Performance of a first-trimester screening of preeclampsia in a routine care low-risk setting

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

PURPOSE OF REVIEW

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

RECENT FINDINGS

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

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Performance of machine-learning approach for prediction of pre-eclampsia in a middle-income country

OBJECTIVE

Pre-eclampsia (PE) is a serious complication of pregnancy associated with maternal and fetal morbidity and mortality. As current prediction models have limitations and may not be applicable in resource-limited settings, we aimed to develop a machine-learning (ML) algorithm that offers a potential solution for developing accurate and efficient first-trimester prediction of PE.

METHODS

We conducted a prospective cohort study in Mexico City, Mexico to develop a first-trimester prediction model for preterm PE (pPE) using ML. Maternal characteristics and locally derived multiples of the median (MoM) values for mean arterial pressure, uterine artery pulsatility index and serum placental growth factor were used for variable selection. The dataset was split into training, validation and test sets. An elastic-net method was employed for predictor selection, and model performance was evaluated using area under the receiver-operating-characteristics curve (AUC) and detection rates (DR) at 10% false-positive rates (FPR).

RESULTS

The final analysis included 3050 pregnant women, of whom 124 (4.07%) developed PE. The ML model showed good performance, with AUCs of 0.897, 0.963 and 0.778 for pPE, early-onset PE (ePE) and any type of PE (all-PE), respectively. The DRs at 10% FPR were 76.5%, 88.2% and 50.1% for pPE, ePE and all-PE, respectively.

CONCLUSIONS

Our ML model demonstrated high accuracy in predicting pPE and ePE using first-trimester maternal characteristics and locally derived MoM. The model may provide an efficient and accessible tool for early prediction of PE, facilitating timely intervention and improved maternal and fetal outcome. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

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.

Clinical effectiveness of routine first-trimester combined screening for pre-eclampsia in Spain with the addition of placental growth factor

INTRODUCTION

Pre-eclampsia affects 2%-8% of pregnancies and is one of the leading causes of maternal and perinatal morbidity and mortality. First-trimester screening using an algorithm that combines maternal characteristics, mean arterial blood pressure, uterine artery pulsatility index and biomarkers (pregnancy-associated plasma protein-A and placental growth factor) is the method that achieves a greater diagnostic accuracy. It has been shown that daily salicylic acid administration before 16 weeks in women at a high risk for pre-eclampsia can reduce the incidence of preterm pre-eclampsia. However, no previous studies have evaluated the impact of routine first-trimester combined screening for pre-eclampsia with placental growth factor after being implemented in the clinical practice.

MATERIAL AND METHODS

This was a multicenter cohort study conducted in eight different maternities across Spain. Participants in the reference group were prospectively recruited between October 2015 and September 2017. Participants in the study group were retrospectively recruited between March 2019 and May 2021. Pre-eclampsia risk was calculated between 11+0 and 13+6  weeks using the Gaussian algorithm combining maternal characteristics, mean arterial pressure, uterine arteries pulsatility index, pregnancy-associated plasma protein-A and placental growth factor. Patients with a risk greater than 1/170 were prescribed daily salicylic acid 150 mg until 36 weeks. Patients in the reference group did not receive salicylic acid during gestation.

RESULTS

A significant reduction was observed in preterm pre-eclampsia (OR 0.47; 95% CI: 0.30-0.73), early-onset (<34 weeks) pre-eclampsia (OR 0.35; 95% CI: 0.16-0.77), preterm small for gestational age newborn (OR 0.57; 95% CI: 0.40-0.82), spontaneous preterm birth (OR 0.72; 95% CI: 0.57-0.90), and admission to intensive care unit (OR 0.55; 95% CI: 0.37-0.81). A greater treatment adherence resulted in a significant reduction in adverse outcomes.

CONCLUSIONS

Routine first-trimester screening for pre-eclampsia with placental growth factor leads to a reduction in preterm pre-eclampsia and other pregnancy complications. Aspirin treatment compliance has a great impact on the effectiveness of this screening program.

Early Prediction of Hypertensive Diseases of Pregnancy by Using Combined Screening Methods in a Rural Population

INTRODUCTION

The most frequent medical issue during pregnancy is hypertension, which can complicate up to 10% to 15% of pregnancies worldwide. An estimated 14% of all maternal fatalities worldwide are thought to be caused by hypertensive disease of pregnancy, one of the main causes of maternal and fetal morbidity and mortality. Despite the fact that maternal mortality is substantially lower in high-income countries than in low- and middle-income countries, hypertension is still one of the leading causes of maternal death globally. Maternal mortality associated with hypertension fluctuated between 0.08 and 0.42 per 100,000 births between 2009 and 2015. In India, the estimated overall pooled prevalence of HDP was determined to be one out of 11 women, or 11% (95% CI, 5%-17%). Despite various government programs, there is still a high prevalence of hypertension, which calls for stakeholders and healthcare professionals to focus on providing both therapeutic and preventive care. The best solution is to concentrate more on the early detection of pregnancy-related hypertension and to guarantee its universal application so that proper care can be carried out to prevent maternal and fetal morbidity.

AIM

To estimate the predictive value of the combination of maternal characteristics, i.e., mean arterial pressure (MAP), biophysical evaluation (uterine artery Doppler), and biochemical markers (pregnancy-associated plasma protein A (PAPP-A)), in the first trimester of pregnancy for hypertensive diseases of pregnancy.

METHODOLOGY

It was a prospective observational study of longitudinal variety that took over 18 months in a tertiary care rural hospital. The number of women admitted to the hospital for labor care during 2019 was 5261. A total of 513 were diagnosed with hypertensive illnesses during pregnancy. At a prevalence rate of 10%, we calculated a sample size of 350 to achieve a sensitivity of 85% with an absolute error of 12.5% at a 95% CI. Maternal histories, such as age, education, socio-economic status, gravidity, and BMI, were taken along with three parameters, i.e., MAP, which was significant above 90 mmHg, uterine artery Doppler, which was taken significant above 1.69, and serum PAPP-A, which was significant at less than 0.69 ml/IU.

OBSERVATION AND RESULTS

We have found that the following are associated with the prediction of hypertension: among the maternal characteristics are advanced age >35 years, presence of body edema, and urine proteins along with MAP, uterine artery pulsatility index (UtA-PI), and PAPP-A are significant. The predictive accuracy of the combination of MAP, UtA-PI, and PAPP-A is also significant. We also found that there is a significant increase in cesarean sections and NICU admissions in hypertensive patients.

CONCLUSION

A combination of screening parameters, including MAP, UtA-PI, and PAPP-A, to predict early hypertensive disease of pregnancy is developed and tested.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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.

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

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

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.

Reduction in Preterm Preeclampsia after Contingent First-Trimester Screening and Aspirin Prophylaxis in a Routine Care Setting

Objectives: Several multivariate algorithms for preeclampsia (PE) screening in the first trimester have been developed over the past few years. These models include maternal factors, mean arterial pressure (MAP), uterine artery Doppler (UtA-PI), and biochemical markers (pregnancy-associated plasma protein-A (PAPP-A) or placental growth factor (PlGF)). Treatment with low-dose aspirin (LDA) has shown a reduction in the incidence of preterm PE in women with a high-risk assessment in the first trimester. An important barrier to the implementation of first-trimester screening is the cost of performing tests for biochemical markers in the whole population. Theoretical contingent strategies suggest that two-stage screening models could also achieve high detection rates for preterm PE with lower costs. However, no data derived from routine care settings are currently available. This study was conducted to validate and assess the performance of a first-trimester contingent screening process using PlGF for PE, with prophylactic LDA, for decreasing the incidence of preterm PE. Methods: This was a two-phase study. In phase one, a contingent screening model for PE was developed using a multivariate validated model and a historical cohort participating in a non-interventional PE screening study (n = 525). First-stage risk assessment included maternal factors, MAP, UtA-PI, and PAPP-A. Several cut-off levels were tested to determine the best screening performance, and three groups were then defined (high-, medium-, and low-risk groups). PlGF was determined in the medium-risk group to calculate the final risk. Phase two included a validation cohort of 847 singleton pregnancies prospectively undergoing first-trimester PE screening using this approach. Women at high risk of PE received prophylactic treatment with 150 mg of LDA. The clinical impact of the model was evaluated by comparing the incidence of early-onset (<34 weeks) and preterm (<37 weeks) PE between groups. Results: Cut-off levels for the contingent screening model were chosen in the first and second stages of screening to achieve a performance with sensitivities of 100% and 80% for early-onset and preterm PE detection, respectively, with a 15% false positive rate. In the development phase, 21.5% (n = 113) of the women had a medium risk of PE and required second-stage screening. In the prospective validation phase, 15.3% (n = 130) of the women required second-stage screening for PlGF, yielding an overall screen-positive rate of 14.9% (n = 126). The incidence of preterm PE was reduced by 68.4% (1.9% vs. 0.6%, p = 0.031) after one year of screening implementation. Conclusions: Implementation of contingent screening for PE using PlGF in a routine care setting led to a significant reduction (68.4%) in preterm PE, suggesting that contingent screening can achieve similar results to protocols using PlGF in the whole population. This could have financial benefits, with a similar reduction in the rate of preterm PE.

First-Trimester Sequential Screening for Preeclampsia Using Angiogenic Factors: Study Protocol for a Prospective, Multicenter, Real Clinical Setting Study

Introduction

The incidence of preeclampsia (PE) is about 2–8%, making it one of the leading causes of perinatal morbidity and maternal mortality in the world. Early prophylactic low dose administration (150 mg) of acetylsalicylic acid is associated with a significant reduction in the incidence of early-onset PE, intrauterine growth restriction (IUGR), and neonatal mean stay in the intensive care unit (ICU). Universal implementation of a first-trimester screening system including angiogenic and antiangiogenic markers [the Placental Growth Factor (PlGF) and/or soluble fms-like Tyrosine Kinase-1 (sFlt-1)] has shown a prediction rate of 90% for early-onset PE but entails a high financial cost. The aim of this study is to determine the predictive and preventive capacity of a universal PE first-trimester two-step sequential screening model, determining the PlGF only in patients previously classified as intermediate risk by means of a multivariate model based on resources already used in the standard pregnancy control, in a real clinical setting. We hypothesize that this screening model will achieve similar diagnostic performance as the universal determination of PlGF but at a lower economic cost.

Methods and Analysis

This is a prospective, multicentric, cohort study in a real-world clinical setting. Every singleton pregnancy will be recruited at the routine first pregnancy visit. In a first step, the first-trimester risk of PE will be calculated using a multivariate Gaussian distribution model, based on medical history, mean blood pressure, Pregnancy-Associated Plasma Protein A (PAPP-A), and Uterine Artery Doppler Pulsatility Index (UTPI). Patients will be classified into three risk groups for PE: (1) risk ≥ 1/50, high-risk with no further testing (blinded PlGF); (2) risk between 1/51 and 1/500, medium-risk requiring further testing; and (3) risk ≤ 1/501, low-risk with no further testing. In a second step, the PlGF will only be determined in those patients classified as intermediate risk after this first step, and then reclassified into high- or low-risk groups. Prophylactic administration of aspirin (150 mg/day) will be prescribed only in high risk patients. As a secondary objective, sFlt-1 values will be blindly determined in patients with high and intermediate risk to assess its potential performance in the screening for PE.

Ethics and Dissemination

The study will be conducted in accordance with the principles of Good Clinical Practice. This study is approved by the Aragon Research Ethics Committee (CEICA) on 3 July 2020 (15/2020).

Clinical Trial Registration

ClinicalTrials.gov, identifier: NCT04767438.

An imbalance-aware deep neural network for early prediction of preeclampsia

Preeclampsia (PE) is a hypertensive complication affecting 8-10% of US pregnancies annually. While there is no cure for PE, aspirin may reduce complications for those at high risk for PE. Furthermore, PE disproportionately affects racial minorities, with a higher burden of morbidity and mortality. Previous studies have shown early prediction of PE would allow for prevention. We approached the prediction of PE using a new method based on a cost-sensitive deep neural network (CSDNN) by considering the severe imbalance and sparse nature of the data, as well as racial disparities. We validated our model using large extant rich data sources that represent a diverse cohort of minority populations in the US. These include Texas Public Use Data Files (PUDF), Oklahoma PUDF, and the Magee Obstetric Medical and Infant (MOMI) databases. We identified the most influential clinical and demographic features (predictor variables) relevant to PE for both general populations and smaller racial groups. We also investigated the effectiveness of multiple network architectures using three hyperparameter optimization algorithms: Bayesian optimization, Hyperband, and random search. Our proposed models equipped with focal loss function yield superior and reliable prediction performance compared with the state-of-the-art techniques with an average area under the curve (AUC) of 66.3% and 63.5% for the Texas and Oklahoma PUDF respectively, while the CSDNN model with weighted cross-entropy loss function outperforms with an AUC of 76.5% for the MOMI data. Furthermore, our CSDNN model equipped with focal loss function leads to an AUC of 66.7% for Texas African American and 57.1% for Native American. The best results are obtained with 62.3% AUC with CSDNN with weighted cross-entropy loss function for Oklahoma African American, 58% AUC with DNN and balanced batch for Oklahoma Native American, and 72.4% AUC using either CSDNN with weighted cross-entropy loss function or CSDNN with focal loss with balanced batch method for MOMI African American dataset. Our results provide the first evidence of the predictive power of clinical databases for PE prediction among minority populations.

Elevated mid-trimester 4-h postprandial triglycerides for predicting late-onset preeclampsia: a prospective screening study

Background

Abnormal maternal lipid concentrations are associated with increased risk of preeclampsia. However, previous studies mainly focused on fasting lipid concentrations, scarce data have been published on the relationship between postprandial triglyceride (TG) concentrations in the second trimester and the risk of preeclampsia. Our aim is to evaluate the potential of triglyceride (TG) concentrations at the time of oral lipid tolerance test (OLTT) measurement in the second trimester to predict preeclampsia and to elucidate the lipid metabolic changes related to these diseases.

Methods

This is a prospective cohort study of Pregnant women at 12–24 weeks of gestation undergone an OLTT in a university affiliated hospital between May 2019 and January 2020. Data were stratified into binaries according to the OLTT results. The receiver operating characteristic (ROC) curve analysis was conducted to determine the optimal cut-off points of TG, HDL-C, LDL-C, sd-LDL, FFA, and BG for predicting preeclampsia.

Results

438 pregnant women were recruited to undergo an OLTT at 12–24 weeks of gestation. Among these, 24 women developed preeclampsia and 414 women remained normotensive. Women who subsequently developed preeclampsia had higher concentrations of 4-h postprandial TG than those who remained normotensive. In the linear logistic regression analyses of potential confounding factors, mid-trimester 4-h postprandial TG concentrations at the time of OLTT measurement were significantly higher in preeclamptic cases than in controls.

Conclusions

Dyslipidemia in the second trimester of pregnancy, particularly postprandial hypertriglyceridemia, appears to be associated with an increased risk of preeclampsia. Mid-trimester 4-h postprandial TG concentration at the time of OLTT measurement may be a potential predictive marker of preeclampsia.

Trial registration Data of registration: 2018/10/15. Date of initial participant enrollment: 2019/05/01. Clinical trial identification number: chiCTR1800018884. URL of the registration site: http://www.chictr.org.cn/showproj.aspx?proj=25526. Data sharing information: The data including individual participant data, detailed study protocols, statistical analysis plans will be shared upon request to the corresponding author.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03261-6.

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.

BCG-trained innate immunity leads to fetal growth restriction by altering immune cell profile in the mouse developing placenta

Trained immunity is a new concept illustrating that innate immune cells are able to undergo a long-term metabolic and epigenetic reprogramming after infection or vaccination, thus displaying either a pro- or an anti-inflammatory phenotype during a sequential unrelated challenge. Innate immune cells such as natural killer (NK) cells and macrophages constitute a large part of the decidual leukocyte population at the maternal-fetal interface, playing an important role in placental development and as such in fetal growth and development. In this study, we hypothesized that training the innate immune cells before pregnancy could have an impact on pregnancy. To test this hypothesis, we used CBA/J x DBA/2 mouse model to investigate pregnancy outcomes and leukocyte population at the maternal-fetal interface. Although we were not able to show a beneficial effect of LPS-tolerogenic training on fetal resorption, Bacillus Calmette-Guérin (BCG) training, known to prime innate immune cells to be proinflammatory, led to fetal growth restriction, without aggravating the fetal resorption rate. We also found that BCG training led to less NK cells and macrophages at the maternal-fetal interface at the early stage of placentation (E9.5), associated with a down-regulation of Ccr3 and Lif mRNA expression. This induced altered leucocyte population profile can be an explanation for the subsequent fetal growth restriction. These data suggest that preconceptional infections-induced trained immunity could influence pregnancy outcomes.

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.

Diagnostic accuracy of the Gaussian first-trimester combined screening for pre-eclampsia to predict small-for-gestational-age neonates

OBJECTIVE

Pre-eclampsia and delivery of small-for-gestational-age (SGA) neonates can be predicted from the first trimester. A Gaussian model for prediction of PE has recently been described, although its capacity to predict SGA is still unknown.

METHODS

This was a secondary analysis of a prospective cohort study conducted at Vall d'Hebron University Hospital (Barcelona) in 2483 single pregnancies from October 2015 to September 2017. Mean arterial blood pressure and mean uterine artery pulsatility index were recorded at the first-trimester scan. Serum concentrations of placental growth factor and pregnancy-associated plasma protein-A were assessed between 8⁺⁰ and 13⁺⁶  weeks. The predictive capacities of early (<32 weeks) and preterm (<37 weeks) SGA were tested.

RESULTS

For SGA without pre-eclampsia, detection rates of 25.0% (95% confidence interval [CI] 0-75.0) for early SGA and 14.3% (95% CI 3.6-28.6) for preterm SGA were achieved. For SGA with pre-eclampsia, the algorithm showed detection rates of 100.0% (95% CI 25.0-100.0) for early SGA and 56.3% (95% CI 31.3-81.3) for preterm SGA.

CONCLUSION

This algorithm identifies 62.5% of early SGA and 27.3% of preterm SGA. Combined screening for predicting both pre-eclampsia and SGA by using the Gaussian algorithm is feasible and would simplify clinical practice.

Variation in the uterine arteries Doppler parameters when obtained transvaginally or transabdominally at different sampling locations

INTRODUCTION

To evaluate differences in Doppler velocimetry parameters when the uterine arteries (UtA) are evaluated transabdominally (TA) at different sampling locations and transvaginally (TV).

MATERIALS AND METHODS

Five hundred and fifty-seven pregnant women were evaluated between 11 and 39 weeks of gestation. The mean UtA pulsatility index (PI) and prevalence of bilateral notching were obtained at four different locations: (1) TA just above the crossing with the iliac artery; (2) TA just below the crossing with the iliac artery; (3) TA well above approximately 3 cm away from the crossing with the iliac artery; and (4) TV at the point closest to the internal cervical os. Measurements obtained just above the external iliac artery were considered the standard for comparison. Differences among different locations per gestational week were calculated.

RESULTS

The mean UtA-PI and prevalence of bilateral notching were similar when the uterine arteries were sampled TA just above or just below the crossing with the external iliac artery. The mean UtA-PI values and prevalence of bilateral notching were significantly higher (p < .0001) when obtained TV and significantly lower when obtained 3 cm above the crossing with the external iliac artery (p = .004), as compared to the standard plane just above the crossing.

CONCLUSION

The mean UtA-PI and prevalence of bilateral notching vary significantly when the uterine arteries are sampled far above the crossing with the external iliac artery or when obtained transvaginally.Key MessageThe predictive performance of the uterine arteries during pregnancy can significantly vary in relation to the approach selected for evaluation and to the location of the Doppler sampling gate.

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 .

"Screening for small-for-gestational age neonates at early third trimester in a high-risk population for preeclampsia"

BACKGROUND

Strategies to improve prenatal detection of small-for-gestational age (SGA) neonates are necessary because its association with poorer perinatal outcome. This study evaluated, in pregnancies with first trimester high risk of early preeclampsia, the performance of a third trimester screening for SGA combining biophysical and biochemical markers.

METHODS

This is a prospective longitudinal study on 378 singleton pregnancies identified at high risk of early preeclampsia according to a first trimester multiparametric algorithm with the cutoff corresponding to 15% false positive rate. This cohort included 50 cases that delivered SGA neonates with birthweight < 10th centile (13.2%) and 328 cases with normal birthweight (86.8%). At 27-30 weeks' gestation, maternal weight, blood pressure, estimated fetal weight, mean uterine artery pulsatility index and maternal biochemical markers (placental growth factor and soluble FMS-Like Tyrosine Kinase-1) were assessed. Different predictive models were created to evaluate their performance to predict SGA neonates.

RESULTS

For a 15% FPR, a model that combines maternal characteristics, estimated fetal weight, mean uterine artery pulsatility index and placental growth factor achieved a detection rate (DR) of 56% with a negative predictive value of 92.2%. The area under receiver operating characteristic curve (AUC) was 0.79 (95% confidence interval (CI), 0.72-0.86). The DR of a model including maternal characteristics, estimated fetal weight and mean uterine artery pulsatility index was 54% (AUC, 0.77 (95% CI, 0.70-0.84)). The DR of a model that includes maternal characteristics and placental growth factor achieved a similar performance (DR 56%, AUC 0.75, 95% CI (0.67-0.83)).

CONCLUSIONS

The performance of screening for SGA neonates at early third trimester combining biophysical and biochemical markers in a high-risk population is poor. However, a high negative predictive value could help in reducing maternal anxiety, avoid iatrogenic interventions and propose a specific plan for higher risk patients.

Early Prophylactic Enoxaparin for the Prevention of Preeclampsia and Intrauterine Growth Restriction: A Randomized Trial

BACKGROUND

Preeclampsia (PE) and intrauterine growth restriction (IUGR) are major causes of maternal and perinatal morbidity and mortality. Previous studies have shown that intervention with low-dose aspirin resulted in a reduction in the occurrence of preterm PE. However, no data are currently available on the effect of low-molecular-weight heparin (LMWH) for the prevention of pregnancy complications in women enrolled at first trimester screening.

OBJECTIVE

We aimed to assess the effectiveness of LMWH in the prevention of PE, IUGR, fetal death, and abruptio placentae in women classified as high risk based on their medical history and in women selected by first trimester screening of PE. Study -Design: This was a multicenter, randomized, open-label, parallel controlled trial in women without thrombophilia between 6.0 and 15.6 weeks of gestation. Inclusion criteria were severe PE or IUGR before 34 weeks of gestation and/or abruptio placentae or unexplained intrauterine death in a previous pregnancy; uterine artery mean pulsatility index Doppler >95th percentile and/or positive first trimester screening for PE. Pregnant women were randomly assigned to receive no intervention or LMWH until the 36th week of gestation. The primary composite outcome consisted of 1 or more of the following: development of PE, IUGR, abruptio placentae, and intrauterine fetal death.

RESULTS

A total of 278 pregnant women were randomly allocated to receive LMWH (n = 134) or no intervention (n = 144). Overall, 115 (41%) women experienced placental insufficiency complications, with no significant differences between the 2 arms: 50/144 (34.7%) in the LMWH arm and 43/134 (32%) in the control arm (p = 0.64, OR: 1.13, 95% CI: 0.68-1.85).

CONCLUSION

LMWH did not reduce the incidence of placenta-mediated complications either in women with previous adverse obstetric history without thrombophilia or in women selected by first trimester screening for PE. Based on these results, we cannot recommend the use of LMWH alone in women at risk of placental complications.

The competing risk approach for prediction of preeclampsia

The established method of the assessment of the risk for development of preeclampsia is to identify risk factors from maternal demographic characteristics and medical history; in the presence of such factors, the patient is classified as high risk and in their absence as low risk. Although this approach is simple to perform, it has poor performance of the prediction of preeclampsia and does not provide patient-specific risks. This review describes a new approach that allows the estimation of patient-specific risks of delivery with preeclampsia before any specified gestational age by maternal demographic characteristics and medical history with biomarkers obtained either individually or in combination at any stage in pregnancy. In the competing risks approach, every woman has a personalized distribution of gestational age at delivery with preeclampsia; whether she experiences preeclampsia or not before a specified gestational age depends on competition between delivery before or after the development of preeclampsia. The personalized distribution comes from the application of Bayes theorem to combine a previous distribution, which is determined from maternal factors, with likelihoods from biomarkers. As new data become available, what were posterior probabilities take the role as the previous probability, and data collected at different stages are combined by repeating the application of Bayes theorem to form a new posterior at each stage, which allows for dynamic prediction of preeclampsia. The competing risk model can be used for precision medicine and risk stratification at different stages of pregnancy. In the first trimester, the model has been applied to identify a high-risk group that would benefit from preventative therapeutic interventions. In the second trimester, the model has been used to stratify the population into high-, intermediate-, and low-risk groups in need of different intensities of subsequent monitoring, thereby minimizing unexpected adverse perinatal events. The competing risks model can also be used in surveillance of women presenting to specialist clinics with signs or symptoms of hypertensive disorders; combination of maternal factors and biomarkers provide patient-specific risks for preeclampsia that lead to personalized stratification of the intensity of monitoring, with risks updated on each visit on the basis of biomarker measurements.

A new model for screening for early-onset preeclampsia

BACKGROUND

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

OBJECTIVE

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

STUDY DESIGN

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

RESULTS

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

CONCLUSION

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

Early prediction of preeclampsia via machine learning

BACKGROUND

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

OBJECTIVE

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

STUDY DESIGN

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

RESULTS

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

CONCLUSION

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

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.

Aspirin delays the development of preeclampsia

BACKGROUND

In the Combined Multimarker Screening and Randomized Patient Treatment with Aspirin for Evidence-Based Preeclampsia Prevention trial, risks of preterm preeclampsia were obtained from the competing risk model. Consenting women with risks of greater than 1 in 100 were randomized to treatment with aspirin or placebo. The trial showed strong evidence of an effect (odds ratio, 0.38, 95% confidence interval, 0.20-0.74) on the incidence of preterm preeclampsia, which was the primary outcome of Aspirin for Evidence-Based Preeclampsia Prevention. There was a small and insignificant effect on the incidence of term preeclampsia, which was a secondary outcomes (odds ratio, 0.95, 95% confidence interval, 0.64-1.39). These differential effects on term and preterm preeclampsia could reflect a mechanism in which the action of aspirin is to delay the delivery with preeclampsia, thereby converting what would be, without treatment, preterm preeclampsia to term preeclampsia.

OBJECTIVE

The objective of the study was to examine the hypothesis that the effect of aspirin is to delay the time of delivery in women who have preeclampsia.

STUDY DESIGN

This was an unplanned exploratory analysis of data from the Aspirin for Evidence-Based Preeclampsia Prevention trial. The delay hypothesis predicts that in groups for which preterm preeclampsia, without aspirin, were infrequent relative to term preeclampsia, a reduction in term preeclampsia would be expected because few cases of preterm preeclampsia would be converted to term preeclampsia. In contrast, in groups for which preterm preeclampsia were frequent relative to term preeclampsia, the conversion of preterm preeclampsia to term preeclampsia by aspirin would reduce or even reverse any effect on the incidence term preeclampsia. This is examined using the Aspirin for Evidence-Based Preeclampsia Prevention trial data by analysis of the effect of aspirin on the incidence of term preeclampsia stratified according to the risk of preterm preeclampsia at randomization. Given that women were included in Aspirin for Evidence-Based Preeclampsia Prevention with risks of preterm preeclampsia >1 in 100, a risk cutoff if 1 in 50 was used to define higher risk and lower risk strata. A statistical model in which the effect of aspirin is to delay the gestational age at delivery was fitted to the Aspirin for Evidence-Based Preeclampsia Prevention trial data and the consistency of the predictions from this model with the observed incidence was demonstrated.

RESULTS

In the lower-risk group (<1 in 50), there was a reduction in the incidence of term preeclampsia (odds ratio, 0.62, 95% confidence interval, 0.29-1.30). In contrast, in the higher risk group (≥1 in 50) there was a small increase in the incidence of term- preeclampsia (odds ratio 1.11, 95% confidence interval, 0.71- .75). Although these effects fail to achieve significance, they are consistent with the delay hypothesis. Within the framework of the aspirin-related delay hypothesis, the effect of aspirin was to delay the gestational age at delivery with preeclampsia by an estimated 4.4 weeks (95% confidence interval, 1.4-7.1 weeks) for those that in the placebo group would be delivered at 24 weeks and the effect decreased by an estimated 0.23 weeks (95% confidence interval, 0.021-0.40 weeks) for each week of gestation so that at 40⁺⁰ weeks, the estimated delay was by 0.8 weeks (95% confidence interval, -0.03 to 1.7 weeks).

CONCLUSION

The Aspirin for Evidence-Based Preeclampsia Prevention trial data are consistent with the hypothesis that aspirin delays the gestational age at delivery with preeclampsia.

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

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

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Biochemical Markers for Prediction of Hypertensive Disorders of Pregnancy

Background

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

Methods

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

Results

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

Conclusions

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

Two-stage screening for preterm preeclampsia at 11-13 weeks' gestation

BACKGROUND

Screening for preeclampsia at 11-13 weeks' gestation by a combination of maternal factors, mean arterial pressure, uterine artery pulsatility index, and serum placental growth factor (triple test) can predict about 90% of preeclampsia, with delivery at <32 weeks (early-preeclampsia), and 75% of preeclampsia with delivery at <37 weeks (preterm preeclampsia), at a screen-positive rate of 10%. In pregnancies identified as being at high risk for preeclampsia by such screening, administration of aspirin (150 mg/d from 11 to 14 weeks' gestation to 36 weeks) reduces the rate of early preeclampsia by about 90% and preterm preeclampsia by about 60%. Recording of maternal history and blood pressure are part of routine prenatal care, but measurement of uterine artery pulsatility index and placental growth factor require additional costs.

OBJECTIVE

To explore the possibility of carrying out first-stage screening in the whole population by maternal factors alone or a combination of maternal factors, mean arterial pressure and uterine artery pulsatility index or maternal factors, mean arterial pressure, and placental growth factor and proceeding to second-stage screening by the triple test only for a subgroup of the population selected on the basis of the risk derived from first-stage screening.

STUDY DESIGN

The data for this study were derived from prospective nonintervention screening for preeclampsia at 11⁺⁰ to 13⁺⁶ weeks' gestation in 61,174 singleton pregnancies. Patient-specific risks of delivery with preeclampsia at <37 and <32 weeks' gestation were calculated using the competing risks model to combine the prior distribution of the gestational age at delivery with preeclampsia, obtained from maternal characteristics and medical history, with various combinations of multiple of the median values of mean arterial pressure, uterine artery pulsatility index, and placental growth factor. We estimated the detection rate of preterm-preeclampsia and early-preeclampsia at overall screen-positive rate of 10%, 15%, and 20% from a policy in which first-stage screening of the whole population is carried out by some of the components of the triple test and second-stage screening by the full triple test on women selected on the basis of results from first-stage screening.

RESULTS

If the method of first-stage screening is maternal factors, then measurements of mean arterial pressure, uterine artery pulsatility index, and placental growth factor can be reserved for only 70% of the population, achieving similar detection rate and screen-positive rate as with screening the whole population with the triple test. In the case of first-stage screening by maternal factors, mean arterial pressure, and uterine artery pulsatility index, then measurement of placental growth factor can be reserved for only 30-40% of the population, and if first-stage screening is by maternal factors, mean arterial pressure, and placental growth factor, measurement of uterine artery pulsatility index can be reserved for only 20-30% of the population. Empirical results were consistent with model-based performance.

CONCLUSION

Two-stage screening and biomarker testing for only part of the population will have financial benefits over conducting the test for the entire population.

Predictive performance of the competing risk model in screening for preeclampsia

BACKGROUND

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

OBJECTIVE

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

STUDY DESIGN

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

RESULTS

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

CONCLUSION

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

[Aspirin and preeclampsia]

Indications for aspirin during pregnancy are a matter of debate and there is a recent trend to an extended prescription and an overuse of aspirin in pregnancy. Aspirin is efficient in secondary prevention of preeclampsia essentially in patients with a personal history of preeclampsia. The effect of aspirin on platelet aggregation and on the TXA2/PGI2 balance is dose-dependent. The optimum dosage, from 75mg/day to 150mg/day, needs to be determined. Fetal safety data at 150mg/day are still limited. The efficacy of aspirin seems to be subject to a chronobiological effect. It is recommended to prescribe an evening or bedtime intake. Aspirin, in primary prevention of preeclampsia, given to high-risk patients identified in the first trimester by screening tests, seems to reduce the occurrence of early-onset preeclampsia. Nevertheless, there are insufficient data for the implementation of such screening procedures in practice.

First trimester screening for preterm and term pre-eclampsia by maternal characteristics and biophysical markers in a low-risk population

AIM

To develop a combined predictive model for preterm and term pre-eclampsia (PE) during the first trimester of pregnancy.

METHODS

This investigation was a nested case-control study in singleton pregnancies at the Maternal-Fetal Medicine Unit, University of Chile Hospital. A priori risks for preterm and term PE were calculated by multivariate logistic regression analyses. Biophysical markers were log₁₀ -transformed and expressed as multiples of the median. A multivariate logistic regression analysis was used to estimate a combined predictive model of preterm and term PE. Detection rates at different cut-off points were determined by a receiver operator curve analysis of a posteriori risks.

RESULTS

First trimester mean arterial pressure and uterine artery Doppler pulsatility index were significantly higher in women who develop PE than in the unaffected group. The detection rate of preterm PE based on maternal characteristics and biophysical markers was 72% at a 10% false-positive rate, corresponding to a cut-off risk of 1 in 50. The detection rate for term PE was 30% at a 10% false-positive rate.

CONCLUSION

Preterm PE can be predicted by a combination of maternal characteristics and biophysical markers. However, first trimester screening is less valuable for term PE.

Preeclampsia and the challenge of early prediction: reality or utopia? State of art and critical review of literature

Purpose: The challenge to obtain improved predictive tools, able to identify women destined to develop preeclampsia (PE), is raising the interest of researchers for the attractive chance to allow for timely initiation of prophylactic therapy, appropriate antenatal surveillance, and better-targeted research into preventive interventions. We aimed to gather all the evidence reported up to now in scientific literature relating to all prediction tests for PE.Materials and methods: We searched articles on conventional literature platforms from January 1952 to August 2016, using the terms "preeclampsia," "gestational preeclampsia," and "gestational hypertensive disorders" combined with "predictive test" and "risk assessment." Abstracts/titles identified by the search were screened by three investigators.Results: The search identified 203 citations, of which 154 potentially relevant after the initial evaluation. Among these studies, 20 full articles were excluded, therefore, 134 primary studies met the criteria for inclusion and were analyzed.Conclusions: Current evidence suggests that a combination of several features may provide the best predictive accuracy for the identification of PE. Large-scale, multicenter, multiethnic, prospective trials are required to propose an ideal combination of markers for routine screening.

[Preeclampsia: A challenge also for cardiologists]

Due to its short-term consequences on perinatal outcome, preeclampsia has been long regarded as an obstetrical disease, strictly confined to a management by OB/GYNs. It has been now widely accepted that preeclampsia is most a systemic inflammatory and systemic vascular disease during pregnancy and then a lifelong risk factor for subsequent cardiovascular event in women's life. The aim of this review is to propose an overview in the current state-of-art in definition, early identification and management of preeclampsia. We will also discuss the growing evidence that support that cardiologists must be fully involved in screening and prevention of preeclampsia during pregnancy and beyond in the subsequent medical follow-up of women who have experienced a preeclampsia.

Prospective biomarkers in preterm preeclampsia: A review

Preterm pre-eclampsia (prior to 37 weeks' gestation) remains a major cause of maternal and fetal morbidity and mortality particularly in low to middle income countries. Much research has focused on first and second trimester predictors of pre-eclampsia with the aim of allowing stratification of antenatal care and trialling of potential preventative and therapeutic agents. However, none have been shown to be of benefit in randomised controlled trials. In this literature review we critically evaluate predictive and diagnostic tests for preterm pre-eclampsia and discuss their clinical use and potential value in the management of preterm pre-eclampsia. We defined preterm pre-eclampsia as pre-eclampsia occurring prior to 37 weeks' gestation. Substantial progress has been made in the development of predictive screening tests for preterm pre-eclampsia, but further research is needed prior to their introduction and integration into routine clinical practice. The performance of diagnostic tests mainly utilising angiogenic and anti-angiogenic factors for determining time to delivery in later pregnancy currently hold more promise than first trimester predictive tests, possible reflecting the heterogeneity of pre-eclampsia.

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.

Aspirin for Prevention of Preeclampsia

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

Placenta-derived exosomes: potential biomarkers of preeclampsia

Preeclampsia remains a leading cause of maternal and fetal mortality, due to ineffective treatment and diagnostic strategies, compounded by the lack of clarity on the etiology of the disorder. Although several clinical and biological markers of preeclampsia have been evaluated, they have proven to be ineffective in providing a definitive diagnosis during the various stages of the disorder. Exosomes have emerged as ideal biomarkers of pathological states, such as cancer, and have more recently gained interest in pregnancy-related complications, due to their role in cellular communication in normal and complicated pregnancies. This occurs as a result of the specific placenta-derived exosomal molecular cargo, which may be involved in normal pregnancy-associated immunological events, such as the maintenance of maternal-fetal tolerance. This review provides perspectives on placenta-derived exosomes as possible biomarkers for the diagnosis/prognosis of preeclampsia. Using keywords, online databases were searched to identify relevant publications to review the potential use of placenta-derived exosomes as biomarkers of preeclampsia.

Second-trimester maternal serum markers in the prediction of preeclampsia

AIM

To determine whether late second-trimester maternal serum biomarkers are useful for the prediction of preeclampsia during the third trimester, a case-control study including 33 preeclamptic and 71 healthy pregnancies was conducted. Maternal serum concentrations of placental protein 13 (PP13), pregnancy-associated plasma protein (PAPP-A), pentraxin3 (PTX3), soluble FMS-like tyrosine kinase-1 (sFlt-1), myostatin and follistatin-like-3 (FSLT-3) were measured at 24-28 weeks' gestation. All the concentrations of these markers were compared between the preeclamptic and control groups. Receiver operating characteristic (ROC) curve analysis was applied to assess sensitivity and specificity of serum markers with significant difference.

RESULTS

The levels of PP13 and sFlt-1 were significantly increased and FSLT3 was significantly decreased in patients with preeclampsia. However, the concentration of PAPPA, PTX3 and myostatin did not differ significantly. In screening for preeclampsia during the third trimester by PP13, sFlt-1 and FSLT3, the detection rate was 61.3%, 48.1% and 39.1%, respectively, at 80% specificity, and the detection rate increased to 69.8% by combination of these three markers.

CONCLUSION

Maternal serum levels of PP13, sFlt-1 and FSLT3 play an important role in predicting late-onset preeclampsia, and the combination of these three markers significantly increases the detection rate for prediction.

Validation of Fetal Medicine Foundation algorithm for prediction of pre-eclampsia in the first trimester in an unselected Brazilian population

OBJECTIVE

The objective of this study is to evaluate the predictive performance of the Fetal Medicine Foundation (FMF) algorithm for prediction of preeclampsia (PE) between 11 and 14 weeks of gestation in an unselected Brazilian population.

METHODS

We conducted a prospective cohort study with 617 singleton pregnancies of unselected risk. Biophysical markers (mean pulsatility index, mean arterial pressure) and biochemical markers (placental growth factor (PLGF) and PAPP-A) were inserted into the FMF software and converted into multiples of the median (MoM). The subjects were divided into five groups: early-onset PE, parturition <34 weeks' gestation; preterm PE, parturition <37 weeks; PE, parturition at any gestational age; gestational hypertension (GH); and control group. Areas under the receiver operating characteristics curve (AUC) were calculated for the outcomes.

RESULTS

Among 617 patients, seven developed early-onset PE, 18 developed preterm PE (seven early PE plus 11 delivered between 34 and 36 + 6 weeks gestation), 34 developed PE (18 preterm PE plus 16 delivered after 37-week gestation), 12 pregnant women developed GH, and 517 women comprised the control group. The best predictive performance using the FMF algorithm occurred in the early-onset PE group, with AUC = 0.946 (95% CI 0.919-0.973) and the detection rate of 28.6% and 85.7% for 5% and 10% false-positive (FP), respectively.

CONCLUSIONS

The FMF algorithm to predict PE was effective in a Brazilian population, mainly in the early-onset form of the disease at 10% FP.