 1.188 vs 1.063[P=.006]), and the mean placental growth factor multiples of the median was significantly lower (0.760 vs 1.100 [P<.001]) than in women without preeclampsia. The Fetal Medicine Foundation triple test achieved detection rates of 48.2%, 64.0%, 71.8%, and 75.8% at 5%, 10%, 15%, and 20% fixed false-positive rates, respectively, for the prediction of preterm preeclampsia. These were comparable with those of previously published data from the Fetal Medicine Foundation study. Screening that used the American College of Obstetricians and Gynecologists recommendations achieved detection rate of 54.6% at 20.4% false-positive rate. The detection rate with the use of National Institute for Health and Care Excellence guideline was 26.3% at 5.5% false-positive rate.

CONCLUSION

Based on a large number of women, this study has demonstrated that the Fetal Medicine Foundation Bayes theorem-based model is effective in the prediction of preterm preeclampsia in an Asian population and that this method of screening is superior to the approach recommended by American College of Obstetricians and Gynecologists and the National Institute for Health and Care Excellence. We have also shown that the Fetal Medicine Foundation prediction model can be implemented as part of routine prenatal care through the use of the existing infrastructure of routine prenatal care.

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.

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.

First trimesters Pregnancy-Associated Plasma Protein-A levels value to Predict Gestational diabetes Mellitus: A systematic review and meta-analysis of the literature

Detecting pregnant women at risk of diabetes in first months can help them by early intervention for delaying or preventing onset of GDM. In this study, we aimed to assess the Predictive value of first trimester Pregnancy related plasma protein-A (PAPP-A) levels for detecting Gestational diabetes Mellitus (GDM). This systematic review and meta-analysis was conducted through probing in databases. PubMed, Scopus, Medline and Google scholar citations were searched to find the published papers from 1974 to 2017. Studies were considered eligible if they were cohorts, case-control studies, reported GDM result, not other types, conducted on singleton pregnancy, measured Serum pregnancy associated plasma protein A in the first trimester and evaluated the relation of first trimester pregnancy associated plasma protein-A and GDM. Two reviewers independently assessed the quality with Newcastle-Ottawa and extracted data in the Pre-defined checklist. Analysis of the data was carried out by "Comprehensive Meta-analysis Version 2 (CAM)" and Metadisc software. 17 articles have our inclusion criteria and were considered in our systematic review, 5 studies included in Meta-analysis. Meta-analysis of these articles showed that the predictive value of PAPP-A for GDM has 55% sensitivity (53-58), 90% (89-90) specificity, LR + 2.48 (0.83-7.36) and LR - 0.70 (0.45-1.09) with 95% confidence intervals. In our study PAPP-A has low predictive accuracy overall, but it may be useful when combined with other tests, and this is an active part for future research. One limitation of our study is significant heterogeneity because of different adjusted variables and varied diagnostic criteria.

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 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.

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).

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.

Predictive Value of Free β-hCG Multiple of the Median for Women with Preeclampsia

BACKGROUND

Preeclampsia (PE) is relatively common and is unpredictable in its onset and progression.

AIMS

We investigated the clinical value of using the multiple of the median (MoM) of free β-human chorionic gonadotropin (β-hCG) concentrations in women with normal pregnancy and PE.

METHODS

This study was based on a dataset available from published studies, and the relevant studies were retrieved from multiple electronic databases. Data were extracted from case-control studies; a random-effects model was employed, and standardized mean difference and 95% confidence intervals were calculated. Twelve case-control studies (eleven English-based articles and one Chinese-based article) were analyzed in the current meta-analysis and included 702 patients with PE and 8,233 women with normal pregnancies.

RESULTS

Statistical analysis revealed a higher MoM of β-hCG serum levels in patients with PE. Ethnicity subgroup analysis showed that the MoM of serum β-hCG levels was significantly higher in women with PE in both Asian and Caucasian populations.

CONCLUSION

The MoM of β-hCG serum levels was significantly increased in women with PE compared to women with normal pregnancies. Screening for serum β-hCG MoM levels will be helpful in the early identification of pregnancies at risk of developing PE. © 2015 S. Karger AG, Basel.

First-trimester uterine artery Doppler analysis in the prediction of later pregnancy complications

Uterine artery Doppler waveform analysis has been extensively studied in the second trimester of pregnancy as a predictive marker for the later development of preeclampsia and fetal growth restriction. The use of Doppler interrogation of this vessel in the first trimester has gained momentum in recent years. Various measurement techniques and impedance indices have been used to evaluate the relationship between uterine artery Doppler velocimetry and adverse pregnancy outcomes. Overall, first-trimester Doppler interrogation of the uterine artery performs better in the prediction of early-onset than late-onset preeclampsia. As an isolated marker of future disease, its sensitivity in predicting preeclampsia and fetal growth restriction in low risk pregnant women is moderate, at 40–70%. Multiparametric predictive models, combining first-trimester uterine artery pulsatility index with maternal characteristics and biochemical markers, can achieve a detection rate for early-onset preeclampsia of over 90%. The ideal combination of these tests and validation of them in various patient populations will be the focus of future research.

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.

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.

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.

Gestational diabetes mellitus: including serum pregnancy-associated plasma protein-A testing in the clinical management of primiparous women? A case-control study

AIMS

To assess pregnancy-associated plasma protein A (PAPP-A) correlation with GDM and its usefulness in predicting GDM in primiparous women.

METHODS

First trimester data related to 307 pregnant women affected by GDM and 366 control pregnant women were retrieved from a computer data base and integrated with ad hoc data. Clinical data were recorded at delivery. A logistic model was used to analyze the association between first trimester data and subsequent clinical outcomes. We derived a risk score using both classical risk factors for GDM and PAPP-A.

RESULTS

Diabetic and control women were significantly different in terms of age (p<0.001), BMI (p<0.001), weight (p<0.001), family history of diabetes (p<0.001), PAPP-A concentration and PAPP-A corrected multiple of the median (MoM) (p<0.001). The ROC-AUC of the clinical risk score was 0.60 (95%CI 0.56-0.64), the adjusted score including PAPP-A MoM was 0.70 (95%CI 0.66-0.74).

CONCLUSIONS

Low PAPP-A was strongly associated with GDM and lower values were found in diabetic women needing insulin therapy. Adding PAPP-A to first trimester screening could improve the prediction of women at high risk who will develop GDM. Further studies are needed to validate the applicability of our findings in different populations and settings.