Multicenter screening for pre-eclampsia by maternal factors and biomarkers at 11-13 weeks' gestation: comparison with NICE guidelines and ACOG recommendations

Clinical utility of a glycosylated fibronectin test (LumellaTM) for assessment of impending preeclampsia

OBJECTIVE

Preeclampsia is a major pregnancy complication that results in significant maternal and infant mortality and morbidity, yet difficulties remain in the diagnosis of preeclampsia based on clinical parameters alone. The objective was to assess the performance of a hand-held point-of-care (POC) immunoassay in a clinical environment for glycosylated fibronectin (GlyFn) for the prediction of preeclampsia within 4 weeks of sampling.

METHODS

Multinational European prospective observational pilot study of predominantly high-risk patients in the second half of pregnancy to assess a point-of-care immunoassay for GlyFn in predicting preeclampsia within 4 weeks of sampling. GlyFn was measured using a second generation hand held POC immunoassay. Results were considered normal for GlyFn concentrations of < 350 µg/mL, positive for GlyFn concentrations of 351-600 µg/mL, and high-positive for GlyFn concentrations > 600 µg/mL.

RESULTS

Preeclampsia developed in 16 (19%) of 84 subjects and was associated with a shorter gestational age at delivery 35.3 weeks vs. 37.3 weeks for non-preeclamptics, n = 82; p = 0.001), a higher risk of fetal growth restriction (FGR; 31.2% vs. 10.3% for non-preeclamptics, p = 0.046), and an increased risk of preterm birth < 37 weeks gestation (83.3% vs. 33.3% for non-preeclamptics, (n = 78; p = 0.003). GlyFn positive or high positive was seen in 13/16 (81%) and in 35/68 (51.5%), yielding a sensitivity of 81%, a specificity of 49%, a positive predictive value of 27%, and a negative predictive value of 92%. GlyFn positive or high positive was also associated with preterm birth < 37 weeks in singleton pregnancy non-preeclamptic patients. Preterm birth occurred in 4.8% of those with normal GlyFn, in 26.7% with positive GlyFn, and in 50% of those with high GlyFn in singleton gestations without preeclampsia (p = 0.008).

CONCLUSION

The ability to use this test in a POC format provides a method for practitioners to quickly determine risk for preeclampsia in their pregnant patients and offers an affordable alternative, as a single analyte to other diagnostic or screening tests that require laboratory-based testing or ultrasound equipment. Independent of preeclampsia, an elevated GlyFn was also correlated with preterm delivery and requires further study.

Comparison of Maternal Serum Neuropilin-1 (NRP-1) and Fetal Cord Blood NRP-1 Concentrations in Between Normotensive Pregnant Women and Those with Preeclampsia

Background: The aim of this study was to compare the levels of Neuropilin-1 (NRP-1) in maternal plasma and fetal cord blood plasma between pregnancies complicated by preeclampsia (PE) and those in normotensive pregnant women. Materials and Methods: This prospectively designed study included 53 pregnant women aged 18 years or older and at least 20 weeks into gestation, who were admitted to the Maternity Department of Izmir Katip Çelebi University Atatürk Training and Research Hospital. The patient group consisted of 28 pregnant women who met the diagnostic criteria for PE, while the control group included 25 normotensive pregnant women. The diagnosis of PE was established based on the 2020 diagnostic criteria of the American College of Obstetricians and Gynecologists (ACOG). After detailed anamnesis, blood samples were collected immediately after delivery in EDTA tubes to assess serum NRP-1 levels. These samples included maternal blood, fetal cord blood, and additional tests such as CBC, liver and kidney function tests, serum electrolytes, spot urinalysis, prothrombin time (PT), and activated partial thromboplastin time (APTT). Results: There was a statistically significant difference between the two groups in terms of gestational week, presence of comorbidities, hypertension (HT), diabetes mellitus (DM), history of PE, and protein detected in spot urine examinations. Pregnant women in the PE group had significantly higher rates of comorbidities, HT, and DM compared to the control group (p < 0.001, 0.002, and 0.007, respectively). No statistically significant differences were observed between the two groups regarding hemoglobin, platelet count, aspartate aminotransferase (AST), alanine aminotransferase (ALT), or fetal cord plasma NRP-1 levels (p: 0.736, 0.831, 0.561, and 0.734, respectively). However, a statistically significant difference was found in maternal plasma NRP-1 levels (p: 0.02), which were lower in the control group compared to the PE group (median: 473.3 pg/mL vs. 587.7 pg/mL, respectively). The optimal cut-off value for maternal plasma NRP-1 to predict PE, with the best sensitivity and specificity, was determined to be 358.4 pg/mL. Among the study participants, 40 pregnant women had maternal plasma NRP-1 levels above the cut-off value, while 13 had levels below it. PE occurred significantly more frequently in the high NRP-1 group than in the low group. When demographic and clinical characteristics were analyzed, a statistically significant but weak positive correlation was found between body mass index (BMI) and maternal plasma NRP-1 levels (p: 0.02, Rho: 0.304). No strong or statistically significant relationships were identified with other variables. There was no significant difference in fetal cord plasma NRP-1 levels between the PE group and the normotensive group. In contrast, maternal plasma NRP-1 levels were significantly higher in the PE group. The cut-off value for maternal plasma NRP-1, providing optimal sensitivity and specificity for predicting PE, remained 358.4 pg/mL. Conclusions: While further studies involving larger cohorts of pregnant women from diverse racial backgrounds and various hospitals are needed to better understand the relationship between NRP-1 and PE, maternal NRP-1 concentration shows promise as a diagnostic marker.

Comparison of first trimester preeclampsia combined screening performances with various approaches in the Indonesian population

INTRODUCTION

This study aimed to compare Fetal Medicine Foundation (FMF), Indonesian Maternal and Children Health Handbook (MCH-HB), and Indonesian Prenatal Institute (IPI) models for predicting PE.

MATERIALS/SUBJECTS AND METHODS

Maternal risk factors, biophysical, and biochemical markers were recorded from women screened for PE at 11-14 gestational weeks. The receiving operator curve (ROC) analysis was used to compare the detection rate (DR) among prediction models.

RESULTS

For all PE at a 10% false-positive rate (FPR), FMF had a DR 62.9%; MCH-HB had a DR 50.0%; IPI had a DR 66.9%. For early-onset PE, at 10% FPR FMF had a DR 80.3%; MCH-HB had a DR 71.4%; IPI had a DR 81.5%. For preterm PE at 10% FPR, FMF had a DR 70.2%; MCH-HB had a DR 38.5%; IPI had a DR 66.9%.

DISCUSSION

IPI algorithm is comparable to FMF and outperforms MCH-HB algorithm for all, early-onset, and preterm PE screening.

Maternal cell-free DNA in early pregnancy for preeclampsia screening: a systematic review

PURPOSE

To quantify the separation between maternal blood cell-free (cf)DNA markers in preeclampsia and unaffected pregnancies and compare with existing markers. This approach has not been used in previous studies.

METHODS

Comprehensive systematic literature search of PubMed to identify studies measuring total cfDNA, fetal cf(f)DNA or the fetal fraction (FF) in pregnant women. Included-studies of asymptomatic pregnancies with subsequent preeclampsia (cases) and unaffected pregnancies (controls) tested in the first or second trimester and before the clinical onset of preeclampsia. Excluded-studies not reporting the median or mean, standard deviation, inter-quartile range or range in cases and controls. Information from 26 eligible studies was entered into a meta-analysis to estimate, for each marker, the Mahalanobis distance, a measure of separation between the overlapping distributions in preeclampsia and unaffected pregnancies. This was compared with estimates for mean arterial pressure (MAP), uterine artery Doppler pulsatility index (UtA-PI), pregnancy associated plasma protein (PAPP)-A and placental growth factor (PlGF).

RESULTS

The mean Mahalanobis distance for total cfDNA was 0.44 (95% CI 0.12-0.76), which fell between UtA-PI (0.53) and the absolute value of PAPP-A (- 0.36). For cffDNA the distance was 1.03 (0.37-1.69), which is superior to MAP (0.74), UtA-PI, PlGF (- 0.57) and PAPP-A. The distance for FF was - 0.34 (- 0.56 to - 0.12), similar to PAPP-A.

CONCLUSION

All three markers have a potential preeclampsia screening role, particularly cffDNA. However, to estimate the screening performance in combination with existing markers further large studies are needed. The current analysis will help in the power calculation for such studies.

Senescent Syncytiotrophoblast Secretion During Early Onset Preeclampsia

BACKGROUND

Preeclampsia is a severe hypertensive disorder in pregnancy that causes preterm delivery, maternal and fetal morbidity, mortality, and life-long sequelae. Understanding the pathogenesis of preeclampsia is a critical first step toward protecting mother and child from this syndrome and increased risk of cardiovascular disease later in life. However, effective early predictive tests and therapies for preeclampsia are scarce.

METHODS

To identify novel markers and signaling pathways for early onset preeclampsia, we profiled human maternal-fetal interface units (fetal villi and maternal decidua) from early onset preeclampsia and healthy controls using single-nucleus RNA sequencing combined with spatial transcriptomics. The placental syncytiotrophoblast is in direct contact with maternal blood and forms the barrier between fetal and maternal circulation.

RESULTS

We identified different transcriptomic states of the endocrine syncytiotrophoblast nuclei with patterns of dysregulation associated with a senescence-associated secretory phenotype and a spatial dysregulation of senescence in the placental trophoblast layer. Elevated senescence markers were validated in placental tissues of clinical multicenter cohorts. Importantly, several secreted senescence-associated secretory phenotype factors were elevated in maternal blood already in the first trimester. We verified the secreted senescence markers, PAI-1 (plasminogen activator inhibitor 1) and activin A, as identified in our single-nucleus RNA sequencing model as predictive markers before clinical preeclampsia diagnosis.

CONCLUSIONS

This indicates that increased syncytiotrophoblast senescence appears weeks before clinical manifestation of early onset preeclampsia, suggesting that the dysregulated preeclamptic placenta starts with higher cell maturation resulting in premature and increased senescence-associated secretory phenotype release. These senescence-associated secretory phenotype markers may serve as an additional early diagnostic tool for this syndrome.

Prevention of Pre-Eclampsia: Modern Strategies and the Role of Early Screening

Background: Pre-eclampsia (PE) remains a leading cause of maternal and perinatal morbidity and mortality worldwide. Early detection and risk stratification are critical for improving pregnancy outcomes. This review aims to summarize current advancements in PE screening, including clinical risk factors, biomarkers, imaging techniques, and predictive models. Methods: A comprehensive literature search was conducted using PubMed, Scopus, Web of Science, and Google Scholar to identify relevant studies on PE screening and prediction. Peer-reviewed original studies, systematic reviews, and meta-analyses published in English were included, while case reports and conference abstracts were excluded. Results: Traditional screening methods rely on maternal history and clinical risk factors, while emerging approaches incorporate biochemical markers and ultrasound parameters to enhance predictive accuracy. Machine learning models and artificial intelligence (AI)-driven algorithms are being explored for improved risk stratification. However, challenges such as data heterogeneity, lack of external validation, and integration into clinical practice remain. Conclusions: Advances in PE screening hold promise for early identification and targeted prevention strategies. Future research should focus on validating predictive models in diverse populations, integrating AI with traditional screening methods, and developing personalized approaches to reduce PE-associated complications.

The Role of Mid-Trimester BUN and Creatinine Assessment in Predicting Preeclampsia: Retrospective Case-Control Study

Background and Objectives: Preeclampsia (PE) is a major cause of adverse perinatal outcomes. Early diagnosis of pregnant women at risk of PE can facilitate disease prevention and management. However, the presence of different phenotypes of the disease complicates its prediction. In particular, the challenges in the early diagnosis of term PE cases necessitate research on PE prediction in the second and third trimesters. This study aims to examine the association between PE development and mid-trimester blood urea nitrogen (BUN), serum creatinine, and the BUN/creatinine ratio in pregnant women. Materials and Methods: This retrospective case-control study was conducted on women diagnosed with PE. Pregnant women who underwent routine biochemical blood tests between the 18th and 24th weeks of gestation and subsequently gave birth at our hospital between January 2022 and May 2023 were categorized into three groups. Accordingly, healthy women who had term deliveries were classified as Group 1 (150 cases), women diagnosed with PE were classified as Group 2 (58 cases), and those diagnosed with severe PE were classified as Group 3 (44 cases). Results: There were no significant differences in age, gravidity, parity, body mass index, or gestational week at blood sampling between the patient and control groups (p > 0.05). When comparing the mean blood urea nitrogen, serum creatinine, and BUN/creatinine ratios, a significant difference was observed between the control group and those who developed PE (p = 0.001, p < 0.001, and p = 0.031, respectively). Univariate analysis revealed a significant association between BUN levels and PE development (OR 1.083; 95% CI, 1.031-1.139; p = 0.002). A stronger association was observed between serum creatinine levels and PE development (OR 112.344; 95% CI, 11.649-1083.416; p < 0.001). However, no significant association was found between the BUN/creatinine ratio and PE in univariate analysis (OR 1.003; 95% CI, 0.979-1.028; p > 0.05). Mid-trimester BUN and serum creatinine levels were significantly higher in patients who developed PE and severe PE. The AUC value for the BUN parameter in predicting PE was 0.614 (AUC 0.614; 95% CI, 0.539-0.689; p = 0.002). A BUN cut-off value of 16.2 mg/dL predicted disease development with a sensitivity of 52.9% and a specificity of 74%. Similarly, the AUC value for the serum creatinine parameter in predicting PE was 0.644 (AUC 0.644; 95% CI, 0.574-0.751; p < 0.001). A serum creatinine cut-off value of 0.58 mg/dL was able to predict disease development with 37.2% sensitivity and 88% specificity. No significant AUC value was obtained for the BUN/creatinine ratio (p > 0.05). Conclusions: Our findings indicate that elevated BUN and serum creatinine levels measured during the mid-trimester (18-24 weeks) are associated with an increased risk of developing preeclampsia.

Exploring new predictors for hypertensive disorders of pregnancy

The best performing predicting Bayesian algorithm for preeclampsia, endorsed by FIGO, identifies high-risk women at first trimester screening who benefits of a closer monitoring and possibly preventive measures. Unfortunately, the most frequent late term and term preeclampsia are less efficiently predicted. This algorithm is based on statistical assumptions at odds with the physiopathology: preeclampsia is a disease and not a syndrome, as we know it is, and the contingent time-based criteria according to which all pregnancies if not terminated by nature should develop this "disease". In addition to this, we know that gestational hypertension might cause in fifty percent of cases severe outcome, comparable to preeclampsia. The very definition of preeclampsia as proteinuric hypertension is now extended to hypertension associated with other end-organ damage, including fetal growth restriction (FGR), this latter condition proceeding, in early onset cases, hypertension. Predicting phenotypes of hypertensive Disorders of pregnancy (HDP) could better help clinical practice. This study reports exploratory observations in women resulted at high and low risk at first trimester screening followed up at second and third trimester, to term. The co-variates interrogated were sFlt1/PlGF ratio, the uterine arteries PI, the systemic vascular resistances (SVR), maternal total body water and visceral fat. Women were classified as HDP-AGA, HDP-FGR, normotensive-FGR and uneventful pregnancies (controls). We performed a longitudinal Bayesian multivariate mixed-effects model corrected both for pre-gestational BMI and trimester of analysis. The sFlt-1/PlGF ratio and SVR confirmed their significant difference in HDP-AGA, in normotensive FGR, and HDP-FGR along the three trimesters from controls, but with different strength along the three trimesters. The bioimpedance analysis of total body water and visceral fat confirmed the association of these co-factors with women who will develop HDP-AGA. The strength of longitudinal changes observed, even on a limited number of cases, provide evidence that Bayesian algorithms applied at screening tests at different gestational ages, should be based on co-variates significantly associated either with HDP-FGR or with HDP-AGA provided that the main causative co-factors involved are adopted by predictive models aimed at these distinct diseases.

Formative research to optimize pre-eclampsia risk-screening and prevention (PEARLS): study protocol

BACKGROUND

Pre-eclampsia is a leading cause of maternal and neonatal mortality, affecting nearly 5% of pregnant women worldwide. Accurate and timely risk-screening of pregnant women is essential to start preventive therapies as early as possible, including low-dose aspirin and calcium supplementation. In the formative phase for the "Preventing pre-eclampsia: Evaluating AspiRin Low-dose regimens following risk Screening" (PEARLS) trial, we aim to validate and implement a pre-eclampsia risk-screening algorithm, and validate an artificial intelligence (AI) ultrasound for gestational age estimation. In the trial phase, we will compare different daily aspirin doses (75 mg v 150 mg) for pre-eclampsia prevention and postpartum bleeding. This study protocol outlines the mixed-methods formative phase of PEARLS, which will identify challenges and the feasibility of implementing these activities in participating facilities in Ghana, Kenya, and South Africa.

METHODS

We will employ qualitative and quantitative methods to identify factors that may influence trial implementation. In-depth interviews and focus group discussions with policy stakeholders, research midwives, health workers, and pregnant women will explore the barriers, facilitators, and acceptability of pre-eclampsia risk screening, AI ultrasound, and aspirin uptake. A cross-sectional survey of antenatal care and maternity health workers will assess current clinical practices around pre-eclampsia and willingness to participate in the trial activities. Data will be analyzed using thematic analysis and triangulated across sources and participant groups. The findings will inform trial design and help optimize implementation.

DISCUSSION

The research will provide critical insights into the feasibility of pre-eclampsia risk screening and AI ultrasound for gestational age estimation in resource-limited settings. By identifying factors that can influence implementation of pre-eclampsia prevention and care pathways, the findings will inform successful execution of the PEARLS trial, and post-research scale-up activities. This, in turn, can help reduce the prevalence of pre-eclampsia, and improve maternal and newborn outcomes in high-burden settings.

TRIAL REGISTRATION

PACTR202403785563823 || pactr.samrc.ac.za (Date of registration: 12 March 2024).

Advancing preeclampsia prediction: a tailored machine learning pipeline integrating resampling and ensemble models for handling imbalanced medical data

BACKGROUND

Constructing a predictive model is challenging in imbalanced medical dataset (such as preeclampsia), particularly when employing ensemble machine learning algorithms.

OBJECTIVE

This study aims to develop a robust pipeline that enhances the predictive performance of ensemble machine learning models for the early prediction of preeclampsia in an imbalanced dataset.

METHODS

Our research establishes a comprehensive pipeline optimized for early preeclampsia prediction in imbalanced medical datasets. We gathered electronic health records from pregnant women at the People's Hospital of Guangxi from 2015 to 2020, with additional external validation using three public datasets. This extensive data collection facilitated the systematic assessment of various resampling techniques, varied minority-to-majority ratios, and ensemble machine learning algorithms through a structured evaluation process. We analyzed 4,608 combinations of model settings against performance metrics such as G-mean, MCC, AP, and AUC to determine the most effective configurations. Advanced statistical analyses including OLS regression, ANOVA, and Kruskal-Wallis tests were utilized to fine-tune these settings, enhancing model performance and robustness for clinical application.

RESULTS

Our analysis confirmed the significant impact of systematic sequential optimization of variables on the predictive performance of our models. The most effective configuration utilized the Inverse Weighted Gaussian Mixture Model for resampling, combined with Gradient Boosting Decision Trees algorithm, and an optimized minority-to-majority ratio of 0.09, achieving a Geometric Mean of 0.6694 (95% confidence interval: 0.5855-0.7557). This configuration significantly outperformed the baseline across all evaluated metrics, demonstrating substantial improvements in model performance.

CONCLUSIONS

This study establishes a robust pipeline that significantly enhances the predictive performance of models for preeclampsia within imbalanced datasets. Our findings underscore the importance of a strategic approach to variable optimization in medical diagnostics, offering potential for broad application in various medical contexts where class imbalance is a concern.

The relationship between renal function indicators and preeclampsia in the second trimester of pregnancy: a retrospective study

Background

To investigate the relationship between serum renal function indicators and preeclampsia (PE) in pregnant women at second trimester of pregnancy, determine the optimal critical values of the above indicators, and further identify the independent risk factors of PE.

Methods

We assessed the renal function indicators in second trimesters of 137 pregnant women with PE and 137 normal pregnant women who delivered at Shijiazhuang Fourth Hospital between January 2020 to December 2022. Paired t-tests, paired Mann-Whitney U tests, and Chi-square tests were used to evaluate differences of clinical data between the two groups. Receiver operating characteristic (ROC) curves was employed to establish the optimal critical values of the above indicators. A 1:1 matched case-control logistic regression analysis was conducted to identify the independent risk factors for PE.

Results

The levels of serum uric acid and the ratio of serum uric acid to serum creatinine were significantly higher in the PE group compared to the control (P < 0.001), while the serum creatinine levels were higher in control group (P = 0.002). The incidence of adverse maternal (P < 0.001) and neonatal outcomes (P < 0.001) in the PE group were higher than those in the control group. A ROC analysis based on the occurrence of PE showed that the levels of serum uric acid (P < 0.001), serum creatinine (P = 0.006) and the ratio of serum uric acid to serum creatinine (P < 0.001) were statistically significant. After adjusting for confounding factors, elevated serum uric acid (a OR = 1.012, 95% CI [1.005-1.019], P < 0.001) and an increased serum uric acid to serum creatinine ratio (a OR = 1.190, 95% CI [1.053-1.346], P = 0.005) were identified as independent risk factors for PE. There was no significant difference in renal function between maternal and newborn group in relation to the occurrence of adverse outcomes (P > 0.05 vs. all groups).

Conclusions

Through the analysis of renal function indicators in patients with PE in the second trimester of pregnancy and those in a normal control group, it is found that elevated serum uric acid and serum uric acid to serum creatinine ratio in PE individual may serve as indicative markers for the onset of PE. Targeting this subset of the population for monitoring and management during the second trimester could enhance the efficacy of medical interventions.

Placental growth factor as a predictive marker of preeclampsia in twin pregnancy

OBJECTIVES

Placental growth factor (PlGF) has been reported as a good biomaker for the prediction of preeclampsia occurring in the short term in singleton pregnancies, in women presenting with clinical suspicion of preeclampsia. This study aims to evaluate the predictive value of the PlGF in twin pregnancies.

METHODS

Twin pregnancies with clinically suspected preeclampsia (24 weeks 0 days-36 weeks 6 days of gestation) were enrolled in this study. The threshold of PlGF for predicting preeclampsia was determined on the basis of a receiver-operating characteristic curve to predict preeclampsia and the short-term occurrence of preeclampsia.

RESULTS

Within 1 week, 2 weeks, and 4 weeks of testing respectively, a cutoff value of 215 pg/mL for PlGF to predict preeclamsia in twin pregnancies suspected to have preeclampsia has a specificity of 100 %[51.7 %, 100 %], 100 %[62.9 %, 100 %], 93.8 %[667.6 %, 99.7 %], and a negative predictive value of 100 %[94.8 %, 100 %], 100 %[95.0 %, 100 %], and 98.9 %[93.0 %, 99.9 %].

CONCLUSIONS

A cutoff value of 215 pg/mL for PlGF is a useful tool to exclude the development of preeclampsia within 4 weeks of measurement.

Prediction of pre-eclampsia using maternal hemodynamic parameters at 12 + 0 to 15 + 6 weeks

OBJECTIVES

To compare the maternal hemodynamic profile at 12 + 0 to 15 + 6 weeks' gestation in women who subsequently developed pre-eclampsia (PE) and those who did not, and to assess the screening performance of maternal hemodynamic parameters for PE in combination with the Fetal Medicine Foundation (FMF) triple test, including maternal factors (MF), mean arterial pressure (MAP), uterine artery pulsatility index and placental growth factor.

METHODS

This was a prospective case-control study involving Chinese women with a singleton pregnancy who underwent preterm PE screening at 11 + 0 to 13 + 6 weeks' gestation using the FMF triple test, between February 2020 and February 2023. Women identified as being at high risk (≥ 1:100) for preterm PE by the FMF triple test were matched 1:1 with women identified as low risk (< 1:100) for maternal age ± 3 years, maternal weight ± 5 kg and date of screening ± 14 days. Two-dimensional transthoracic echocardiography was performed at 12 + 0 to 15 + 6 weeks to evaluate maternal hemodynamic parameters (heart rate (HR), stroke volume (SV), cardiac output (CO) and systemic vascular resistance (SVR)). Maternal hemodynamic parameters were expressed as multiples of the median (MoM) values, determined by linear regression models to adjust for gestational age (GA) and MF. The distribution of log10 MoM values of maternal hemodynamic parameters in cases of PE and unaffected pregnancies, and the association between these hemodynamic parameters and GA at delivery, were assessed. The risks of preterm PE (delivery before 37 weeks) and any-onset PE (delivery at any time) were reassessed using Bayes' theorem after maternal hemodynamic parameters were added to the FMF triple test. The screening performance for preterm PE and any-onset PE was determined by the area under the receiver-operating-characteristics curve (AUC) and detection rate at a 10% fixed false-positive rate (FPR). Differences in AUC (ΔAUC) were assessed using DeLong's test.

RESULTS

A total of 743 cases were analyzed, of whom 39 (5.2%) subsequently developed PE, including 29 (3.9%) cases of preterm PE and 10 (1.3%) cases of term PE. Mean log10 SVR MoM was significantly higher in cases of preterm PE and any-onset PE compared with unaffected pregnancies. Mean log10 SV MoM and log10 CO MoM were significantly lower in cases of preterm PE and any-onset PE compared with unaffected pregnancies. Mean log10 HR MoM was not significantly different between the study groups. Mean log10 CO MoM and log10 SVR MoM were not significantly correlated with GA at delivery in preterm PE and any-onset PE. For the prediction of preterm PE and any-onset PE, adding CO or SVR or replacing MAP with CO and SVR in the FMF triple test achieved an identical or greater AUC compared with the FMF triple test, but ΔAUC was not significantly different. In addition, adding CO or SVR or replacing MAP by CO and SVR in the FMF triple test did not improve the detection rate for preterm PE and any-onset PE at a fixed FPR of 10%.

CONCLUSIONS

Women with preterm PE or any-onset PE exhibited increased SVR and decreased CO before the clinical manifestations of PE became apparent. These changes may serve as early indicators of cardiovascular maladaptation. However, assessment of maternal hemodynamics at 12 + 0 to 15 + 6 weeks does not enhance the screening performance for preterm PE and any-onset PE of these parameters. The FMF triple test remains superior to other biomarker combinations for predicting PE. © 2025 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Preterm preeclampsia screening and prevention: a comprehensive approach to implementation in a real-world setting

BACKGROUND

Preeclampsia significantly impacts maternal and perinatal health. Early screening using advanced models and primary prevention with low-dose acetylsalicylic acid for high-risk populations is crucial to reduce the disease's incidence. This study assesses the feasibility of implementing preterm preeclampsia screening and prevention by leveraging information from our current aneuploidy screening program in a real-world setting with geographic separation clinical site and laboratory analysis site.

METHODS

A prospective cohort study involved pregnant individuals undergoing nuchal translucency scans between 11 and 14 weeks. Risk for preterm preeclampsia was assessed using the Fetal Medicine Foundation algorithm, which includes maternal risk factors, uterine artery Doppler, mean arterial pressure and serum markers (Placental growth factor, PlGF and Pregnancy-associated plasma protein-A, PAPP-A). High-risk patients were offered low-dose acetylsalicylic acid prophylaxis. Feasibility outcomes, such as recruitment rates, protocol adherence, operational impact, integration with existing workflows, screening performance and pregnancy outcomes, were evaluated.

RESULTS

Out of 974 participants, 15.6% were deemed high-risk for preterm preeclampsia. The study achieved high recruitment (82.1%) and adherence rates, with 95.4% of high-risk patients prescribed low-dose acetylsalicylic acid. Screening performance, adjusted for low-dose acetylsalicylic acid use, showed a detection rate of 88.9-90% (FPR 13.0% and 12.7%) for preterm preeclampsia. High-risk group for preeclampsia had higher incidences of adverse outcomes, including preterm preeclampsia (7.5 vs 0.4%; p < 0.001), preterm delivery (21.2 vs 6.2%; p < 0.001), low birth weight (23.3 vs 5.6%; p < 0.001) and birthweight < 10th percentile (11% vs 5.6%; p = 0.015) compared to low-risk group. The integration of preeclampsia screening had a minimal effect on the time required for aneuploidy screening, with results obtained within a rapid turnaround time.

CONCLUSIONS

The study confirms the feasibility of integrating comprehensive preeclampsia screening into clinical practice, notwithstanding geographic separation between laboratory and clinical settings. It underscores the need for broader adoption and enhanced infrastructure to optimize patient care and outcomes across diverse healthcare settings.

TRIAL REGISTRATION

Clinical trial: NCT04412681 (2020-06-02).

Advancing diagnosis and early risk assessment of preeclampsia through noninvasive cell-free DNA methylation profiling

BACKGROUND

Aberrant embryo implantation and suboptimal placentation can lead to (severe) complications such as preeclampsia and fetal growth restriction later in pregnancy. Current identification of high-risk pregnancies relies on a combination of risk factors, biomarkers, and ultrasound examinations, a relatively inaccurate approach. Previously, aberrant DNA methylation due to placental hypoxia has been identified as a potential marker of placental insufficiency and, hence, potential (future) pregnancy complications. The goal of the Early Prediction of prEgnancy Complications Testing, or the ExPECT study, is to validate a genome-wide, cell-free DNA (cfDNA) methylation strategy to diagnose preeclampsia accurately. More importantly, the predictive potential of this strategy is also explored to reliably identify high-risk pregnancies early in gestation. Furthermore, a longitudinal study was conducted, including sequential blood samples from pregnant individuals experiencing both uneventful and complicated gestations, to assess the methylation dynamics of cfDNA throughout these pregnancies. A significant strength of this study is its enzymatic digest, which enriches CpG-rich regions across the genome without the need for proprietary reagents or prior selection of regions of interest. This makes it useful for the cost-effective discovery of novel markers.

RESULTS

Investigation of methylation patterns throughout pregnancy showed different methylation trends between unaffected and affected pregnancies. We detected differentially methylated regions (DMRs) in pregnancies complicated with preeclampsia as early as 12 weeks of gestation, with distinct differences in the methylation profile between early and late pregnancy. Two classification models were developed to diagnose and predict preeclampsia, demonstrating promising results on a small set of validation samples.

CONCLUSIONS

This study offers valuable insights into methylation changes at specific genomic regions throughout pregnancy, revealing critical differences between normal and complicated pregnancies. The power of noninvasive cfDNA methylation profiling was successfully proven, suggesting the potential to integrate this noninvasive approach into routine prenatal care.

Preeclampsia and its prediction: traditional versus contemporary predictive methods

OBJECTIVE

Preeclampsia (PE) poses a significant threat to maternal and perinatal health, so its early prediction, prevention, and management are of paramount importance to mitigate adverse pregnancy outcomes. This article provides a brief review spanning epidemiology, etiology, pathophysiology, and risk factors associated with PE, mainly discussing the emerging role of Artificial Intelligence (AI) deep learning (DL) technology in predicting PE, to advance the understanding of PE and foster the clinical application of early prediction methods.

METHODS

Our narrative review comprehensively examines the PE epidemiology, etiology, pathophysiology, risk factors and predictive approaches, including traditional models and AI deep learning technology.

RESULTS

Preeclampsia involves a wide range of biological and biochemical risk factors, among which poor uterine artery remodeling, excessive immune response, endothelial dysfunction, and imbalanced angiogenesis play important roles. Traditional PE prediction models exhibit significant limitations in sensitivity and specificity, particularly in predicting late-onset PE, with detection rates ranging from only 30% to 50%. AI models have exhibited a notable level of predictive accuracy and value across various populations and datasets, achieving detection rates of approximately 70%. Particularly, they have shown superior predictive capabilities for late-onset PE, thereby presenting novel opportunities for early screening and management of the condition.

CONCLUSION

AI DL technology holds promise in revolutionizing the prediction and management of PE. AI-based approaches offer a pathway toward more effective risk assessment methods by addressing the shortcomings of traditional prediction models. Ongoing research efforts should focus on expanding databases and validating the performance of AI in diverse populations, leading to the development of more sophisticated prediction models with improved accuracy.

Development and validation of a spontaneous preterm birth risk prediction algorithm based on maternal bioinformatics: A single-center retrospective study

BACKGROUND

Spontaneous preterm birth (sPTB) is a primary cause of adverse neonatal outcomes. The objective of this study is to analyze the factors influencing the occurrence of sPTB in pregnant women and to construct and validate a predictive model for sPTB risk based on big data from clinical and laboratory assessments during pregnancy.

METHODS

A retrospective analysis was conducted on the clinical data of 3,082 pregnant women, categorizing those who delivered before 37 weeks of gestation as the sPTB group and those who delivered at or after 37 weeks as the full-term group. The performance of five machine learning models was compared using metrics such as the AUC, accuracy, sensitivity, specificity, and precision to identify the optimal predictive model. The top 10 predictive variables were selected based on their significance in disease prediction. The data were then divided into a training set (70%) and a validation set (30%) for validation. External data were also utilized to validate the model's predictive performance.

RESULTS

A total of 24 indicators with significant differences were identified. In terms of predicting the risk of preterm birth, the XGBoost algorithm demonstrated the most outstanding performance, with an AUCROC of 0.89 (95% CI: 0.88-0.90). The top 10 critical indicators included ALP, AFP, ALB, HCT, TC, DBP, ALT, PLT, height, and SBP, which are essential for constructing an accurate predictive model. The model exhibited stable performance on both the training and validation sets, with AUC values of 0.93 and 0.87, respectively. Furthermore, the external testing set also showed superior performance, with an AUC of 0.79.

CONCLUSIONS

At the time of delivery, ALP, AFP, ALB, HCT, TC, DBP, ALT, PLT, height, and SBP are influential factors for sPTB in pregnant women. The XGBoost algorithm, constructed based on these factors, demonstrated the most outstanding performance.

Arterial stiffness as a novel tool for the early prediction of preeclampsia: a perspective

Preeclampsia is a leading complication of pregnancy that lacks accurate tools for its early prediction. Improved risk stratification tools early in pregnancy would enable more efficient allocation of limited healthcare resources while ensuring that pregnant women destined to develop preeclampsia receive appropriate care. This brief perspective highlights the current state of first-trimester preeclampsia prediction. We focus on arterial stiffness, an important hemodynamic indicator of vascular health that has shown promising results for improved early prediction of preeclampsia by our and independent research groups. Further, we outline the promise, applicability, and feasibility of integrating arterial stiffness assessments into clinical practice.

The role of cell-free DNA biomarkers and patient data in the early prediction of preeclampsia: an artificial intelligence model

BACKGROUND

Accurate individualized assessment of preeclampsia risk enables the identification of patients most likely to benefit from initiation of low-dose aspirin at 12 to 16 weeks of gestation when there is evidence for its effectiveness, and enables the guidance of appropriate pregnancy care pathways and surveillance.

OBJECTIVE

The primary objective of this study was to evaluate the performance of artificial neural network models for the prediction of preterm preeclampsia (<37 weeks' gestation) using patient characteristics available at the first antenatal visit and data from prenatal cell-free DNA screening. Secondary outcomes were prediction of early-onset preeclampsia (<34 weeks' gestation) and term preeclampsia (≥37 weeks' gestation).

METHODS

This secondary analysis of a prospective, multicenter, observational prenatal cell-free DNA screening study (SMART) included singleton pregnancies with known pregnancy outcomes. Thirteen patient characteristics that are routinely collected at the first prenatal visit and 2 characteristics of cell-free DNA (total cell-free DNA and fetal fraction) were used to develop predictive models for early-onset (<34 weeks), preterm (<37 weeks), and term (≥37 weeks) preeclampsia. For the models, the "reference" classifier was a shallow logistic regression model. We also explored several feedforward (nonlinear) neural network architectures with ≥1 hidden layers, and compared their performance with the logistic regression model. We selected a simple neural network model built with 1 hidden layer and made up of 15 units.

RESULTS

Of the 17,520 participants included in the final analysis, 72 (0.4%) developed early-onset, 251 (1.4%) preterm, and 420 (2.4%) term preeclampsia. Median gestational age at cell-free DNA measurement was 12.6 weeks, and 2155 (12.3%) had their cell-free DNA measurement at ≥16 weeks' gestation. Preeclampsia was associated with higher total cell-free DNA (median, 362.3 vs 339.0 copies/mL cell-free DNA; P<.001) and lower fetal fraction (median, 7.5% vs 9.4%; P<.001). The expected, cross-validated area under the curve scores for early-onset, preterm, and term preeclampsia were 0.782, 0.801, and 0.712, respectively, for the logistic regression model, and 0.797, 0.800, and 0.713, respectively, for the neural network model. At a screen-positive rate of 15%, sensitivity for preterm preeclampsia was 58.4% (95% confidence interval, 0.569-0.599) for the logistic regression model and 59.3% (95% confidence interval, 0.578-0.608) for the neural network model. The contribution of both total cell-free DNA and fetal fraction to the prediction of term and preterm preeclampsia was negligible. For early-onset preeclampsia, removal of the total cell-free DNA and fetal fraction features from the neural network model was associated with a 6.9% decrease in sensitivity at a 15% screen-positive rate, from 54.9% (95% confidence interval, 52.9-56.9) to 48.0% (95% confidence interval, 45.0-51.0).

CONCLUSION

Routinely available patient characteristics and cell-free DNA markers can be used to predict preeclampsia with performance comparable to that of other patient characteristic models for the prediction of preterm preeclampsia. Logistic regression and neural network models showed similar performance.

Validation of the first-trimester machine learning model for predicting pre-eclampsia in an Asian population

OBJECTIVES

To evaluate the performance of an artificial intelligence (AI) and machine learning (ML) model for first-trimester screening for pre-eclampsia in a large Asian population.

METHODS

This was a secondary analysis of a multicenter prospective cohort study in 10 935 participants with singleton pregnancies attending for routine pregnancy care at 11-13+6 weeks of gestation in seven regions in Asia between December 2016 and June 2018. We applied the AI+ML model for the first-trimester prediction of preterm pre-eclampsia (<37 weeks), term pre-eclampsia (≥37 weeks), and any pre-eclampsia, which was derived and tested in a cohort of pregnant participants in the UK (Model 1). This model comprises maternal factors with measurements of mean arterial pressure, uterine artery pulsatility index, and serum placental growth factor (PlGF). The model was further retrained with adjustments for analyzers used for biochemical testing (Model 2). Discrimination was assessed by area under the receiver operating characteristic curve (AUC). The Delong test was used to compare the AUC of Model 1, Model 2, and the Fetal Medicine Foundation (FMF) competing risk model.

RESULTS

The predictive performance of Model 1 was significantly lower than that of the FMF competing risk model in the prediction of preterm pre-eclampsia (0.82, 95% confidence interval [CI] 0.77-0.87 vs. 0.86, 95% CI 0.811-0.91, P = 0.019), term pre-eclampsia (0.75, 95% CI 0.71-0.80 vs. 0.79, 95% CI 0.75-0.83, P = 0.006), and any pre-eclampsia (0.78, 95% CI 0.74-0.81 vs. 0.82, 95% CI 0.79-0.84, P < 0.001). Following the retraining of the data with adjustments for the PlGF analyzers, the performance of Model 2 for predicting preterm pre-eclampsia, term pre-eclampsia, and any pre-eclampsia was improved with the AUC values increased to 0.84 (95% CI 0.80-0.89), 0.77 (95% CI 0.73-0.81), and 0.80 (95% CI 0.76-0.83), respectively. There were no differences in AUCs between Model 2 and the FMF competing risk model in the prediction of preterm pre-eclampsia (P = 0.135) and term pre-eclampsia (P = 0.084). However, Model 2 was inferior to the FMF competing risk model in predicting any pre-eclampsia (P = 0.024).

CONCLUSION

This study has demonstrated that following adjustment for the biochemical marker analyzers, the predictive performance of the AI+ML prediction model for pre-eclampsia in the first trimester was comparable to that of the FMF competing risk model in an Asian population.

Comparison of National Factor-Based Models for Preeclampsia Screening

OBJECTIVE

 This study aimed to compare the predictive values of the American College of Obstetricians and Gynecologists (ACOG), the National Institute for Health and Care Excellence (NICE), and the Society of Obstetricians and Gynecologists of Canada (SOGC) factor-based models for preeclampsia (PE) screening.

STUDY DESIGN

 We conducted a secondary analysis of maternal and birth data from 32 hospitals. For each delivery, we calculated the risk of PE according to the ACOG, the NICE, and the SOGC models. Our primary outcomes were PE and preterm PE (PE combined with preterm birth) using the ACOG criteria. We calculated the detection rate (DR or sensitivity), the false positive rate (FPR or 1 - specificity), the positive (PPV) and negative (NPV) predictive values of each model for PE and for preterm PE using receiver operator characteristic (ROC) curves.

RESULTS

 We used 130,939 deliveries including 4,635 (3.5%) cases of PE and 823 (0.6%) cases of preterm PE. The ACOG model had a DR of 43.6% for PE and 50.3% for preterm PE with FPR of 15.6%; the NICE model had a DR of 36.2% for PE and 41.3% for preterm PE with FPR of 12.8%; and the SOGC model had a DR of 49.1% for PE and 51.6% for preterm PE with FPR of 22.2%. The PPV for PE of the ACOG (9.3%) and NICE (9.4%) models were both superior than the SOGC model (7.6%; p < 0.001), with a similar trend for the PPV for preterm PE (1.9 vs. 1.9 vs. 1.4%, respectively; p < 0.01). The area under the ROC curves suggested that the ACOG model is superior to the NICE for the prediction of PE and preterm PE and superior to the SOGC models for the prediction of preterm PE (all with p < 0.001).

CONCLUSION

 The current ACOG factor-based model for the prediction of PE and preterm PE, without considering race, is superior to the NICE and SOGC models.

KEY POINTS

· Clinical factor-based model can predict PE in approximately 44% of the cases for a 16% false positive.. · The ACOG model is superior to the NICE and SOGC models to predict PE.. · Clinical factor-based models are better to predict PE in parous than in nulliparous..

First-trimester predictive models for adverse pregnancy outcomes-a base for implementation of strategies to prevent cardiovascular disease development

Introduction

This study aimed to establish efficient, cost-effective, and early predictive models for adverse pregnancy outcomes based on the combinations of a minimum number of miRNA biomarkers, whose altered expression was observed in specific pregnancy-related complications and selected maternal clinical characteristics.

Methods

This retrospective study included singleton pregnancies with gestational hypertension (GH, n = 83), preeclampsia (PE, n = 66), HELLP syndrome (n = 14), fetal growth restriction (FGR, n = 82), small for gestational age (SGA, n = 37), gestational diabetes mellitus (GDM, n = 121), preterm birth in the absence of other complications (n = 106), late miscarriage (n = 34), stillbirth (n = 24), and 80 normal term pregnancies. MiRNA gene expression profiling was performed on the whole peripheral venous blood samples collected between 10 and 13 weeks of gestation using real-time reverse transcription polymerase chain reaction (RT-PCR).

Results

Most pregnancies with adverse outcomes were identified using the proposed approach (the combinations of selected miRNAs and appropriate maternal clinical characteristics) (GH, 69.88%; PE, 83.33%; HELLP, 92.86%; FGR, 73.17%; SGA, 81.08%; GDM on therapy, 89.47%; and late miscarriage, 84.85%). In the case of stillbirth, no addition of maternal clinical characteristics to the predictive model was necessary because a high detection rate was achieved by a combination of miRNA biomarkers only [91.67% cases at 10.0% false positive rate (FPR)].

Conclusion

The proposed models based on the combinations of selected cardiovascular disease-associated miRNAs and maternal clinical variables have a high predictive potential for identifying women at increased risk of adverse pregnancy outcomes; this can be incorporated into routine first-trimester screening programs. Preventive programs can be initiated based on these models to lower cardiovascular risk and prevent the development of metabolic/cardiovascular/cerebrovascular diseases because timely implementation of beneficial lifestyle strategies may reverse the dysregulation of miRNAs maintaining and controlling the cardiovascular system.

Aspirin for evidence-based preeclampsia prevention trial: effects of aspirin on maternal serum pregnancy-associated plasma protein A and placental growth factor trajectories in pregnancy

BACKGROUND

The exact mechanism by which aspirin prevents preeclampsia remains unclear. Its effects on serum placental biomarkers throughout pregnancy are also unknown.

OBJECTIVE

To investigate the effects of aspirin on serum pregnancy-associated plasma protein A and placental growth factor trajectories using repeated measures from women at increased risk of preterm preeclampsia.

STUDY DESIGN

This was a longitudinal secondary analysis of the Combined Multimarker Screening and Randomized Patient Treatment with Aspirin for Evidence-based Preeclampsia Prevention trial using repeated measures of pregnancy-associated plasma protein A and placental growth factor. In the trial, 1620 women at increased risk of preterm preeclampsia were identified using the Fetal Medicine Foundation algorithm at 11 to 13+6 weeks of gestation, of whom 798 were randomly assigned to receive aspirin 150 mg and 822 to receive placebo daily from before 14 weeks to 36 weeks of gestation. Serum biomarkers were measured at baseline and follow-up visits at 19 to 24, 32 to 34, and 36 weeks of gestation. Generalized additive mixed models with treatment by gestational age interaction terms were used to investigate the effect of aspirin on biomarker trajectories over time.

RESULTS

Overall, there were 5507 pregnancy-associated plasma protein A and 5523 placental growth factor measurements. Raw pregnancy-associated plasma protein A values increased over time, and raw placental growth factor increased until 32 weeks of gestation followed by a decline. The multiple of the median mean values of the same biomarkers were consistently below 1.0 multiple of the median, reflecting the high-risk profile of the study population. Trajectories of mean pregnancy-associated plasma protein A and placental growth factor multiple of the median values did not differ significantly between the aspirin and placebo groups (aspirin treatment by gestational age interaction P values: .259 and .335, respectively).

CONCLUSION

In women at increased risk of preterm preeclampsia, aspirin 150 mg daily had no significant effects on pregnancy-associated plasma protein A or placental growth factor trajectories when compared to placebo.

Can short- and long-term maternal and infant risks linked to hypertension and diabetes during pregnancy be reduced by therapy?

Hypertensive disorders of pregnancy (HDP), especially preeclampsia, and diabetes during pregnancy pose significant risks for both maternal and infant health, extending to long-term outcomes such as early-onset cardiovascular disease and metabolic disorders. Current strategies for managing HDP focus on screening, prevention, surveillance, and timely intervention. No disease-modifying therapies exist so far for established preeclampsia; delivery remains the definitive resolution. Preventive measures-including early pregnancy screening, exercise, and low-dose aspirin-show promise. Antihypertensive treatments reduce severe hypertension risks, whereas magnesium sulfate remains the standard for preventing eclampsia. Planned delivery from gestational week 37 can balance maternal benefits and neonatal risks in women with established preeclampsia. Delivery between 34 and 37 weeks gestation in women with preeclampsia has to balance risks for mother and infant. Lifestyle interventions-particularly diet and physical activity-are pivotal in managing gestational diabetes mellitus and type 2 diabetes. The oral antidiabetic metformin has shown benefits in glycaemic control and reducing maternal weight gain, although its long-term effects on offspring remain uncertain. The safety of other peroral antidiabetics in pregnancy is less studied. Advancements in glucose monitoring and insulin administration present encouraging prospects for enhancing outcomes in women with diabetes types 1 and 2. Both HDP and diabetes during pregnancy necessitate vigilant management through a combination of lifestyle modifications, pharmacological interventions, and timely obstetric care. Although certain treatments such as low-dose aspirin and metformin show efficacy in risk reduction, further research is ongoing to ensure safety for both mothers and their offspring to reduce short- and long-term adverse effects.

Therapeutic Value of Low-Dose Acetylsalicylic Acid for the Prevention of Preeclampsia in High-Risk Bulgarian Women

Introduction Preeclampsia (PE) is a syndrome that affects pregnant women after 20 weeks of gestation and involves numerous organ systems. Screening for PE is essential to prevent complications and guide management. Some existing guidelines for screening have limitations in terms of detection rates and false positives. The aim of this study is to assess the therapeutic value of low-dose acetylsalicylic acid (ASA) for the prevention of PE in high-risk Bulgarian women. Methodology A prospective cohort research was carried out, encompassing women who were recruited from several routine consultations, such as booking, scanning, and regular prenatal visits. We utilized the purposive sampling technique to carefully choose potential participants. The study was conducted by a maternal-fetal medicine center located in Plovdiv, Bulgaria. The data-gathering period spanned from January 2018 to November 2020. At the appointment, the following procedures were conducted: 1) recording history; 2) assessing height, weight, and blood pressure; 3) collecting blood specimens for biochemical markers; and 4) ultrasound examination. Results A total sample size of 1,383 individuals was categorized into two distinct groups: high-risk patients (n = 506) and low-risk patients (n = 877). The mean uterine artery pulsatility index (UtA-PI) and mean arterial pressure (MAP) ratios were all greater in high-risk group women (p < 0.05). The data revealed that a significant number of high-risk women failed to adhere to the prescribed dosage or regular use of ASA as recommended by their doctor. There were only 384 (75.9%) high-risk women who took low-dose ASA regularly. Conclusion The findings emphasize the importance of personalized prenatal care and early risk assessment to improve maternal and fetal outcomes. Therefore, it is crucial to educate pregnant women, considering the benefits and risks of low-dose ASA when appropriately indicated.

Prevention of Pregnancy Complications Using a Multimodal Lifestyle, Screening, and Medical Model

Prevention of pregnancy complications related to the "great obstetrical syndromes" (preeclampsia, fetal growth restriction, spontaneous preterm labor, and stillbirth) is a global research and clinical management priority. These syndromes share many common pathophysiological mechanisms that may contribute to altered placental development and function. The resulting adverse pregnancy outcomes are associated with increased maternal and perinatal morbidity and mortality and increased post-partum risk of cardiometabolic disease. Maternal nutritional and environmental factors are known to play a significant role in altering bidirectional communication between fetal-derived trophoblast cells and maternal decidual cells and contribute to abnormal placentation. As a result, lifestyle-based interventions have increasingly been recommended before, during, and after pregnancy, in order to reduce maternal and perinatal morbidity and mortality and decrease long-term risk. Antenatal screening strategies have been developed following extensive studies in diverse populations. Multivariate preeclampsia screening using a combination of maternal, biophysical, and serum biochemical markers is recommended at 11-14 weeks' gestation and can be performed at the same time as the first-trimester ultrasound and blood tests. Women identified as high-risk can be offered prophylactic low dose aspirin and monitored with angiogenic factor assessment from 22 weeks' gestation, in combination with clinical assessment, serum biochemistry, and ultrasound. Lifestyle factors can be reassessed during counseling related to antenatal screening interventions. The integration of lifestyle interventions, pregnancy screening, and medical management represents a conceptual advance in pregnancy care that has the potential to significantly reduce pregnancy complications and associated later life cardiometabolic adverse outcomes.

Prospective Validation of First-Trimester Screening for Preterm Preeclampsia in Nulliparous Women (PREDICTION Study)

BACKGROUND

Fetal Medicine Foundation (FMF) studies suggest that preterm preeclampsia can be predicted in the first trimester by combining biophysical, biochemical, and ultrasound markers and prevented using aspirin. We aimed to evaluate the FMF preterm preeclampsia screening test in nulliparous women.

METHODS

We conducted a prospective multicenter cohort study of nulliparous women recruited at 11 to 14 weeks. Maternal characteristics, mean arterial blood pressure, PAPP-A (pregnancy-associated plasma protein A), PlGF (placental growth factor) in maternal blood, and uterine artery pulsatility index were collected at recruitment. The risk of preterm preeclampsia was calculated by a third party blinded to pregnancy outcomes. Receiver operating characteristic curves were used to estimate the detection rate (sensitivity) and the false-positive rate (1-specificity) for preterm (<37 weeks) and for early-onset (<34 weeks) preeclampsia according to the FMF screening test and according to the American College of Obstetricians and Gynecologists criteria.

RESULTS

We recruited 7554 participants including 7325 (97%) who remained eligible after 20 weeks of which 65 (0.9%) developed preterm preeclampsia, and 22 (0.3%) developed early-onset preeclampsia. Using the FMF algorithm (cutoff of ≥1 in 110 for preterm preeclampsia), the detection rate was 63.1% for preterm preeclampsia and 77.3% for early-onset preeclampsia at a false-positive rate of 15.8%. Using the American College of Obstetricians and Gynecologists criteria, the equivalent detection rates would have been 61.5% and 59.1%, respectively, for a false-positive rate of 34.3%.

CONCLUSIONS

The first-trimester FMF preeclampsia screening test predicts two-thirds of preterm preeclampsia and three-quarters of early-onset preeclampsia in nulliparous women, with a false-positive rate of ≈16%.

REGISTRATION

URL: https://www.clinicaltrials.gov; Unique identifier: NCT02189148.

Management of hypertensive disorders in pregnancy: a Position Statement of the European Society of Hypertension Working Group 'Hypertension in Women'

Hypertensive disorders in pregnancy (HDP), remain the leading cause of adverse maternal, fetal, and neonatal outcomes. Epidemiological factors, comorbidities, assisted reproduction techniques, placental disorders, and genetic predisposition determine the burden of the disease. The pathophysiological substrate and the clinical presentation of HDP are multifarious. The latter and the lack of well designed clinical trials in the field explain the absence of consensus on disease management among relevant international societies. Thus, the usual clinical management of HDP is largely empirical. The current position statement of the Working Group 'Hypertension in Women' of the European Society of Hypertension (ESH) aims to employ the current evidence for the management of HDP, discuss the recommendations made in the 2023 ESH guidelines for the management of hypertension, and shed light on controversial issues in the field to stimulate future research.

Model construction and application for predicting pre-eclampsia by Sonoclot coagulation analyzer

Maternal age has significantly increased among Chinese women, thereby posing risk of pregnancy-related complications. Preeclampsia is a leading cause of maternal and perinatal morbidity and mortality, and coagulation analysis in conjunction with clinical signs and symptoms are generally used for its diagnosis with limited efficacy. Sonoclot coagulation analyzer is effective in assessing coagulation function used during cerebral surgery and cardiovascular surgery. However, its use has not been explored in preeclampsia. Here, we investigated the potential use of Sonoclot in diagnosing preeclampsia in obstetrics cases. Subjects meeting the screening criteria were divided either into a test group or a control group, according to whether they were preeclamptic or not. We recorded the Sonoclot-derived coagulation and the routine coagulation parameters including platelet function (PF), activated clotting time (ACT) and clot rate (CR), prothrombin time (PT), activated partial thromboplastin time (APTT), thrombin time (TT), fibrinogen (FIB), and platelet count. Regression analysis was done on the relevant parameters to assess the feasibility of Sonoclot analyzer in preeclampsia diagnosis. In parallel, changes in preeclampsia lncRNAs was also evaluated. Significant differences were recorded in PT and ACT between the two groups. In the monovariant logistic regression, PT and ACT appeared to be reliable predictor variables. In the multinomial logistic regression, a total of five regression steps were performed with decreasing AIC values. The K-fold cross validation resulted in an accuracy rate (ACC) of 77.5%, a false positive rate of 16.4%, and a false negative rate of 33.2%. lncRNAs ANRIL and HOXD-AS1 were found deregulated. Our findings indicate that Sonoclot may be useful for diagnosis of preeclampsia in obstetrics.

Association between plasma growth differentiation factor 15 levels and pre-eclampsia in China

Background

Growth differentiation factor-15 (GDF-15) is a stress response protein and is related to cardiovascular diseases (CVD). This study aimed to investigate the association between GDF-15 and pre-eclampsia (PE).

Method

The study involved 299 pregnant women, out of which 236 had normal pregnancies, while 63 participants had PE. Maternal serum levels of GDF-15 were measured by using enzyme-linked immunosorbent assay kits and then translated into multiple of median (MOM) to avoid the influence of gestational week at blood sampling. Logistic models were performed to estimate the association between GDF-15 MOM and PE, presenting as odd ratios (ORs) and 95% confidence intervals (CIs).

Results

MOM of GDF-15 in PE participants was higher compared with controls (1.588 vs. 1.000, p < 0.001). In the logistic model, pregnant women with higher MOM of GDF-15 (>1) had a 4.74-fold (95% CI = 2.23-10.08, p < 0.001) increased risk of PE, adjusted by age, preconceptional body mass index, gravidity, and parity.

Conclusions

These results demonstrated that higher levels of serum GDF-15 were associated with PE. GDF-15 may serve as a biomarker for diagnosing PE.

Etiologically Based Functional Taxonomy of the Preterm Birth Syndrome

Preterm birth (PTB) is a complex syndrome traditionally defined by a single parameter, namely, gestational age at birth (ie, ˂37 weeks). This approach has limitations for clinical usefulness and may explain the lack of progress in identifying cause-specific effective interventions. The authors offer a framework for a functional taxonomy of PTB based on (1) conceptual principles established a priori; (2) known etiologic factors; (3) specific, prospectively identified obstetric and neonatal clinical phenotypes; and (4) postnatal follow-up of growth and development up to 2 years of age. This taxonomy includes maternal, placental, and fetal conditions routinely recorded in data collection systems.

Feasibility of Universal Screening for Preeclampsia Risk and Aspirin Recommendation in the Ultrasound Unit

OBJECTIVE

 To evaluate the feasibility and impact of using the first-trimester ultrasound visit to identify and counsel women at increased risk of preeclampsia about the benefits of low-dose aspirin (LDA) for preventing preeclampsia. We also assessed patient-reported utilization of LDA, perceived risk for preeclampsia, and clinical outcomes.

STUDY DESIGN

 Women presenting for routine first-trimester nuchal-translucency (NT) ultrasounds were screened for clinical preeclampsia risks using a self-administered risk assessment. Women at moderate or high risk for preeclampsia were counseled to take LDA, if not already taking it. LDA utilization and perceived risk for preeclampsia were assessed during the second-trimester ultrasound. Factors associated with LDA utilization were analyzed. Pregnancy outcomes were compared between those who used LDA and those who did not.

RESULTS

 Slightly more than 20% of patients (765/3,669) screened at increased risk for developing preeclampsia. Of those, 67.8% (519/765) had not received LDA recommendations from their referring obstetrician and 97 had not been taking LDA despite being advised to do so. Combined, 94.6% (583/616) of these patients eligible to start LDA prophylaxis received the indicated counseling during the ultrasound visit. A total of 61.4% (358/583) of women completed the follow-up form and of those 77.9% (279/358) reported taking LDA. Screening at increased risk for preeclampsia and perception of increased risk were positively associated with LDA utilization, whereas concerns for LDA safety were negatively associated with use. African American/Black patients and Medicaid recipients were less likely to use LDA. Pregnancy outcomes were similar between those who used LDA and those who did not.

CONCLUSION

 Assessing preeclampsia risk and counseling patients about LDA at the time of the NT ultrasound are feasible in the ultrasound unit and led to good LDA utilization among women at increased risk for preeclampsia. This intervention may standardize patient care and help close the disparity in maternal health.

KEY POINTS

· A simple intervention captured 2/3 of eligible patients.. · Aspirin utilization rate was good after the intervention.. · Screening high risk for preeclampsia and self-perception of risk correlated with aspirin use..

Cell-free DNA methylation-based preeclampsia prediction: A journey to improve maternal health

Presymptomatic prediction of preeclampsia (PE) is crucial to enable early prophylactic treatment. Current screening tools are either complex or lack predictive value. We recently demonstrated that cell-free DNA methylation can be leveraged to predict early-onset PE in 57% at a 10% false positive rate. Importantly, this minimally invasive screening test can be implemented as an add-on to current widespread noninvasive prenatal aneuploidy screening. Here, we highlight the pitfalls and promising prospects of this research.

Cell-Free Nucleic Acids for Early Prediction of Preeclampsia

PURPOSE OF REVIEW

This review summarizes the potential of cell-free nucleic acids for predicting preeclampsia, contrasts them with other methods, and discusses these findings' relevance to preeclampsia's pathogenesis and care.

RECENT FINDINGS

Recent studies have demonstrated the utility of cell-free nucleic acids in early preeclampsia risk prediction. Encouragingly, nucleic acid measurement exhibits similar or better sensitivity as compared to standard screening assays and furthermore sheds light on preeclampsia's underlying placental biology. Over the past decade, liquid biopsies measuring cell-free nucleic acids have found diverse applications, including in prenatal care. Recent advances have extended their utility to predict preeclampsia, a major cause of maternal mortality. These assays assess methylation patterns in cell-free DNA (cfDNA) or gene levels in cell-free RNA (cfRNA). Currently, preeclampsia care focuses on blood pressure control, seizure prevention, and delivery. If validated, early prediction of preeclampsia through liquid biopsies can improve maternal health and deepen our understanding of its causes.

Prediction of preeclampsia from retinal fundus images via deep learning in singleton pregnancies: a prospective cohort study

INTRODUCTION

Early prediction of preeclampsia (PE) is of universal importance in controlling the disease process. Our study aimed to assess the feasibility of using retinal fundus images to predict preeclampsia via deep learning in singleton pregnancies.

METHODS

This prospective cohort study was conducted at Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine. Eligible participants included singleton pregnancies who presented for prenatal visits before 14 weeks of gestation from September 1, 2020, to February 1, 2022. Retinal fundus images were obtained using a nonmydriatic digital retinal camera during their initial prenatal visit upon admission before 20 weeks of gestation. In addition, we generated fundus scores, which indicated the predictive value of hypertension, using a hypertension detection model. To evaluate the predictive value of the retinal fundus image-based deep learning algorithm for preeclampsia, we conducted stratified analyses and measured the area under the curve (AUC), sensitivity, and specificity. We then conducted sensitivity analyses for validation.

RESULTS

Our study analyzed a total of 1138 women, 92 pregnancies developed into hypertension disorders of pregnancy (HDP), including 26 cases of gestational hypertension and 66 cases of preeclampsia. The adjusted odds ratio (aOR) of the fundus scores was 2.582 (95% CI, 1.883-3.616; P  < 0.001). Otherwise, in the categories of prepregnancy BMI less than 28.0 and at least 28.0, the aORs were 3.073 (95%CI, 2.265-4.244; P  < 0.001) and 5.866 (95% CI, 3.292-11.531; P  < 0.001). In the categories of maternal age less than 35.0 and at least 35.0, the aORs were 2.845 (95% CI, 1.854-4.463; P  < 0.001) and 2.884 (95% CI, 1.794-4.942; P  < 0.001). The AUC of the fundus score combined with risk factors was 0.883 (sensitivity, 0.722; specificity, 0.934; 95% CI, 0.834-0.932) for predicting preeclampsia.

CONCLUSION

Our study demonstrates that the use of deep learning algorithm-based retinal fundus images offers promising predictive value for the early detection of preeclampsia.

An early screening model for preeclampsia: utilizing zero-cost maternal predictors exclusively

To provide a reliable, low-cost screening model for preeclampsia, this study developed an early screening model in a retrospective cohort (25,709 pregnancies) and validated in a validation cohort (1760 pregnancies). A data augmentation method (α-inverse weighted-GMM + RUS) was applied to a retrospective cohort before 10 machine learning models were simultaneously trained on augmented data, and the optimal model was chosen via sensitivity (at a false positive rate of 10%). The AdaBoost model, utilizing 16 predictors, was chosen as the final model, achieving a performance beyond acceptable with Area Under the Receiver Operating Characteristic Curve of 0.8008 and sensitivity of 0.5190. All predictors were derived from clinical characteristics, some of which were previously unreported (such as nausea and vomiting in pregnancy and menstrual cycle irregularity). Compared to previous studies, our model demonstrated superior performance, exhibiting at least a 50% improvement in sensitivity over checklist-based approaches, and a minimum of 28% increase over multivariable models that solely utilized maternal predictors. We validated an effective approach for preeclampsia early screening incorporating zero-cost predictors, which demonstrates superior performance in comparison to similar studies. We believe the application of the approach in combination with high performance approaches could substantially increase screening participation rate among pregnancies. Machine learning model for early preeclampsia screening, using 16 zero-cost predictors derived from clinical characteristics, was built on a 10-year Chinese cohort. The model outperforms similar research by at least 28%; validated on an independent cohort.

Hypertensive states of pregnancy

Hypertensive disorders in pregnancy (HDP) remain a leading cause of pregnancy-related maternal and foetal morbidity and mortality worldwide, including chronic hypertension, gestational hypertension, and pre-eclampsia. Affected women and newborns also have an increased risk of cardiovascular disease later in life, independent of traditional cardiovascular disease risks. Despite these risks, recommendations for optimal diagnosis and treatment have changed little in recent decades, probably due to fear of the foetal repercussions of decreased blood pressure and possible drug toxicity. In this document we review the diagnostic criteria and classification of (HDP), as well as important aspects regarding pathophysiology and early detection that allows early identification of women at risk, with the aim of preventing both immediate and long-term consequences. Prophylactic treatment with aspirin is also reviewed early and a therapeutic approach is carried out that involves close maternal and foetal monitoring, and if necessary, the use of safe drugs in each situation. This review aims to provide an updated vision for the prevention, diagnosis, and treatment of HDP that is useful in our usual clinical practice.

Comparison of Pre-eclampsia Screening Algorithms in the First Trimester of Pregnancy Being Used in Thai Nguyen Province

Objective To compare the rates of pregnancies in high-risk groups for preeclampsia recommended for aspirin prophylactic when screened using various common algorithms. Methods This cross-sectional study was conducted on 1726 pregnant women from 11 to 13 weeks six days of gestation receiving antenatal care at two hospitals: Thai Nguyen National Hospital and a hospital in Thai Nguyen Province from October 2022 to October 2023. All participants provided consent for the study. We collected maternal characteristics, obstetric history, mean arterial pressure (MAP), mean uterine artery-PI (UtA-PI), and placental growth factor serum (PLGF). Screening performance estimates were calculated using the Fetal Medicine Foundation (FMF) and National Institute for Health and Care Excellence (NICE) guidelines. All pregnant women in the study had their preeclampsia risk assessed using all three algorithms with two cut-off points. Our data was collected, entered and analyzed using SPSS software 20.0 (IBM Corp., Armonk, NY). Categorical data was reported as frequency and percentage. McNemar's test was used for analyzing differences in the sizes of individual groups. Results In our study, the most common high-risk factor identified was the history of preeclampsia, 132 cases (7.6%). According to the NICE guideline, BMI ≥ 35 (kg/m²) is considered a moderate risk factor for preeclampsia. Several risk factors, such as BMI ≥ 35 kg/m² and history of diabetes mellitus type 1, were not present in any participants. Only one pregnant woman had chronic kidney disease (0.06%). Out of the 1726 pregnant women surveyed, the rates of high-risk preeclampsia were as follows: 9.9% (171 cases) based on algorithm 1; 10.8% (187 cases) based on algorithm 2 with a cut-off point > 1/100, 11.8% (203 cases) with a cut-off point > 1/150; 10.3 % (178 cases) based on algorithm 3 with a cut-off point > 1/100, and 11.6% (201 cases) with a cut-off point > 1/150. Among these algorithms, pregnant women in the high-risk preeclampsia group were advised to consider taking low-dose aspirin. Conclusion Screening for pre-eclampsia based on NICE recommendations resulted in a lower number of high-risk pregnant women requiring prophylactic aspirin use compared to other algorithms. This means that some pregnant women at risk of developing preeclampsia are not recommended to use aspirin as a preventive measure. Adding PLGF to the screening strategy will help us get closer to pregnant women who are truly at risk of progressing to preterm preeclampsia.

Prevention of preeclampsia

Preeclampsia is a relatively common pregnancy complication and constitutes a major cause of morbidity and mortality for mothers and children worldwide. It disproportionally affects low-resource countries. Appropriate identification of individuals at increased risk and prevention of the disease and its complications remain healthcare and research priorities, and the investigation of potential interventions to prevent preeclampsia has driven much of the obstetric research in recent decades. In this article, we review the scientific literature on the topic, highlighting established benefits and remaining questions regarding different non-pharmacological and pharmacological strategies, including exercise, the timing of birth, aspirin and calcium use, among others, as well as potential novel therapies under investigation.

Screening for pre-eclampsia with competing-risks model using placental growth factor measurement in blood samples collected before 11 weeks' gestation

OBJECTIVES

To describe the distributional properties and assess the performance of placental growth factor (PlGF) measured in blood samples collected before 11 weeks' gestation in the prediction of pre-eclampsia (PE).

METHODS

The study population consisted of pregnant women included in the Pre-eclampsia Screening in Denmark (PRESIDE) study with a PlGF measurement from the routine combined first-trimester screening (cFTS) blood sample collected at 8-14 weeks' gestation. PRESIDE was a prospective multicenter study investigating the predictive performance of the Fetal Medicine Foundation (FMF) first-trimester screening algorithm for PE in a Danish population. In the current study, serum concentration of PlGF in the cFTS blood samples was analyzed in batches between January and June 2021.

RESULTS

A total of 8386 pregnant women were included. The incidence of PE was 0.7% at < 37 weeks' gestation and 3.0% at ≥ 37 weeks. In blood samples collected at 10 weeks' gestation, PlGF multiples of the median (MoM) were significantly lower in pregnancies with preterm PE < 37 weeks compared to unaffected pregnancies. However, PlGF MoM did not differ significantly between pregnancies with PE and unaffected pregnancies in samples collected before 10 weeks' gestation.

CONCLUSIONS

The gestational-age range for PlGF sampling may be expanded from 11-14 to 10-14 weeks when assessing the risk for PE using the FMF first-trimester screening model. There is little evidence to support the use of PlGF in blood samples collected before 10 weeks' gestation. © 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.

Hypertensive Disorders in Pregnancy: Global Burden From 1990 to 2019, Current Research Hotspots and Emerging Trends

Hypertensive disorders in pregnancy (HDP) constitute a worldwide health problem for pregnant women and their infants. This study provided HDP burden over 1990 to 2019 by region and age distribution, and predicted changes in related values for the next 25 years. We then conducted an econometric analysis of the author distribution, collaborative networks, keyword burst clustering, and spatio-temporal analysis of HDP-related publications from 2012 to 2022 to access current scientific developments and hotspots. The number of pregnant women with HDP has been increasing over the past 30 years, with regional and age-stratified differences in the burden of disease. Additionally, projections suggest an increase of deaths due to maternal HDP among adolescents younger than 20 years. Current research is mostly centered on pre-eclampsia, with hot keywords including trophoblast, immune tolerance, frozen-thawed embryo transfer, aspirin, gestational diabetes association, and biomarkers. Researches on the pathological mechanism, classification, and subtypes of HDP need to be further advanced.

First trimester prediction models for small-for- gestational age and fetal growth restricted fetuses without the presence of preeclampsia

We established efficient first trimester prediction models for small-for-gestational age (SGA) and fetal growth restriction (FGR) without the presence of preeclampsia (PE) regardless of the gestational age of the onset of the disease [early FGR occurring before 32 gestational week or late FGR occurring after 32 gestational week]. The retrospective study was performed on singleton Caucasian pregnancies (n = 6440) during the period 11/2012-3/2020. Finally, 4469 out of 6440 pregnancies had complete medical records since they delivered in the Institute for the Care of Mother and Child, Prague, Czech Republic. The study included all cases diagnosed with SGA (n = 37) or FGR (n = 82) without PE, and 80 selected normal pregnancies. Four microRNAs (miR-1-3p, miR-20a-5p, miR-146a-5p, and miR-181a-5p) identified 75.68 % SGA cases at 10.0 % false positive rate (FPR). Eight microRNAs (miR-1-3p, miR-20a-5p, miR-20b-5p, miR-126-3p, miR-130b-3p, miR-146a-5p, miR-181a-5p, and miR-499a-5p) identified 83.80 % SGA cases at 10.0 % FPR. The prediction model for SGA based on microRNAs was further improved via implementation of maternal clinical characteristics [maternal age and BMI, an infertility treatment by assisted reproductive technology (ART), first trimester screening for PE and/or FGR and for spontaneous preterm, both by FMF algorithm]. Then 81.08 % and 89.19 % pregnancies developing SGA were identified at 10.0 % FPR in case of utilization of 4 microRNA and 8 microRNA biomarkers. Simplified prediction model for SGA based on limited number of maternal clinical characteristics (maternal age and BMI, an infertility treatment by ART, and 4 microRNAs) does not improve the detection rate of SGA (70.27 % SGA cases at 10.0 % FPR) when compared with prediction model for SGA based just on the expression profile of 4 or 8 microRNAs biomarkers. Seven microRNAs only (miR-16-5p, miR-20a-5p, miR-145-5p, miR-146a-5p, miR-181a-5p, miR-342-3p, and miR-574-3p) identified 42.68 % FGR cases at 10.0 % FPR (AUC 0.725). However, the combination of 10 microRNAs only (miR-16-5p, miR-20a-5p, miR-100-5p, miR-143-3p, miR-145-5p, miR-146a-5p, miR-181a-5p, miR-195-5p, miR-342-3p, and miR-574-3p) reached a higher discrimination power (AUC 0.774). It identified 40.24 % FGR cases at 10.0 % FPR. The prediction model for any subtype of FGR based on microRNAs was further improved via implementation of maternal clinical characteristics [maternal age and BMI, an infertility treatment by ART, the parity (nulliparity), the occurrence of SGA or FGR in previous gestation, and the occurrence of any autoimmune disorder, and the presence of chronic hypertension]. Then 64.63 % and 65.85 % pregnancies destinated to develop FGR were identified at 10.0 % FPR in case of utilization of 7 microRNA biomarkers or 10 microRNA biomarkers. When other clinical variables next to those ones mentioned above such as first trimester screening for PE and/or FGR and for spontaneous preterm, both by FMF algorithm, were added to the prediction model for FGR, the detection power was even increased to 74.39 % cases and 78.05 % cases at 10.0 % FPR.

Epigenetic phenotype of plasma cell-free DNA in the prediction of early-onset preeclampsia

BACKGROUND

In the current study, we sought to characterise the methylation haplotypes and nucleosome positioning patterns of placental DNA and plasma cell-free DNA of pregnant women with early-onset preeclampsia using whole genome bisulphite sequencing (WGBS) and methylation capture bisulphite sequencing (MCBS) and further develop and examine the diagnostic performance of a generalised linear model (GLM) by incorporating the epigenetic features for early-onset preeclampsia.

METHODS

This case-control study recruited pregnant women aged at least 18 years who delivered their babies at our Hospital. In addition, non-pregnant women with no previous history of diseases were included. Placental samples of the villous parenchyma were taken at the time of delivery and venous blood was drawn from pregnant women during non-invasive prenatal testing at 12-15 weeks of pregnancy and nonpregnant women during the physical check-up. WGBS and MCBS were carried out of extracted genomic DNA. Then, we established the GLM by incorporating preeclampsia-specific methylation haplotypes and nucleosome positioning patterns and examined the diagnostic performance of the model by receiver operating characteristic (ROC) curve analysis.

RESULTS

The study included 135 pregnant women and 50 non-pregnant women. Our high-depth MCBS revealed notably different DNA methylation and nucleosome positioning patterns between women with and without preeclampsia. Preeclampsia-specific hypermethylated sites were found predominantly in the promoter regions and particularly enriched in CTCF on the X chromosome. Totally, 2379 preeclampsia-specific methylation haplotypes were found across the entire genome. ROC analysis showed that the area under the ROC curve (AUC) was 0.938 (95%CI 0.877, 1.000). At a GLM cut-off of 0.341, the AUC was the maximum, with a sensitivity of 95.6% and a specificity of 89.7%.

CONCLUSION

Pregnant women with early-onset preeclampsia exhibit DNA methylation and nucleosome positioning patterns in placental and plasma DNA.

Motivation towards first trimester screening for preeclampsia among pregnant women in Denmark: A cross-sectional questionnaire study

INTRODUCTION

The aim of this cross-sectional questionnaire study was to investigate motivation to participate in a possible new screening for preeclampsia in the first trimester of pregnancy among Danish pregnant women through a questionnaire based on Theory of Planned Behavior developed for this specific purpose. The new screening combines maternal characteristics with mean arterial pressure, uterine artery pulsatility index and biochemical markers to predict the risk of preeclampsia, whereas the current Danish screening uses maternal characteristics alone.

MATERIAL AND METHODS

Participation was offered to a proportion of women attending a first or a second trimester screening scan at two University Hospitals in Copenhagen. The questionnaire was set up in REDCap® and answers were entered directly into the database, which was accessed via a QR-code.

RESULTS

We invited 772 pregnant women to participate in the questionnaire survey between November 2021 and April 2022 at Copenhagen University Hospital Rigshospitalet (study site one) (n = 238) and Copenhagen University Hospital Hvidovre (study site two) (n = 534). The response rate was 71.8% (171/238) at study site one and 33.9% (181/534) at study site two. A total of 352 women were included in the study (total participation rate 45.6%). Most women had a positive attitude towards preeclampsia screening in pregnancy, and 99.4% said they would participate in a risk assessment for preeclampsia if given the opportunity. A total of 97.4% answered "yes" to whether a first trimester preeclampsia screening should be offered to all pregnant women in Denmark. Positive motivation to participate in preeclampsia screening was correlated with having a network with a positive attitude towards preeclampsia screening.

CONCLUSIONS

The results of this study indicate that Danish pregnant women have a positive attitude towards participation in a first trimester screening for preeclampsia. This observation might be useful in relation to possible future implementation in Denmark.

Impact of Endometrial Preparation on the Maternal and Fetal Cardiovascular Variables of the First Trimester Combined Screening Test

The modality of endometrial preparation for the transfer of frozen-thawed embryos may influence maternal and fetal adaptation to pregnancy and could thus impact the results of the first trimester combined screening test. We conducted a retrospective cross-sectional study on singleton pregnancies achieved by embryo transfer of a single frozen-thawed blastocyst, comparing two different endometrial preparation protocols: natural cycle (n = 174) and hormone replacement therapy (HRT) (n = 122). The primary outcome was the risk of preeclampsia at the first trimester combined screening test. Secondary endpoints included variable reflecting fetal cardiac function (nuchal translucency and fetal heart rate), maternal adaptation (median arterial blood pressure-MAP and uterine arteries pulsatility index-UtA-PI), and placentation (pregnancy associated plasma protein A and placental growth factor). The risk of early preeclampsia was comparable in the two groups (38% vs. a 28%, p = 0.12). However, women in the natural cycle group showed lower fetal heart rate (159 [155-164] vs. 164 [158-168], p = 0.002) and higher UtA-PI (0.96 [0.74-1.18] vs. 0.72 [0.58-0.90], p < 0.001). The frequency of a screening test at high risk for aneuploidies was similar. The modality of transfer of frozen-thawed embryos is associated with changes in the variables reflecting maternal and fetal cardiovascular function.

Glycosylated fibronectin improves first-trimester prediction of pre-eclampsia

OBJECTIVE

To determine whether maternal serum glycosylated fibronectin (GlyFn) level in the first trimester increases the sensitivity of the Fetal Medicine Foundation (FMF) triple test, which incorporates mean arterial pressure, uterine artery pulsatility index and placental growth factor, when screening for pre-eclampsia (PE) in an Asian population.

METHODS

This was a nested case-control study of Chinese women with a singleton pregnancy who were screened for PE at 11-13 weeks' gestation as part of a non-intervention study between December 2016 and June 2018. GlyFn levels were measured retrospectively in archived serum from 1685 pregnancies, including 101 with PE, using an enzyme-linked immunosorbent assay (ELISA), and from 448 pregnancies, including 101 with PE, using a point-of-care (POC) device. Concordance between ELISA and POC tests was assessed using Lin's correlation coefficient and Passing-Bablok and Bland-Altman analyses. GlyFn was transformed into multiples of the median (MoM) to adjust for maternal and pregnancy characteristics. GlyFn MoM was compared between PE and non-PE pregnancies, and the association between GlyFn MoM and gestational age at delivery with PE was assessed. Risk for developing PE was estimated using the FMF competing-risks model. Screening performance for preterm and any-onset PE using different biomarker combinations was quantified by area under the receiver-operating-characteristics curve (AUC) and detection rate (DR) at a 10% fixed false-positive rate (FPR). Differences in AUC between biomarker combinations were compared using the DeLong test.

RESULTS

The concordance correlation coefficient between ELISA and POC measurements was 0.86 (95% CI, 0.83-0.88). Passing-Bablok analysis indicated proportional bias (slope, 1.08 (95% CI, 1.04-1.14)), with POC GlyFn being significantly higher compared with ELISA GlyFn. ELISA GlyFn in non-PE pregnancies was independent of gestational age at screening (P = 0.11), but significantly dependent on maternal age (P < 0.003), weight (P < 0.0002), height (P = 0.001), parity (P < 0.02) and smoking status (P = 0.002). Compared with non-PE pregnancies, median GlyFn MoM using ELISA and POC testing was elevated significantly in those with preterm PE (1.23 vs 1.00; P < 0.0001 and 1.18 vs 1.00; P < 0.0001, respectively) and those with term PE (1.26 vs 1.00; P < 0.0001 and 1.22 vs 1.00; P < 0.0001, respectively). GlyFn MoM was not correlated with gestational age at delivery with PE (P = 0.989). Adding GlyFn to the FMF triple test for preterm PE increased significantly the AUC from 0.859 to 0.896 (P = 0.012) and increased the DR at 10% FPR from 64.9% (95% CI, 48.7-81.1%) to 82.9% (95% CI, 66.4-93.4%). The corresponding DRs at 10% FPR for any-onset PE were 52.5% (95% CI, 42.3-62.5%) and 65.4% (95% CI, 55.2-74.5%), respectively.

CONCLUSIONS

Adding GlyFn to the FMF triple test increased the screening sensitivity for both preterm and any-onset PE in an Asian population. Prospective non-intervention studies are needed to confirm these initial findings. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.