Prediction of early and late pre-eclampsia from maternal characteristics, uterine artery Doppler and markers of vasculogenesis during first trimester of pregnancy

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.

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.

Comprehensive analysis of transcriptomic profiling of 5-methylcytosin modification in placentas from preeclampsia and normotensive pregnancies

Increasing evidence suggests that RNA m5C modification and its regulators have been confirmed to be associated with the pathogenesis of many diseases. However, the distribution and biological functions of m5C in mRNAs of placental tissues remain unknown. we collected placentae from normotensive pregnancies (CTR) and preeclampsia patients (PE) to analyze the transcriptomic profiling of m5C RNA methylation through m5C RNA immunoprecipitation (UMI-MeRIP-Seq). we discovered that overall m5C methylation peaks were decreased in placental tissues from PE patients. And, 2844 aberrant m5C peaks were identified, of which respectively 1304 m5C peaks were upregulated and 1540 peaks were downregulated. The distribution of m5C peaks were mainly located in CDS (coding sequences) regions in placental tissues of both groups, but compared with the CTR group, the m5C peak in PE group before the stop code of CDS was significantly increased and even higher than the peak value after start code in CDS. Differentially methylated genes were mainly enriched in MAPK/cAMP signaling pathway. Moreover, the up-regulated genes with hypermethylated modification were enriched in the processes of hypoxia, inflammation/immune response. Finally, through analyzing the mRNA expression levels of m5C RNA methylation regulators, we found only DNMT3B and TET3 were significantly upregulated in PE samples than in control group. And they are not only negatively correlated with each other, but also closely related to those differentially expressed genes modified by differential methylation.Our findings provide new insights regarding alterations of m5C RNA modification into the pathogenic mechanisms of PE.

Multi-omics and machine learning for the prevention and management of female reproductive health

Females typically carry most of the burden of reproduction in mammals. In humans, this burden is exacerbated further, as the evolutionary advantage of a large and complex human brain came at a great cost of women's reproductive health. Pregnancy thus became a highly demanding phase in a woman's life cycle both physically and emotionally and therefore needs monitoring to assure an optimal outcome. Moreover, an increasing societal trend towards reproductive complications partly due to the increasing maternal age and global obesity pandemic demands closer monitoring of female reproductive health. This review first provides an overview of female reproductive biology and further explores utilization of large-scale data analysis and -omics techniques (genomics, transcriptomics, proteomics, and metabolomics) towards diagnosis, prognosis, and management of female reproductive disorders. In addition, we explore machine learning approaches for predictive models towards prevention and management. Furthermore, mobile apps and wearable devices provide a promise of continuous monitoring of health. These complementary technologies can be combined towards monitoring female (fertility-related) health and detection of any early complications to provide intervention solutions. In summary, technological advances (e.g., omics and wearables) have shown a promise towards diagnosis, prognosis, and management of female reproductive disorders. Systematic integration of these technologies is needed urgently in female reproductive healthcare to be further implemented in the national healthcare systems for societal benefit.

Early prediction and longitudinal modeling of preeclampsia from multiomics

Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear. We developed machine-learning models for early prediction of preeclampsia (first 16 weeks of pregnancy) and over gestation by analyzing six omics datasets from a longitudinal cohort of pregnant women. For early pregnancy, a prediction model using nine urine metabolites had the highest accuracy and was validated on an independent cohort (area under the receiver-operating characteristic curve [AUC] = 0.88, 95% confidence interval [CI] [0.76, 0.99] cross-validated; AUC = 0.83, 95% CI [0.62,1] validated). Univariate analysis demonstrated statistical significance of identified metabolites. An integrated multiomics model further improved accuracy (AUC = 0.94). Several biological pathways were identified including tryptophan, caffeine, and arachidonic acid metabolisms. Integration with immune cytometry data suggested novel associations between immune and proteomic dynamics. While further validation in a larger population is necessary, these encouraging results can serve as a basis for a simple, early diagnostic test for preeclampsia.

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.

First trimester preeclampsia screening and prediction

Preeclampsia is a major cause of maternal and perinatal morbidity and mortality. Early-onset disease requiring preterm delivery is associated with a higher risk of complications in both mothers and babies. Evidence suggests that the administration of low-dose aspirin initiated before 16 weeks' gestation significantly reduces the rate of preterm preeclampsia. Therefore, it is important to identify pregnant women at risk of developing preeclampsia during the first trimester of pregnancy, thus allowing timely therapeutic intervention. Several professional organizations such as the American College of Obstetricians and Gynecologists (ACOG) and National Institute for Health and Care Excellence (NICE) have proposed screening for preeclampsia based on maternal risk factors. The approach recommended by ACOG and NICE essentially treats each risk factor as a separate screening test with additive detection rate and screen-positive rate. Evidence has shown that preeclampsia screening based on the NICE and ACOG approach has suboptimal performance, as the NICE recommendation only achieves detection rates of 41% and 34%, with a 10% false-positive rate, for preterm and term preeclampsia, respectively. Screening based on the 2013 ACOG recommendation can only achieve detection rates of 5% and 2% for preterm and term preeclampsia, respectively, with a 0.2% false-positive rate. Various first trimester prediction models have been developed. Most of them have not undergone or failed external validation. However, it is worthy of note that the Fetal Medicine Foundation (FMF) first trimester prediction model (namely the triple test), which consists of a combination of maternal factors and measurements of mean arterial pressure, uterine artery pulsatility index, and serum placental growth factor, has undergone successful internal and external validation. The FMF triple test has detection rates of 90% and 75% for the prediction of early and preterm preeclampsia, respectively, with a 10% false-positive rate. Such performance of screening is superior to that of the traditional method by maternal risk factors alone. The use of the FMF prediction model, followed by the administration of low-dose aspirin, has been shown to reduce the rate of preterm preeclampsia by 62%. The number needed to screen to prevent 1 case of preterm preeclampsia by the FMF triple test is 250. The key to maintaining optimal screening performance is to establish standardized protocols for biomarker measurements and regular biomarker quality assessment, as inaccurate measurement can affect screening performance. Tools frequently used to assess quality control include the cumulative sum and target plot. Cumulative sum is a sensitive method to detect small shifts over time, and point of shift can be easily identified. Target plot is a tool to evaluate deviation from the expected multiple of median and the expected median of standard deviation. Target plot is easy to interpret and visualize. However, it is insensitive to detecting small deviations. Adherence to well-defined protocols for the measurements of mean arterial pressure, uterine artery pulsatility index, and placental growth factor is required. This article summarizes the existing literature on the different methods, recommendations by professional organizations, quality assessment of different components of risk assessment, and clinical implementation of the first trimester screening for preeclampsia.

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

OBJECTIVES

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

STUDY DESIGN

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

MAIN OUTCOME MEASURES

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

RESULTS

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

CONCLUSIONS

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

Distinct placental molecular processes associated with early-onset and late-onset preeclampsia

Background: Patients with preeclampsia display a spectrum of onset time and severity of clinical presentation, yet the underlying molecular bases for the early-onset and late-onset clinical subtypes are not known. Although several transcriptome studies have been done on placentae from PE patients, only a small number of differentially expressed genes have been identified due to very small sample sizes and no distinguishing of clinical subtypes. Methods: We carried out RNA-seq on 65 high-quality placenta samples, including 33 from 30 patients and 32 from 30 control subjects, to search for dysregulated genes and the molecular network and pathways they are involved in. Results: We identified two functionally distinct sets of dysregulated genes in the two major subtypes: 2,977 differentially expressed genes in early-onset severe preeclampsia, which are enriched with metabolism-related pathways, notably transporter functions; and 375 differentially expressed genes in late-onset severe preeclampsia, which are enriched with immune-related pathways. We also identified some key transcription factors, which may drive the widespread gene dysregulation in both early-onset and late-onset patients. Conclusion: These results suggest that early-onset and late-onset severe preeclampsia have different molecular mechanisms, whereas the late-onset mild preeclampsia may have no placenta-specific causal factors. A few regulators may be the key drivers of the dysregulated molecular pathways.

Metabolomics for prediction of hypertension in pregnancy: a systematic review and meta-analysis protocol

INTRODUCTION

Hypertension is a very important cause of maternal morbidity and mortality worldwide, despite efforts on prevention. The lack of a tool to provide effective and early prediction of hypertension for a high-risk group may contribute to improving maternal and fetal outcomes. Metabolomics has figured out as a promised technology to contribute to the improvement of hypertension in pregnancy prediction.

METHODS AND ANALYSIS

Our primary outcome is hypertensive disorders of pregnancy. A detailed systematic literature search will be performed in electronic databases PubMed, EMBASE, Scopus, Web of Science, Latin America and Caribbean Health Sciences Literature, Scientific Electronic Library Online, Health Technology Assessment and Database of Abstracts of Reviews of Effects using controlled terms 'pre-eclampsia', 'hypertensive disorders', 'metabolomics' and 'prediction' (and their variations). Studies from the latest 20 years will be included, except case reports, reviews, cross-sectional studies, letter to editors, expert opinions, commentaries papers or non-human research. If possible, we will perform a meta-analysis. Two peer-reviewers will independently perform the search and in cases of discordance, a third reviewer will be consulted.

ETHICS AND DISSEMINATION

As a systematic review, ethics approval is not required. The results of this review will present the current use and performance of metabolomics for predicting gestational hypertension. Such data could potentially guide future studies and interventions to improve existing prediction models.

PROSPERO REGISTRATION NUMBER

CRD42018097409.

Late first trimester circulating microparticle proteins predict the risk of preeclampsia < 35 weeks and suggest phenotypic differences among affected cases

We hypothesize that first trimester circulating micro particle (CMP) proteins will define preeclampsia risk while identifying clusters of disease subtypes among cases. We performed a nested case–control analysis among women with and without preeclampsia. Cases diagnosed < 34 weeks’ gestation were matched to controls. Plasma CMPs were isolated via size exclusion chromatography and analyzed using global proteome profiling based on HRAM mass spectrometry. Logistic models then determined feature selection with best performing models determined by cross-validation. K-means clustering examined cases for phenotypic subtypes and biological pathway enrichment was examined. Our results indicated that the proteins distinguishing cases from controls were enriched in biological pathways involved in blood coagulation, hemostasis and tissue repair. A panel consisting of C1RL, GP1BA, VTNC, and ZA2G demonstrated the best distinguishing performance (AUC of 0.79). Among the cases of preeclampsia, two phenotypic sub clusters distinguished cases; one enriched for platelet degranulation and blood coagulation pathways and the other for complement and immune response-associated pathways (corrected p < 0.001). Significantly, the second of the two clusters demonstrated lower gestational age at delivery (p = 0.049), increased protein excretion (p = 0.01), more extreme laboratory derangement (p < 0.0001) and marginally increased diastolic pressure (p = 0.09). We conclude that CMP-associated proteins at 12 weeks’ gestation predict the overall risk of developing early preeclampsia and indicate distinct subtypes of pathophysiology and clinical morbidity.

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.

Systematic review of prediction models for gestational hypertension and preeclampsia

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

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

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.

The role of biomarkers in predicting pre-eclampsia in high-risk women

Background

There are limited data on performance of biomarkers to predict pre-eclampsia (PE) in high-risk women. This study investigated the ability of FABP4, PAPP-A, PlGF, sFlt-1 and sEng to predict PE in a high-risk group.

Methods

Non-fasting samples were analysed at 11 + 0–13 + 6 (V1) and 19 + 0–21 + 6 weeks (V2) ( n = 195). Logistic regression models were determined. Area under (AUC) the receiver operating characteristic (ROC) curve analysis was performed. The added value of biomarkers to clinical characteristics for PE prediction was quantified using integrated discrimination improvement (IDI) and net reclassification improvement (NRI) indices.

Results

Prevalence of PE was 12%. Lower concentrations of sFlt-1:PlGF (V1) and PlGF and PlGF:sEng (V2) were seen in women who developed PE. Controlling for baseline characteristics (V1), a doubling of sFlt-1 (pg/mL) (median 896.0, IQR 725.5–1097.0) and sFlt-1:PlGF (median 21.2, IQR 14.7–32.3) was associated with reduction in odds of PE (OR 0.20, 95% CI 0.06–0.65, P = 0.007 and OR 0.48, 95% CI 0.25–0.92, P = 0.04). Addition of sFlt-1 and sFlt-1:PlGF to baseline characteristics non-significantly improved AUC (0.74) (AUC 0.77, P = 0.40 and 0.76, P = 0.39). NRI and IDI analyses confirmed added clinical utility of sFlt-1 (NRI = 0.539, P = 0.01 and IDI = 0.052, P = 0.03). In V2, doubling of PlGF:sEng (median 71.9, IQR 47.0–102.8) was associated with reduction in the risk of PE (OR 0.56, 95% CI 0.35–0.98, P = 0.04). The addition of PlGF:sEng to baseline characteristics non-significantly improved AUC from 0.78 to 0.82 ( P = 0.25) and improved reclassification of cases (NRI = 0.682, P = 0.002).

Conclusions

Screening tests incorporating first trimester sFlt-1 and second trimester PlGF:sEng have potential to aid PE prediction in high-risk pregnancies.

The prediction of early preeclampsia: Results from a longitudinal proteomics study

OBJECTIVES

To identify maternal plasma protein markers for early preeclampsia (delivery <34 weeks of gestation) and to determine whether the prediction performance is affected by disease severity and presence of placental lesions consistent with maternal vascular malperfusion (MVM) among cases.

STUDY DESIGN

This longitudinal case-control study included 90 patients with a normal pregnancy and 33 patients with early preeclampsia. Two to six maternal plasma samples were collected throughout gestation from each woman. The abundance of 1,125 proteins was measured using high-affinity aptamer-based proteomic assays, and data were modeled using linear mixed-effects models. After data transformation into multiples of the mean values for gestational age, parsimonious linear discriminant analysis risk models were fit for each gestational-age interval (8-16, 16.1-22, 22.1-28, 28.1-32 weeks). Proteomic profiles of early preeclampsia cases were also compared to those of a combined set of controls and late preeclampsia cases (n = 76) reported previously. Prediction performance was estimated via bootstrap.

RESULTS

We found that 1) multi-protein models at 16.1-22 weeks of gestation predicted early preeclampsia with a sensitivity of 71% at a false-positive rate (FPR) of 10%. High abundance of matrix metalloproteinase-7 and glycoprotein IIbIIIa complex were the most reliable predictors at this gestational age; 2) at 22.1-28 weeks of gestation, lower abundance of placental growth factor (PlGF) and vascular endothelial growth factor A, isoform 121 (VEGF-121), as well as elevated sialic acid binding immunoglobulin-like lectin 6 (siglec-6) and activin-A, were the best predictors of the subsequent development of early preeclampsia (81% sensitivity, FPR = 10%); 3) at 28.1-32 weeks of gestation, the sensitivity of multi-protein models was 85% (FPR = 10%) with the best predictors being activated leukocyte cell adhesion molecule, siglec-6, and VEGF-121; 4) the increase in siglec-6, activin-A, and VEGF-121 at 22.1-28 weeks of gestation differentiated women who subsequently developed early preeclampsia from those who had a normal pregnancy or developed late preeclampsia (sensitivity 77%, FPR = 10%); 5) the sensitivity of risk models was higher for early preeclampsia with placental MVM lesions than for the entire early preeclampsia group (90% versus 71% at 16.1-22 weeks; 87% versus 81% at 22.1-28 weeks; and 90% versus 85% at 28.1-32 weeks, all FPR = 10%); and 6) the sensitivity of prediction models was higher for severe early preeclampsia than for the entire early preeclampsia group (84% versus 71% at 16.1-22 weeks).

CONCLUSION

We have presented herein a catalogue of proteome changes in maternal plasma proteome that precede the diagnosis of preeclampsia and can distinguish among early and late phenotypes. The sensitivity of maternal plasma protein models for early preeclampsia is higher in women with underlying vascular placental disease and in those with a severe phenotype.

Omega-3 fatty acids differentially influences embryotoxicity in subtypes of preeclampsia

Background: Early (EOP) and late onset (LOP) preeclampsia are two subtypes of preeclampsia. This study examines the effect of maternal omega-3 fatty acids and vitamin E supplementation in a rat model of preeclampsia.Method: Pregnant Wistar rats were assigned to control; EOP; LOP; EOP+omega-3 fatty acid supplementation+vitamin E and LOP+omega-3 fatty acid supplementation+vitamin E. L-Nitroarginine methylester was used to induce preeclampsia. Blood Pressure (BP) was recorded during pregnancy and dams were dissected at d14 and d20 of gestation.Results: Animals from EOP and LOP groups demonstrated higher systolic and diastolic BP, lower weight gain, lower conceptuses size, lower conceptuses weight and fetal weight as compared to control. EOP and LOP groups showed higher percentage of fetal resorptions and embryotoxicity (deformities and hematomas).Conclusion: Supplementation reduced the diastolic BP, percentage of resorptions and embryotoxicity only in the LOP group, suggesting a need for differential supplementation regime for the two subtypes of preeclampsia.

Using proteomics to advance the search for potential biomarkers for preeclampsia: A systematic review and meta-analysis

BACKGROUND

Preeclampsia (PE) is a leading cause of maternal and perinatal morbidity and mortality worldwide. Although predictive multiparametric screening is being developed, it is not applicable to nulliparous women, and is not applied to low-risk women. As PE is considered a heterogenous disorder, it is unlikely that any single multiparametric screening protocol containing a small group of biomarkers could have the required accuracy to predict all PE subgroups. Given the etiology of PE is complex and not fully understood, it begs the question, whether the search for biomarkers based on the predominant view of impaired placentation involving factors predominately implicated in angiogenesis and inflammation, has been too limiting. Here we highlight the enormous potential of state-of-the-art, high-throughput proteomics, to provide a comprehensive and unbiased approach to biomarker identification.

METHODS AND FINDINGS

Our literature search identified 1336 articles; after review, 45 studies with proteomic data from PE women that were eligible for inclusion. From 710 proteins with altered abundance, we identified 13 common circulating proteins, some of which had not been previously considered as prospective biomarkers of PE. An additional search of the literature for original publications testing any of the 13 common proteins using non-proteomic techniques was also undertaken. Strikingly, 9 of these common proteins had been independently evaluated in PE studies as potential biomarkers.

CONCLUSION

This study highlights the potential of using high-throughput data sets, which are comprehensive and without bias, to identify a profile of proteins that may improve predictions of PE and understanding of its etiology. We bring to the attention of the medical and research communities that the strengths and advantages of using data from high-throughput studies for biomarker discovery would be increased dramatically, if first and second trimester samples were collected for proteomics, and if standardized guidelines for patient reporting and data collection were implemented.

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.

[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.

Comparative Study of Abdominal Versus Transvaginal Ultrasound for Uterine Artery Doppler Velocimetry at 11 to 13 Weeks

OBJECTIVES

To compare the first-trimester uterine artery pulsatility index (PI) measured by abdominal and transvaginal ultrasound (US).

METHODS

We performed a prospective study of singleton pregnant women recruited at 11 to 13 weeks' gestation. The mean uterine artery PI was obtained by abdominal followed by transvaginal US. The mean of the left and right uterine artery PIs was used, and differences between approaches were computed. The intraclass correlation coefficient and a Bland-Altman plot were used to compare the two approaches.

RESULTS

Data were available for 940 participants, including 928 (99%) with uterine artery PIs obtained on both uterine sides. The mean uterine artery PI decreased with gestational age in both approaches (P < .001). We observed a moderate correlation between abdominal and transvaginal mean uterine artery PIs (intraclass correlation coefficient, 0.72; 95% confidence interval, 0.69 to 0.75). Values obtained by abdominal US (median, 1.70, interquartile range, 1.35 to 2.09) were greater than those obtained by transvaginal US (median, 1.65; interquartile range, 1.37 to 1.99). There was a significant increase in differences as average measurements became higher (P < .01).

CONCLUSIONS

The first-trimester mean uterine artery PI decreases with gestational age in both approaches. Abdominal US could be associated with greater uterine artery PI values than transvaginal US, especially at higher measurements. The first-trimester uterine artery PI for prediction of adverse perinatal outcomes should be adjusted for gestational age and possibly for the US approach.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Mechanisms of the effect of magnesium salts in preeclampsia

Preeclampsia is a heterogeneous pregnancy-specific syndrome associated with abnormal trophoblast invasion and endothelial dysfunction. Magnesium (Mg²⁺) level may be normal or decreased in women with preeclampsia. However, the use of Mg²⁺ salts, such as Mg²⁺ sulphate, are useful in reducing the pathophysiological consequences of preeclampsia with severe features and eclampsia. Although the mechanism of action of this Mg²⁺ salt is not well understood, the available evidence suggests a beneficial effect of Mg²⁺ for the mother and foetus. The mechanisms include a lower level of soluble fms-like tyrosine kinase 1 and endoglin, blockage of brain N-methyl-D-aspartate receptors, decreased inflammation mediators, activation of nitric oxide synthases, blockage of arginases, and reduced free radicals level. The maintenance of Mg²⁺ homeostasis in pregnancy is crucial for an appropriate pregnancy progression. Oral Mg²⁺ salts can be used for this purpose which could result in mitigating the deleterious consequences of this syndrome to the mother, foetus, and newborn.

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.

Integrated Proteomic and Metabolomic prediction of Term Preeclampsia

Term preeclampsia (tPE), ≥37 weeks, is the most common form of PE and the most difficult to predict. Little is known about its pathogenesis. This study aims to elucidate the pathogenesis and assess early prediction of tPE using serial integrated metabolomic and proteomic systems biology approaches. Serial first- (11-14 weeks) and third-trimester (30-34 weeks) serum samples were analyzed using targeted metabolomic (¹H NMR and DI-LC-MS/MS) and proteomic (MALDI-TOF/TOF-MS) platforms. We analyzed 35 tPE cases and 63 controls. Serial first- (sphingomyelin C18:1 and urea) and third-trimester (hexose and citrate) metabolite screening predicted tPE with an area under the receiver operating characteristic curve (AUC) (95% CI) = 0.817 (0.732-0.902) and a sensitivity of 81.6% and specificity of 71.0%. Serial first [TATA box binding protein-associated factor (TBP)] and third-trimester [Testis-expressed sequence 15 protein (TEX15)] protein biomarkers highly accurately predicted tPE with an AUC (95% CI) of 0.987 (0.961-1.000), sensitivity 100% and specificity 98.4%. Integrated pathway over-representation analysis combining metabolomic and proteomic data revealed significant alterations in signal transduction, G protein coupled receptors, serotonin and glycosaminoglycan metabolisms among others. This is the first report of serial integrated and combined metabolomic and proteomic analysis of tPE. High predictive accuracy and potentially important pathogenic information were achieved.

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.

Pendrin Expression in Preeclampsia: A Prospective Immunohistochemical Staining Study on Placental Bed Biopsies

INTRODUCTION

To assess the pendrin expression density in placental bed biopsies from preeclampsia cases in comparison with healthy term controls.

MATERIAL AND METHODS

A prospective case-control study with 106 placental bed biopsies obtained during cesarean deliveries. Pendrin expression was evaluated by immunohistochemical staining in different hypertensive disorders of pregnancy.

RESULTS

Pendrin immunostaining frequency was higher in the hypertensive disorders group (p: 0.024), which was a result of the high frequency in the early-onset preeclampsia group. Uterine artery pulsatility indices were higher in pendrin positive patients than in the negatives in the case group. Gravidity was not found to affect the pendrin expression frequency in the placental bed.

CONCLUSION

Placental ischemia seems to be an important determinant of pendrin expression in pregnant decidua. Increased pendrin density in early-onset preeclampsia could be a pathogenetic mechanism in or a part of the adaptational response to the development of the hypertension.

External validation of preexisting first trimester preeclampsia prediction models

OBJECTIVE

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

STUDY DESIGN

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

RESULTS

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

CONCLUSION

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

The prediction of late-onset preeclampsia: Results from a longitudinal proteomics study

BACKGROUND

Late-onset preeclampsia is the most prevalent phenotype of this syndrome; nevertheless, only a few biomarkers for its early diagnosis have been reported. We sought to correct this deficiency using a high through-put proteomic platform.

METHODS

A case-control longitudinal study was conducted, including 90 patients with normal pregnancies and 76 patients with late-onset preeclampsia (diagnosed at ≥34 weeks of gestation). Maternal plasma samples were collected throughout gestation (normal pregnancy: 2-6 samples per patient, median of 2; late-onset preeclampsia: 2-6, median of 5). The abundance of 1,125 proteins was measured using an aptamers-based proteomics technique. Protein abundance in normal pregnancies was modeled using linear mixed-effects models to estimate mean abundance as a function of gestational age. Data was then expressed as multiples of-the-mean (MoM) values in normal pregnancies. Multi-marker prediction models were built using data from one of five gestational age intervals (8-16, 16.1-22, 22.1-28, 28.1-32, 32.1-36 weeks of gestation). The predictive performance of the best combination of proteins was compared to placental growth factor (PIGF) using bootstrap.

RESULTS

1) At 8-16 weeks of gestation, the best prediction model included only one protein, matrix metalloproteinase 7 (MMP-7), that had a sensitivity of 69% at a false positive rate (FPR) of 20% (AUC = 0.76); 2) at 16.1-22 weeks of gestation, MMP-7 was the single best predictor of late-onset preeclampsia with a sensitivity of 70% at a FPR of 20% (AUC = 0.82); 3) after 22 weeks of gestation, PlGF was the best predictor of late-onset preeclampsia, identifying 1/3 to 1/2 of the patients destined to develop this syndrome (FPR = 20%); 4) 36 proteins were associated with late-onset preeclampsia in at least one interval of gestation (after adjustment for covariates); 5) several biological processes, such as positive regulation of vascular endothelial growth factor receptor signaling pathway, were perturbed; and 6) from 22.1 weeks of gestation onward, the set of proteins most predictive of severe preeclampsia was different from the set most predictive of the mild form of this syndrome.

CONCLUSIONS

Elevated MMP-7 early in gestation (8-22 weeks) and low PlGF later in gestation (after 22 weeks) are the strongest predictors for the subsequent development of late-onset preeclampsia, suggesting that the optimal identification of patients at risk may involve a two-step diagnostic process.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Renal Interlobar Vein Impedance Index as a First-Trimester Marker Does Not Predict Hypertensive Disorders of Pregnancy

OBJECTIVES

The purpose of this study was to examine whether the maternal renal interlobar vein impedance index as assessed by first-trimester sonography is able to predict the later development of hypertensive disorders of pregnancy.

METHODS

Venous Doppler parameters of both maternal kidneys were studied in 214 pregnant women at gestational ages of 11 weeks to 13 weeks 6 days. Patients were classified according to outcomes related to hypertensive disorders. Detection rates and areas under receiver operating characteristic curves were determined for the maternal renal interlobar vein impedance index as a first-trimester predictor of preeclampsia and gestational hypertension.

RESULTS

Among the 214 patients, 22 (10.3%) developed preeclampsia; 10 (4.7%) developed gestational hypertension; and 182 were unaffected by hypertensive disorders (controls; 85.0%). In the overall study population, there was no difference in the impedance index between the right (0.44; 95% confidence interval, 0.35-0.50) and left (0.43; 95% confidence interval, 0.35-0.53) sides (P = .86). The average impedance index did not differ among women destined to develop preeclampsia (0.46; 95% confidence interval, 0.38-0.57), gestational hypertension (0.39; 95% confidence interval, 0.33-0.46), or pregnancies uncomplicated by hypertensive disease (0.42; 95% confidence interval, 0.37-0.50; P = .15). Low detection rates and the area under the curve analysis demonstrated that the impedance index was not predictive of hypertensive disorders of pregnancy.

CONCLUSIONS

The maternal renal interlobar vein impedance index should not be considered a first-trimester marker of hypertensive disorders of pregnancy.

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

Background

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

Methods

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

Results

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

Conclusions

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

Hypoxic Preconditioning Augments the Therapeutic Efficacy of Bone Marrow Stromal Cells in a Rat Ischemic Stroke Model

Transplantation of bone marrow stromal cells (BMSCs) is a promising therapy for ischemic stroke, but the poor oxygen environment in brain lesions limits the efficacy of cell-based therapies. Here, we tested whether hypoxic preconditioning (HP) could augment the efficacy of BMSC transplantation in a rat ischemic stroke model and investigated the underlying mechanism of the effect of HP. In vitro, BMSCs were divided into five passage (P0, P1, P2, P3, and P4) groups, and HP was applied to the groups by incubating the cells with 1% oxygen for 0, 4, 8, 12, and 24 h, respectively. We demonstrated that the expression of hypoxia-inducible factor-1α (HIF-1α) was increased in the HP-treated BMSCs, while their viability was unchanged. We also found that HP decreased the apoptosis of BMSCs during subsequent simulated ischemia-reperfusion (I/R) injury, especially in the 8-h HP group. In vivo, a rat transient focal cerebral ischemia model was established. These rats were administered normal cultured BMSCs (N-BMSCs), HP-treated BMSCs (H-BMSCs), or DMEM cell culture medium (control) at 24 h after the ischemic insult. Compared with the DMEM control group, the two BMSC-transplanted groups exhibited significantly improved functional recovery and reduced infarct volume, especially the H-BMSC group. Moreover, HP decreased neuronal apoptosis and enhanced the expression of BDNF and VEGF in the ischemic brain. Survival and differentiation of transplanted BMSCs were also increased by HP, and the quantity of engrafted BMSCs was significantly correlated with neurological function improvement. These results suggest that HP may enhance the therapeutic efficacy of BMSCs in an ischemic stroke model. The underlying mechanism likely involves the inhibition of caspase-3 activation and an increasing expression of HIF-1α, which promotes angiogenesis and neurogenesis and thereby reduces neuronal death and improves neurological function.

First trimester screening for pre-eclampsia

The commercial availability of tests in the first trimester of pregnancy that predict the later development of pre-eclampsia has prompted considerable debate regarding their clinical utility and the degree to which they fulfil the longstanding principles of screening. Such tests have been shown to achieve detection rates for early pre-eclampsia (requiring delivery prior to 34 weeks) of over 90%, for a false positive rate of 10%. However, their capacity to predict later onset pre-eclampsia, which accounts for the bulk of the disease burden, is much more limited. The relatively few studies validating the performance of these tests in different populations have demonstrated significant variations in performance. Moreover, prospective research confirming that the administration of aspirin to those screened to be high risk reduces the incidence of pre-eclampsia is yet to be completed, and there may be harms in restricting aspirin therapy to this group, given its broader beneficial effect. In light of these limitations, further development of these tests is recommended prior to their introduction to clinical practice.

Angiogenic balance (sFlt-1/PlGF) and preeclampsia

Preeclampsia is a hypertensive disorder of pregnancy associated with important maternal and perinatal mortality and morbidity. Although symptomatic management has improved, there is currently no curative treatment, and only childbirth and delivery of the placenta, usually prematurely, alleviate the mother's symptoms. Placental insufficiency plays a central role in the pathophysiology of preeclampsia. Abnormal placentation during the first trimester leads to defective remodeling of the uterine vascularization. This results progressively in placental hypoperfusion, which induces trophoblast dysfunction and the release in maternal circulation of trophoblastic factors leading to an excessive inflammatory response, endothelial dysfunction and glomerular damage. Among these factors, the most important is sFlt-1, which is a soluble form of the VEGF and PlGF receptor. sFlt-1 binds to free VEGF and PlGF in the maternal circulation, thus reducing their bioavailability for their membrane receptor. The result is inhibition of the effects of VEGF and PlGF on maternal endothelial cells and podocytes. The sFlt-1/PlGF ratio reflects the circulating angiogenic balance and is correlated with severity of the disease.

Fetal hemoglobin, α1-microglobulin and hemopexin are potential predictive first trimester biomarkers for preeclampsia

OBJECTIVE

Overproduction of cell-free fetal hemoglobin (HbF) in the preeclamptic placenta has been recently implicated as a new etiological factor of preeclampsia. In this study, maternal serum levels of HbF and the endogenous hemoglobin/heme scavenging systems were evaluated as predictive biomarkers for preeclampsia in combination with uterine artery Doppler ultrasound.

STUDY DESIGN

Case-control study including 433 women in early pregnancy (mean 13.7weeks of gestation) of which 86 subsequently developed preeclampsia. The serum concentrations of HbF, total cell-free hemoglobin, hemopexin, haptoglobin and α1-microglobulin were measured in maternal serum. All patients were examined with uterine artery Doppler ultrasound. Logistic regression models were developed, which included the biomarkers, ultrasound indices, and maternal risk factors.

RESULTS

There were significantly higher serum concentrations of HbF and α1-microglobulin and significantly lower serum concentrations of hemopexin in patients who later developed preeclampsia. The uterine artery Doppler ultrasound results showed significantly higher pulsatility index values in the preeclampsia group. The optimal prediction model was obtained by combining HbF, α1-microglobulin and hemopexin in combination with the maternal characteristics parity, diabetes and pre-pregnancy hypertension. The optimal sensitivity for all preeclampsia was 60% at 95% specificity.

CONCLUSIONS

Overproduction of placentally derived HbF and depletion of hemoglobin/heme scavenging mechanisms are involved in the pathogenesis of preeclampsia. The combination of HbF and α1-microglobulin and/or hemopexin may serve as a prediction model for preeclampsia in combination with maternal risk factors and/or uterine artery Doppler ultrasound.

Role of Extracellular Vesicles and microRNAs on Dysfunctional Angiogenesis during Preeclamptic Pregnancies

Preeclampsia is a syndrome characterized by hypertension during pregnancy, which is a leading cause of morbidity and mortality in both mother and newborn in developing countries. Some advances have increased the understanding of pathophysiology of this disease. For example, reduced utero-placental blood flow associated with impaired trophoblast invasion may lead to a hypoxic placenta that releases harmful materials into the maternal and feto-placental circulation and impairs endothelial function. Identification of these harmful materials is one of the hot topics in the literature, since these provide potential biomarkers. Certainty, such knowledge will help us to understand the miscommunication between mother and fetus. In this review we highlight how placental extracellular vesicles and their cargo, such as small RNAs (i.e., microRNAs), might be involved in endothelial dysfunction, and then in the angiogenesis process, during preeclampsia. Currently only a few reports have addressed the potential role of endothelial regulatory miRNA in the impaired angiogenesis in preeclampsia. One of the main limitations in this area is the variability of the analyses performed in the current literature. This includes variability in the size of the particles analyzed, and broad variation in the exosomes considered. The quantity of microRNA targets genes suggest that practically all endothelial cell metabolic functions might be impaired. More studies are required to investigate mechanisms underlying miRNA released from placenta upon endothelial function involved in the angiogenenic process.