The performance of risk prediction models for pre-eclampsia using routinely collected maternal characteristics and comparison with models that include specialised tests and with clinical guideline decision rules: a systematic review

Does the use of angiogenic biomarkers for the management of preeclampsia and fetal growth restriction improve outcomes?: Challenging the current status quo

Monitoring and timing of delivery in preterm preeclampsia and fetal growth restriction is one of the biggest challenges in Obstetrics. Finding the optimal time of delivery of these fetuses usually involves a trade-off between the severity of the disease and prematurity. So far, most clinical guidelines recommend the use of a combination between clinical, laboratory and ultrasound markers to guide the time of delivery. Angiogenic biomarkers, especially placental growth factor (PlGF) and soluble fms-like tyrosine kinase-1 (sFlt-1), have gained significant attention in recent years for their potential role in the prediction and diagnosis of placenta-related disorders including preeclampsia and fetal growth restriction. Another potential clinical application of the angiogenic biomarkers is for the differential diagnosis of patients with chronic kidney disease, as this condition shares similar clinical features with preeclampsia. Consequently, angiogenic biomarkers have been advocated as tools for monitoring and deciding the optimal time of the delivery of fetuses affected by placental dysfunction. In this clinical opinion, we critically review the available literature on PlGF and sFlt-1 for the surveillance and time of the delivery in fetuses affected by preterm preeclampsia and fetal growth restriction. Moreover, we explore the use of angiogenic biomarkers for the differentiation between chronic kidney disease and superimposed preeclampsia.

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

PURPOSE OF REVIEW

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

RECENT FINDINGS

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

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Comparing urine point-of-care tests to screen preeclampsia: Congo-red dot paper test versus dipstick urinalysis

To compare the urine Congo-red dot paper test (CRD) with dipstick urinalysis to screen preeclampsia (PE). A total of 409 paired spot urine samples were obtained prospectively from women with suspected pre-eclampsia attending for routine hospital visits. Congo-red dot paper test and dipstick urinalysis were examined and compared to screen pre-eclampsia. The agreement between the two urinary test is modest (kappa coefficient = 0.28, 95% CI 0.14-0.42). The specificity of CRD was higher than urinalysis (97.4% vs. 90.4%, p < .001). Urinalysis performed better in sensitivity (77.3% vs. 40.9%, p = .04) and the area under the receiver operating characteristic curves (AUC) (0.84 [95% CI 0.74-0.94] vs. 0.69 [95% CI 0.55-0.83], p = .04) than CRD, respectively. The sensitivity, specificity, AUC of the parallel test of them is 86.4% (64.0%-96.4%), 89.1% (85.5%-92.0%), and 0.88 (95% CI 0.79-0.96). And the serial test is 31.8% (14.7%-54.9%), 98.7% (96.8%-99.5%), 0.65 (95% CI 0.51-0.79), accordingly. The urinalysis is a better diagnosing test for preeclampsia. CRD could aid in the diagnosis of patients with preeclampsia. Combined the two tests in suspected patients may further improve the performance in the diagnosis of preeclampsia. Further study need to be made for its potential clinical practice.

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.

Machine learning models for predicting preeclampsia: a systematic review

BACKGROUND

This systematic review provides an overview of machine learning (ML) approaches for predicting preeclampsia.

METHOD

This review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) guidelines. We searched the Cochrane Central Register, PubMed, EMBASE, ProQuest, Scopus, and Google Scholar up to February 2023. Search terms were limited to "preeclampsia" AND "artificial intelligence" OR "machine learning" OR "deep learning." All studies that used ML-based analysis for predicting preeclampsia in pregnant women were considered. Non-English articles and those that are unrelated to the topic were excluded. The PROBAST was used to assess the risk of bias and applicability of each included study.

RESULTS

The search strategy yielded 128 citations; after duplicates were removed and title and abstract screening was completed, 18 full-text articles were evaluated for eligibility. Four studies were included in this review. Two studies were at low risk of bias, and two had low to moderate risk. All of the study designs included were retrospective cohort studies. Nine distinct models were chosen as ML models from the four studies. Maternal characteristics, medical history, medication intake, obstetrical history, and laboratory and ultrasound findings obtained during prenatal care visits were candidate predictors to train the ML model. Elastic net, stochastic gradient boosting, extreme gradient boosting, and Random forest were among the best models to predict preeclampsia. All four studies used metrics such as the area under the curve, true positive rate, negative positive rate, accuracy, precision, recall, and F1 score. The AUC of ML models varied from 0.860 to 0.973 in four studies.

CONCLUSION

The results of studies yielded high prediction performance of ML models for preeclampsia risk from routine early pregnancy information.

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

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

Prediction of Pregnancy-Associated Hypertension Using a Scoring System: A Multicenter Cohort Study

This study aimed to develop an early pregnancy risk scoring model for pregnancy-associated hypertension (PAH) based on maternal pre-pregnancy characteristics, such as mean arterial pressure (MAP), pregnancy-associated plasma protein-A (PAPP-A) or neither. The perinatal databases of seven hospitals from January 2009 to December 2020 were randomly divided into a training set and a test set at a ratio of 70:30. The data of a total pregnant restricted population (women not taking aspirin during pregnancy) were analyzed separately. Three models (model 1, pre-pregnancy factors only; model 2, adding MAP; model 3, adding MAP and PAPP-A) and the American College of Obstetricians and Gynecologists (ACOG) risk factors model were compared. A total of 2840 (8.11%) and 1550 (3.3%) women subsequently developed PAH and preterm PAH, respectively. Performances of models 2 and 3 with areas under the curve (AUC) over 0.82 in both total population and restricted population were superior to those of model 1 (with AUCs of 0.75 and 0.748, respectively) and the ACOG risk model (with AUCs of 0.66 and 0.66) for predicting PAH and preterm PAH. The final scoring system with model 2 for predicting PAH and preterm PAH showed moderate to good performance (AUCs of 0.78 and 0.79, respectively) in the test set. "A risk scoring model for PAH and preterm PAH with pre-pregnancy factors and MAP showed moderate to high performances. Further prospective studies for validating this scoring model with biomarkers and uterine artery Doppler or without them might be required".

A novel approach to joint prediction of preeclampsia and delivery timing using semicompeting risks

BACKGROUND

Preeclampsia is a pregnancy complication that contributes substantially to perinatal morbidity and mortality worldwide. Existing approaches to modeling and prediction of preeclampsia typically focus either on predicting preeclampsia risk alone, or on the timing of delivery following a diagnosis of preeclampsia. As such, they are misaligned with typical healthcare interactions during which the 2 events are generally considered simultaneously.

OBJECTIVE

This study aimed to describe the "semicompeting risks" framework as an innovative approach for jointly modeling the risk and timing of preeclampsia and the timing of delivery simultaneously. Through this approach, one can obtain, at any point during the pregnancy, clinically relevant summaries of an individual's predicted outcome trajectories in 4 risk categories: not developing preeclampsia and not having delivered, not developing preeclampsia but having delivered because of other causes, developing preeclampsia but not having delivered, and developing preeclampsia and having delivered.

STUDY DESIGN

To illustrate the semicompeting risks methodology, we presented an example analysis of a pregnancy cohort from the electronic health record of an urban, academic medical center in Boston, Massachusetts (n=9161 pregnancies). We fit an illness-death model with proportional-hazards regression specifications describing 3 hazards for timings of preeclampsia, delivery in the absence of preeclampsia, and delivery following preeclampsia diagnosis.

RESULTS

The results indicated nuanced relationships between a variety of risk factors and the timings of preeclampsia diagnosis and delivery, including maternal age, race/ethnicity, parity, body mass index, diabetes mellitus, chronic hypertension, cigarette use, and proteinuria at 20 weeks' gestation. Sample predictions for a diverse set of individuals highlighted differences in projected outcome trajectories with regard to preeclampsia risk and timing, and timing of delivery either before or after preeclampsia diagnosis.

CONCLUSION

The semicompeting risks framework enables characterization of the joint risk and timing of preeclampsia and delivery, providing enhanced, meaningful information regarding clinical decision-making throughout the pregnancy.

Prediction of pre-eclampsia at St. Mary's hospital lacor, a low-resource setting in northern Uganda, a prospective cohort study

BACKGROUND

Pre-eclampsia is the second leading cause of maternal death in Uganda. However, mothers report to the hospitals late due to health care challenges. Therefore, we developed and validated the prediction models for prenatal screening for pre-eclampsia.

METHODS

This was a prospective cohort study at St. Mary's hospital lacor in Gulu city. We included 1,004 pregnant mothers screened at 16-24 weeks (using maternal history, physical examination, uterine artery Doppler indices, and blood tests), followed up, and delivered. We built models in RStudio. Because the incidence of pre-eclampsia was low (4.3%), we generated synthetic balanced data using the ROSE (Random Over and under Sampling Examples) package in RStudio by over-sampling pre-eclampsia and under-sampling non-preeclampsia. As a result, we got 383 (48.8%) and 399 (51.2%) for pre-eclampsia and non-preeclampsia, respectively. Finally, we evaluated the actual model performance against the ROSE-derived synthetic dataset using K-fold cross-validation in RStudio.

RESULTS

Maternal history of pre-eclampsia (adjusted odds ratio (aOR) = 32.75, 95% confidence intervals (CI) 6.59-182.05, p = 0.000), serum alkaline phosphatase(ALP) < 98 IU/L (aOR = 7.14, 95% CI 1.76-24.45, p = 0.003), diastolic hypertension ≥ 90 mmHg (aOR = 4.90, 95% CI 1.15-18.01, p = 0.022), bilateral end diastolic notch (aOR = 4.54, 95% CI 1.65-12.20, p = 0.003) and body mass index of ≥ 26.56 kg/m² (aOR = 3.86, 95% CI 1.25-14.15, p = 0.027) were independent risk factors for pre-eclampsia. Maternal age ≥ 35 years (aOR = 3.88, 95% CI 0.94-15.44, p = 0.056), nulliparity (aOR = 4.25, 95% CI 1.08-20.18, p = 0.051) and white blood cell count ≥ 11,000 (aOR = 8.43, 95% CI 0.92-70.62, p = 0.050) may be risk factors for pre-eclampsia, and lymphocyte count of 800 - 4000 cells/microliter (aOR = 0.29, 95% CI 0.08-1.22, p = 0.074) may be protective against pre-eclampsia. A combination of all the above variables predicted pre-eclampsia with 77.0% accuracy, 80.4% sensitivity, 73.6% specificity, and 84.9% area under the curve (AUC).

CONCLUSION

The predictors of pre-eclampsia were maternal age ≥ 35 years, nulliparity, maternal history of pre-eclampsia, body mass index, diastolic pressure, white blood cell count, lymphocyte count, serum ALP and end-diastolic notch of the uterine arteries. This prediction model can predict pre-eclampsia in prenatal clinics with 77% accuracy.

Effects of maternal characteristics and medical history on first trimester biomarkers for preeclampsia

Objective

To identify and quantify the effects of maternal characteristics and medical history on the distribution of Placental Growth Factor (PlGF), mean arterial pressure (MAP), and Uterine Artery Mean Pulsatility Index (UtA-PI); and to standardize the expected values for these biomarkers in the first trimester to create unique multiples of the median (MoMs) for Latin-American population.

Methods

This is a prospective cohort built exclusively for research purposes of consecutive pregnant women attending their first-trimester screening ultrasound at a primary care center for the general population in Mexico City between April 2019 and October 2021. We excluded fetuses with chromosomal abnormalities, major fetal malformations, and women delivering in another care center. Linear regression was used on log-transformed biomarkers to assess the influence of maternal characteristics on non-preeclamptic women to create MoM.

Results

Of a total of 2,820 pregnant women included in the final analysis, 118 (4.18%) developed PE, of which 22 (0.78%) delivered before 34 weeks of gestation, 74 (2.62%) before 37 weeks, and 44 (1.56%) from 37 weeks gestation. Characteristics that significantly influenced PLGF were fetal crown rump length (CRL), maternal age, nulliparity, body mass index (BMI), chronic hypertension, Lupus, spontaneous pregnancy, polycystic ovary syndrome (PCOS), hypothyroidism, preeclampsia (PE) in a previous pregnancy, and mother with PE. MAP had significant influence from CRL, maternal age, PE in a previous pregnancy, induction of ovulation, a mother with PE, chronic hypertension, BMI, and hypothyroidism. UtA-PI was influenced by CRL, maternal age, a mother with PE, chronic hypertension, and gestational diabetes mellitus (GDM) in a previous pregnancy.

Conclusion

Population-specific multiples of the median (MoMs) for PlGF, MAP, and UtA-PI in the first trimester adequately discriminate among women developing preeclampsia later in pregnancy.

Placenta autophagy is closely associated with preeclampsia

The pathogenesis of preeclampsia (PE) is complex and placental internal homeostasis is regulated by cellular autophagy. However, there are fewer studies related to the role of placental autophagy in the pathogenesis of PE. The GSE75010 and GSE10588 datasets were downloaded from the gene expression omnibus (GEO) database. In the GSE75010 (test cohort), 103 differentially expressed genes (DEGs) were screened using "Limma" package, and 281 PE characteristic genes were screened by weighted gene coexpression network analysis (WGCNA). Combined with the autophagy gene set, a total of 5 autophagy-related hub genes were obtained. Three biomarkers (HK2, PLOD2, and TREM1) were then further screened by random forest(RF) model and least absolute shrinkage and selection operator(LASSO) algorithm as diagnostic of PE. In the unsupervised consensus clustering analysis, HK2, PLOD2, and TREM1 may be synergistically involved in hypoxia-induced autophagy and hypoxia-inducible factor 1(HIF-1) signaling pathway to induce PE. In addition, we constructed and evaluated a nomogram model for PE diagnosis using these three key diagnostic biomarkers, and the results showed that the model had significantly excellent predictive power (AUC values of GSE75010 and GSE10588 datasets were 0.869 and 0.876, respectively). In terms of immune infiltration, a higher proportion of T cells CD8, and a lower proportion of Macrophages M2 were found in PE placentas compared to normal tissue, and high expression of HK2, PLOD2, and TREM1 were accompanied by low levels of Macrophages M2 infiltration. HK2, PLOD2, and TREM1 may be associated with the development of pre-eclampsia, and their mechanisms of action in preeclampsia need to be further investigated.

Prediction of Adverse Outcomes in De Novo Hypertensive Disorders of Pregnancy: Development and Validation of Maternal and Neonatal Prognostic Models

Effectively identifying high-risk patients with de novo hypertensive disorder of pregnancy (HDP) is required to enable timely intervention and to reduce adverse maternal and perinatal outcomes. Electronic medical record of pregnant women with de novo HDP were extracted from a birth cohort in Beijing, China. The adverse outcomes included maternal and fetal morbidities, mortality, or any other adverse complications. A multitude of machine learning statistical methods were employed to develop two prediction models, one for maternal complications and the other for perinatal deteriorations. The maternal model using the random forest algorithm produced an AUC of 0.984 (95% CI (0.978, 0.991)). The strongest predictors variables selected by the model were platelet count, fetal head/abdominal circumference ratio, and gestational age at the diagnosis of de novo HDP; The perinatal model using the boosted tree algorithm yielded an AUC of 0.925 (95% CI (0.907, 0.945]). The strongest predictor variables chosen were gestational age at the diagnosis of de novo HDP, fetal femur length, and fetal head/abdominal circumference ratio. These prediction models can help identify de novo HDP patients at increased risk of complications who might need intense maternal or perinatal care.

Development of a prediction model on preeclampsia using machine learning-based method: a retrospective cohort study in China

Objective: The aim of this study was to use machine learning methods to analyze all available clinical and laboratory data obtained during prenatal screening in early pregnancy to develop predictive models in preeclampsia (PE).

Material and Methods: Data were collected by retrospective medical records review. This study used 5 machine learning algorithms to predict the PE: deep neural network (DNN), logistic regression (LR), support vector machine (SVM), decision tree (DT), and random forest (RF). Our model incorporated 18 variables including maternal characteristics, medical history, prenatal laboratory results, and ultrasound results. The area under the receiver operating curve (AUROC), calibration and discrimination were evaluated by cross-validation.

Results: Compared with other prediction algorithms, the RF model showed the highest accuracy rate. The AUROC of RF model was 0.86 (95% CI 0.80–0.92), the accuracy was 0.74 (95% CI 0.74–0.75), the precision was 0.82 (95% CI 0.79–0.84), the recall rate was 0.42 (95% CI 0.41–0.44), and Brier score was 0.17 (95% CI 0.17–0.17).

Conclusion: The machine learning method in our study automatically identified a set of important predictive features, and produced high predictive performance on the risk of PE from the early pregnancy information.

First-trimester prediction model for placental vascular disorders: An observational prospective study

This study aims to develop a multivariable predictive model for the risk of placental vascular complications (PVC), by using biochemical, biophysical, anamnestic and clinical maternal features available at the first trimester. PVC include gestational hypertension, preeclampsia, placenta abruption, intrauterine growth restriction (IUGR), and stillbirth. Prospective study that included all singleton pregnancies attending the first-trimester aneuploidy screening (11 +0-12 +6 weeks) at Obstetrics Unit of the University Hospital of Modena, in Northern Italy, between June 2018 and December 2019. In a total of 503 women included in the analysis, 40 patients were in the PVC group. The final prediction model for PVC included the following independent variables: pre-pregnancy BMI ≥ 30 (OR = 2.65, 95% CI = 1.04; 6.75, p = 0.0415), increasing values of mean arterial pressure (OR = 1.06, 95% CI = 1.02; 1.10, p = 0.0008), PAPP-A < 2.40465 U/L (OR = 0.43, 95% CI = 0.19; 0.96, p = 0.0388) and decreasing values of PlGf (MoM) (OR = 0.28, 95% CI = 0.10; 0.79, p = 0.0153). The area under the ROC curve was 79.4% indicating a satisfactory predictive accuracy. The best predictive cut-off for this score was equal to -2.562, which corresponds to a 7.2 % probability of having PVC. By using such a cut-off, the risk of PVC can be predicted in our sample with sensitivity equal to 82,4 % and specificity equal to 69,9 %. This model for early prediction of PVC is a promising tool to early identify women at greater risk for placenta vascular complications.

Toward personalized management of chronic hypertension in pregnancy

Chronic hypertension complicates 1% to 2% of pregnancies, and it is increasingly common. Women with chronic hypertension are an easily recognized group who are in touch with a wide variety of healthcare providers before, during, and after pregnancy, mandating that chronic hypertension in pregnancy be within the scope of many practitioners. We reviewed recent data on management to inform current care and future research. This study is a narrative review of published literature. Compared with normotensive women, women with chronic hypertension are at an increased risk of maternal and perinatal complications. Women with chronic hypertension who wish to be involved in their care can do by measuring blood pressure at home. Accurate devices for home blood pressure monitoring are now readily available. The diagnostic criteria for superimposed preeclampsia remain problematic because most guidelines continue to include deteriorating blood pressure control in the definition. It has not been established how angiogenic markers may aid in confirmation of the diagnosis of superimposed preeclampsia when suspected, over and above information provided by routinely available clinical data and laboratory results. Although chronic hypertension is a strong risk factor for preeclampsia, and aspirin decreases preeclampsia risk, the effectiveness specifically among women with chronic hypertension has been questioned. It is unclear whether calcium has an independent effect in preeclampsia prevention in such women. Treating hypertension with antihypertensive therapy halves the risk of progression to severe hypertension, thrombocytopenia, and elevated liver enzymes, but a reduction in preeclampsia or serious maternal complications has not been observed; however, the lack of evidence for the latter is possibly owing to few events. In addition, treating chronic hypertension neither reduces nor increases fetal or newborn death or morbidity, regardless of the gestational age at which the antihypertensive treatment is started. Antihypertensive agents are not teratogenic, but there may be an increase in malformations associated with chronic hypertension itself. At present, blood pressure treatment targets used in clinics are the same as those used at home, although blood pressure values tend to be inconsistently lower at home among women with hypertension. Although starting all women on the same antihypertensive medication is usually effective in reducing blood pressure, it remains unclear whether there is an optimal agent for such an approach or how best to use combinations of antihypertensive medications. An alternative approach is to individualize care, using maternal characteristics and blood pressure features beyond blood pressure level (eg, variability) that are of prognostic value. Outcomes may be improved by timed birth between 38 0/7 and 39 6/7 weeks' gestation based on observational literature; of note, confirmatory trial evidence is pending. Postnatal care is facilitated by the acceptability of most antihypertensives (including angiotensin-converting enzymes inhibitors) for use in breastfeeding. The evidence base to guide the care of pregnant women with chronic hypertension is growing and aligning with international guidelines. Addressing outstanding research questions would inform personalized care of chronic hypertension in pregnancy.

First trimester preeclampsia screening and prediction

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

Development and Validation of an Obstetric Comorbidity Risk Score for Clinical Use

Background: A comorbidity summary score may support early and systematic identification of women at high risk for adverse obstetric outcomes. The objective of this study was to conduct the initial development and validation of an obstetrics comorbidity risk score for automated implementation in the electronic health record (EHR) for clinical use. Methods: The score was developed and validated using EHR data for a retrospective cohort of pregnancies with delivery between 2010 and 2018 at Kaiser Permanente Northern California, an integrated health care system. The outcome used for model development consisted of adverse obstetric events from delivery hospitalization (e.g., eclampsia, hemorrhage, death). Candidate predictors included maternal age, parity, multiple gestation, and any maternal diagnoses assigned in health care encounters in the 12 months before admission for delivery. We used penalized regression for variable selection, logistic regression to fit the model, and internal validation for model evaluation. We also evaluated prenatal model performance at 18 weeks of pregnancy. Results: The development cohort (n = 227,405 pregnancies) had an outcome rate of 3.8% and the validation cohort (n = 41,683) had an outcome rate of 2.9%. Of 276 candidate predictors, 37 were included in the final model. The final model had a validation c-statistic of 0.72 (95% confidence interval [CI] 0.70-0.73). When evaluated at 18 weeks of pregnancy, discrimination was modestly diminished (c-statistic 0.68 [95% CI 0.67-0.70]). Conclusions: The obstetric comorbidity score demonstrated good discrimination for adverse obstetric outcomes. After additional appropriate validation, the score can be automated in the EHR to support early identification of high-risk women and assist efforts to ensure risk-appropriate maternal care.

Aspirin Use to Prevent Preeclampsia and Related Morbidity and Mortality: Updated Evidence Report and Systematic Review for the US Preventive Services Task Force

IMPORTANCE

Preeclampsia is a hypertensive disorder of pregnancy that poses serious maternal and infant health risks. Previous systematic reviews have established benefits of low-dose aspirin taken during pregnancy to prevent preeclampsia and its sequelae.

OBJECTIVE

To update evidence for the US Preventive Services Task Force (USPSTF) on effectiveness of aspirin use in preventing preeclampsia in individuals at increased risk based on clinical risk factors or measurements associated with higher disease incidence than in the general population.

DATA SOURCES

Studies from previous USPSTF review (2014), literature published January 2013 through May 15, 2020, in MEDLINE, PubMed (for publisher-supplied records only), EMBASE, and Cochrane Central Register of Controlled Trials. Ongoing surveillance through January 22, 2021.

STUDY SELECTION

Good- and fair-quality randomized clinical trials (RCTs) of low-dose aspirin use during pregnancy to prevent preeclampsia among individuals at increased risk; studies conducted in general populations to evaluate potential harms.

DATA EXTRACTION AND SYNTHESIS

Dual article screening and risk-of-bias assessment. Study data abstracted into prespecified forms, checked for accuracy. Random-effects meta-analysis.

MAIN OUTCOMES AND MEASURES

Diagnosis of preeclampsia; adverse pregnancy health outcomes and complications including eclampsia, perinatal mortality, preterm birth, small for gestational age, and potential bleeding harms or infant/child harms from aspirin exposure.

RESULTS

A total of 23 randomized clinical trials (RCTs) (N = 26 952) were included; 18 were conducted among participants at increased preeclampsia risk. Aspirin dosages ranged from 50 mg/d to 150 mg/d. Most trials enrolled majority White populations selected based on a range of risk factors. The incidence of preeclampsia among the trials of participants at increased risk ranged from 4% to 30%. Aspirin use was significantly associated with lower risk of preeclampsia (pooled relative risk [RR], 0.85 [95% CI, 0.75-0.95]; 16 RCTs [n = 14 093]; I2 = 0%), perinatal mortality (pooled RR, 0.79 [95% CI, 0.66-0.96]; 11 RCTs [n = 13 860]; I2 = 0%), preterm birth (pooled RR, 0.80 [95% CI, 0.67-0.95]; 13 RCTs [n = 13 619]; I2 = 49%), and intrauterine growth restriction (pooled RR, 0.82 [95% CI, 0.68-0.99]; 16 RCTs [n = 14 385]; I2 = 41%). There were no significant associations of aspirin use with risk of postpartum hemorrhage (pooled RR, 1.03 [95% CI, 0.94-1.12]; 9 RCTs [n = 23 133]; I2 = 0%) and other bleeding-related harms, or with rare perinatal or longer-term harms. Absolute risk reductions for preeclampsia associated with aspirin use ranged from -1% to -6% across larger trials (n >300) and were greater in smaller trials. For perinatal mortality, absolute risk reductions ranged from 0.5% to 1.1% in the 3 largest trials.

CONCLUSIONS AND RELEVANCE

Daily low-dose aspirin during pregnancy was associated with lower risks of serious perinatal outcomes for individuals at increased risk for preeclampsia, without evident harms.

Routinely collected antenatal data for longitudinal prediction of preeclampsia in nulliparous women: a population-based study

The objective was to evaluate the sequentially updated predictive capacity for preeclampsia during pregnancy, using multivariable longitudinal models including data from antenatal care. This population-based cohort study in the Stockholm-Gotland Counties, Sweden, included 58,899 pregnancies of nulliparous women 2008-2013. Prospectively collected data from each antenatal care visit was used, including maternal characteristics, reproductive and medical history, and repeated measurements of blood pressure, weight, symphysis-fundal height, proteinuria, hemoglobin and blood glucose levels. We used a shared-effects joint longitudinal model including all available information up until a given gestational length (week 24, 28, 32, 34 and 36), to update preeclampsia prediction sequentially. Outcome measures were prediction of preeclampsia, preeclampsia with delivery < 37, and preeclampsia with delivery ≥ 37 weeks' gestation. The area under the curve (AUC) increased with gestational length. AUC for preeclampsia with delivery < 37 weeks' gestation was 0.73 (95% CI 0.68-0.79) at week 24, and increased to 0.87 (95% CI 0.84-0.90) in week 34. For preeclampsia with delivery ≥ 37 weeks' gestation, the AUC in week 24 was 0.65 (95% CI 0.63-0.68), but increased to 0.79 (95% CI 0.78-0.80) in week 36. The addition of routinely collected clinical measurements throughout pregnancy improve preeclampsia prediction and may be useful to individualize antenatal care.

Use of FMF algorithm for prediction of preeclampsia in high risk pregnancies: a single center longitudinal study

OBJECTIVE: This study aimed to assess the accuracy of The Fetal Medicine Foundation (FMF) screening algorithm for the prediction of preeclampsia.METHODS: Out of 138 women with high-risk pregnancies prospectively followed, 30 developed preeclampsia. The clinical examination and biochemical measurements were performed at first, second, early and late third trimester.RESULTS: A lower PAPP-A levels were found in the first trimester, while sFlt/PlGF was increased in the second and early third trimester in preeclampsia (p>0.05). FMF algorithm presented higher specificity (>70%), but had a drawback of lower sensitivity (35-77%).CONCLUSION: FMF algorithm had modest performance in the prediction of preeclampsia for high-risk pregnancies.

Recent advances in the diagnosis and management of pre-eclampsia

Pre-eclampsia is an elusive condition to diagnose and a complex disease to manage. There have been recent developments in prediction, prevention, diagnosis, and management. Risk modelling has been used to identify women at highest risk of developing pre-eclampsia as well as predicting maternal adverse outcomes in confirmed disease. New evidence has shown that aspirin prophylaxis significantly reduces early onset pre-eclampsia as well as preterm birth. The criteria for the diagnosis of pre-eclampsia are evolving, and proteinuria is no longer a pre-requisite to make a diagnosis. Angiogenic biomarker testing accelerates diagnosis as well as minimises adverse maternal outcomes and has been incorporated into national guidelines. Emerging evidence demonstrates that expedited delivery in late preterm pre-eclampsia may be protective against maternal adverse outcomes but increase the risk of neonatal unit admission. Both women and their offspring are at increased risk of long-term health complications following pre-eclampsia, and it is important that postnatal health is optimised. This article summarises recent developments in the field of pre-eclampsia research, evaluating the impact on clinical care for women at risk of, or with suspected or confirmed, pre-eclampsia.

Prevalence of hypertensive disorders in pregnancy in China: A systematic review and meta-analysis

Hypertensive disorders in pregnancy (HDP) are associated with various maternal and fetal adverse outcomes and become an increasingly significant threat to Chinese pregnant women. Yet, the prevalence of HDP in China is not clear. We conducted this meta-analysis to estimate the prevalence of HDP and specific subtypes in China. We searched PubMed, Embase, Web of Science, CNKI, Wangfang, and CMB for studies on prevalence of HDP and specific subtypes, published from 1990 to Jan 21, 2020, without language restrictions. We included all studies reported the prevalence of HDP and specific subtypes in Chinese pregnant women. We excluded qualitative studies, case reports, reviews, conference presentations, and studies only provided abstracts. We using a standard self-developed form to extract information from eligible studies. We did meta-analyses by random-effect models and estimated the pooled prevalence of HDP and specific subtypes. In order to explore potential sources of heterogeneity and subgroup effects, we did and meta-regression and subgroup analyses by pre-specified covariates. This study is registered with PROSPERO, number CRD42020166001. We initially identified 4179 records, of which 92 studies with 1,377,448 participants were eligible in the final systematic review and meta-analyses. The pooled prevalence (95% CI) of HDP, gestational hypertension, preeclampsia, mild preeclampsia, severe preeclampsia, eclampsia, chronic hypertension, and chronic hypertension with superimposed preeclampsia were 7.30% (6.60%-8.00%), 3.30% (2.90%-3.70%), 4.50% (4.00%-5.00%), 2.00% (1.70%-2.30%), 2.60% (2.10%-3.00%), 0.11% (0.08%-0.15%), 0.60% (0.30%-0.90%), and 0.60% (0.40%-0.80%), respectively. No publication bias was identified, although heterogeneity was high (I² statistics: 92.0%-99.3%). High prevalence of HDP and the subtypes frequently reported in Western and Northern China. Pregnant women who were aged 35 years and above had high prevalence of HDP and subtypes; women who were overweight or obese had high prevalence of HDP, gestational hypertension and preeclampsia. The prevalence of HDP and the subtypes vary in different areas in China. Given to increasingly prevalent of the risk factors, such as overweight, obesity, and advance maternal age, strategies to prevent and manage HDP need to be improved, especially for women living in Western and Northern China.

Current update of first trimester preeclampsia screening in Asia

In consideration of high prevalence of preeclampsia, enormous studies attempted to look for strategies in early gestation. Hence, a powerful screening should be built up in first trimester. Then, Aspirin could be administrated for proper prevention. The objective of this article is reviewing the screening for preeclampsia in first trimester recently. To identify the high-risk group precisely, an effective model should be recommended to Asian population. Articles related to first trimester screening of PE in Asia from databases of PubMed, ScienceDirect and Scopus were searched for this narrative review. The criteria included randomized clinical trials, observational prospective and retrospective cohort studies, case-control studies, systematic review and meta-analysis and professional review articles. Screening models combining maternal factors, biophysical factors, ultrasound studies and biochemical factors achieved high predictive performance of preeclampsia. In Asia, the detection rate of the Fetal Medicine Foundation is superior to those of the American College of Obstetricians and Gynecologists and the National Institute for Health and Care Excellence. Consequently, this effective model from the Fetal Medicine Foundation should be continuously used for screening in first trimester for the Asian.

Preterm parturition and pre-eclampsia: The confluence of two great gestational syndromes

BACKGROUND

Preterm birth (PTB) and pre-eclampsia independently, and frequently concurrently, adversely affect the pregnancy outcomes of millions of mothers and infants worldwide each year.

OBJECTIVES

To fill the gap between PTB and pre-eclampsia, which continue to constitute the two most important current global challenges to maternal and perinatal health.

METHODS

Pubmed, Embase, and Cochrane databases were searched from inception until December 2019 using the terms spontaneous PTB (SPTB), indicated preterm delivery (IPTD), early-onset pre-eclampsia, and pre-eclampsia.

RESULTS

History of PTB and pre-eclampsia were the strongest risk factors contributing to the occurrence of SPTB or IPTB. The risk of PTB and pre-eclampsia among non-Hispanic African American women was higher than the rate among all other racial/ethnic groups in the United States. Low-dose aspirin (LDA) has been reported to reduce the risk of pre-eclampsia by at least 10% and PTB by at least 14%. Lastly, women and their fetuses who develop early-onset pre-eclampsia are at higher risk for developing hypertension and cardiovascular disease later in life.

CONCLUSIONS

While better clarity is needed, efforts to coordinate prevention of both PTB and pre-eclampsia, even though imperfect, are critically important as part of any program to make motherhood as safe as possible.

Study for Improving Maternal Pregnancy And Child ouTcomes (IMPACT): a study protocol for a Swedish prospective multicentre cohort study

INTRODUCTION

First-trimester pregnancy risk evaluation facilitates individualised antenatal care, as well as application of preventive strategies for pre-eclampsia or birth of a small for gestational age infant. A range of early intervention strategies in pregnancies identified as high risk at the end of the first trimester has been shown to decrease the risk of preterm pre-eclampsia (<37 gestational weeks). The aim of this project is to create the Improving Maternal Pregnancy And Child ouTcomes (IMPACT) database; a nationwide database with individual patient data, including predictors recorded at the end of the first trimester and later pregnancy outcomes, to identify women at high risk of pre-eclampsia. A second aim is to link the IMPACT database to a biobank with first-trimester blood samples.

METHODS AND ANALYSIS

This is a Swedish prospective multicentre cohort study. Women are included between the 11th and 14th weeks of pregnancy. At inclusion, pre-identified predictors are retrieved by interviews and medical examinations. Blood samples are collected and stored in a biobank. Additional predictors and pregnancy outcomes are retrieved from the Swedish Pregnancy Register. Inclusion in the study began in November 2018 with a targeted sample size of 45 000 pregnancies by end of 2021. Creation of a new risk prediction model will then be developed, validated and implemented. The database and biobank will enable future research on prediction of various pregnancy-related complications.

ETHICS AND DISSEMINATION

Confidentiality aspects such as data encryption and storage comply with the General Data Protection Regulation and with ethical committee requirements. This study has been granted national ethical approval by the Swedish Ethical Review Authority (Uppsala 2018-231) and national biobank approval at Uppsala Biobank (18237 2 2018 231). Results from the current as well as future studies using information from the IMPACT database will be published in peer-reviewed journals.

TRIAL REGISTRATION NUMBER

NCT03831490.

Occupational exposure to whole-body vibrations and pregnancy complications: a nationwide cohort study in Sweden

Objectives

Pregnancy complications are common contributors to perinatal mortality and morbidity. Still, the cause(s) of gestational hypertensive disorders and diabetes are largely unknown. Some occupational exposures have been inconsistently associated with pregnancy complications, but exposure to whole-body vibrations (WBV) has been largely overlooked even though it has been associated with adverse birth outcomes. Therefore, the aim was to assess whether occupational WBV exposure during pregnancy is associated with pregnancy complications in a nationwide, prospective cohort study.

Methods

The Fetal Air Pollution Exposure cohort was formed by merging multiple Swedish, national registers containing information on occupation during pregnancy and diagnosis codes, and includes all working women who gave birth between 1994 and 2014 (n=1 091 044). WBV exposure was derived from a job-exposure matrix and was divided into categories (0, 0.1–0.2, 0.3–0.4 and ≥0.5 m/s²). ORs with 95% CIs were calculated using logistic regression adjusted for potential confounders.

Results

Among women working full time (n=646 490), we found increased risks of all pregnancy complications in the highest exposure group (≥0.5 m/s²), compared with the lowest. The adjusted ORs were 1.76 (95% CI 1.41 to 2.20), 1.55 (95% CI 1.26 to 1.91) and 1.62 (95% CI 1.07 to 2.46) for preeclampsia, gestational hypertension and gestational diabetes, respectively, and were similar in all sensitivity analyses. There were no clear associations for part-time workers.

Conclusions

The results suggest that women should not be exposed to WBV at/above the action limit value of 0.5 m/s² (European directive) continuously through pregnancy. However, these results need further confirmation.

Associations of maternal age at the start of pregnancy with placental function throughout pregnancy: The Generation R Study

OBJECTIVE

To examine the associations of maternal age at the start of pregnancy across the full range with second and third trimester uterine and umbilical artery flow indices, and placental weight.

STUDY DESIGN

In a population-based prospective cohort study among 8271 pregnant women, we measured second and third trimester uterine artery resistance and umbilical artery pulsatility indices and the presence of third trimester uterine artery notching using Doppler ultrasound.

RESULTS

Compared to women aged 25-29.9 years, higher maternal age was associated with a higher third trimester uterine artery resistance index (difference for women 30-34.9 years was 0.10 SD (95% Confidence Interval (CI) 0.02 to 0.17), and for women aged ≥40 years 0.33 SD (95% CI 0.08 to 0.57), overall linear trend 0.02 SD (95% CI 0.01 to 0.03) per year). Compared to women aged 25-29.9 years, women younger than 20 years had an increased risk of third trimester uterine artery notching (Odds Ratio (OR) 1.97 (95% CI 1.30-3.00)). A linear trend was present with a decrease in risk of third trimester uterine artery notching per year increase in maternal age (OR 0.96 (95% CI 0.94 to 0.98)). Maternal age was not consistently associated with umbilical artery pulsatility indices or placental weight.

CONCLUSIONS

Young maternal age is associated with higher risk of third trimester uterine artery notching, whereas advanced maternal age is associated with a higher third trimester uterine artery resistance index, which may predispose to an increased risk of pregnancy complications.

Population screening for gestational hypertensive disorders using maternal, fetal and placental characteristics: A population-based prospective cohort study

Objective

To determine screening performance of maternal, fetal and placental characteristics for selecting pregnancies at risk of gestational hypertension and preeclampsia in a low‐risk multi‐ethnic population.

Method

In a prospective population‐based cohort among 7124 pregnant women, we collected maternal characteristics including body mass index, ethnicity, parity, smoking and blood pressure in early‐pregnancy. Fetal characteristics included second and third trimester estimated fetal weight and sex determined by ultrasound. Placental characteristics included first and second trimester placental growth factor concentrations and second and third trimester uterine artery resistance indices.

Results

Maternal characteristics provided the best screening result for gestational hypertension (area‐under‐the‐curve [AUC] 0.79 [95% Confidence interval {CI} 0.76‐0.81]) with 40% sensitivity at 90% specificity. For preeclampsia, the maternal characteristics model led to a screening performance of AUC 0.74 (95% CI 0.70‐0.78) with 33% sensitivity at 90% specificity. Addition of second and third trimester placental ultrasound characteristics only improved screening performance for preeclampsia (AUC 0.78 [95% CI 0.75‐0.82], with 48% sensitivity at 90% specificity).

Conclusion

Routinely measured maternal characteristics, known at the start of pregnancy, can be used in screening for pregnancies at risk of gestational hypertension or preeclampsia within a low‐risk multi‐ethnic population. Addition of combined second and third trimester placental ultrasound characteristics only improved screening for preeclampsia.

A risk factor-based predictive model for new-onset hypertension during pregnancy in Chinese Han women

BACKGROUND

Hypertensive disorders of pregnancy (HDP) is one of the leading causes of maternal and neonatal mortality, increasing the long-term incidence of cardiovascular diseases. Preeclampsia and gestational hypertension are the major components of HDP. The aim of our study is to establish a prediction model for pregnant women with new-onset hypertension during pregnancy (increased blood pressure after gestational age > 20 weeks), thus to guide the clinical prediction and treatment of de novo hypertension.

METHODS

A total of 117 pregnant women with de novo hypertension who were admitted to our hospital's obstetrics department were selected as the case group and 199 healthy pregnant women were selected as the control group from January 2017 to June 2018. Maternal clinical parameters such as age, family history and the biomarkers such as homocysteine, cystatin C, uric acid, total bile acid and glomerular filtration rate were collected at a mean gestational age in 16 to 20 weeks. The prediction model was established by logistic regression.

RESULTS

Eleven indicators have statistically significant difference between two groups (P < 0.05). These 11 factors were substituted into the logistic regression equation and 7 independent predictors were obtained. The equation expressed including 7 factors. The calculated area under the curve was 0.884(95% confidence interval: 0.848-0.921), the sensitivity and specificity were 88.0 and 75.0%. A scoring system was established to classify pregnant women with scores ≤15.5 as low-risk pregnancy group and those with scores > 15.5 as high-risk pregnancy group.

CONCLUSIONS

Our regression equation provides a feasible and reliable means of predicting de novo hypertension after pregnancy. Risk stratification of new-onset hypertension was performed to early treatment interventions in high-risk populations.

Prediction of pre-eclampsia in nulliparous women using routinely collected maternal characteristics: a model development and validation study

Background

Guidelines recommend identifying in early pregnancy women at elevated risk of pre-eclampsia. The aim of this study was to develop and validate a pre-eclampsia risk prediction model for nulliparous women attending routine antenatal care “the Western Sydney (WS) model”; and to compare its performance with the National Institute of Health and Care Excellence (NICE) risk factor-list approach for classifying women as high-risk.

Methods

This retrospective cohort study included all nulliparous women who gave birth in three public hospitals in the Western-Sydney-Local-Health-District, Australia 2011–2014. Using births from 2011 to 2012, multivariable logistic regression incorporated established maternal risk factors to develop and internally validate the WS model. The WS model was then externally validated using births from 2013 to 2014, assessing its discrimination and calibration. We fitted the final WS model for all births from 2011 to 2014, and compared its accuracy in predicting pre-eclampsia with the NICE approach.

Results

Among 12,395 births to nulliparous women in 2011–2014, there were 293 (2.4%) pre-eclampsia events. The WS model included: maternal age, body mass index, ethnicity, multiple pregnancy, family history of pre-eclampsia, autoimmune disease, chronic hypertension and chronic renal disease. In the validation sample (6201 births), the model c-statistic was 0.70 (95% confidence interval 0.65–0.75). The observed:expected ratio for pre-eclampsia was 0.91, with a Hosmer-Lemeshow goodness-of-fit test p-value of 0.20. In the entire study sample of 12,395 births, 374 (3.0%) women had a WS model-estimated pre-eclampsia risk ≥8%, the pre-specified risk-threshold for considering aspirin prophylaxis. Of these, 54 (14.4%) developed pre-eclampsia (sensitivity 18% (14–23), specificity 97% (97–98)). Using the NICE approach, 1173 (9.5%) women were classified as high-risk, of which 107 (9.1%) developed pre-eclampsia (sensitivity 37% (31–42), specificity 91% (91–92)). The final model showed similar accuracy to the NICE approach when using lower risk-threshold of ≥4% to classify women as high-risk for pre-eclampsia.

Conclusion

The WS risk model that combines readily-available maternal characteristics achieved modest performance for prediction of pre-eclampsia in nulliparous women. The model did not outperform the NICE approach, but has the advantage of providing individualised absolute risk estimates, to assist with counselling, inform decisions for further testing, and consideration of aspirin prophylaxis.

Clinical risk assessment in early pregnancy for preeclampsia in nulliparous women: A population based cohort study

Objective

To evaluate the capacity of multivariable prediction of preeclampsia during pregnancy, based on detailed routinely collected early pregnancy data in nulliparous women.

Design and setting

A population-based cohort study of 62 562 pregnancies of nulliparous women with deliveries 2008–13 in the Stockholm-Gotland Counties in Sweden.

Methods

Maternal social, reproductive and medical history and medical examinations (including mean arterial pressure, proteinuria, hemoglobin and capillary glucose levels) routinely collected at the first visit in antenatal care, constitute the predictive variables. Predictive models for preeclampsia were created by three methods; logistic regression models using 1) pre-specified variables (similar to the Fetal Medicine Foundation model including maternal factors and mean arterial pressure), 2) backward selection starting from the full suite of variables, and 3) a Random forest model using the same candidate variables. The performance of the British National Institute for Health and Care Excellence (NICE) binary risk classification guidelines for preeclampsia was also evaluated. The outcome measures were diagnosis of preeclampsia with delivery <34, <37, and ≥37 weeks’ gestation.

Results

A total of 2 773 (4.4%) nulliparous women subsequently developed preeclampsia. The pre-specified variables model was superior the other two models, regarding prediction of preeclampsia with delivery <34 and <37 weeks, both with areas under the curve of 0.68, and sensitivity of 30.6% (95% CI 24.5–37.2) and 29.2% (95% CI 25.2–33.4) at a 10% false positive rate, respectively. The performance of these customizable multivariable models at the chosen false positive rate, was significantly better than the binary NICE-guidelines for preeclampsia with delivery <37 and ≥37 weeks’ gestation.

Conclusion

Multivariable models in early pregnancy had a modest performance, although providing advantages over the NICE-guidelines, in predicting preeclampsia in nulliparous women. Use of a machine learning algorithm (Random forest) did not result in superior prediction.

Postpartum Interventions to Reduce Long-Term Cardiovascular Disease Risk in Women After Hypertensive Disorders of Pregnancy: A Systematic Review

Introduction: Hypertensive disorders (HDP) affect ~7% of pregnancies. Epidemiological evidence strongly suggests HDP independently increases that individual's risk of later cardiovascular disease (CVD). Focus on reduction or mitigation of this risk has been limited. This review seeks to identify trialed interventions to reduce cardiovascular risk after HDP. Methods: Online medical databases were searched to identify full-text published results of randomized controlled trials (RCT) in women <10 years postpartum after HDP that trialed interventions to reduce cardiovascular risk. Outcomes sought included cardiovascular disease events, chronic hypertension, and other measures of cardiovascular risk such as obesity, smoking status, diet, and physical activity. Publications from January 2008 to July 2019 were included. Results: Two RCTs were identified. One, a trial of calcium vs. placebo in 201 women with calcium commenced from the first follow-up visit outside of pregnancy and continued until 20 weeks' gestation if another pregnancy occurred. A non-significant trend toward decreased blood pressure was noted. The second RCT of 151 women tested an online education programme (vs. general information to control group) to increase awareness of risk factors and personalized phone-based lifestyle coaching in women who had a preeclampsia affected pregnancy in the 5 years preceding enrolment. Significant findings included increase in knowledge of CVD risk factors, reported healthy eating and decreased physical inactivity, however adoption of a promoted heart healthy diet and physical activity levels did not differ significantly between groups. Several observational studies after HDP, and one meta-analysis of studies of lifestyle interventions not performed specifically after HDP but used to extrapolate likely benefits of lifestyle interventions, were identified which supported the use of lifestyle interventions. Several ongoing RCTs were also noted. Discussion: There is a paucity of intervention trials in the early years after HDP to guide evidence-based cardiovascular risk reduction in affected women. Limited evidence suggests lifestyle intervention may be effective, however degree of any risk reduction remains uncertain. Conclusion: Sufficiently powered randomized controlled trials of appropriate interventions (e.g., lifestyle behavior change, pharmacological) are required to assess the best method of reducing the risk of cardiovascular disease in this at-risk population of women.

Prognostic models for adverse pregnancy outcomes in low-income and middle-income countries: a systematic review

INTRODUCTION

Ninety-nine per cent of all maternal and neonatal deaths occur in low-income and middle-income countries (LMIC). Prognostic models can provide standardised risk assessment to guide clinical management and can be vital to reduce and prevent maternal and perinatal mortality and morbidity. This review provides a comprehensive summary of prognostic models for adverse maternal and perinatal outcomes developed and/or validated in LMIC.

METHODS

A systematic search in four databases (PubMed/Medline, EMBASE, Global Health Library and The Cochrane Library) was conducted from inception (1970) up to 2 May 2018. Risk of bias was assessed with the PROBAST tool and narratively summarised.

RESULTS

1741 articles were screened and 21 prognostic models identified. Seventeen models focused on maternal outcomes and four on perinatal outcomes, of which hypertensive disorders of pregnancy (n=9) and perinatal death including stillbirth (n=4) was most reported. Only one model was externally validated. Thirty different predictors were used to develop the models. Risk of bias varied across studies, with the item 'quality of analysis' performing the least.

CONCLUSION

Prognostic models can be easy to use, informative and low cost with great potential to improve maternal and neonatal health in LMIC settings. However, the number of prognostic models developed or validated in LMIC settings is low and mirrors the 10/90 gap in which only 10% of resources are dedicated to 90% of the global disease burden. External validation of existing models developed in both LMIC and high-income countries instead of developing new models should be encouraged.

PROSPERO REGISTRATION NUMBER

CRD42017058044.

Preeclampsia: Risk Factors, Diagnosis, Management, and the Cardiovascular Impact on the Offspring

Hypertensive disorders of pregnancy affect up to 10% of pregnancies worldwide, which includes the 3%–5% of all pregnancies complicated by preeclampsia. Preeclampsia is defined as new onset hypertension after 20 weeks’ gestation with evidence of maternal organ or uteroplacental dysfunction or proteinuria. Despite its prevalence, the risk factors that have been identified lack accuracy in predicting its onset and preventative therapies only moderately reduce a woman’s risk of preeclampsia. Preeclampsia is a major cause of maternal morbidity and is associated with adverse foetal outcomes including intra-uterine growth restriction, preterm birth, placental abruption, foetal distress, and foetal death in utero. At present, national guidelines for foetal surveillance in preeclamptic pregnancies are inconsistent, due to a lack of evidence detailing the most appropriate assessment modalities as well as the timing and frequency at which assessments should be conducted. Current management of the foetus in preeclampsia involves timely delivery and prevention of adverse effects of prematurity with antenatal corticosteroids and/or magnesium sulphate depending on gestation. Alongside the risks to the foetus during pregnancy, there is also growing evidence that preeclampsia has long-term adverse effects on the offspring. In particular, preeclampsia has been associated with cardiovascular sequelae in the offspring including hypertension and altered vascular function.

The association between ethnicity and pre-eclampsia in Australia: A multicentre retrospective cohort study

BACKGROUND

Rates of pre-eclampsia vary between countries and certain ethnic groups. However, there is limited evidence about the impact of ethnicity on risk of pre-eclampsia, beyond established clinical risk factors.

AIMS

To assess the association between ethnicity and pre-eclampsia in Australia's diverse multi-ethnic population.

MATERIALS AND METHODS

We conducted a retrospective cohort study using the ObstetriX database. We included all women with a birth between January 2011 and December 2014, at Auburn, Blacktown/Mount-Druitt and Westmead Hospitals in the Western Sydney Local Health District. We estimated the pre-eclampsia rate overall, and by maternal ethnic group, defined by country of birth and primary language. We developed multivariable logistic regression models to estimate odds ratios (OR) and 95% confidence intervals (CIs) for pre-eclampsia, adjusting for maternal age, body mass index, autoimmune disease, chronic hypertension, chronic renal disease, diabetes mellitus (type 1 or 2), and multiple pregnancy. A secondary analysis was restricted to nulliparous women.

RESULTS

There were 40 824 women evaluated, including 12 743 nulliparous women. Of these, 1448 (3.5%) developed pre-eclampsia (range: Australian/New Zealand-born English speakers 735/15 422 (4.8%); North-East Asian women 51/4470 (1.1%)). Relative to Australian/New Zealand-born English speakers, immigrants had a lower risk of pre-eclampsia overall (adjusted OR 0.67; 95% CI 0.60-0.75); as did the three largest immigrant groups examined: Southern Asian (0.73; 0.62-0.85), Middle-Eastern/African (0.55; 0.47-0.66) and North-East Asian (0.33; 0.25-0.45) women. Findings were similar for nulliparous women.

CONCLUSIONS

Certain immigrant groups are at lower risk of pre-eclampsia than Australian/New Zealand-born English-speaking women. Understanding why this is so may lead to better screening and preventive strategies in higher-risk women.

Statistical risk prediction models for adverse maternal and neonatal outcomes in severe preeclampsia in a low-resource setting: proposal for a single-centre cross-sectional study at Mpilo Central Hospital, Bulawayo, Zimbabwe

Hypertensive disorders in pregnancy are a leading cause of maternal and perinatal morbidity and mortality, especially in low-resource settings. Identifying mothers and babies at greatest risk of complications would enable intervention to be targeted to those most likely to benefit from them. However, current risk prediction models have a wide range of sensitivity (42-81%) and specificity (87-92%) indicating that improvements are needed. Furthermore, no predictive models have been developed or evaluated in Zimbabwe. This proposal describes a single centre retrospective cross-sectional study which will address the need to further develop and test statistical risk prediction models for adverse maternal and neonatal outcomes in low-resource settings; this will be the first such research to be carried out in Zimbabwe. Data will be collected on maternal demographics characteristics, outcome of prior pregnancies, past medical history, symptoms and signs on admission, results of biochemical and haematological investigations. Adverse outcome will be defined as a composite of maternal morbidity and mortality and perinatal morbidity and mortality. Association between variables and outcomes will be explored using multivariable logistic regression. Critically, new risk prediction models introduced for our clinical setting may reduce avoidable maternal and neonatal morbidity and mortality at local, national, regional and international level.

Prediction of pre-eclampsia: review of reviews

OBJECTIVE

Primary studies and systematic reviews provide estimates of varying accuracy for different factors in the prediction of pre-eclampsia. The aim of this study was to review published systematic reviews to collate evidence on the ability of available tests to predict pre-eclampsia, to identify high-value avenues for future research and to minimize future research waste in this field.

METHODS

MEDLINE, EMBASE and The Cochrane Library including DARE (Database of Abstracts of Reviews of Effects) databases, from database inception to March 2017, and bibliographies of relevant articles were searched, without language restrictions, for systematic reviews and meta-analyses on the prediction of pre-eclampsia. The quality of the included reviews was assessed using the AMSTAR tool and a modified version of the QUIPS tool. We evaluated the comprehensiveness of search, sample size, tests and outcomes evaluated, data synthesis methods, predictive ability estimates, risk of bias related to the population studied, measurement of predictors and outcomes, study attrition and adjustment for confounding.

RESULTS

From 2444 citations identified, 126 reviews were included, reporting on over 90 predictors and 52 prediction models for pre-eclampsia. Around a third (n = 37 (29.4%)) of all reviews investigated solely biochemical markers for predicting pre-eclampsia, 31 (24.6%) investigated genetic associations with pre-eclampsia, 46 (36.5%) reported on clinical characteristics, four (3.2%) evaluated only ultrasound markers and six (4.8%) studied a combination of tests; two (1.6%) additional reviews evaluated primary studies investigating any screening test for pre-eclampsia. Reviews included between two and 265 primary studies, including up to 25 356 688 women in the largest review. Only approximately half (n = 67 (53.2%)) of the reviews assessed the quality of the included studies. There was a high risk of bias in many of the included reviews, particularly in relation to population representativeness and study attrition. Over 80% (n = 106 (84.1%)) summarized the findings using meta-analysis. Thirty-two (25.4%) studies lacked a formal statement on funding. The predictors with the best test performance were body mass index (BMI) > 35 kg/m² , with a specificity of 92% (95% CI, 89-95%) and a sensitivity of 21% (95% CI, 12-31%); BMI > 25 kg/m² , with a specificity of 73% (95% CI, 64-83%) and a sensitivity of 47% (95% CI, 33-61%); first-trimester uterine artery pulsatility index or resistance index > 90^th centile (specificity 93% (95% CI, 90-96%) and sensitivity 26% (95% CI, 23-31%)); placental growth factor (specificity 89% (95% CI, 89-89%) and sensitivity 65% (95% CI, 63-67%)); and placental protein 13 (specificity 88% (95% CI, 87-89%) and sensitivity 37% (95% CI, 33-41%)). No single marker had a test performance suitable for routine clinical use. Models combining markers showed promise, but none had undergone external validation.

CONCLUSIONS

This review of reviews calls into question the need for further aggregate meta-analysis in this area given the large number of published reviews subject to the common limitations of primary predictive studies. Prospective, well-designed studies of predictive markers, preferably randomized intervention studies, and combined through individual-patient data meta-analysis are needed to develop and validate new prediction models to facilitate the prediction of pre-eclampsia and minimize further research waste in this field. Copyright © 2018 ISUOG. Published by John Wiley & Sons Ltd.

The International Federation of Gynecology and Obstetrics (FIGO) initiative on pre-eclampsia: A pragmatic guide for first-trimester screening and prevention

Pre‐eclampsia (PE) is a multisystem disorder that typically affects 2%–5% of pregnant women and is one of the leading causes of maternal and perinatal morbidity and mortality, especially when the condition is of early onset. Globally, 76 000 women and 500 000 babies die each year from this disorder. Furthermore, women in low‐resource countries are at a higher risk of developing PE compared with those in high‐resource countries. Although a complete understanding of the pathogenesis of PE remains unclear, the current theory suggests a two‐stage process. The first stage is caused by shallow invasion of the trophoblast, resulting in inadequate remodeling of the spiral arteries. This is presumed to lead to the second stage, which involves the maternal response to endothelial dysfunction and imbalance between angiogenic and antiangiogenic factors, resulting in the clinical features of the disorder. Accurate prediction and uniform prevention continue to elude us. The quest to effectively predict PE in the first trimester of pregnancy is fueled by the desire to identify women who are at high risk of developing PE, so that necessary measures can be initiated early enough to improve placentation and thus prevent or at least reduce the frequency of its occurrence. Furthermore, identification of an “at risk” group will allow tailored prenatal surveillance to anticipate and recognize the onset of the clinical syndrome and manage it promptly. PE has been previously defined as the onset of hypertension accompanied by significant proteinuria after 20 weeks of gestation. Recently, the definition of PE has been broadened. Now the internationally agreed definition of PE is the one proposed by the International Society for the Study of Hypertension in Pregnancy (ISSHP). According to the ISSHP, PE is defined as systolic blood pressure at ≥140 mm Hg and/or diastolic blood pressure at ≥90 mm Hg on at least two occasions measured 4 hours apart in previously normotensive women and is accompanied by one or more of the following new‐onset conditions at or after 20 weeks of gestation: 1.Proteinuria (i.e. ≥30 mg/mol protein:creatinine ratio; ≥300 mg/24 hour; or ≥2 + dipstick); 2.Evidence of other maternal organ dysfunction, including: acute kidney injury (creatinine ≥90 μmol/L; 1 mg/dL); liver involvement (elevated transaminases, e.g. alanine aminotransferase or aspartate aminotransferase >40 IU/L) with or without right upper quadrant or epigastric abdominal pain; neurological complications (e.g. eclampsia, altered mental status, blindness, stroke, clonus, severe headaches, and persistent visual scotomata); or hematological complications (thrombocytopenia–platelet count <150 000/μL, disseminated intravascular coagulation, hemolysis); or 3.Uteroplacental dysfunction (such as fetal growth restriction, abnormal umbilical artery Doppler waveform analysis, or stillbirth). It is well established that a number of maternal risk factors are associated with the development of PE: advanced maternal age; nulliparity; previous history of PE; short and long interpregnancy interval; use of assisted reproductive technologies; family history of PE; obesity; Afro‐Caribbean and South Asian racial origin; co‐morbid medical conditions including hyperglycemia in pregnancy; pre‐existing chronic hypertension; renal disease; and autoimmune diseases, such as systemic lupus erythematosus and antiphospholipid syndrome. These risk factors have been described by various professional organizations for the identification of women at risk of PE; however, this approach to screening is inadequate for effective prediction of PE. PE can be subclassified into: 1.Early‐onset PE (with delivery at <34+0 weeks of gestation); 2.Preterm PE (with delivery at <37+0 weeks of gestation); 3.Late‐onset PE (with delivery at ≥34+0 weeks of gestation); 4.Term PE (with delivery at ≥37+0 weeks of gestation). These subclassifications are not mutually exclusive. Early‐onset PE is associated with a much higher risk of short‐ and long‐term maternal and perinatal morbidity and mortality. Obstetricians managing women with preterm PE are faced with the challenge of balancing the need to achieve fetal maturation in utero with the risks to the mother and fetus of continuing the pregnancy longer. These risks include progression to eclampsia, development of placental abruption and HELLP (hemolysis, elevated liver enzyme, low platelet) syndrome. On the other hand, preterm delivery is associated with higher infant mortality rates and increased morbidity resulting from small for gestational age (SGA), thrombocytopenia, bronchopulmonary dysplasia, cerebral palsy, and an increased risk of various chronic diseases in adult life, particularly type 2 diabetes, cardiovascular disease, and obesity. Women who have experienced PE may also face additional health problems in later life, as the condition is associated with an increased risk of death from future cardiovascular disease, hypertension, stroke, renal impairment, metabolic syndrome, and diabetes. The life expectancy of women who developed preterm PE is reduced on average by 10 years. There is also significant impact on the infants in the long term, such as increased risks of insulin resistance, diabetes mellitus, coronary artery disease, and hypertension in infants born to pre‐eclamptic women. The International Federation of Gynecology and Obstetrics (FIGO) brought together international experts to discuss and evaluate current knowledge on PE and develop a document to frame the issues and suggest key actions to address the health burden posed by PE. FIGO's objectives, as outlined in this document, are: (1) To raise awareness of the links between PE and poor maternal and perinatal outcomes, as well as to the future health risks to mother and offspring, and demand a clearly defined global health agenda to tackle this issue; and (2) To create a consensus document that provides guidance for the first‐trimester screening and prevention of preterm PE, and to disseminate and encourage its use. Based on high‐quality evidence, the document outlines current global standards for the first‐trimester screening and prevention of preterm PE, which is in line with FIGO good clinical practice advice on first trimester screening and prevention of pre‐eclampsia in singleton pregnancy.1 It provides both the best and the most pragmatic recommendations according to the level of acceptability, feasibility, and ease of implementation that have the potential to produce the most significant impact in different resource settings. Suggestions are provided for a variety of different regional and resource settings based on their financial, human, and infrastructure resources, as well as for research priorities to bridge the current knowledge and evidence gap. To deal with the issue of PE, FIGO recommends the following: Public health focus: There should be greater international attention given to PE and to the links between maternal health and noncommunicable diseases (NCDs) on the Sustainable Developmental Goals agenda. Public health measures to increase awareness, access, affordability, and acceptance of preconception counselling, and prenatal and postnatal services for women of reproductive age should be prioritized. Greater efforts are required to raise awareness of the benefits of early prenatal visits targeted at reproductive‐aged women, particularly in low‐resource countries. Universal screening: All pregnant women should be screened for preterm PE during early pregnancy by the first‐trimester combined test with maternal risk factors and biomarkers as a one‐step procedure. The risk calculator is available free of charge at https://fetalmedicine.org/research/assess/preeclampsia. FIGO encourages all countries and its member associations to adopt and promote strategies to ensure this. The best combined test is one that includes maternal risk factors, measurements of mean arterial pressure (MAP), serum placental growth factor (PLGF), and uterine artery pulsatility index (UTPI). Where it is not possible to measure PLGF and/or UTPI, the baseline screening test should be a combination of maternal risk factors with MAP, and not maternal risk factors alone. If maternal serum pregnancy‐associated plasma protein A (PAPP‐A) is measured for routine first‐trimester screening for fetal aneuploidies, the result can be included for PE risk assessment. Variations to the full combined test would lead to a reduction in the performance screening. A woman is considered high risk when the risk is 1 in 100 or more based on the first‐trimester combined test with maternal risk factors, MAP, PLGF, and UTPI. Contingent screening: Where resources are limited, routine screening for preterm PE by maternal factors and MAP in all pregnancies and reserving measurements of PLGF and UTPI for a subgroup of the population (selected on the basis of the risk derived from screening by maternal factors and MAP) can be considered. Prophylactic measures: Following first‐trimester screening for preterm PE, women identified at high risk should receive aspirin prophylaxis commencing at 11–14+6 weeks of gestation at a dose of ~150 mg to be taken every night until 36 weeks of gestation, when delivery occurs, or when PE is diagnosed. Low‐dose aspirin should not be prescribed to all pregnant women. In women with low calcium intake (<800 mg/d), either calcium replacement (≤1 g elemental calcium/d) or calcium supplementation (1.5–2 g elemental calcium/d) may reduce the burden of both early‐ and late‐onset PE.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Maternity Log study: a longitudinal lifelog monitoring and multiomics analysis for the early prediction of complicated pregnancy

PURPOSE

A prospective cohort study for pregnant women, the Maternity Log study, was designed to construct a time-course high-resolution reference catalogue of bioinformatic data in pregnancy and explore the associations between genomic and environmental factors and the onset of pregnancy complications, such as hypertensive disorders of pregnancy, gestational diabetes mellitus and preterm labour, using continuous lifestyle monitoring combined with multiomics data on the genome, transcriptome, proteome, metabolome and microbiome.

PARTICIPANTS

Pregnant women were recruited at the timing of first routine antenatal visits at Tohoku University Hospital, Sendai, Japan, between September 2015 and November 2016. Of the eligible women who were invited, 65.4% agreed to participate, and a total of 302 women were enrolled. The inclusion criteria were age ≥20 years and the ability to access the internet using a smartphone in the Japanese language.

FINDINGS TO DATE

Study participants uploaded daily general health information including quality of sleep, condition of bowel movements and the presence of nausea, pain and uterine contractions. Participants also collected physiological data, such as body weight, blood pressure, heart rate and body temperature, using multiple home healthcare devices. The mean upload rate for each lifelog item was ranging from 67.4% (fetal movement) to 85.3% (physical activity), and the total number of data points was over 6 million. Biospecimens, including maternal plasma, serum, urine, saliva, dental plaque and cord blood, were collected for multiomics analysis.

FUTURE PLANS

Lifelog and multiomics data will be used to construct a time-course high-resolution reference catalogue of pregnancy. The reference catalogue will allow us to discover relationships among multidimensional phenotypes and novel risk markers in pregnancy for the future personalised early prediction of pregnancy complications.

External validation and clinical usefulness of first-trimester prediction models for small- and large-for-gestational-age infants: a prospective cohort study

Objective

To assess the external validity of all published first‐trimester prediction models based on routinely collected maternal predictors for the risk of small‐ and large‐for‐gestational‐age (SGA and LGA) infants. Furthermore, the clinical potential of the best‐performing models was evaluated.

Design

Multicentre prospective cohort.

Setting

Thirty‐six midwifery practices and six hospitals (in the Netherlands).

Population

Pregnant women were recruited at <16 weeks of gestation between 1 July 2013 and 31 December 2015.

Methods

Prediction models were systematically selected from the literature. Information on predictors was obtained by a web‐based questionnaire. Birthweight centiles were corrected for gestational age, parity, fetal sex, and ethnicity.

Main outcome measures

Predictive performance was assessed by means of discrimination (C‐statistic) and calibration.

Results

The validation cohort consisted of 2582 pregnant women. The outcomes of SGA <10th percentile and LGA >90th percentile occurred in 203 and 224 women, respectively. The C‐statistics of the included models ranged from 0.52 to 0.64 for SGA (n = 6), and from 0.60 to 0.69 for LGA (n = 6). All models yielded higher C‐statistics for more severe cases of SGA (<5th percentile) and LGA (>95th percentile). Initial calibration showed poor‐to‐moderate agreement between the predicted probabilities and the observed outcomes, but this improved substantially after recalibration.

Conclusion

The clinical relevance of the models is limited because of their moderate predictive performance, and because the definitions of SGA and LGA do not exclude constitutionally small or large infants. As most clinically relevant fetal growth deviations are related to ‘vascular’ or ‘metabolic’ factors, models predicting hypertensive disorders and gestational diabetes are likely to be more specific.

Tweetable abstract

The clinical relevance of prediction models for the risk of small‐ and large‐for‐gestational‐age is limited.

The clinical relevance of prediction models for the risk of small‐ and large‐for‐gestational‐age is limited.