First trimester screening for early and late preeclampsia based on maternal characteristics, biophysical parameters, and angiogenic factors

Comparison of different methods of first-trimester screening for preterm pre-eclampsia: cohort study

OBJECTIVE

To compare the predictive performance of three different mathematical models for first-trimester screening of pre-eclampsia (PE), which combine maternal risk factors with mean arterial pressure (MAP), uterine artery pulsatility index (UtA-PI) and serum placental growth factor (PlGF), and two risk-scoring systems.

METHODS

This was a prospective cohort study performed in eight fetal medicine units in five different regions of Spain between September 2017 and December 2019. All pregnant women with singleton pregnancy and a non-malformed live fetus attending their routine ultrasound examination at 11 + 0 to 13 + 6 weeks' gestation were invited to participate in the study. Maternal characteristics and medical history were recorded and measurements of MAP, UtA-PI, serum PlGF and pregnancy-associated plasma protein-A (PAPP-A) were converted into multiples of the median (MoM). Risks for term PE, preterm PE (< 37 weeks' gestation) and early PE (< 34 weeks' gestation) were calculated according to the FMF competing-risks model, the Crovetto et al. logistic regression model and the Serra et al. Gaussian model. PE classification was also performed based on the recommendations of the National Institute for Health and Care Excellence (NICE) and the American College of Obstetricians and Gynecologists (ACOG). We estimated detection rates (DR) with their 95% CIs at a fixed 10% screen-positive rate (SPR), as well as the area under the receiver-operating-characteristics curve (AUC) for preterm PE, early PE and all PE for the three mathematical models. For the scoring systems, we calculated DR and SPR. Risk calibration was also assessed.

RESULTS

The study population comprised 10 110 singleton pregnancies, including 32 (0.3%) that developed early PE, 72 (0.7%) that developed preterm PE and 230 (2.3%) with any PE. At a fixed 10% SPR, the FMF, Crovetto et al. and Serra et al. models detected 82.7% (95% CI, 69.6-95.8%), 73.8% (95% CI, 58.7-88.9%) and 79.8% (95% CI, 66.1-93.5%) of early PE; 72.7% (95% CI, 62.9-82.6%), 69.2% (95% CI, 58.8-79.6%) and 74.1% (95% CI, 64.2-83.9%) of preterm PE; and 55.1% (95% CI, 48.8-61.4%), 47.1% (95% CI, 40.6-53.5%) and 53.9% (95% CI, 47.4-60.4%) of all PE, respectively. The best correlation between predicted and observed cases was achieved by the FMF model, with an AUC of 0.911 (95% CI, 0.879-0.943), a slope of 0.983 (95% CI, 0.846-1.120) and an intercept of 0.154 (95% CI, -0.091 to 0.397). The NICE criteria identified 46.7% (95% CI, 35.3-58.0%) of preterm PE at 11% SPR and ACOG criteria identified 65.9% (95% CI, 55.4-76.4%) of preterm PE at 33.8% SPR.

CONCLUSIONS

The best performance of screening for preterm PE is achieved by mathematical models that combine maternal factors with MAP, UtA-PI and PlGF, as compared to risk-scoring systems such as those of NICE and ACOG. While all three algorithms show similar results in terms of overall prediction, the FMF model showed the best performance at an individual level. © 2024 International Society of Ultrasound in Obstetrics and Gynecology.

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.

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.

Predictive value of the sFlt‑1/PlGF ratio in women with suspected preeclampsia: An update (Review)

Preeclampsia (PE) is a major complication of pregnancy with an incidence rate of 2‑8% and is a leading cause of maternal mortality and morbidity. The various consequences of severe preeclampsia for the fetus, neonate and child include intrauterine growth retardation (IUGR), fetal hypoxia, oligohydramnios, intrauterine fetal demise, increased perinatal mortality and morbidity, neurodevelopmental disorders and even irreversible brain damage (cerebral palsy). A number of studies have demonstrated that differences in maternal serum concentrations of angiogenic factors between preeclampsia and normotensive pregnancies can be used as biomarkers, either alone or in combination with other markers, to predict the development of PE. The presence in the maternal circulation of two proteins of placental origin, placental growth factor (PlGF) and soluble fms‑like tyrosine kinase 1 (sFlt‑1), has been shown to be of clinical value, as the sFlt‑1/PlGF ratio appears to be the optimal predictive tool for the development of PE. The measurement of their concentration in maternal serum in screening models, serves as predictive marker for the development of PE or IUGR later in gestation. However, further research is required to improve its clinical applicability and provide guidelines for its use worldwide to achieve more consistent clinical management of women with PE.

Machine-learning predictive model of pregnancy-induced hypertension in the first trimester

In the first trimester of pregnancy, accurately predicting the occurrence of pregnancy-induced hypertension (PIH) is important for both identifying high-risk women and adopting early intervention. In this study, we used four machine-learning models (LASSO logistic regression, random forest, backpropagation neural network, and support vector machines) to predict the occurrence of PIH in a prospective cohort. Candidate features for predicting the occurrence of middle and late PIH were acquired using a LASSO algorithm. The performance of predictive models was assessed using receiver operating characteristic analysis. Finally, a nomogram was established with the model scores, age, and nulliparity. Calibration, clinical usefulness, and internal validation were used to assess the performance of the nomogram. In the training set (2258 pregnant women), eleven candidate factors in the first trimester were significantly associated with the occurrence of PIH (P < 0.001 in the training set). Four models showed AUCs from 0.780 to 0.816 in the training set. For the validation set (939 pregnant women), AUCs varied from 0.516 to 0.795. The nomogram showed good discrimination, with an AUC of 0.847 (95% CI: 0.805-0.889) in the training set and 0.753 (95% CI: 0.653-0.853) in the validation set. Decision curve analysis suggested that the model was clinically useful. The model developed using LASSO logistic regression achieved the best performance in predicting the occurrence of PIH. The derived nomogram, which incorporates the model score and maternal risk factors, can be used to predict PIH in clinical practice. We develop a model with good performance for clinical prediction of PIH in the first trimester.

A Balancing Act: Navigating Hypertensive Disorders of Pregnancy at Very Advanced Maternal Age, from Preconception to Postpartum

The decision to postpone parenting has gained momentum in recent years, a shift driven by evolving social dynamics and improved access to fertility treatments. Despite their increasing prevalence, pregnancies at advanced maternal ages are associated with increased risks of adverse maternal and neonatal outcomes. This article addresses the association between advanced maternal age and hypertensive disorders of pregnancies (HDPs), which are more prevalent and a significant cause of maternal morbidity and mortality in this population. This review explores the biological mechanisms and age-related risk factors that underpin this increased susceptibility and offers practical management strategies that can be implemented prior to, as well as during, each stage of pregnancy to mitigate the incidence and severity of HDPs in this group. Lastly, this review acknowledges both the short-term and long-term postpartum implications of HDPs in women of advanced maternal age.

Lack of an association between first-trimester concentration of mid-regional pro-atrial natriuretic peptide and risk of early-onset preeclampsia <34 weeks' gestation

AIM

We examined the heart failure biomarker mid-regional pro-atrial natriuretic peptide during the first trimester of pregnancy in relation to early-onset preeclampsia <34 weeks.

MATERIALS AND METHODS

This case-control study included 34 women with singleton pregnancies with a preeclampsia diagnosis and delivery before 34 weeks of gestation who had attended the routine first-trimester ultrasound scan at 11-13+6 weeks of gestation between August 2010 and October 2015 at the Copenhagen University Hospital Rigshospitalet, Denmark, and 91 uncomplicated singleton pregnancies matched by time of the routine first-trimester blood sampling at 8-13+6 weeks. Descriptive statistical analyses were performed for maternal characteristics and obstetric and medical history for the case versus the control group. Concentrations of mid-regional pro-atrial natriuretic peptide, placental growth factor, soluble fms-like tyrosine kinase-1, and pregnancy-associated plasma protein A between early-onset preeclampsia cases and the control group were compared using Students t-test and the Mann-Whitney U test. Biochemical marker concentrations were converted into multiples of the expected median values after adjustment for gestational age.

RESULTS

Mid-regional pro-atrial natriuretic peptide levels were not significantly different between early-onset preeclampsia cases and the control group in the first trimester of pregnancy. As expected, both placental growth factor and pregnancy-associated plasma protein A levels were significantly lower in early-onset preeclampsia, whereas soluble fms-like tyrosine kinase-1 levels were not statistically significantly different.

CONCLUSION

The maternal first-trimester concentration of mid-regional pro-atrial natriuretic peptide, a peptide with multiple biological functions including a relation to cardiovascular disease, was not significantly different in women with early-onset preeclampsia.

Preeclampsia at term can be classified into 2 clusters with different clinical characteristics and outcomes based on angiogenic biomarkers in maternal blood

BACKGROUND

An antiangiogenic state has emerged as a mechanism of disease in preeclampsia. Angiogenic biomarkers are used in the risk assessment of this syndrome, particularly of early disease. The role of an antiangiogenic state in late preeclampsia is unclear.

OBJECTIVE

This study aimed to determine the prevalence, characteristics, and clinical significance of angiogenic/antiangiogenic factor abnormalities in women with preeclampsia stratified according to gestational age at delivery.

STUDY DESIGN

Two studies were conducted: (1) a longitudinal nested case-control study comprising women with preeclampsia (n=151) and a control group (n=540); and (2) a case series of patients with preeclampsia (n=452). In patients with preeclampsia, blood was collected at the time of diagnosis. Plasma concentrations of placental growth factor and soluble fms-like tyrosine kinase-1 were determined by enzyme-linked immunosorbent assays. An abnormal angiogenic profile was defined as a plasma ratio of placental growth factor and soluble fms-like tyrosine kinase-1 expressed as a multiple of the median <10th percentile for gestational age based on values derived from the longitudinal study. The proportion of patients diagnosed with preeclampsia who had an abnormal angiogenic profile was determined in the case-series participants and stratified by gestational age at delivery into early (≤34 weeks), intermediate (34.1-36.9 weeks), and term (≥37 weeks) preeclampsia. The demographics, clinical characteristics, and pregnancy outcomes of women with preeclampsia with and without an abnormal angiogenic profile were compared.

RESULTS

The prevalence of an abnormal angiogenic profile was higher in preterm than in term preeclampsia (for early, intermediate, and term in the case-control study: 90%, 100%, and 39%; for the case series: 98%, 80%, and 55%, respectively). Women with preeclampsia at term who had an abnormal angiogenic profile were more frequently nulliparous (57% vs 35%), less likely to smoke (14% vs 26%), at greater risk for maternal (14% vs 5%) or neonatal (7% vs 1%) complications, and more often had placental lesions consistent with maternal vascular malperfusion (42% vs 23%; all, P<.05) than those without an abnormal profile. Women with preeclampsia at term who had a normal angiogenic profile had a higher frequency of chronic hypertension (36% vs 21%) and were more likely to have class ≥2 obesity (41% vs 23%) than those with an abnormal profile (both, P<.05).

CONCLUSION

Patients with early preeclampsia had an abnormal angiogenic profile in virtually all cases, whereas only 50% of women with preeclampsia at term had such abnormalities. The profile of angiogenic biomarkers can be used to classify patients with preeclampsia at term, on the basis of mechanisms of disease, into 2 clusters, which have different demographics, clinical characteristics, and risks of adverse maternal and neonatal outcomes. These findings provide a simple approach to classify preeclampsia at term and have implications for future clinical care and research.

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.

Perspectives on the Use of Placental Growth Factor (PlGF) in the Prediction and Diagnosis of Pre-Eclampsia: Recent Insights and Future Steps

Pre-eclampsia (PE) is a complex multisystem disease of pregnancy that is becoming increasingly recognized as a state of angiogenic imbalance characterized by low concentrations of placental growth factor (PlGF) and elevated soluble fms-like tyrosine kinase (sFlt-1). PlGF is a protein highly expressed by the placenta with vasculogenic and angiogenic properties, which has a central role in spiral artery remodeling and the development of a low-resistance placental capillary network. PlGF concentrations are significantly lower in women with preterm PE, and these reduced levels have been shown to precede the clinical onset of disease. Subsequently, the clinical utility of maternal serum PlGF has been extensively studied in singleton gestations from as early as 11 to 13 weeks' gestation, utilizing a validated multimarker prediction model, which performs superiorly to the National Institute for Health and Care Excellence (NICE) and American College of Obstetricians and Gynecologists (ACOG) guidelines in the detection of preterm PE. There is extensive research highlighting the role of PlGF-based testing utilizing commercially available assays in accelerating the diagnosis of PE in symptomatic women over 20 weeks' gestation and predicting time-to-delivery, allowing individualized risk stratification and appropriate antenatal surveillance to be determined. "Real-world" data has shown that interpretation of PlGF-based test results can aid clinicians in improving maternal outcomes and a growing body of evidence has implied a role for sFlt-1/PlGF in the prognostication of adverse pregnancy and perinatal events. Subsequently, PlGF-based testing is increasingly being implemented into obstetric practice and is advocated by NICE. This literature review aims to provide healthcare professionals with an understanding of the role of angiogenic biomarkers in PE and discuss the evidence for PlGF-based screening and triage. Prospective studies are warranted to explore if its implementation significantly improves perinatal outcomes, explore the value of repeat PlGF testing, and its use in multiple pregnancies.

First trimester serum apolipoproteins in the prediction of late-onset preeclampsia

Late-onset preeclampsia occurring after 34 weeks of gestation is the most common form of preeclampsia, but little is known about either etiology or prevention. Current detection methods for preeclampsia in early pregnancy have not shown promising results in detecting late-onset preeclampsia. The aim of this study was to assess whether apolipoproteins in combination with maternal medical history and biophysical factors can be used as an early detection method for late-onset preeclampsia. This nested case-cohort study was based at Odense University Hospital, Denmark. Women attending their first trimester scan were invited to participate if they understood Danish or English, were above the age of 18, and had singleton pregnancies. Blood pressure, maternal medical history, uterine artery pulsatility indices, and blood samples were collected at inclusion. Outcome data were collected from participants' medical files postpartum, and cases were selected when preeclampsia diagnostics were present. Serum samples were analyzed by targeted mass spectrometry using a biomarker panel consisting of 12 apolipoproteins. Logistic regression analyses were performed and finally receiver operating curves were completed. The cohort consisted of 27 cases and 194 normotensive controls, randomized from 340 eligible participants. Significant differences were found between the two groups' baseline characteristics but none of the apolipoproteins showed significant difference (p < 0.05). The ROC-curve combining maternal characteristics, mean arterial pressure and two apolipoproteins showed the best sensitivity of 55.5% at a 10% false-positive rate and an area under the curve of 0.873. In conclusion, apolipoproteins did not improve the detection of late-onset preeclampsia in a combined screening model.

Evaluation of placental growth potential and placental bed perfusion by 3D ultrasound for early second-trimester prediction of preeclampsia

PURPOSE

This study aimed to investigate whether placental parameters measured by three-dimensional ultrasound are associated with preeclampsia (PE) and small-for-gestational-age (SGA).

METHODS

In total, 1163 pregnancies at 11-14 weeks of gestation were recruited between October 8, 2020, and April 30, 2021. Placenta volume (PV), placental bed vascularization flow index (PBVFI), and uterine arteries pulse index (UtA-PI) were measured. Placental quotient (PQ = PV/weeks of gestation) was calculated. All participants were re-examined 4 weeks later. The placental volume growth rate (PVGR = placental volume difference between the two examinations/interval days) was also calculated. Patients were divided into four groups by the gestational age at the onset of PE and birth weight: early-onset PE (E-PE, n = 18), late-onset PE (L-PE, n = 36), isolated SGA5 (birth weight less than the fifth percentile for gestational age without PE, n = 9), and unaffected (n = 1100) groups.

RESULTS

A predictive model for E-PE was established, which consisted of unnatural conception, chronic hypertension, PBVFI (of second examination), and PVGR for E-PE; 94.4% sensitivity and 96.7% specificity by receiver operating characteristic curve analysis.

CONCLUSIONS

Overall, decreased placental growth potential and low placental bed perfusion in the early second trimester have potential in predicting E-PE.

A mathematical model of maternal vascular growth and remodeling and changes in maternal hemodynamics in uncomplicated pregnancy

The maternal vasculature undergoes tremendous growth and remodeling (G&R) that enables a > 15-fold increase in blood flow through the uterine vasculature from conception to term. Hemodynamic metrics (e.g., uterine artery pulsatility index, UA-PI) are useful for the prognosis of pregnancy complications; however, improved characterization of the maternal hemodynamics is necessary to improve prognosis. The goal of this paper is to develop a mathematical framework to characterize maternal vascular G&R and hemodynamics in uncomplicated human pregnancies. A validated 1D model of the human vascular tree from the literature was adapted and inlet blood flow waveforms at the ascending aorta at 4 week increments from 0 to 40 weeks of gestation were prescribed. Peripheral resistances of each terminal vessel were adjusted to achieve target flow rates and mean arterial pressure at each gestational age. Vessel growth was governed by wall shear stress (and axial lengthening in uterine vessels), and changes in vessel distensibility were related to vessel growth. Uterine artery velocity waveforms generated from this model closely resembled ultrasound results from the literature. The literature UA-PI values changed significantly across gestation, increasing in the first month of gestation, then dramatically decreasing from 4 to 20 weeks. Our results captured well the time-course of vessel geometry, material properties, and UA-PI. This 1D fluid-G&R model captured the salient hemodynamic features across a broad range of clinical reports and across gestation for uncomplicated human pregnancy. While results capture available data well, this study highlights significant gaps in available data required to better understand vascular remodeling in pregnancy.

A risk model that combines MAP, PlGF, and PAPP-A in the first trimester of pregnancy to predict hypertensive disorders of pregnancy

Whether the first trimester maternal mean arterial pressure (MAP), placental growth factor (PlGF), and pregnancy-associated plasma protein A (PAPP-A) can predict hypertensive disorders of pregnancy (HDP) is unclear. We conducted a retrospective case-control study with the total population of 539 gravidas, of these 447 had normal pregnancy, 27 had gestational hypertension (GH), 36 had preeclampsia (PE), and 29 had preeclampsia with severe features (SPE). Prediction for HDP was determined by the area under curve (AUC). Compared to the healthy group, the multiple of the median (MoM) for MAP was increased in the study groups, while PlGF and PAPP-A were decreased. When the cutoff values for MAP, PlGF, and PAPP-A were 1.069, 0.769, and 0.673 MoM, respectively, the sensitivities for predicting HDP were 0.517, 0.446, and 0.500 and the specificities were 0.744, 0.826, and 0.769, respectively. To predict GH, the highest AUC was 0.755 (95% CI: 0.655-0.856, p < 0.001) based on MAP, PlGF, and PAPP-A. The combined PlGF and PAPP-A had the highest AUC (0.683 [95% CI: 0.584-0.782, p < 0.001] and 0.755 [95% CI: 0.682-0.829, p < 0.001]) for prediction of PE and SPE. We found that MAP, serum levels of PlGF, and PAPP-A in the first trimester pregnancy are markers that predict HDP in the third trimester. The combination of markers is far superior to single markers alone. To improve the diagnostic value, specific cutoff values should be applied to GH, PE, SPE in each condition.

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.

Cardiac dysfunction and remodeling regulated by anti-angiogenic environment in patients with preeclampsia: the ANGIOCOR prospective cohort study protocol

Background

Cardiovascular diseases (CVD) are cause of increased morbidity and mortality in spite of advances for diagnosis and treatment. Changes during pregnancy affect importantly the maternal CV system. Pregnant women that develop preeclampsia (PE) have higher risk (up to 4 times) of clinical CVD in the short- and long-term. Predominance of an anti-angiogenic environment during pregnancy is known as main cause of PE, but its relationship with CV complications is still under research. We hypothesize that angiogenic factors are associated to maternal cardiac dysfunction/remodeling and that these may be detected by new cardiac biomarkers and maternal echocardiography.

Methods

Prospective cohort study of pregnant women with high-risk of PE in first trimester screening, established diagnosis of PE during gestation, and healthy pregnant women (total intended sample size n = 440). Placental biochemical and biophysical cardiovascular markers will be assessed in the first and third trimesters of pregnancy, along with maternal echocardiographic parameters. Fetal cardiac function at third trimester of pregnancy will be also evaluated and correlated with maternal variables. Maternal cardiac function assessment will be determined 12 months after delivery, and correlation with CV and PE risk variables obtained during pregnancy will be evaluated.

Discussion

The study will contribute to characterize the relationship between anti-angiogenic environment and maternal CV dysfunction/remodeling, during and after pregnancy, as well as its impact on future CVD risk in patients with PE. The ultimate goal is to improve CV health of women with high-risk or previous PE, and thus, reduce the burden of the disease.

Trial registration

NCT04162236

Prospective Evaluation of International Prediction of Pregnancy Complications Collaborative Network Models for Prediction of Preeclampsia: Role of Serum sFlt-1 at 11-13 Weeks' Gestation

The study aimed to investigate whether serum sFlt-1 (soluble fms-like tyrosine kinase-1) at 11-13 weeks' gestation in pregnancies that subsequently developed preeclampsia was different from those without preeclampsia and compare screening performance of the International Prediction of Pregnancy Complications (IPPIC) reported models, which include various combinations of maternal factors, systolic blood pressure, diastolic blood pressure, PlGF (placental growth factor) and sFlt-1 and the competing risk (CR) models, which include various combinations of maternal factors, mean arterial pressure (MAP) and PlGF for predicting any-onset, early-onset, and late-onset preeclampsia. This was a prospective multicenter study in 7877 singleton pregnancies. The differences of the predictive performance between the IPPIC and CR models were compared. There were 141 women (1.79%) who developed preeclampsia, including 13 cases (0.17%) of early-onset preeclampsia and 128 cases (1.62%) of late-onset preeclampsia. In pregnancies that developed preeclampsia compared to unaffected pregnancies, median serum sFlt-1 levels and its MoMs were not significantly different (p>0.05). There was no significant association between gestational age at delivery and log₁₀ sFlt-1 and log₁₀ sFlt-1 MoM (p>0.05). The areas under the curve of CR models were significantly higher than the IPPIC models for the prediction of any-onset and late-onset preeclampsia but not for early-onset preeclampsia. In conclusion, there are no significant differences in the maternal serum sFlt-1 levels at 11-131 weeks' gestation between women who subsequently develop preeclampsia and those who do not. Moreover, the CR models for the prediction of any-onset and late-onset preeclampsia perform better than the IPPIC reported model.

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.

Incidence of pre-eclampsia and other perinatal complications among pregnant women with congenital heart disease: systematic review and meta-analysis

OBJECTIVE

It has been proposed recently that pre-eclampsia (PE) may originate from maternal cardiac maladaptation rather than primary placental insult. As congenital heart disease (CHD) is associated with reduced adaptation to the hemodynamic needs of pregnancy, it is hypothesized that women with CHD have an increased risk of PE. The aim of this systematic review was to investigate the risk of PE in pregnant women with CHD.

METHODS

A systematic search was performed to identify relevant studies published in English, Spanish, French, Italian, Chinese or German, with no time restrictions, using databases such as PubMed, Web of Science and SCOPUS. Randomized controlled trials and observational studies (prospective or retrospective cohorts) of pregnant women with a history of CHD were sought. The main outcome was the incidence of PE (including eclampsia and HELLP syndrome). For quality assessment of the included studies, two reviewers assessed independently the risk of bias. For the meta-analysis, the incidence of PE in pregnancies (those beyond 20 weeks' gestation) was calculated using single-proportion analysis by random-effects modeling (weighted by inverse variance). Heterogeneity between studies was assessed using the χ² (Cochran's Q), tau² and I² statistics. Subgroup analysis was performed, and meta-regression was used to assess the influence of several covariates on the pooled results.

RESULTS

A total of 33 studies were included in the meta-analysis, including 40 449 women with CHD and a total of 40 701 pregnancies. The weighted incidence of PE was 3.1% (95% CI, 2.2-4.0%), with true-effect heterogeneity of 93% according to I² , and no publication bias found. No difference was found in the weighted incidence of PE between studies including cyanotic CHD vs those excluding (or not reporting) cyanotic CHD (2.5% (95% CI, 1.6-3.4%) vs 4.1% (95% CI, 2.4-5.7%); P = 0.0923). Meta-regression analysis showed that the only cofactor that significantly influenced the incidence of PE in each study was the reported incidence of aortic stenosis; studies with a higher incidence of aortic stenosis had a higher incidence of PE (estimate: 0.0005; P = 0.038).

CONCLUSIONS

We failed to demonstrate an incidence of PE above the expected baseline risk in women with CHD. This observation contradicts the theory of the cardiac origin of PE. © 2020 International Society of Ultrasound in Obstetrics and Gynecology.

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.

Predictive Performance of Serum β-hCG MoM Levels for Preeclampsia Screening: A Meta-Analysis

OBJECTIVE

The aim of the present study was to investigate the predictive value of using the multiple of the median (MoM) of β-human chorionic gonadotropin (β-hCG) levels in patients with preeclampsia (PE) and healthy pregnant women.

METHODS

Electronic databases including PubMed, EBSCO, Ovid, Web of Science, China National Knowledge Infrastructure (CNKI), SinoMed, Wangfang and the Weipu Journal were searched up to May 31, 2020. Two reviewers independently selected the articles and extracted data on study characteristics, quality and results. A random-effects model was employed, and standardized mean difference and 95% confidence intervals were calculated. Twenty-one case-control studies were analyzed in the present meta-analysis, including a total of 2,266 cases and 25,872 healthy controls.

RESULTS

Women who were diagnosed with PE were found to have higher early second-trimester levels of serum β-hCG MoM compared with healthy controls, although the levels in the first trimester were not significantly different. Ethnicity subgroup analysis demonstrated that the MoM of β-hCG serum levels was significantly higher in PE patients in both Asian and Caucasian populations during the early second trimester.

CONCLUSION

The MoM of β-hCG serum levels was found to be a valuable clinical indicator for predicting PE in the early second trimester, but had little predictive value in the first trimester. However, further assessment of the predictive capacity of β-hCG within larger, diverse populations is required.

Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis

BACKGROUND

Predictions in pregnancy care are complex because of interactions among multiple factors. Hence, pregnancy outcomes are not easily predicted by a single predictor using only one algorithm or modeling method.

OBJECTIVE

This study aims to review and compare the predictive performances between logistic regression (LR) and other machine learning algorithms for developing or validating a multivariable prognostic prediction model for pregnancy care to inform clinicians' decision making.

METHODS

Research articles from MEDLINE, Scopus, Web of Science, and Google Scholar were reviewed following several guidelines for a prognostic prediction study, including a risk of bias (ROB) assessment. We report the results based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies were primarily framed as PICOTS (population, index, comparator, outcomes, timing, and setting): Population: men or women in procreative management, pregnant women, and fetuses or newborns; Index: multivariable prognostic prediction models using non-LR algorithms for risk classification to inform clinicians' decision making; Comparator: the models applying an LR; Outcomes: pregnancy-related outcomes of procreation or pregnancy outcomes for pregnant women and fetuses or newborns; Timing: pre-, inter-, and peripregnancy periods (predictors), at the pregnancy, delivery, and either puerperal or neonatal period (outcome), and either short- or long-term prognoses (time interval); and Setting: primary care or hospital. The results were synthesized by reporting study characteristics and ROBs and by random effects modeling of the difference of the logit area under the receiver operating characteristic curve of each non-LR model compared with the LR model for the same pregnancy outcomes. We also reported between-study heterogeneity by using τ2 and I2.

RESULTS

Of the 2093 records, we included 142 studies for the systematic review and 62 studies for a meta-analysis. Most prediction models used LR (92/142, 64.8%) and artificial neural networks (20/142, 14.1%) among non-LR algorithms. Only 16.9% (24/142) of studies had a low ROB. A total of 2 non-LR algorithms from low ROB studies significantly outperformed LR. The first algorithm was a random forest for preterm delivery (logit AUROC 2.51, 95% CI 1.49-3.53; I2=86%; τ2=0.77) and pre-eclampsia (logit AUROC 1.2, 95% CI 0.72-1.67; I2=75%; τ2=0.09). The second algorithm was gradient boosting for cesarean section (logit AUROC 2.26, 95% CI 1.39-3.13; I2=75%; τ2=0.43) and gestational diabetes (logit AUROC 1.03, 95% CI 0.69-1.37; I2=83%; τ2=0.07).

CONCLUSIONS

Prediction models with the best performances across studies were not necessarily those that used LR but also used random forest and gradient boosting that also performed well. We recommend a reanalysis of existing LR models for several pregnancy outcomes by comparing them with those algorithms that apply standard guidelines.

TRIAL REGISTRATION

PROSPERO (International Prospective Register of Systematic Reviews) CRD42019136106; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=136106.

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 .

Utility of biochemical tests in prediction, diagnostics and clinical management of preeclampsia: a review

The most widely accepted theory for the development of preeclampsia is the "two-stage theory". An imbalance between antiangiogenic and proangiogenic factors is considered the link between the two stages. Nowadays, an increasing amount of data is available on the use of measurements of serum concentrations of these factors in the prediction, diagnosis and management of preeclampsia. The most useful, modern biochemical test that may help in making crucial clinical decisions in patients with preeclampsia is the sFlt-1/PlGF (soluble fms-like tyrosine kinase 1/placental growth factor) ratio. The aim of this review is to present the current use of different biochemical tests in the prediction, diagnosis and management of preeclampsia. Development of these diagnostic methods in recent years and a belief in their ground-breaking role in modern management of preeclampsia make this review especially important.

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.

Salivary uric acid as a predictive test of preeclampsia, pregnancy-induced hypertension and preterm delivery: A pilot study

INTRODUCTION

There remains a need for a non-invasive, low-cost and easily accessible way of identifying women at risk of developing hypertensive disorders in pregnancy. This study evaluated the predictive value of longitudinal salivary uric acid measurement.

MATERIAL AND METHODS

Pregnant women (n = 137) from 20 weeks of gestation were recruited at St Richards Hospital, Chichester, UK, for this prospective cohort study. Weekly samples of salivary uric acid were analyzed until delivery. Information regarding pregnancy and labor were obtained from the patient's record after delivery. Independent t tests were used to compare mean levels of salivary uric acid in women with hypertensive complications and adverse fetal outcomes with women with normal pregnancies. Main outcome measures were preeclampsia, pregnancy-induced hypertension, spontaneous preterm delivery and small-for-gestational-age babies.

RESULTS

From 21 weeks of gestation until delivery, levels of salivary uric acid increased significantly in women who subsequently developed preeclampsia and pregnancy-induced hypertension compared with women with normal pregnancies (preeclampsia-mean at gestational age 21-24, 95% confidence interval [95% CI] [mean _GA21-24 ): 108 [63-185] vs 47 (39-55) µmol/L; P = .005; pregnancy-induced hypertension-mean _GA21-24 : 118 [54-258] vs 47 [39-55] µmol/L; P = .004). In women who had spontaneous preterm delivery, salivary uric acid levels increased significantly from 29 to 32 weeks of gestation compared with women with normal pregnancies (mean _GA29-32 : 112 (57-221) vs 59 (50-71) µmol/L; P = .04). In women who had babies small-for-gestational-age <10th percentile and small-for-gestational-age <3rd percentile, differences in salivary uric acid levels were insignificant.

CONCLUSIONS

Elevated levels of salivary uric acid precede the onset of preeclampsia, pregnancy-induced hypertension and preterm delivery. Salivary uric acid may prove to be an early biomarker of hypertensive complications of pregnancy and spontaneous preterm delivery.

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.

Concordance-analysis and evaluation of different diagnostic algorithms used in first trimester screening for late-onset preeclampsia

Objective: Concordance-analysis and evaluation of existing algorithms detecting late-onset preeclampsia during first trimester screeningMethods: Retrospective cohort study investigating risk algorithms of late-onset preeclampsia during first trimester screening in a German prenatal center. Three previously developed algorithms including anamnestic factors (Apriori) and biophysical markers (BioM) were investigated by using detection rates (DR) with fixed FPR 10% and fixed cutoff >1:100. Furthermore, we set up a concordance-analysis of test results in late-onset preeclampsia cases to examine the effect of influencing factors and to detect potential weaknesses of the algorithms. Therefore, we modeled the probability of discordances as a function of the influencing factors based on a logistic regression, that was fitted using a Bayesian approach.Results: 6,113 pregnancies were considered, whereof 700 have been excluded and 5,413 pregnancies were analyzed. 98 (1.8%) patients developed preeclampsia (79 late-onsets, 19 early-onsets). The Apriori-algorithm reaches a DR of 34.2%, by adding BioM (MAP and UtA-PI) the DR improves to 57.0% (FPR of 10%). In concordance-analysis of Apriori algorithm and Apriori+BioM algorithms, influencing factor BMI<25 increases the chance of discordances sigificantly. Additional, in the subgroup of late-onset preeclampsias with BMI<25 the DR is higher in Apriori+BioM algorithms than in Apriori algorithm alone. If both compared algorithms include BioM, influencing factor MAP decreases the chance of discordances significantly. All other tested influencing factors do not have a statistically significant effect on discordancesConclusion: Normal-weight patients benefit more from the integration of MAP and UtA-PI compared to overweight/obese patients.

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.

Doppler Indices and Notching Assessment of Uterine Artery Between the 19th and 22nd Week of Pregnancy in the Prediction of Pregnancy Outcome

BACKGROUND/AIM

The aim of this study was to determine the value of Doppler indices and notching assessment of uterine artery between the 19th and 22nd week of gestation in the prediction of pregnancy outcome such as delivery mode, birth weight, Apgar score, afterbirth pH, fetal presentation, preeclampsia and fetal growth restriction in singleton pregnancy.

PATIENTS AND METHODS

This is a retrospective cohort study of Doppler ultrasound of the uterine arteries at 19-22 week of gestation in 1,472 women with singleton pregnancies.

RESULTS

Patients with bilateral high resistance-index (RI) and pulsatility-index (RI) or with the presence of a notch showed a significantly higher prevalence of small for gestational age (SGA) fetuses and intrauterine growth restriction (IUGR), low Apgar Scores at the 1st and the 5th min, high c-section rate, preterm birth, breech birth, placental insufficiency and placental abruption. The presence of a notch significantly increased the prevalence of severe preeclampsia, HELLP-syndrome and oligohydramnios. Also, patients with a bilateral uterine notching had a higher c-section rate along with higher prevalence of SGA and IUGR at screening time.

CONCLUSION

Uterine artery Doppler waveform analysis as well as the assessment of the presence of a notch in the second trimester can be used as a screening method to identify women who will thereafter develop a severe adverse outcome.

Altered level of salivary placental growth factor is associated with preeclampsia

A pilot, case-control study was conducted to compare the concentrations of placental growth factor (PlGF) and placental alkaline phosphatase (PLAP) in saliva of preeclampsia (PE) patients with normotensive controls in the second and third trimesters. Measured by ELISA assays, levels of salivary PlGF were significantly lower in PE patients (n = 13) compared to controls (n = 15) (two-way ANOVA, p = 0.0208) independent of gestational age at time of collection (p = 0.49). Salivary PLAP differences between PE and controls were not statistically significant. Placenta-specific proteins are detectable in maternal saliva and may serve as noninvasive biomarkers to monitor placenta health and disease during pregnancy.

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.

Predictive Performance of PlGF (Placental Growth Factor) for Screening Preeclampsia in Asymptomatic Women: A Systematic Review and Meta-Analysis

Preeclampsia is a systemic syndrome that seems to originate from the placenta and is associated with an imbalance between angiogenic factors in the maternal circulation. One of the well-studied and widely used factors is PlGF (placental growth factor), the levels of which drop in women destined to develop preeclampsia. This drop is known to precede the development of actual signs and symptoms of preeclampsia, thus proving to be a useful screening tool in predicting the disease. The literature varies widely in terms of the clinical usefulness of the test. We conducted a meta-analysis to study the predictive accuracy of PlGF in asymptomatic women. Our analysis included 40 studies with 3189 cases of preeclampsia and 89 498 controls. The overall predictive odds ratio of the test was 9 (6-13). Subgroup analysis evaluating various PlGF thresholds demonstrated that the predictive values were highest for PlGF levels between 80 and 120 pg/mL with a high predictive odds ratio of 25 (7-88), a sensitivity of 0.78 (95% CI, 0.67-0.86), a specificity of 0.88 (95% CI, 0.75-0.95), a positive likelihood ratio of 6.3 (95% CI, 2.7-14.7), and a negative likelihood ratio of 0.26 (95% CI, 0.16-0.42). Additionally, the accuracy was higher when the test was performed after 14 weeks of gestation (OR, 10 [7-15]) and for prediction of early onset preeclampsia (OR, 18 [9-37]). We conclude that PlGF is a useful screening tool to predict preeclampsia. Nonetheless, its utility should be judged with caution and randomized controlled trials are warranted to explore if its implementation improves perinatal outcomes in asymptomatic women.

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.

Does Uterine Doppler Add Information to the Cerebroplacental Ratio for the Prediction of Adverse Perinatal Outcome at the End of Pregnancy?

OBJECTIVE

To evaluate whether the addition of the mean uterine arteries pulsatility index (mUtA PI) to the cerebroplacental ratio (CPR) improves its ability to predict adverse perinatal outcome (APO) at the end of pregnancy.

METHODS

This was a prospective study of 891 fetuses that underwent an ultrasound examination at 34-41 weeks. The CPR and the mUtA PI were converted into multiples of the median (MoM) and the estimated fetal weight (EFW) into centiles according to local references. APO was defined as a composite of abnormal cardiotocogram, intrapartum pH requiring cesarean section, 5' Apgar score <7, neonatal pH <7.10 and admission to pediatric care units. The accuracies of the different parameters were evaluated alone and in combination with gestational characteristics using univariate and multivariate analyses by means of the Akaike Information Criteria (AIC) and the area under the curve (AUC). Finally, a comparison was similarly performed between the CPR and the cerebro-placental-uterine ratio (CPUR; CPR/mUtA PI) for the prediction of APO.

RESULTS

The univariate analysis showed that CPR MoM was the best parameter predicting APO (AIC 615.71, AUC 0.675). The multivariate analysis including clinical data showed that the best prediction was also achieved with the CPR MoM (AIC 599.39, AUC 0.718). Moreover, when EFW centiles were considered, the addition of UtA PI MoM did not improve the prediction already obtained with CPR MoM (AIC 591.36, AUC 0.729 vs. AIC 589.86, AUC 0.731). Finally, the prediction by means of CPUR did not improve that of CPR alone (AIC 623.38, AUC 0.674 vs. AIC 623.27, AUC 0.66).

CONCLUSION

The best prediction of APO at the end of pregnancy is obtained with CPR whatever is the combination of parameters. The addition of uterine Doppler to the information yielded by CPR does not result in any prediction improvement.

Development of a biophysical screening model for gestational hypertensive diseases

Background

To investigate the possibility of using maternal biophysical parameters only in screening for the different types of gestational hypertensive diseases.

Methods

A total of 969 pregnant women were randomly screened in first and second trimester, of which 8 developed Early-onset Preeclampsia, 29 Late-onset Preeclampsia, 35 Gestational Hypertension and 897 women had a normal outcome. An observational maternal hemodynamics assessment was done via standardized electrocardiogram-Doppler ultrasonography, Impedance Cardiography and bio-impedance, acquiring functional information on heart, arteries, veins and body fluid. Preliminary prediction models were developed to test the screening potential for early preeclampsia, late preeclampsia and gestational hypertension using a Partial Least Square Discriminant Analysis.

Results

A combined model using maternal characteristics with cardiovascular parameters in first and second trimester offers high screening performance with Area Under the Curve of 99,9% for Early-onset Preeclampsia, 95,3% for Late-onset Preeclampsia and 94% for Gestational Hypertension.

Conclusions

Using biophysical parameters as fundament for a new prediction model, without the need of biochemical parameters, seems feasible. However, validation in a large prospective study will reveal its true potential.

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.

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.

First-Trimester Placental Growth Factor for the Prediction of Preeclampsia in Nulliparous Women: The Great Obstetrical Syndromes Cohort Study

BACKGROUND

First-trimester maternal serum markers have been associated with preeclampsia (PE). We aimed to evaluate the performance of first-trimester placental growth factor (PlGF) for the prediction of PE in nulliparous women.

SUBJECTS AND METHODS

We conducted a prospective cohort study of nulliparous women with singleton pregnancy at 11-13 weeks. Maternal serum PlGF concentration was measured using B·R·A·H·M·S PlGFplus KRYPTOR automated assays and reported in multiple of the median adjusted for gestational age. We used proportional hazard models, along with receiver operating characteristic curves and areas under the curve (AUC).

RESULTS

Out of 4,652 participants, we observed 232 (4.9%) cases of PE including 202 (4.3%) term and 30 (0.6%) preterm PE. PlGF was associated with the risk of term (AUC = 0.61, 95% confidence interval [CI] 0.57-0.65) and preterm PE (AUC = 0.73, 95% CI 0.64-0.83). The models were improved with the addition of maternal characteristics (AUC for term PE 0.66, 95% CI 0.62-0.71; AUC for preterm PE 0.81, 95% CI 0.72-0.91; p < 0.01). At a false-positive rate of 10%, PlGF combined with maternal characteristics could have predicted 26% of term and 55% of preterm PE. The addition of pregnancy-associated plasma protein A did not significantly improve the prediction models.

CONCLUSION

First-trimester PlGF combined with maternal characteristics is useful to predict preterm PE in nulliparous women.

Integrated Systems Biology Approach Identifies Novel Maternal and Placental Pathways of Preeclampsia

Preeclampsia is a disease of the mother, fetus, and placenta, and the gaps in our understanding of the complex interactions among their respective disease pathways preclude successful treatment and prevention. The placenta has a key role in the pathogenesis of the terminal pathway characterized by exaggerated maternal systemic inflammation, generalized endothelial damage, hypertension, and proteinuria. This sine qua non of preeclampsia may be triggered by distinct underlying mechanisms that occur at early stages of pregnancy and induce different phenotypes. To gain insights into these molecular pathways, we employed a systems biology approach and integrated different "omics," clinical, placental, and functional data from patients with distinct phenotypes of preeclampsia. First trimester maternal blood proteomics uncovered an altered abundance of proteins of the renin-angiotensin and immune systems, complement, and coagulation cascades in patients with term or preterm preeclampsia. Moreover, first trimester maternal blood from preterm preeclamptic patients in vitro dysregulated trophoblastic gene expression. Placental transcriptomics of women with preterm preeclampsia identified distinct gene modules associated with maternal or fetal disease. Placental "virtual" liquid biopsy showed that the dysregulation of these disease gene modules originates during the first trimester. In vitro experiments on hub transcription factors of these gene modules demonstrated that DNA hypermethylation in the regulatory region of ZNF554 leads to gene down-regulation and impaired trophoblast invasion, while BCL6 and ARNT2 up-regulation sensitizes the trophoblast to ischemia, hallmarks of preterm preeclampsia. In summary, our data suggest that there are distinct maternal and placental disease pathways, and their interaction influences the clinical presentation of preeclampsia. The activation of maternal disease pathways can be detected in all phenotypes of preeclampsia earlier and upstream of placental dysfunction, not only downstream as described before, and distinct placental disease pathways are superimposed on these maternal pathways. This is a paradigm shift, which, in agreement with epidemiological studies, warrants for the central pathologic role of preexisting maternal diseases or perturbed maternal-fetal-placental immune interactions in preeclampsia. The description of these novel pathways in the "molecular phase" of preeclampsia and the identification of their hub molecules may enable timely molecular characterization of patients with distinct preeclampsia phenotypes.

ELABELA plasma concentrations are increased in women with late-onset preeclampsia

Objective: ELABELA is a newly discovered peptide hormone that appears to be implicated in the mechanisms leading to preeclampsia, independently of angiogenic factors. The aim of the current study was to investigate whether women with early- or late-onset preeclampsia have altered ELABELA plasma concentrations compared to gestational-age-matched normal pregnant women.Methods: This retrospective cross-sectional study focused on the maternal plasma samples collected from 232 women with a singleton pregnancy who were allocated into the following groups: (1) early-onset preeclampsia (<34 weeks of gestation, N = 56); (2) late-onset preeclampsia (≥34 weeks of gestation, N = 57); and (3) gestational-age-matched controls with a normal pregnancy [(<34 weeks of gestation, N = 59); (≥34 weeks of gestation, N = 60)]. ELABELA plasma concentrations were determined using a validated enzyme immunoassay.Results: (1) ELABELA plasma concentrations are higher in patients with late-onset preeclampsia compared with those from gestational-age-matched controls with a normal pregnancy [median: 7.99 ng/mL (IQR, 5.3-13.95 ng/mL) versus median: 4.17 ng/mL (IQR, 3-11.19 ng/mL), p =.001]; (2) ELABELA plasma concentrations in patients with early-onset preeclampsia do not differ from those of normal pregnant women [median: 6.09 ng/mL (IQR, 2.8-10.66 ng/mL) versus median: 4.02 ng/mL (IQR, 3.26-7.49), p = .32]; and (3) ELABELA plasma concentrations are higher in patients with late-onset preeclampsia compared to those with early-onset preeclampsia [median: 7.99 ng/mL (IQR, 5.3-13.95 ng/mL) versus median: 6.09 ng/mL (IQR, 2.8-10.66 ng/mL), p = .01].Conclusion: ELABELA plasma concentrations are higher in patients with late-onset preeclampsia than in those with a normal pregnancy. However, women with early-onset preeclampsia have similar ELABELA plasma concentrations to those with a normal pregnancy. These findings provide insight into the ELABELA axis during the human syndrome of preeclampsia. In addition, these data support the concept that different pathophysiologic mechanisms are implicated in early- and late-onset preeclampsia.