Competing risks model in screening for preeclampsia by maternal characteristics and medical history

Targeting oxidative stress in preeclampsia

Preeclampsia is a complex condition characterized by elevated blood pressure and organ damage involving kidneys or liver, resulting in significant morbidity and mortality for both the mother and the fetus. Increasing evidence suggests that oxidative stress, often caused by mitochondrial dysfunction within fetal trophoblast cells may play a major role in the development and progression of preeclampsia. Oxidative stress occurs as a result of an imbalance between the production of reactive oxygen species (ROS) and the capacity of antioxidant defenses, which can lead to placental cellular damage and endothelial cell dysfunction. Targeting oxidative stress appears to be a promising therapeutic approach that has the potential to improve both short- and long-term maternal and fetal outcomes, thus reducing the global burden of preeclampsia. The purpose of this review is to provide a comprehensive account of the mechanisms of oxidative stress in preeclampsia. Furthermore, it also examines potential interventions for reducing oxidative stress in preeclampsia, including natural antioxidant supplements, lifestyle modifications, mitochondrial targeting antioxidants, and pharmacological agents.A better understanding of the mechanism of action of proposed therapeutic strategies targeting oxidative stress is essential for the identification of companion biomarkers and personalized medicine approaches for the development of effective treatments of preeclampsia.

Causal association of hypertension in family members with preeclampsia-eclampsia in pregnant women: A two-sample Mendelian randomization study

OBJECTIVES

The genetic risk factors for hypertension are also high-risk factors for preeclampsia-eclampsia. This study examined the association of hypertension in family members with preeclampsia-eclampsia in pregnant women through two-sample Mendelian randomization (MR).

STUDY DESIGN

Mendelian randomization.

MAIN OUTCOME MEASURES

The data for hypertension in siblings, mother, and father were from the UK Biobank, including 364,661, 426,391, and 402,899 individuals, respectively. The data for preeclampsia-eclampsia were FinnGEN R9 (7217 cases and 194,266 controls). Inverse-variance weighted was used as the main analysis method. Weighted median, MR-Egger, simple mode, and weighted mode were complementary MR methods. Heterogeneity was detected using Cochran's Q-test, horizontal pleiotropy using MR-Egger regression, and driving single-nucleotide polymorphisms (SNPs) using the leave-one-out method.

RESULTS

Mendelian randomization analysis showed that hypertension in family members was positively correlated with preeclampsia-eclampsia risk. The risk of preeclampsia-eclampsia in pregnant women who have siblings with hypertension was the highest (OR = 179.41, 95 % CI: 23.10-1393.65, P = 6.98E-07), followed by hypertension in the mothers (OR = 26.83, 95 % CI: 5.42-132.87, P = 5.56E-05) and the fathers (OR = 18.97, 95 % CI: 1.28-281.29, P = 0.032). The MR-Egger regression test indicated no horizontal pleiotropy (P > 0.05). Cochran's Q-test showed that the effects of the included SNPs exhibited heterogeneity (P < 0.05). The leave-one-out analysis did not reveal SNPs driving the results by themselves.

CONCLUSION

The risk of preeclampsia-eclampsia in pregnant women who have siblings with hypertension was the highest, followed by pregnant women with a mother or father with hypertension. Having siblings with hypertension should be considered as a high-risk factor for the early prediction of preeclampsia-eclampsia.

Early prediction of preeclampsia from clinical, multi-omics and laboratory data using random forest model

BACKGROUND

Predicting preeclampsia (PE) within the first 16 weeks of gestation is difficult due to various risk factors, poorly understood causes and likely multiple pathogenic phenotypes of preeclampsia.  OBJECTIVES: In this study, we aimed to develop prediction models for early-onset preeclampsia (EPE) and late-onset preeclampsia (LPE) respectively using clinical data, metabolome and proteome analyses on plasma samples and laboratory data.

METHODS

We retrospectively recruited 56 EPE, 50 LPE patients and 92 normotensive controls from three tertiary hospitals and used clinical and laboratory data in early pregnancy. Models for EPE and LPE were fitted with the use of patient' clinical, multi-omics and laboratory data.

RESULTS

By comparing multi-omics and laboratory test variables between EPE, LPE and healthy controls, we identified sets of differentially expressed biomarkers, including 49 and 33 metabolites, 28 and 36 proteins as well as 5 and 7 laboratory variables associated with EPE and LPE respectively. Using the random forest algorithm, we developed a prediction model using seven clinical factors, seven metabolites, five laboratory test variables. The model yielded the highest accuracy for EPE prediction with good sensitivity (87.5%, 95% confidence interval [CI]: 67.64%-97.34%) and specificity (94.1%, 95% CI: 80.32%-99.28%). We also developed a prediction model that exhibited high accuracy in separating LPE from controls (sensitivity: 66.67%, 95% CI: 43.03%-85.41%; specificity: 94.12%, 95% CI: 80.32%-99.28%) using seven clinical factors, five metabolites and eight proteins.

CONCLUSION

Our study has identified a set of significant omics and laboratory features for PE prediction. The established models yielded high prediction performance for preeclampsia risk from clinical, multi-omics and laboratory information.

Predicting preeclampsia in early pregnancy using clinical and laboratory data via machine learning model

BACKGROUND

This study was performed to characterize the relationship of various laboratory test indicators with clinical information and Preeclampsia (PE) development. Then, prediction models for early-onset preeclampsia (EOPE), late-onset preeclampsia (LOPE), and preterm preeclampsia (Preterm PE) were developed using maternal characteristics and laboratory data.

METHODS

Between January 2019 and December 2021, we retrospectively recruited 144 EOPE, 363 LOPE, 231 Preterm PE, and 1458 healthy participants from six hospitals. We utilized all available clinical and laboratory data obtained during routine prenatal visits in early pregnancy. The models for EOPE, LOPE, and Preterm PE were created using ensemble machine learning models with patient clinical and laboratory data.

RESULTS

By comparing laboratory variables between PE patients and healthy controls, we identified 7, 18, 8, 15, 7,29 laboratory markers for EOPE, LOPE, and Preterm PE, severe PE, superimposed PE, first-time PE respectively. The ensemble EOPE and LOPE models incorporating clinical and laboratory predictors outperformed the clinical factor models respectively. The ensemble EOPE model demonstrated good sensitivity (72.22%,95% confidence interval [CI]: 57.59%-86.85%) and specificity (85.25%,95% CI: 80.54%-89.97%) in distinguishing EOPE from controls in early pregnancy. Similarly, the ensemble LOPE model showed good accuracy in differentiating LOPE from healthy participants (sensitivity: 69.57%, 95% CI: 56.27%-82.86%; specificity: 85.25%, 95% CI: 80.54%-89.97%). The prediction scores demonstrated notable positive correlations with blood pressure at admission, while they showed inverse correlations with 24-hour urine protein levels and fetal growth restriction among PE patients. In conclusion, our study identified key laboratory indicators for forecasting PE. The developed models exhibited good predictive capability for assessing preeclampsia risk and severity based on clinical and laboratory data.

CLINICAL TRIAL NUMBER

Not applicable.

A Bayesian Network model of pregnancy outcomes for England and Wales

Efforts to fully exploit the rich potential of Bayesian Networks (BNs) have hitherto not seen a practical approach for development of domain-specific models using large-scale public statistics which have the potential to reduce the time required to develop probability tables and train the model. As a result, the duration of projects seeking to develop health BNs tend to be measured in years due to their reliance on obtaining ethics approval and collecting, normalising, and discretising collections of patient EHRs. This work addresses this challenge by investigating a new approach to developing health BNs that combines expert elicitation with knowledge from literature and national health statistics. The approach presented here is evaluated through the development of a BN for pregnancy complications and outcomes using national health statistics for all births in England and Wales during 2021. The result is a BN that when validated using vignettes against other common types of predictive models including logistic regression and nomograms produces comparable predictions. The BN using our approach and large-scale public statistics was also developed in a project with a duration measured in months rather than years. The unique contributions of this paper are a new efficient approach to BN development and a working BN capable of reasoning over a broad range of pregnancy-related conditions and outcomes.

Screening for pre-eclampsia using pregnancy-associated plasma protein-A or placental growth factor measurements in blood samples collected at 8-14 weeks' gestation

OBJECTIVES

To assess the value of pregnancy-associated plasma protein-A (PAPP-A) in screening for preterm pre-eclampsia (PE) (delivery < 37 weeks' gestation) measured in maternal blood samples collected before 11 weeks, and to compare the screening performance of PAPP-A with that of placental growth factor (PlGF) from blood samples collected at 8-14 weeks.

METHODS

This study analyzed data from women who participated in the PRESIDE (Pre-eclampsia Screening in Denmark) study, a prospective, non-interventional multicenter study investigating the predictive performance of the Fetal Medicine Foundation first-trimester screening algorithm for PE in a Danish population. As part of combined first-trimester screening, a routine blood sample was collected at 8-14 weeks' gestation and PAPP-A was measured. Excess serum was stored at -80°C and analyzed for PlGF in batches after delivery. Most women in the PRESIDE study had an extra blood sample collected at the time of the first-trimester scan at 11-14 weeks, which was also analyzed for PlGF and PAPP-A in batches after all the participants had delivered. Screening performance was assessed in terms of the detection rate at a 10% screen-positive rate (SPR) for a combination of PAPP-A or PlGF with maternal factors alone and for a combination of each of these biomarkers with maternal factors, mean arterial pressure (MAP) and uterine artery pulsatility index (UtA-PI).

RESULTS

The study population comprised 8386 women who had a routine combined first-trimester aneuploidy screening blood sample collected at 8-14 weeks' gestation. In pregnancies that developed preterm PE, the median PAPP-A multiples of the median from routine blood samples were 0.78 (95% CI, 0.67-0.90) before 10 weeks, 0.80 (95% CI, 0.58-1.10) at 10 weeks and 0.64 (95% CI, 0.53-0.78) at 11-14 weeks. In women with samples collected before 10 weeks, there was no significant improvement in the detection rate of preterm PE when PAPP-A or PlGF was combined with maternal factors alone or when combined with maternal factors, MAP and UtA-PI. In routine samples collected at or after 10 weeks, PAPP-A only increased the detection rate of preterm PE slightly. However, PlGF in samples collected at or after 10 weeks increased the detection rate from 31.3% (95% CI, 16.1-50.0%) to 56.3% (95% CI, 37.7-73.6%) at a 10% SPR, i.e. an increase in the detection rate of 25.0% (95% CI, 4.3-44.4%), when combined with maternal factors alone. When PlGF collected from the PRESIDE sample at 11-14 weeks was combined with maternal factors, MAP and UtA-PI, there was an increase in the detection rate from 50.9% (95% CI, 37.1-64.6%) to 67.3% (95% CI, 53.3-79.3%), i.e. an increase of 16.4% (95% CI, 5.6-29.0%) at a 10% SPR.

CONCLUSIONS

PAPP-A has limited value in first-trimester screening for PE, whereas PlGF adds significantly to the detection rate of preterm PE at 10-14 weeks' gestation. © 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.

Preeclampsia prediction with maternal and paternal polygenic risk scores: the TMM BirThree Cohort Study

Genomic information from pregnant women and the paternal parent of their fetuses may provide effective biomarkers for preeclampsia (PE). This study investigated the association of parental polygenic risk scores (PRSs) for blood pressure (BP) and PE with PE onset and evaluated predictive performances of PRSs using clinical predictive variables. In the Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study, 19,836 participants were genotyped using either Affymetrix Axiom Japonica Array v2 (further divided into two cohorts-the PRS training cohort and the internal-validation cohort-at a ratio of 1:2) or Japonica Array NEO (external-validation cohort). PRSs were calculated for systolic BP (SBP), diastolic BP (DBP), and PE and hyperparameters for PRS calculation were optimized in the training cohort. PE onset was associated with maternal SBP-, DBP-, and PE-PRSs and paternal SBP- and DBP-PRSs only in the external-validation cohort. Meta-analysis revealed overall associations with maternal PRSs but highlighted significant heterogeneity between cohorts. Maternal DBP-PRS calculated using "LDpred2" presented the most improvement in prediction models and provided additional predictive information on clinical predictive variables. Paternal DBP-PRS improved prediction models in the internal-validation cohort. In conclusion, Parental PRS, along with clinical predictive variables, is potentially useful for predicting PE.

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.

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.

Artificial Intelligence and Machine Learning in Preeclampsia

Preeclampsia is a multisystem hypertensive disorder that manifests itself after 20 weeks of pregnancy, along with proteinuria. The pathophysiology of preeclampsia is incompletely understood. Artificial intelligence, especially machine learning with its capability to identify patterns in complex data, has the potential to revolutionize preeclampsia research. These data-driven techniques can improve early diagnosis, personalize risk assessment, uncover the disease's molecular basis, optimize treatments, and enable remote monitoring. This brief review discusses the recent applications of artificial intelligence and machine learning in preeclampsia management and research, including the improvements these approaches have brought, along with their challenges and limitations.

Low-dose aspirin for the prevention of preeclampsia in women with polycystic ovary syndrome: a retrospective cohort study

BACKGROUND

The objective of this study was to investigate the efficacy of low-dose aspirin (LDA) in preventing preeclampsia among pregnant women with polycystic ovary syndrome (PCOS), given the increased susceptibility of this population to preeclampsia development.

METHODS

A retrospective cohort study was conducted on pregnant women with PCOS who delivered between January 1, 2018 and February 10, 2024 at our institution. Clinical characteristics and obstetric data were extracted from medical records. Propensity score matching (PSM) was employed to analyze the association between LDA use and PE incidence.

RESULTS

The study cohort comprised 1522 pregnant women with PCOS. Among 395 pregnant women identified as high-risk for preeclampsia, 98 were administered LDA for preeclampsia prevention, while 297 did not receive LDA. Following PSM, no statistically significant difference was observed in preeclampsia risk between the LDA and non-LDA groups. Additionally, maternal and neonatal outcomes were comparable between the two groups.

CONCLUSIONS

This cohort analysis did not provide sufficient evidence to support the efficacy of LDA in preventing preeclampsia among PCOS patients at high risk for preeclampsia.

A review of ethnic disparities in preeclampsia

PURPOSE OF REVIEW

Recent reports have reiterated the inequities in maternal morbidity and mortality for minority ethnic groups, with preeclampsia being a significant concern. Females of Black and South Asian ethnicity have an increased risk of preeclampsia with disproportionately higher adverse outcomes compared to white females.

RECENT FINDINGS

This review will explore ethnic disparities in preeclampsia outcomes, prediction, diagnosis, prevention and management. Recent evidence has demonstrated that biochemical and biophysical markers that are used for preeclampsia prediction and diagnosis vary for females of different ethnic groups. This needs careful consideration given the current need for accurate prediction models. Furthermore, recent reports have highlighted the disparity in maternal morbidity for those of minority ethnic groups. The reasons for this are multifactorial but underlying biases and racism have been attributed as major contributors to poor care and adverse outcomes.

SUMMARY

Exploring disparities in preeclampsia care is essential to address ethnic inequities that lead to increased adverse outcomes. We must alter current clinical practice to break down the barriers that result in substandard care for females from minority ethnic backgrounds.

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.

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.

Implementation of First-Trimester Screening and Prevention of Preeclampsia: A Stepped Wedge Cluster-Randomized Trial in Asia

BACKGROUND

This trial aimed to assess the efficacy, acceptability, and safety of a first-trimester screen-and-prevent strategy for preterm preeclampsia in Asia.

METHODS

Between August 1, 2019, and February 28, 2022, this multicenter stepped wedge cluster randomized trial included maternity/diagnostic units from 10 regions in Asia. The trial started with a period where all recruiting centers provided routine antenatal care without study-related intervention. At regular 6-week intervals, one cluster was randomized to transit from nonintervention phase to intervention phase. In the intervention phase, women underwent first-trimester screening for preterm preeclampsia using a Bayes theorem-based triple-test. High-risk women, with adjusted risk for preterm preeclampsia ≥1 in 100, received low-dose aspirin from <16 weeks until 36 weeks.

RESULTS

Overall, 88.04% (42 897 of 48 725) of women agreed to undergo first-trimester screening for preterm preeclampsia. Among those identified as high-risk in the intervention phase, 82.39% (2919 of 3543) received aspirin prophylaxis. There was no significant difference in the incidence of preterm preeclampsia between the intervention and non-intervention phases (adjusted odds ratio [aOR], 1.59 [95% CI, 0.91-2.77]). However, among high-risk women in the intervention phase, aspirin prophylaxis was significantly associated with a 41% reduction in the incidence of preterm preeclampsia (aOR, 0.59 [95% CI, 0.37-0.92]). In addition, it correlated with 54%, 55%, and 64% reduction in the incidence of preeclampsia with delivery at <34 weeks (aOR, 0.46 [95% CI, 0.23-0.93]), spontaneous preterm birth <34 weeks (aOR, 0.45 [95% CI, 0.22-0.92]), and perinatal death (aOR, 0.34 [95% CI, 0.12-0.91]), respectively. There was no significant between-group difference in the incidence of aspirin-related severe adverse events.

CONCLUSIONS

The implementation of the screen-and-prevent strategy for preterm preeclampsia is not associated with a significant reduction in the incidence of preterm preeclampsia. However, low-dose aspirin effectively reduces the incidence of preterm preeclampsia by 41% among high-risk women. The screen-and-prevent strategy for preterm preeclampsia is highly accepted by a diverse group of women from various ethnic backgrounds beyond the original population where the strategy was developed. These findings underpin the importance of the widespread implementation of the screen-and-prevent strategy for preterm preeclampsia on a global scale.

REGISTRATION

URL: https://www.clinicaltrials.gov; Unique identifier: NCT03941886.

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..

Preeclampsia and eclampsia: Enhanced detection and treatment for morbidity reduction

Preeclampsia is a life-threatening complication that develops in 2-8% of pregnancies. It is characterized by elevated blood pressure after 20 weeks of gestation and may progress to multiorgan dysfunction, leading to severe maternal and fetal morbidity and mortality. The only definitive treatment is delivery, and efforts are focused on early risk prediction, surveillance, and severity mitigation. Anesthesiologists, as part of the interdisciplinary team, should evaluate patients early in labor in order to optimize cardiovascular, pulmonary, and coagulation status. Neuraxial techniques are safe in the absence of coagulopathy and aid avoidance of general anesthesia, which is associated with high risk in these patients. This review aims to provide anaesthesiologists with a comprehensive update on the latest strategies and evidence-based practices for managing preeclampsia, with an emphasis on perioperative care.

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.

A Scoping Review of Risk Factors of Hypertensive Disorders of Pregnancy in Black Women Living in High-Income Countries: An Intersectional Approach

BACKGROUND

Hypertensive disorders of pregnancy (HDP) are maternity-related increases in blood pressure (eg, gestational hypertension, preeclampsia, and eclampsia). Compared with women of other races in high-income countries, Black women have a comparatively higher risk of an HDP. Intersectionality helps to provide a deeper understanding of the multifactorial identities that affect health outcomes in this high-risk population.

OBJECTIVE

In this review, we sought to explore the literature on HDP risk factors in Black women living in high-income countries and to assess the interaction of these risk factors using the conceptual framework of intersectionality.

METHODS

We conducted this review using the Arksey and O'Malley methodology with enhancements from Levac and colleagues. Published articles in English on HDP risk factors with a sample of not less than 10% of Black women in high-income countries were included. Six databases, theses, and dissertations were searched from January 2000 to July 2021. A thematic analysis was used to summarize the results.

RESULTS

A final total of 36 studies were included from the 15 480 studies retrieved; 4 key themes of HDP risks were identified: (1) biological; (2) individual traditional; (3) race and ethnicity, geographical location, and immigration status; and (4) gender related. These intersectional HDP risk factors intersect to increase the risk of HDP among Black women living in high-income countries.

CONCLUSION

Upstream approaches are recommended to lower the risks of HDP in this population.

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

PURPOSE OF REVIEW

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

RECENT FINDINGS

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

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.

Prediction model of preeclampsia using machine learning based methods: a population based cohort study in China

Introduction

Preeclampsia is a disease with an unknown pathogenesis and is one of the leading causes of maternal and perinatal morbidity. At present, early identification of high-risk groups for preeclampsia and timely intervention with aspirin is an effective preventive method against preeclampsia. This study aims to develop a robust and effective preeclampsia prediction model with good performance by machine learning algorithms based on maternal characteristics, biophysical and biochemical markers at 11-13 + 6 weeks' gestation, providing an effective tool for early screening and prediction of preeclampsia.

Methods

This study included 5116 singleton pregnant women who underwent PE screening and fetal aneuploidy from a prospective cohort longitudinal study in China. Maternal characteristics (such as maternal age, height, pre-pregnancy weight), past medical history, mean arterial pressure, uterine artery pulsatility index, pregnancy-associated plasma protein A, and placental growth factor were collected as the covariates for the preeclampsia prediction model. Five classification algorithms including Logistic Regression, Extra Trees Classifier, Voting Classifier, Gaussian Process Classifier and Stacking Classifier were applied for the prediction model development. Five-fold cross-validation with an 8:2 train-test split was applied for model validation.

Results

We ultimately included 49 cases of preterm preeclampsia and 161 cases of term preeclampsia from the 4644 pregnant women data in the final analysis. Compared with other prediction algorithms, the AUC and detection rate at 10% FPR of the Voting Classifier algorithm showed better performance in the prediction of preterm preeclampsia (AUC=0.884, DR at 10%FPR=0.625) under all covariates included. However, its performance was similar to that of other model algorithms in all PE and term PE prediction. In the prediction of all preeclampsia, the contribution of PLGF was higher than PAPP-A (11.9% VS 8.7%), while the situation was opposite in the prediction of preterm preeclampsia (7.2% VS 16.5%). The performance for preeclampsia or preterm preeclampsia using machine learning algorithms was similar to that achieved by the fetal medicine foundation competing risk model under the same predictive factors (AUCs of 0.797 and 0.856 for PE and preterm PE, respectively).

Conclusions

Our models provide an accessible tool for large-scale population screening and prediction of preeclampsia, which helps reduce the disease burden and improve maternal and fetal outcomes.

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.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Detection of Urinary Misfolded Proteins for Imminent Prediction of Preeclampsia in Pregnant Women With Suspected Cases: Protocol for a Prospective Noninterventional Study

BACKGROUND

Preeclampsia (PE) is one of the most common hypertensive diseases, affecting 2%-8% of all pregnancies. The high maternal and fetal mortality rates of PE are due to a lack of early identification of affected pregnant women that would have led to closer monitoring and care. Recent data suggest that misfolded proteins might be a promising biomarker for PE prediction, which can be detected in urine samples of pregnant women according to their congophilia (aggregated) characteristic.

OBJECTIVE

The main purpose of this trial is to evaluate the value of the urine congophilia-based detection of misfolded proteins for the imminent prediction of PE in women presenting with suspected PE. The secondary objectives are to demonstrate that the presence of urine misfolded proteins correlates with PE-related maternal or neonatal adverse outcomes, and to establish an accurate PE prediction model by combining misfolded proteins with multiple indicators.

METHODS

At least 300 pregnant women with clinical suspicion of PE will be enrolled in this prospective cohort study. Participants should meet the following inclusion criteria in addition to a suspicion of PE: ≥18 years old, gestational week between 20+0 and 33+6, and single pregnancy. Consecutive urine samples will be collected, blinded, and tested for misfolded proteins and other PE-related biomarkers at enrollment and at 4 follow-up visits. Clinical assessments of PE status and related complications for all participants will be performed at regular intervals using strict diagnostic criteria. Investigators and participants will remain blinded to the results. Follow-up will be performed until 42 days postpartum. Data from medical records, including maternal and fetal outcomes, will be collected. The performance of urine misfolded proteins alone and combined with other biomarkers or clinical variables for the prediction of PE will be statistically analyzed.

RESULTS

Enrollment started in July 2023 and was still open upon manuscript submission. As of March 2024, a total of 251 eligible women have been enrolled in the study and enrollment is expected to continue until August 2024. Results analysis is scheduled to start after all participants reach the follow-up endpoint and complete clinical data are collected.

CONCLUSIONS

Upon completion of the study, we expect to derive an accurate PE prediction model, which will allow for proactive management of pregnant women with clinical suspicion of PE and possibly reduce the associated adverse pregnancy outcomes. The additional prognostic value of misfolded proteins is also expected to be confirmed.

TRIAL REGISTRATION

Chinese Clinical Trials Registry ChiCTR2300074878; https://www.chictr.org.cn/showproj.html?proj=202096.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/54026.

Non-invasive prediction of preeclampsia using the maternal plasma cell-free DNA profile and clinical risk factors

Background

Preeclampsia (PE) is a pregnancy complication defined by new onset hypertension and proteinuria or other maternal organ damage after 20 weeks of gestation. Although non-invasive prenatal testing (NIPT) has been widely used to detect fetal chromosomal abnormalities during pregnancy, its performance in combination with maternal risk factors to screen for PE has not been extensively validated. Our aim was to develop and validate classifiers that predict early- or late-onset PE using the maternal plasma cell-free DNA (cfDNA) profile and clinical risk factors.

Methods

We retrospectively collected and analyzed NIPT data of 2,727 pregnant women aged 24-45 years from four hospitals in China, which had previously been used to screen for fetal aneuploidy at 12 + 0 ~ 22 + 6 weeks of gestation. According to the diagnostic criteria for PE and the time of diagnosis (34 weeks of gestation), a total of 143 early-, 580 late-onset PE samples and 2,004 healthy controls were included. The wilcoxon rank sum test was used to identify the cfDNA profile for PE prediction. The Fisher's exact test and Mann-Whitney U-test were used to compare categorical and continuous variables of clinical risk factors between PE samples and healthy controls, respectively. Machine learning methods were performed to develop and validate PE classifiers based on the cfDNA profile and clinical risk factors.

Results

By using NIPT data to analyze cfDNA coverages in promoter regions, we found the cfDNA profile, which was differential cfDNA coverages in gene promoter regions between PE and healthy controls, could be used to predict early- and late-onset PE. Maternal age, body mass index, parity, past medical histories and method of conception were significantly differential between PE and healthy pregnant women. With a false positive rate of 10%, the classifiers based on the combination of the cfDNA profile and clinical risk factors predicted early- and late-onset PE in four datasets with an average accuracy of 89 and 80% and an average sensitivity of 63 and 48%, respectively.

Conclusion

Incorporating cfDNA profiles in classifiers might reduce performance variations in PE models based only on clinical risk factors, potentially expanding the application of NIPT in PE screening in the future.

MicroRNAs in the Pathogenesis of Preeclampsia-A Case-Control In Silico Analysis

Preeclampsia (PE) occurs in 5% to 7% of all pregnancies, and the PE that results from abnormal placentation acts as a primary cause of maternal and neonatal morbidity and mortality. The objective of this secondary analysis was to elucidate the pathogenesis of PE by probing protein-protein interactions from in silico analysis of transcriptomes between PE and normal placenta from Gene Expression Omnibus (GSE149812). The pathogenesis of PE is apparently determined by associations of miRNA molecules and their target genes and the degree of changes in their expressions with irregularities in the functions of hemostasis, vascular systems, and inflammatory processes at the fetal-maternal interface. These irregularities ultimately lead to impaired placental growth and hypoxic injuries, generally manifesting as placental insufficiency. These differentially expressed miRNAs or genes in placental tissue and/or in blood can serve as novel diagnostic and therapeutic biomarkers.

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.

Maternal vascular indices at 36 weeks' gestation in the prediction of preeclampsia

BACKGROUND

Epidemiological studies have shown that women with preeclampsia (PE) are at increased long term cardiovascular risk. This risk might be associated with accelerated vascular ageing process but data on vascular abnormalities in women with PE are scarce.

OBJECTIVE

This study aimed to identify the most discriminatory maternal vascular index in the prediction of PE at 35 to 37 weeks' gestation and to examine the performance of screening for PE by combinations of maternal risk factors and biophysical and biochemical markers at 35 to 37 weeks' gestation.

STUDY DESIGN

This was a prospective observational nonintervention study in women attending a routine hospital visit at 35 0/7 to 36 6/7 weeks' gestation. The visit included recording of maternal demographic characteristics and medical history, vascular indices, and hemodynamic parameters obtained by a noninvasive operator-independent device (pulse wave velocity, augmentation index, cardiac output, stroke volume, central systolic and diastolic blood pressures, total peripheral resistance, and fetal heart rate), mean arterial pressure, uterine artery pulsatility index, and serum concentration of placental growth factor and soluble fms-like tyrosine kinase-1. The performance of screening for delivery with PE at any time and at <3 weeks from assessment using a combination of maternal risk factors and various combinations of biomarkers was determined.

RESULTS

The study population consisted of 6746 women with singleton pregnancies, including 176 women (2.6%) who subsequently developed PE. There were 3 main findings. First, in women who developed PE, compared with those who did not, there were higher central systolic and diastolic blood pressures, pulse wave velocity, peripheral vascular resistance, and augmentation index. Second, the most discriminatory indices were systolic and diastolic blood pressures and pulse wave velocity, with poor prediction from the other indices. However, the performance of screening by a combination of maternal risk factors plus mean arterial pressure was at least as high as that of a combination of maternal risk factors plus central systolic and diastolic blood pressures; consequently, in screening for PE, pulse wave velocity, mean arterial pressure, uterine artery pulsatility index, placental growth factor, and soluble fms-like tyrosine kinase-1 were used. Third, in screening for both PE within 3 weeks and PE at any time from assessment, the detection rate at a false-positive rate of 10% of a biophysical test consisting of maternal risk factors plus mean arterial pressure, uterine artery pulsatility index, and pulse wave velocity (PE within 3 weeks: 85.2%; 95% confidence interval, 75.6%-92.1%; PE at any time: 69.9%; 95% confidence interval, 62.5%-76.6%) was not significantly different from a biochemical test using the competing risks model to combine maternal risk factors with placental growth factor and soluble fms-like tyrosine kinase-1 (PE within 3 weeks: 80.2%; 95% confidence interval, 69.9%-88.3%; PE at any time: 64.2%; 95% confidence interval, 56.6%-71.3%), and they were both superior to screening by low placental growth factor concentration (PE within 3 weeks: 53.1%; 95% confidence interval, 41.7%-64.3%; PE at any time: 44.3; 95% confidence interval, 36.8%-52.0%) or high soluble fms-like tyrosine kinase-1-to-placental growth factor concentration ratio (PE within 3 weeks: 65.4%; 95% confidence interval, 54.0%-75.7%; PE at any time: 53.4%; 95% confidence interval, 45.8%-60.9%).

CONCLUSION

First, increased maternal arterial stiffness preceded the clinical onset of PE. Second, maternal pulse wave velocity at 35 to 37 weeks' gestation in combination with mean arterial pressure and uterine artery pulsatility index provided effective prediction of subsequent development 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.

Value of Non-Coding RNA Expression in Biofluids to Identify Patients at Low Risk of Pathologies Associated with Pregnancy

Pregnancy-related complications (PRC) impact maternal and fetal morbidity and mortality and place a huge burden on healthcare systems. Thus, effective diagnostic screening strategies are crucial. Currently, national and international guidelines define patients at low risk of PRC exclusively based on their history, thus excluding the possibility of identifying patients with de novo risk (patients without a history of disease), which represents most women. In this setting, previous studies have underlined the potential contribution of non-coding RNAs (ncRNAs) to detect patients at risk of PRC. However, placenta biopsies or cord blood samples are required, which are not simple procedures. Our review explores the potential of ncRNAs in biofluids (fluids that are excreted, secreted, or developed because of a physiological or pathological process) as biomarkers for identifying patients with low-risk pregnancies. Beyond the regulatory roles of ncRNAs in placental development and vascular remodeling, we investigated their specific expressions in biofluids to determine favorable pregnancy outcomes as well as the most frequent pathologies of pregnant women. We report distinct ncRNA panels associated with PRC based on omics technologies and subsequently define patients at low risk. We present a comprehensive analysis of ncRNA expression in biofluids, including those using next-generation sequencing, shedding light on their predictive value in clinical practice. In conclusion, this paper underscores the emerging significance of ncRNAs in biofluids as promising biomarkers for risk stratification in PRC. The investigation of ncRNA expression patterns and their potential clinical applications is of diagnostic, prognostic, and theragnostic value and paves the way for innovative approaches to improve prenatal care and maternal and fetal outcomes.

Evaluation of screening performance of first-trimester competing-risks prediction model for small-for-gestational age in Asian population

OBJECTIVE

To examine the external validity of the Fetal Medicine Foundation (FMF) competing-risks model for the prediction of small-for-gestational age (SGA) at 11-14 weeks' gestation in an Asian population.

METHODS

This was a secondary analysis of a multicenter prospective cohort study in 10 120 women with a singleton pregnancy undergoing routine assessment at 11-14 weeks' gestation. We applied the FMF competing-risks model for the first-trimester prediction of SGA, combining maternal characteristics and medical history with measurements of mean arterial pressure (MAP), uterine artery pulsatility index (UtA-PI) and serum placental growth factor (PlGF) concentration. We calculated risks for different cut-offs of birth-weight percentile (< 10th , < 5th or < 3rd percentile) and gestational age at delivery (< 37 weeks (preterm SGA) or SGA at any gestational age). Predictive performance was examined in terms of discrimination and calibration.

RESULTS

The predictive performance of the competing-risks model for SGA was similar to that reported in the original FMF study. Specifically, the combination of maternal factors with MAP, UtA-PI and PlGF yielded the best performance for the prediction of preterm SGA with birth weight < 10th percentile (SGA < 10th ) and preterm SGA with birth weight < 5th percentile (SGA < 5th ), with areas under the receiver-operating-characteristics curve (AUCs) of 0.765 (95% CI, 0.720-0.809) and 0.789 (95% CI, 0.736-0.841), respectively. Combining maternal factors with MAP and PlGF yielded the best model for predicting preterm SGA with birth weight < 3rd percentile (SGA < 3rd ) (AUC, 0.797 (95% CI, 0.744-0.850)). After excluding cases with pre-eclampsia, the combination of maternal factors with MAP, UtA-PI and PlGF yielded the best performance for the prediction of preterm SGA < 10th and preterm SGA < 5th , with AUCs of 0.743 (95% CI, 0.691-0.795) and 0.762 (95% CI, 0.700-0.824), respectively. However, the best model for predicting preterm SGA < 3rd without pre-eclampsia was the combination of maternal factors and PlGF (AUC, 0.786 (95% CI, 0.723-0.849)). The FMF competing-risks model including maternal factors, MAP, UtA-PI and PlGF achieved detection rates of 42.2%, 47.3% and 48.1%, at a fixed false-positive rate of 10%, for the prediction of preterm SGA < 10th , preterm SGA < 5th and preterm SGA < 3rd , respectively. The calibration of the model was satisfactory.

CONCLUSION

The screening performance of the FMF first-trimester competing-risks model for SGA in a large, independent cohort of Asian women is comparable with that reported in the original FMF study in a mixed European population. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Preeclampsia Management and Maternal Ophthalmic Artery Doppler Measurements between 19 and 23 Weeks of Gestation

Background: The ophthalmic Doppler is a reliable and impartial way to assess the severity of preeclampsia (PE). The study aimed to assess the potential utility of Doppler measurements of the maternal ophthalmic arteries during the weeks 19-23 of gestation, both independently and in combination with established biomarkers for PE. Methods: A prospective cohort study was conducted involving women who were recruited from a variety of standard appointments, including booking, scanning, and regular prenatal visits. A total of 200 women that were divided into high-risk and low-risk groups for developing PE were involved during the period between April 2023 and November 2023. Results: The ophthalmic ratio had significantly higher values in high-risk patients than in low-risk women (p = 0.000). There was a significant relationship between PSV2/PSV1 and gestational age at birth in women with PE compared to the ones who did not develop PE. Conclusions: An ophthalmic artery Doppler can play a crucial role in the early detection of PE, allowing for timely intervention and management. Incorporating the ophthalmic artery Doppler as a screening tool for PE in Bulgaria has the potential to improve early detection, risk stratification, and overall maternal and fetal health outcomes.

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.

Preeclampsia Prediction Using Machine Learning and Polygenic Risk Scores From Clinical and Genetic Risk Factors in Early and Late Pregnancies

BACKGROUND

Preeclampsia, a pregnancy-specific condition associated with new-onset hypertension after 20-weeks gestation, is a leading cause of maternal and neonatal morbidity and mortality. Predictive tools to understand which individuals are most at risk are needed.

METHODS

We identified a cohort of N=1125 pregnant individuals who delivered between May 2015 and May 2022 at Mass General Brigham Hospitals with available electronic health record data and linked genetic data. Using clinical electronic health record data and systolic blood pressure polygenic risk scores derived from a large genome-wide association study, we developed machine learning (XGBoost) and logistic regression models to predict preeclampsia risk.

RESULTS

Pregnant individuals with a systolic blood pressure polygenic risk score in the top quartile had higher blood pressures throughout pregnancy compared with patients within the lowest quartile systolic blood pressure polygenic risk score. In the first trimester, the most predictive model was XGBoost, with an area under the curve of 0.74. In late pregnancy, with data obtained up to the delivery admission, the best-performing model was XGBoost using clinical variables, which achieved an area under the curve of 0.91. Adding the systolic blood pressure polygenic risk score to the models did not improve the performance significantly based on De Long test comparing the area under the curve of models with and without the polygenic score.

CONCLUSIONS

Integrating clinical factors into predictive models can inform personalized preeclampsia risk and achieve higher predictive power than the current practice. In the future, personalized tools can be implemented to identify high-risk patients for preventative therapies and timely intervention to improve adverse maternal and neonatal outcomes.

Diagnostic value of platelet to lymphocyte ratio in preeclampsia: a systematic review and meta-analysis

BACKGROUND

Using straightforward and accessible haematological parameters platelet/lymphocyte ratio (PLR) to diagnose preeclampsia (PE) early and precisely remains a challenge. Although several clinical studies suggested that PLR is able to diagnose PE, there has been no systematic evaluation of the diagnostic utility.

OBJECTIVES

To examine the diagnostic accuracy and potential applicability of PLR in the detection of PE.

STUDY DESIGN

Seven databases were searched using a combination of PLR and PE terms, and all potentially pertinent studies were systematically searched up to March 2023. All potentially relevant studies both prospective and retrospective were reviewed. To assess the diagnostic value of PLR for PE, pooled sensitivity (Sen), specificity (Spe), diagnostic odds ratio (DOR) and area under the summary receiver operating characteristic curve (SROC-AUC) were calculated.

RESULTS

Thirteen studies were enrolled in the meta-analysis. In the second and third trimesters, the PLR suggested a diagnostic value for PE with a pooled Sen of 54.7% [95% confidence interval (CI) (51.7, 57.6)], Spe of 77.8% [95% CI (75.5, 80.0)], + LR of 2.457 [95% CI (1.897, 3.182)], -LR of 0.584 [95% CI (0.491, 0.695)], DOR of 4.434 [95% CI (3.071, 6.402)], the SROC-AUC of 0.7296 and the standard error (SE) of 0.0370.

CONCLUSION

For the diagnosis of PE, PLR has a limited sensitivity but an acceptable specificity, and showed moderate accuracy. Further using complete blood count (CBC) indicators such as PLR alone or in combination to diagnose and predict PE could reduce healthcare costs and improve maternal and child prognosis.

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.

Noninvasive preeclampsia prediction using plasma cell-free RNA signatures

BACKGROUND

Preeclampsia, especially preterm preeclampsia and early-onset preeclampsia, is a life-threating pregnancy disorder, and the heterogeneity and complexity of preeclampsia make it difficult to predict risk and to develop treatments. Plasma cell-free RNA carries unique information from human tissue and may be useful for noninvasive monitoring of maternal, placental, and fetal dynamics during pregnancy.

OBJECTIVE

This study aimed to investigate various RNA biotypes associated with preeclampsia in plasma and to develop classifiers to predict preterm preeclampsia and early-onset preeclampsia before diagnosis.

STUDY DESIGN

We performed a novel, cell-free RNA sequencing method termed polyadenylation ligation-mediated sequencing to investigate the cell-free RNA characteristics of 715 healthy pregnancies and 202 pregnancies affected by preeclampsia before symptom onset. We explored differences in the abundance of different RNA biotypes in plasma between healthy and preeclampsia samples and built preterm preeclampsia and early-onset preeclampsia prediction classifiers using machine learning methods. Furthermore, we validated the performance of the classifiers using the external and internal validation cohorts and assessed the area under the curve and positive predictive value.

RESULTS

We detected 77 genes, including messenger RNA (44%) and microRNA (26%), that were differentially expressed in healthy mothers and mothers with preterm preeclampsia before symptom onset, which could separate participants with preterm preeclampsia from healthy samples and that played critical functional roles in preeclampsia physiology. We developed 2 classifiers for predicting preterm preeclampsia and early-onset preeclampsia before diagnosis based on 13 cell-free RNA signatures and 2 clinical features (in vitro fertilization and mean arterial pressure), respectively. Notably, both classifiers showed enhanced performance when compared with the existing methods. The preterm preeclampsia prediction model achieved 81% area under the curve and 68% positive predictive value in an independent validation cohort (preterm, n=46; control, n=151); the early-onset preeclampsia prediction model had an area under the curve of 88% and a positive predictive value of 73% in an external validation cohort (early-onset preeclampsia, n=28; control, n=234). Furthermore, we demonstrated that downregulation of microRNAs may play vital roles in preeclampsia through the upregulation of preeclampsia-relevant target genes.

CONCLUSION

In this cohort study, a comprehensive transcriptomic landscape of different RNA biotypes in preeclampsia was presented and 2 advanced classifiers with substantial clinical importance for preterm preeclampsia and early-onset preeclampsia prediction before symptom onset were developed. We demonstrated that messenger RNA, microRNA, and long noncoding RNA can simultaneously serve as potential biomarkers of preeclampsia, holding the promise of prevention of preeclampsia in the future. Abnormal cell-free messenger RNA, microRNA, and long noncoding RNA molecular changes may help to elucidate the pathogenic determinants of preeclampsia and open new therapeutic windows to effectively reduce pregnancy complications and fetal morbidity.

Cell free DNA screening for fetal aneuploidy in Ireland: An observational study of outcomes following insufficient fetal fraction

OBJECTIVES

To determine maternal factors associated with low fetal fraction (FF). To determine the proportion of women who receive a result from repeat non-invasive prenatal testing (NIPT) testing. To identify any significant associations between pregnancy interventions or outcomes and low FF.

STUDY DESIGN

Retrospective observational study of 4465 women undergoing antenatal screening by targeted cell free DNA (cfDNA) testing at an Irish tertiary maternity hospital between January 2017 and December 2022. Patients who failed to obtain a result after the first NIPT were analyzed in two cohorts; those who received a result on a repeat sample and those who failed to ever achieve a result despite a second, third or fourth cfDNA test.

RESULTS

Risk of insufficient FF significantly increased with elevated maternal BMI (OR 1.07; 95% CI 1.01-1.13, p = 0.03) and in-vitro fertilization (IVF) (OR 3.4; 95% CI 1.19-9.4, p = 0.02). Women with no result were more likely to have diagnostic invasive testing (p < 0.01), but had no increased risk of aneuploidy. Repeated failed NIPT attempts due to low FF were significantly associated with the subsequent development of hypertensive diseases of pregnancy (p = 0.03). Greater than 70% of patients who were unsuccessful in a first or second attempt at NIPT due to low FF yielded a result following a second or third sample.

CONCLUSIONS

High BMI and IVF conceptions are greater contributors to low FF than fetal aneuploidy. Repeating NIPT yields a result in greater than 70% of cases. WHAT'S ALREADY KNOWN ABOUT THIS TOPIC?: Fetal fraction (FF) in prenatal cfDNA testing is influenced by maternal and pregnancy factors including body mass index (BMI) and IVF. Low FF has been associated with adverse pregnancy outcomes including fetal aneuploidy and hypertensive diseases of pregnancy. WHAT DOES THIS STUDY ADD?: In a large Irish population, increasing maternal BMI and in-vitro fertilization are the most significant contributors to repeated test failures due to low FF. Greater than 70% of patients with test failure due to low FF will receive a result on 2nd and 3rd NIPT attempts. Patients with no result from NIPT were more likely to undergo diagnostic invasive testing but the risk of aneuploidy was not significantly increased.

Fetal Cardiac Function at Midgestation and Subsequent Development of Preeclampsia

OBJECTIVE

To assess differences in cardiac morphology and function at midgestation in fetuses from pregnancies that subsequently developed preeclampsia (PE) or gestational hypertension (GH).

METHODS

This was a prospective study in 5,801 women with singleton pregnancies attending for a routine ultrasound examination at midgestation, including 179 (3.1%) who subsequently developed PE and 149 (2.6%) who developed GH. Conventional and more advanced echocardiographic modalities, such as speckle-tracking, were used to assess fetal cardiac function in the right and left ventricle. The morphology of the fetal heart was assessed by calculating the right and left sphericity index.

RESULTS

In fetuses from the PE group (vs the no PE or GH group) there was a significantly higher left ventricular global longitudinal strain and lower left ventricular ejection fraction that could not be accounted for by fetal size. All other indices of fetal cardiac morphology and function were comparable between groups. There was no significant correlation between fetal cardiac indices and uterine artery pulsatility index multiple of the median or placental growth factor multiple of the median.

CONCLUSION

At midgestation, fetuses of mothers at risk of developing PE, but not those at risk of GH, have mild reduction in left ventricular myocardial function. Although absolute differences were minimal and most likely not clinically relevant, these may suggest an early programming effect on left ventricular contractility in fetuses of mothers who develop PE.

Screening for pre-eclampsia by maternal serum glycosylated fibronectin at 11-13 weeks' gestation

OBJECTIVE

To examine the performance of screening for preterm and term pre-eclampsia (PE) at 11-13 weeks' gestation by maternal factors and combinations of maternal serum glycosylated fibronectin (GlyFn), mean arterial pressure (MAP), uterine artery pulsatility index (UtA-PI) and serum placental growth factor (PlGF).

METHODS

This was a case-control study in which maternal serum GlyFn was measured using a point-of-care device in stored samples from a non-intervention screening study of singleton pregnancies at 11 + 0 to 13 + 6 weeks' gestation. In the same samples, PlGF was measured by time-resolved fluorometry. We used samples from women who delivered with PE at < 37 weeks' gestation (n = 100), PE at ≥ 37 weeks (n = 100), gestational hypertension (GH) at < 37 weeks (n = 100), GH at ≥ 37 weeks (n = 100) and 1000 normotensive controls with no pregnancy complications. In all cases, MAP and UtA-PI had been measured during the routine 11-13-week visit. Levels of GlyFn were transformed to multiples of the expected median (MoM) values after adjusting for maternal demographic characteristics and elements of medical history. Similarly, the measured values of MAP, UtA-PI and PlGF were converted to MoMs. The competing-risks model was used to combine the prior distribution of gestational age at delivery with PE, obtained from maternal characteristics, with various combinations of biomarker MoM values to derive the patient-specific risks of delivery with PE or GH at < 37 and ≥ 37 weeks' gestation. Screening performance was estimated by examining the area under the receiver-operating-characteristics curve (AUC) and detection rate (DR) at 10% fixed false-positive rate (FPR).

RESULTS

The maternal characteristics and elements of medical history with a significant effect on the measurement of GlyFn were maternal age, weight, height, race, smoking status and history of PE. In pregnancies that developed PE, GlyFn MoM was increased and the deviation from normal decreased with increasing gestational age at delivery. The DR and AUC of screening for delivery with PE at < 37 weeks' gestation by maternal factors alone were 50% and 0.834, respectively, and these increased to 80% and 0.949, respectively, when maternal risk factors were combined with MAP, UtA-PI and PlGF (triple test). The performance of the triple test was similar to that of screening by a combination of maternal factors, MAP, UtA-PI and GlyFn (DR, 79%; AUC, 0.946) and that of screening by a combination of maternal factors, MAP, PlGF and GlyFn (DR, 81%; AUC, 0.932). The performance of screening for delivery with PE at ≥ 37 weeks' gestation was poor; the DR for screening by maternal factors alone was 35% and increased to only 39% with use of the triple test. Similar results were obtained when GlyFn replaced PlGF or UtA-PI in the triple test. The DR of screening for GH with delivery at < 37 and ≥ 37 weeks' gestation by maternal factors alone was 34% and 25%, respectively, and increased to 54% and 31%, respectively, with use of the triple test. Similar results were obtained when GlyFn replaced PlGF or UtA-PI in the triple test.

CONCLUSIONS

GlyFn is a potentially useful biomarker in first-trimester screening for preterm PE, but the findings of this case-control study need to be validated by prospective screening studies. The performance of screening for term PE or GH at 11 + 0 to 13 + 6 weeks' gestation by any combination of biomarkers is poor. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

Preterm preeclampsia screening using biomarkers: combining phenotypic classifiers into robust prediction models

BACKGROUND

Preeclampsia screening is a critical component of antenatal care worldwide. Currently, the most developed screening test for preeclampsia at 11 to 13 weeks' gestation integrates maternal demographic characteristics and medical history with 3 biomarkers-serum placental growth factor, mean arterial pressure, and uterine artery pulsatility index-to identify approximately 75% of women who develop preterm preeclampsia with delivery before 37 weeks of gestation. It is generally accepted that further improvements to preeclampsia screening require the use of additional biomarkers. We recently reported that the levels of specific metabolites and metabolite ratios are associated with preterm preeclampsia. Notably, for several of these markers, preterm preeclampsia prediction varied according to maternal body mass index class. These findings motivated us to study whether patient classification allowed for combining metabolites with the current biomarkers more effectively to improve prediction of preterm preeclampsia.

OBJECTIVE

This study aimed to investigate whether metabolite biomarkers can improve biomarker-based preterm preeclampsia prediction in 3 screening resource scenarios according to the availability of: (1) placental growth factor, (2) placental growth factor+mean arterial pressure, and (3) placental growth factor+mean arterial pressure+uterine artery pulsatility index.

STUDY DESIGN

This was an observational case-control study, drawn from a large prospective screening study at 11 to 13 weeks' gestation on the prediction of pregnancy complications, conducted at King's College Hospital, London, United Kingdom. Maternal blood samples were also collected for subsequent research studies. We used liquid chromatography-mass spectrometry to quantify levels of 50 metabolites previously associated with pregnancy complications in plasma samples from singleton pregnancies. Biomarker data, normalized using multiples of medians, on 1635 control and 106 preterm preeclampsia pregnancies were available for model development. Modeling was performed using a methodology that generated a prediction model for preterm preeclampsia in 4 consecutive steps: (1) z-normalization of predictors, (2) combinatorial modeling of so-called (weak) classifiers in the unstratified patient set and in discrete patient strata based on body mass index and/or race, (3) selection of classifiers, and (4) aggregation of the selected classifiers (ie, bagging) into the final prediction model. The prediction performance of models was evaluated using the area under the receiver operating characteristic curve, and detection rate at 10% false-positive rate.

RESULTS

First, the predictor development methodology itself was evaluated. The patient set was split into a training set (2/3) and a test set (1/3) for predictor model development and internal validation. A prediction model was developed for each of the 3 different predictor panels, that is, placental growth factor+metabolites, placental growth factor+mean arterial pressure+metabolites, and placental growth factor+mean arterial pressure+uterine artery pulsatility index+metabolites. For all 3 models, the area under the receiver operating characteristic curve in the test set did not differ significantly from that of the training set. Next, a prediction model was developed using the complete data set for the 3 predictor panels. Among the 50 metabolites available for modeling, 26 were selected across the 3 prediction models; 21 contributed to at least 2 out of the 3 prediction models developed. Each time, area under the receiver operating characteristic curve and detection rate were significantly higher with the new prediction model than with the reference model. Markedly, the estimated detection rate with the placental growth factor+mean arterial pressure+metabolites prediction model in all patients was 0.58 (95% confidence interval, 0.49-0.70), a 15% increase (P<.001) over the detection rate of 0.43 (95% confidence interval, 0.33-0.55) estimated for the reference placental growth factor+mean arterial pressure. The same prediction model significantly improved detection in Black (14%) and White (19%) patients, and in the normal-weight group (18.5≤body mass index<25) and the obese group (body mass index≥30), with respectively 19% and 20% more cases detected, but not in the overweight group, when compared with the reference model. Similar improvement patterns in detection rates were found in the other 2 scenarios, but with smaller improvement amplitudes.

CONCLUSION

Metabolite biomarkers can be combined with the established biomarkers of placental growth factor, mean arterial pressure, and uterine artery pulsatility index to improve the biomarker component of early-pregnancy preterm preeclampsia prediction tests. Classification of the pregnant women according to the maternal characteristics of body mass index and/or race proved instrumental in achieving improved prediction. This suggests that maternal phenotyping can have a role in improving the prediction of obstetrical syndromes such as preeclampsia.

The Value of Ultrasonic Elastography in Detecting Placental Stiffness for the Diagnosis of Preeclampsia: A Meta-Analysis

This meta-analysis evaluated the diagnostic value of ultrasonic elastography in detecting placental stiffness in the diagnosis of preeclampsia (PE). A systematic search was conducted in the EMBASE, Web of Science, Cochrane Library, Scopus database, and PubMed databases to identify studies published before June 2023 using ultrasonic elastography to diagnose PE. The sensitivity, specificity, and diagnostic odds ratio of ultrasonic elastography for diagnosing PE were calculated, and a summary receiver operating characteristic curve model was constructed. The degree of heterogeneity was estimated using the I2 statistic, and a meta-regression analysis was performed to explore its sources. A protocol was determined previously (PROSPERO: CRD42023443646). We included 1188 participants from 11 studies, including 190 patients with PE and 998 patients without PE as controls. Overall sensitivity and specificity of ultrasonic elastography in detecting placental stiffness for the diagnosis of PE were 89% (95% CI: 85-93) and 74% (95% CI: 51-89), respectively. The I2 values for sensitivity and specificity were 59% (95% CI: 29-89) and 96% (95% CI: 95-98), respectively. The area under the receiver operating characteristic curve was 0.90 (95% CI: 0.87-0.92). The meta-regression analysis showed no significant heterogeneity. Ultrasonic elastography exhibits good diagnostic accuracy for detecting placental stiffness and can serve as a non-invasive tool for differentially diagnosing PE.

The prevalence and perinatal repercussions of preeclampsia after the implementation of a prophylaxis protocol with aspirin

OBJECTIVES

To evaluate the prevalence and perinatal repercussions of preeclampsia (PE) after the implementation of a prophylaxis protocol with aspirin in singleton pregnancy at Maternity School of Federal University of Rio de Janeiro, Rio de Janeiro, Brazil (2015-2106).

METHODOLOGY

PE prevalence according to gestational age (GA) and the prevalence ratio (PR) between PE and prematurity, small for gestational age (SGA), and fetal death were calculated in patients assisted during 2015 and 2016.

RESULTS

PE occurred in 373(10.75%) of 3468 investigated cases, where PE < 37 weeks was of 2.79% and PE greater than 37 weeks was of 7.95%. A total of 413 (11.9%) prematurity cases, 320 SGA (9.22%), and 50 fetal deaths (1.44%) occurred. In the PE group, 97 premature newborns (PR 0.90) and 51 SGA (PR 1.16) were born, and two fetal deaths occurred (PR 7.46). Concerning PE < 37 weeks, 27 SGA cases (PR 1.42) and two fetal deaths (PR 2.62) were observed. Regarding PE greater than 37 weeks, 24 SGA (PR 1.09) were born, and no fetal deaths were observed. Our findings were compared to previously published results.

CONCLUSIONS

PE was significantly associated with SGA newborns, especially premature PE. Prescribing aspirin for PE prophylaxis based only on clinical risk factors in a real-life scenario does not appear to be effective but resulted in a PE screening and prophylaxis protocol review and update at ME/UFRJ.

Cell-free DNA methylome analysis for early preeclampsia prediction

Preeclampsia (PE) is a leading cause for peripartal morbidity, especially if developing early in gestation. To enable prophylaxis in the prevention of PE, pregnancies at risk of PE must be identified early-in the first trimester. To identify at-risk pregnancies we profiled methylomes of plasma-derived, cell-free DNA from 498 pregnant women, of whom about one-third developed early-onset PE. We detected DNA methylation differences between control and PE pregnancies that enabled risk stratification at PE diagnosis but also presymptomatically, at around 12 weeks of gestation (range 9-14 weeks). The first-trimester risk prediction model was validated in an external cohort collected from two centers (area under the curve (AUC) = 0.75) and integrated with routinely available maternal risk factors (AUC = 0.85). The combined risk score correctly predicted 72% of patients with early-onset PE at 80% specificity. These preliminary results suggest that cell-free DNA methylation profiling is a promising tool for presymptomatic PE risk assessment, and has the potential to improve treatment and follow-up in the obstetric clinic.

An Interpretable Longitudinal Preeclampsia Risk Prediction Using Machine Learning

Background

Preeclampsia is a pregnancy-specific disease characterized by new onset hypertension after 20 weeks of gestation that affects 2-8% of all pregnancies and contributes to up to 26% of maternal deaths. Despite extensive clinical research, current predictive tools fail to identify up to 66% of patients who will develop preeclampsia. We sought to develop a tool to longitudinally predict preeclampsia risk.

Methods

In this retrospective model development and validation study, we examined a large cohort of patients who delivered at six community and two tertiary care hospitals in the New England region between 02/2015 and 06/2023. We used sociodemographic, clinical diagnoses, family history, laboratory, and vital signs data. We developed eight datasets at 14, 20, 24, 28, 32, 36, 39 weeks gestation and at the hospital admission for delivery. We created linear regression, random forest, xgboost, and deep neural networks to develop multiple models and compared their performance. We used Shapley values to investigate the global and local explainability of the models and the relationships between the predictive variables.

Findings

Our study population (N=120,752) had an incidence of preeclampsia of 5.7% (N=6,920). The performance of the models as measured using the area under the curve, AUC, was in the range 0.73-0.91, which was externally validated. The relationships between some of the variables were complex and non-linear; in addition, the relative significance of the predictors varied over the pregnancy. Compared to the current standard of care for preeclampsia risk stratification in the first trimester, our model would allow 48.6% more at-risk patients to be identified.

Interpretation

Our novel preeclampsia prediction tool would allow clinicians to identify patients at risk early and provide personalized predictions, as well as longitudinal predictions throughout pregnancy.

Funding

National Institutes of Health, Anesthesia Patient Safety Foundation.

RESEARCH IN CONTEXT

Evidence before this study: Current tools for the prediction of preeclampsia are lacking as they fail to identify up to 66% of the patients who develop preeclampsia. We searched PubMed, MEDLINE, and the Web of Science from database inception to May 1, 2023, using the keywords "deep learning", "machine learning", "preeclampsia", "artificial intelligence", "pregnancy complications", and "predictive models". We identified 13 studies that employed machine learning to develop prediction models for preeclampsia risk based on clinical variables. Among these studies, six included biomarkers such as serum placental growth factor, pregnancy-associated plasma protein A, and uterine artery pulsatility index, which are not routinely available in our clinical practice; two studies were in diverse cohorts of more than 100 000 patients, and two studies developed longitudinal predictions using medical records data. However, most studies have limited depth, concerns about data leakage, overfitting, or lack of generalizability.Added value of this study: We developed a comprehensive longitudinal predictive tool based on routine clinical data that can be used throughout pregnancy to predict the risk of preeclampsia. We tested multiple types of predictive models, including machine learning and deep learning models, and demonstrated high predictive power. We investigated the changes over different time points of individual and group variables and found previously known and novel relationships between variables such as red blood cell count and preeclampsia risk.Implications of all the available evidence: Longitudinal prediction of preeclampsia using machine learning can be achieved with high performance. Implementation of an accurate predictive tool within the electronic health records can aid clinical care and identify patients at heightened risk who would benefit from aspirin prophylaxis, increased surveillance, early diagnosis, and escalation in care. These results highlight the potential of using artificial intelligence in clinical decision support, with the ultimate goal of reducing iatrogenic preterm birth and improving perinatal care.

British Society of Gastroenterology Best Practice Guidance: outpatient management of cirrhosis - part 3: special circumstances

The prevalence of cirrhosis has risen significantly over recent decades and is predicted to rise further. Widespread use of non-invasive testing means cirrhosis is increasingly diagnosed at an earlier stage. Despite this, there are significant variations in outcomes in patients with cirrhosis across the UK, and patients in areas with higher levels of deprivation are more likely to die from their liver disease. This three-part best practice guidance aims to address outpatient management of cirrhosis, in order to standardise care and to reduce the risk of progression, decompensation and mortality from liver disease. Part 1 addresses outpatient management of compensated cirrhosis: screening for hepatocellular cancer, varices and osteoporosis, vaccination and lifestyle measures. Part 2 concentrates on outpatient management of decompensated disease including management of ascites, encephalopathy, varices, nutrition as well as liver transplantation and palliative care. In this, the third part of the guidance, we focus on special circumstances encountered in managing people with cirrhosis, namely surgery, pregnancy, travel, managing bleeding risk for invasive procedures and portal vein thrombosis.

Preterm Preeclampsia Risk Modelling: Examining Hemodynamic, Biochemical, and Biophysical Markers Prior to Pregnancy

Preeclampsia (PE) is a leading cause of maternal and perinatal death globally and can lead to unplanned preterm birth. Predicting risk for preterm or early-onset PE, has been investigated primarily after conception, and particularly in the early and mid-gestational periods. However, there is a distinct clinical advantage in identifying individuals at risk for PE prior to conception, when a wider array of preventive interventions are available. In this work, we leverage machine learning techniques to identify potential pre-pregnancy biomarkers of PE in a sample of 80 women, 10 of whom were diagnosed with preterm preeclampsia during their subsequent pregnancy. We explore prospective biomarkers derived from hemodynamic, biophysical, and biochemical measurements and several modeling approaches. A support vector machine (SVM) optimized with stochastic gradient descent yields the highest overall performance with ROC AUC and detection rates up to .88 and .70, respectively on subject-wise cross validation. The best performing models leverage biophysical and hemodynamic biomarkers. While preliminary, these results indicate the promise of a machine learning based approach for detecting individuals who are at risk for developing preterm PE before they become pregnant. These efforts may inform gestational planning and care, reducing risk for adverse PE-related outcomes.Clinical Relevance- This work considers the development and optimization of pre-pregnancy biomarkers for improving the identification of preterm (early-onset) preeclampsia risk prior to conception.

Independent predictors of preeclampsia and their impact on the complication in Bulgarian study group of pregnant women

INTRODUCTION

One of the major obstetrical complications, affecting 2%-8% of all pregnancies, is preeclampsia. To predict the onset of preeclampsia, several methods have recently been put forth. The Fetal Medicine Foundation has developed combined screening that can identify the vast majority of women who will develop preeclampsia using a combination of maternal factors, obstetrical history, biochemical, and biophysical factors.