Prediction of stillbirth from placental growth factor at 19-24 weeks

Antepartum Fetal Demise: Toward Diagnostic and Therapeutic Efficacy of Management

Management of women presenting with intrauterine fetal demise is complex, with medical, psychological, emotional and social variables that need careful consideration when determining the best way forward. The need for diagnostic assessment needs to be balanced against the wishes of the grieving patient and family, to allow adequate data collection that can help with insight into the etiology of the stillbirth as well as planning for management of the recurrence risk in a future pregnancy. Multidisciplinary involvement can aid the formulation of a sensitive patient-centred workup plan that can also enhance the evolution of a therapeutic relationship between the patient and the caregivers.

A prediction model for stillbirth based on first trimester pre-eclampsia combined screening

OBJECTIVE

To evaluate the accuracy of combined models of maternal biophysical factors, ultrasound, and biochemical markers for predicting stillbirths.

METHODS

A retrospective cohort study of pregnant women undergoing first-trimester pre-eclampsia screening at 11-13 gestational weeks was conducted. Maternal characteristics and history, mean arterial pressure (MAP) measurement, uterine artery pulsatility index (UtA-PI) ultrasound, maternal ophthalmic peak ratio Doppler, and placental growth factor (PlGF) serum were collected during the visit. Stillbirth was classified as placental dysfunction-related when it occurred with pre-eclampsia or birth weight <10th percentile. Combined prediction models were developed from significant variables in stillbirths, placental dysfunction-related, and controls. We used the area under the receiver-operating-characteristics curve (AUC), sensitivity, and specificity based on a specific cutoff to evaluate the model's predictive performance by measuring the capacity to distinguish between stillbirths and live births.

RESULTS

There were 13 (0.79%) cases of stillbirth in 1643 women included in the analysis. The combination of maternal factors, MAP, UtA-PI, and PlGF, significantly contributed to the prediction of stillbirth. This model was a good predictor for all (including controls) types of stillbirth (AUC 0.879, 95% CI: 0.799-0.959, sensitivity of 99.3%, specificity of 38.5%), and an excellent predictor for placental dysfunction-related stillbirth (AUC 0.984, 95% CI: 0.960-1.000, sensitivity of 98.5, specificity of 85.7).

CONCLUSION

Screening at 11-13 weeks' gestation by combining maternal factors, MAP, UtA-PI, and PlGF, can predict a high proportion of stillbirths. Our model has good accuracy for predicting stillbirths, predominantly placental dysfunction-related stillbirths.

The role of the PLGF in the prediction of the outcome in pregnancies with a small for gestational age fetus

PURPOSE

To explore the value of measuring maternal serum PLGF in the prediction of the outcome of small for gestational age fetuses (SGA).

METHODS

Singleton pregnancies referred with suspicion of SGA in the third trimester were included if they had: no indication for nor signs of imminent delivery, fetal abdominal circumference (AC) at or below the 10th centile and/or estimated fetal weight (EFW) at or below the 10th centile and/or umbilical artery pulsatility index (Umb-PI) at or above the 90th centile for gestation. Women with pre-eclampsia at presentation were excluded. Maternal blood was drawn at the first (index) visit and analyzed retrospectively.

RESULTS

Fifty-one fetuses were examined. Multiple regression analysis showed that family history of microsomia, index EFW and PLGF were significant predictors of the birthweight centile; index femur length centile and PLGF were significant predictors of pre-eclampsia; PLGF and index systolic blood pressure were significant predictors of iatrogenic preterm delivery < 37 weeks, whereas PLGF and index EFW were significant predictors of birthweight ≤ 5th centile and admission to the neonatal intensive care unit. For all outcomes, the addition of maternal-fetal parameters did not improve the prediction compared to PLGF alone. Using a cutoff of 0.3 MoM for PLGF would identify 94.1% of the pregnancies with iatrogenic preterm delivery and/or intra-uterine death and all of the cases that developed pre-eclampsia, for a screen positive rate of 54.9%. Women with PLGF ≤ 0.3 MoM had a poor fetal/maternal outcome (iatrogenic preterm delivery, pre-eclampsia, intra-uterine death) in 61.5% of cases.

CONCLUSION

In pregnancies complicated by SGA, PLGF identifies a very high-risk group that may benefit from intense surveillance.

External validation of prognostic models to predict stillbirth using International Prediction of Pregnancy Complications (IPPIC) Network database: individual participant data meta-analysis

OBJECTIVE

Stillbirth is a potentially preventable complication of pregnancy. Identifying women at high risk of stillbirth can guide decisions on the need for closer surveillance and timing of delivery in order to prevent fetal death. Prognostic models have been developed to predict the risk of stillbirth, but none has yet been validated externally. In this study, we externally validated published prediction models for stillbirth using individual participant data (IPD) meta-analysis to assess their predictive performance.

METHODS

MEDLINE, EMBASE, DH-DATA and AMED databases were searched from inception to December 2020 to identify studies reporting stillbirth prediction models. Studies that developed or updated prediction models for stillbirth for use at any time during pregnancy were included. IPD from cohorts within the International Prediction of Pregnancy Complications (IPPIC) Network were used to validate externally the identified prediction models whose individual variables were available in the IPD. The risk of bias of the models and cohorts was assessed using the Prediction study Risk Of Bias ASsessment Tool (PROBAST). The discriminative performance of the models was evaluated using the C-statistic, and calibration was assessed using calibration plots, calibration slope and calibration-in-the-large. Performance measures were estimated separately in each cohort, as well as summarized across cohorts using random-effects meta-analysis. Clinical utility was assessed using net benefit.

RESULTS

Seventeen studies reporting the development of 40 prognostic models for stillbirth were identified. None of the models had been previously validated externally, and the full model equation was reported for only one-fifth (20%, 8/40) of the models. External validation was possible for three of these models, using IPD from 19 cohorts (491 201 pregnant women) within the IPPIC Network database. Based on evaluation of the model development studies, all three models had an overall high risk of bias, according to PROBAST. In the IPD meta-analysis, the models had summary C-statistics ranging from 0.53 to 0.65 and summary calibration slopes ranging from 0.40 to 0.88, with risk predictions that were generally too extreme compared with the observed risks. The models had little to no clinical utility, as assessed by net benefit. However, there remained uncertainty in the performance of some models due to small available sample sizes.

CONCLUSIONS

The three validated stillbirth prediction models showed generally poor and uncertain predictive performance in new data, with limited evidence to support their clinical application. The findings suggest methodological shortcomings in their development, including overfitting. Further research is needed to further validate these and other models, identify stronger prognostic factors and develop more robust prediction models. © 2021 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Predicting singleton antepartum stillbirth by the demographic Fetal Medicine Foundation Risk Calculator-A retrospective case-control study

OBJECTIVE

To assess the risk of singleton intrauterine fetal death (IUFD) in women by the demographic setting of the online Fetal Medicine Foundation (FMF) Stillbirth Risk Calculator.

METHODS

Retrospective single-centre case-control study involving 144 women having suffered IUFD and 247 women after delivery of a live-born singleton. Nonparametric receiver operating characteristics (ROC) analyses were performed to predict the prognostic power of the FMF Stillbirth risk score and to generate a cut-off value to discriminate best between the event of IUFD versus live birth.

RESULTS

Women in the IUFD cohort born a significantly higher overall risk with a median FMF risk score of 0.45% (IQR 0.23-0.99) compared to controls [0.23% (IQR 0.21-0.29); p<0.001]. Demographic factors contributing to an increased risk of IUFD in our cohort were maternal obesity (p = 0.002), smoking (p<0.001), chronic hypertension (p = 0.015), antiphospholipid syndrome (p = 0.017), type 2 diabetes (p<0.001), and insulin requirement (p<0.001). ROC analyses showed an area under the curve (AUC) of 0.72 (95% CI 0.67-0.78; p<0.001) for predicting overall IUFD and an AUC of 0.72 (95% CI 0.64-0.80; p<0.001), respectively, for predicting IUFD excluding congenital malformations. The FMF risk score at a cut-off of 0.34% (OR 6.22; 95% CI 3.91-9.89; p<0.001) yielded an 82% specificity and 58% sensitivity in predicting IUFD with a positive and negative predictive value of 0.94% and 99.84%, respectively.

CONCLUSION

The FMF Stillbirth Risk Calculator based upon maternal demographic and obstetric characteristics only may help identify women at low risk of antepartum stillbirth.

Abnormal placental perfusion and the risk of stillbirth: a hospital-based retrospective cohort study

BACKGROUND

A lack of information on specific and interventional factors for stillbirth has made designing preventive strategies difficult, and the stillbirth rate has declined more slowly than the neonatal death rate. We compared the prevalence of stillbirth among the offspring of women with or without abnormal placental perfusion (APP).

METHODS

We conducted a hospital-based retrospective cohort study involving women with a singleton pregnancy between 2012 and 2016 (N = 41,632). Multivariate analysis was performed to compare the prevalence of stillbirth in infants exposed to APP (defined as any abnormality in right or left uterine artery pulsatility index or resistance index [UtA-PI, -RI] [e.g., > 95th percentile] or presence of early diastolic notching) with that in those not exposed to APP.

RESULTS

Stillbirths were more common among women with APP than among those with normal placental perfusion (stillbirth rate, 4.3 ‰ vs 0.9 ‰; odds ratio (OR), 4.2; 95% confidence interval (CI), 2.2 to 8.0). The association strengths were consistent across groups of infants exposed to APP that separately defined by abnormality in right or left UtA-PI or -RI (OR ranged from 3.2 to 5.3; all P ≤ 0.008). The associations were slightly stronger for the unexplained stillbirths. Most of the unexplained stillbirth risk was attributed to APP (59.0%), while a foetal sex disparity existed (94.5% for males and 58.0% for females). Women with normal placental perfusion and a male foetus had higher credibility (e.g., higher specificities) in excluding stillbirths than those with APP and a female foetus at any given false negative rate from 1 to 10% (93.4% ~ 94.1% vs. 12.3% ~ 14.0%).

CONCLUSIONS

APP is associated with and accounts for most of the unexplained stillbirth risk. Different mechanisms exist between the sexes. The performance of screening for stillbirth may be improved by stratification according to sex and placental perfusion.

Proposing a machine-learning based method to predict stillbirth before and during delivery and ranking the features: nationwide retrospective cross-sectional study

Background

Stillbirth is defined as fetal loss in pregnancy beyond 28 weeks by WHO. In this study, a machine-learning based method is proposed to predict stillbirth from livebirth and discriminate stillbirth before and during delivery and rank the features.

Method

A two-step stack ensemble classifier is proposed for classifying the instances into stillbirth and livebirth at the first step and then, classifying stillbirth before delivery from stillbirth during the labor at the second step. The proposed SE has two consecutive layers including the same classifiers. The base classifiers in each layer are decision tree, Gradient boosting classifier, logistics regression, random forest and support vector machines which are trained independently and aggregated based on Vote boosting method. Moreover, a new feature ranking method is proposed in this study based on mean decrease accuracy, Gini Index and model coefficients to find high-ranked features.

Results

IMAN registry dataset is used in this study considering all births at or beyond 28th gestational week from 2016/04/01 to 2017/01/01 including 1,415,623 live birth and 5502 stillbirth cases. A combination of maternal demographic features, clinical history, fetal properties, delivery descriptors, environmental features, healthcare service provider descriptors and socio-demographic features are considered. The experimental results show that our proposed SE outperforms the compared classifiers with the average accuracy of 90%, sensitivity of 91%, specificity of 88%. The discrimination of the proposed SE is assessed and the average AUC of ±95%, CI of 90.51% ±1.08 and 90% ±1.12 is obtained on training dataset for model development and test dataset for external validation, respectively. The proposed SE is calibrated using isotopic nonparametric calibration method with the score of 0.07. The process is repeated 10,000 times and AUC of SE classifiers using random different training datasets as null distribution. The obtained p-value to assess the specificity of the proposed SE is 0.0126 which shows the significance of the proposed SE.

Conclusions

Gestational age and fetal height are two most important features for discriminating livebirth from stillbirth. Moreover, hospital, province, delivery main cause, perinatal abnormality, miscarriage number and maternal age are the most important features for classifying stillbirth before and during delivery.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12884-021-03658-z.

Can risk prediction models help us individualise stillbirth prevention? A systematic review and critical appraisal of published risk models

BACKGROUND

Stillbirth prevention is an international priority - risk prediction models could individualise care and reduce unnecessary intervention, but their use requires evaluation.

OBJECTIVES

To identify risk prediction models for stillbirth, and assess their potential accuracy and clinical benefit in practice.

SEARCH STRATEGY

MEDLINE, Embase, DH-DATA and AMED databases were searched from inception to June 2019 using terms relevant to stillbirth, perinatal mortality and prediction models. The search was compliant with Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines.

SELECTION CRITERIA

Studies developing and/or validating prediction models for risk of stillbirth developed for application during pregnancy.

DATA COLLECTION AND ANALYSIS

Study screening and data extraction were conducted in duplicate, using the CHARMS checklist. Risk of bias was appraised using the PROBAST tool.

RESULTS

The search identified 2751 citations. Fourteen studies reporting development of 69 models were included. Variables consistently included were: ethnicity, body mass index, uterine artery Doppler, pregnancy-associated plasma protein and placental growth factor. For almost all models there were significant concerns about risk of bias. Apparent model performance (i.e. in the development dataset) was highest in models developed for use later in pregnancy and including maternal characteristics, and ultrasound and biochemical variables, but few were internally validated and none were externally validated.

CONCLUSIONS

Almost all models identified were at high risk of bias. There are first-trimester models of possible clinical benefit in early risk stratification; these require validation and clinical evaluation. There were few later pregnancy models but, if validated, these could be most relevant to individualised discussions around timing of birth.

TWEETABLE ABSTRACT

Prediction models using maternal factors, blood tests and ultrasound could individualise stillbirth prevention, but existing models are at high risk of bias.

In vivo evidence of significant placental growth factor release by normal pregnancy placentas

Placental growth factor (PlGF) is an angiogenic factor identified in the maternal circulation, and a key biomarker for the diagnosis and management of placental disorders. Furthermore, enhancing the PlGF pathway is regarded as a promising therapy for preeclampsia. The source of PlGF is still controversial with some believing it to be placental in origin while others refute this. To explore the source of PlGF, we undertook a prospective study enrolling normal pregnant women undergoing elective caesarean section. The level of PlGF was estimated in 17 paired serum samples from the uterine vein (ipsilateral or contralateral to the placental insertion) during caesarean section and from a peripheral vein on the same day and second day post-partum. PlGF levels were higher in the uterine than in the peripheral vein with a median difference of 52.2 (IQR 20.1-85.8) pg/mL p = 0.0006. The difference when the sampled uterine vein was ipsilateral to the placenta was 54.8 (IQR 37.1-88.4) pg/mL (n = 11) and 23.7 (IQR -11; 70.5) pg/mL (n = 6) when the sample was contralateral. Moreover, PlGF levels fell by 83% on day 1-2 post-partum. Our findings strongly support the primary source of PlGF to be placental. These findings will be of value in designing target therapies such as PlGF overexpression, to cure placental disorders during pregnancy.

First-trimester screening-biomarkers and cell-free DNA

Background: The introduction of cell-free DNA into clinical practice has changed the screening approach. Healthcare professionals and future parents tend to overestimate NIPT (noninvasive prenatal testing) capabilities despite its relatively high cost and limited information.Objective: In this review, our aim was to survey how various countries have introduced contingent screening models and to discuss the advantages and disadvantages of the combined screening test and the use of NIPT.Data source: The Web of Science, PubMed database and institutional websites were searched for information regarding screening approaches and the implementation in different countries.Results: There are nine countries and regions that have already approved contingent screening test, while others (e.g. Australia) are discussing the implementation of contingent screening versus universal use of NIPT. There are several recent meta-analyses debating whether to use NIPT for universal screening for trisomies and other fetal conditions.Conclusions: NIPT is a reasonable option as an advanced screening test for trisomy 21, 18 and 13 only. Introducing screening by NIPT instead of a first-trimester screening will cause the loss of other valuable information including accurate dating of pregnancy, diagnosing major structural fetal abnormalities and multiple pregnancies at an early gestational age. Additionally, the opportunity to screen for early preeclampsia will be lost. Currently, the price for NIPT is still high adding extra strain on publicly funded health systems.

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

OBJECTIVES

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

STUDY DESIGN

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

RESULTS

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

CONCLUSION

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

Is it possible to safely prevent late preterm and early term births?

Late preterm and early term birth is associated with adverse short- and long-term consequences, particularly for neurodevelopment. A clear reduction in these births can be achieved by avoidance of non-medically indicated births prior to 39 weeks gestation, as shown following the introduction of prohibitive policies in the USA. However, clinicians and policy-makers must always consider the potential for unintended adverse consequences of such action, such as a potential for an increase in term stillbirth. Finding the balance between optimising long-term neurological outcomes and avoiding rare but devastating term stillbirths is one of the challenges of modern maternity care. In this article we review the current evidence for whether this balance can be found, where early births can be safely prevented, and what remains to be addressed to optimise this balance safely.