Prediction of stillbirth from maternal factors, fetal biometry and uterine artery Doppler at 19-24 weeks

Prenatal diagnosis of vasa praevia in routine clinical practice: Prevention of stillbirths and impact on perinatal outcomes

BACKGROUND

Vasa praevia (VP) is defined as the presence of unsupported fetal blood vessels in close proximity of the internal os of the cervix. There is robust evidence from observational cohort studies and meta-analysis that prenatal diagnosis of VP is associated with excellent perinatal outcomes. We have previously proposed a two-stage strategy for prenatal diagnosis that can be implemented in routine clinical practice leading to effective prenatal diagnosis and prevention of fetal and neonatal mortality and morbidity.

OBJECTIVES

To demonstrate the feasibility and effectiveness of a two-stage screening strategy for prenatal diagnosis of VP in routine clinical practice and to estimate the potential impact on prevention of stillbirths and perinatal deaths.

STUDY DESIGN

This was an observational retrospective cohort study carried out at the Medway Fetal and Maternal Medicine Centre between January 2010 and June 2022. We examined the feasibility and effectiveness of this policy in terms of identification of a high-risk cohort and prenatal diagnosis of VP through routine 11-13 and 20-22 weeks' ultrasound assessments based on the two-stage protocol. We also examined the impact on maternal, neonatal and perinatal outcomes in pregnancies with a confirmed diagnosis of VP. Absolute risks (95 %) were calculated based on rates of events in the two groups. Logistic regression analysis was used to estimate independent contribution from maternal and pregnancy characteristics in prediction of VP.

RESULTS

The study population of 53,648 singleton pregnancies included 45 pregnancies with VP (0.83 per 1,000 pregnancies or an incidence of 1 in 1,192 pregnancies). VP was suspected in 56 cases and were resolved in 11 cases (19.6 %), thus leaving 45 pregnancies with a confirmed diagnosis of VP. The main findings that predicted VP included a low-lying placenta at 20-22 weeks', placenta praevia, bilobed placenta and a velamentous cord insertion. In our study population, pregnancies with a prenatal diagnosis of VP had a livebirth rate of 100 % and an intact perinatal survival rate of 97.8 %.

CONCLUSION

Our study demonstrates that effective prenatal diagnosis of pregnancies with VP can be achieved in routine clinical practice with good perinatal outcomes.

Routine 36-week scan: prediction of small-for-gestational-age neonate

OBJECTIVES

First, to compare the predictive performance of routine ultrasonographic estimated fetal weight (EFW) at 31 + 0 to 33 + 6 and 35 + 0 to 36 + 6 weeks' gestation for delivery of a small-for-gestational-age (SGA) neonate. Second, to compare the predictive performance of EFW at 36 weeks' gestation for SGA vs fetal growth restriction (FGR) at birth. Third, to compare the predictive performance for delivery of a SGA neonate of EFW < 10th percentile vs a model combining maternal demographic characteristics and elements of medical history with EFW.

METHODS

This was a retrospective analysis of prospectively collected data in 21 676 women with a singleton pregnancy who had undergone routine ultrasound examination at 31 + 0 to 33 + 6 weeks' gestation and 107 875 women with a singleton pregnancy who had undergone routine ultrasound examination at 35 + 0 to 36 + 6 weeks. Measurements of fetal head circumference, abdominal circumference and femur length were used to calculate EFW according to the Hadlock formula and this was expressed as a percentile according to the Fetal Medicine Foundation fetal and neonatal population weight charts. The same charts were used to diagnose SGA neonates with birth weight < 10th or < 3rd percentile. FGR was defined as birth weight < 10th percentile in addition to Doppler anomalies. For each gestational-age window at screening, the screen-positive rate and detection rate were calculated at different EFW cut-offs between the 10th and 50th percentiles for predicting the delivery of a SGA neonate with birth weight < 10th or < 3rd percentile, either within 2 weeks or at any time after assessment. The areas under the receiver-operating-characteristics curves (AUC) of screening for a SGA neonate by EFW at 31 + 0 to 33 + 6 weeks and at 35 + 0 to 36 + 6 weeks were compared.

RESULTS

The predictive performance of routine ultrasonographic examination during the third trimester for delivery of a SGA neonate is higher if: first, the scan is carried out at 35 + 0 to 36 + 6 weeks' gestation rather than at 31 + 0 to 33 + 6 weeks; second, the outcome measure is birth weight < 3rd rather than < 10th percentile; third, the outcome measure is FGR rather than SGA; fourth, if delivery occurs within 2 weeks after assessment rather than at any time after assessment; and fifth, prediction is performed using a model that combines maternal demographic characteristics and elements of medical history with EFW rather than EFW < 10th percentile alone. At 35 + 0 to 36 + 6 weeks' gestation, detection of ≥ 85% of SGA neonates with birth weight < 10th percentile born at any time after assessment necessitates the use of EFW < 40th percentile. Screening at this percentile cut-off predicted 95% and 98% of neonates with birth weight < 10th and < 3rd percentile, respectively, born within 2 weeks after assessment, and the respective values for neonates born at any time after assessment were 85% and 93%.

CONCLUSION

Routine third-trimester ultrasonographic screening for a SGA neonate performs best when the scan is carried out at 35 + 0 to 36 + 6 weeks' gestation, rather than at 31 + 0 to 33 + 6 weeks, and when EFW is combined with maternal risk factors to estimate the patient-specific risk. © 2024 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

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.

Prognostic prediction models for adverse birth outcomes: A systematic review

Background

Despite progress in reducing maternal and child mortality worldwide, adverse birth outcomes such as preterm birth, low birth weight (LBW), small for gestational age (SGA), and stillbirth continue to be a major global health challenge. Developing a prediction model for adverse birth outcomes allows for early risk detection and prevention strategies. In this systematic review, we aimed to assess the performance of existing prediction models for adverse birth outcomes and provide a comprehensive summary of their findings.

Methods

We used the Population, Index prediction model, Comparator, Outcome, Timing, and Setting (PICOTS) approach to retrieve published studies from PubMed/MEDLINE, Scopus, CINAHL, Web of Science, African Journals Online, EMBASE, and Cochrane Library. We used WorldCat, Google, and Google Scholar to find the grey literature. We retrieved data before 1 March 2022. Data were extracted using CHecklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies. We assessed the risk of bias with the Prediction Model Risk of Bias Assessment tool. We descriptively reported the results in tables and graphs.

Results

We included 115 prediction models with the following outcomes: composite adverse birth outcomes (n = 6), LBW (n = 17), SGA (n = 23), preterm birth (n = 71), and stillbirth (n = 9). The sample sizes ranged from composite adverse birth outcomes (n = 32-549), LBW (n = 97-27 233), SGA (n = 41-116 070), preterm birth (n = 31-15 883 784), and stillbirth (n = 180-76 629). Only nine studies were conducted on low- and middle-income countries. 10 studies were externally validated. Risk of bias varied across studies, in which high risk of bias was reported on prediction models for SGA (26.1%), stillbirth (77.8%), preterm birth (31%), LBW (23.5%), and composite adverse birth outcome (33.3%). The area under the receiver operating characteristics curve (AUROC) was the most used metric to describe model performance. The AUROC ranged from 0.51 to 0.83 in studies that reported predictive performance for preterm birth. The AUROC for predicting SGA, LBW, and stillbirth varied from 0.54 to 0.81, 0.60 to 0.84, and 0.65 to 0.72, respectively. Maternal clinical features were the most utilised prognostic markers for preterm and LBW prediction, while uterine artery pulsatility index was used for stillbirth and SGA prediction.

Conclusions

A varied prognostic factors and heterogeneity between studies were found to predict adverse birth outcomes. Prediction models using consistent prognostic factors, external validation, and adaptation of future risk prediction models for adverse birth outcomes was recommended at different settings.

Registration

PROSPERO CRD42021281725.

Fetal growth restriction and small for gestational age as predictors of neonatal morbidity: which growth nomogram to use?

BACKGROUND

Fetal growth nomograms were developed to screen for fetal growth restriction and guide clinical care to improve perinatal outcomes; however, existing literature remains inconclusive regarding which nomogram is the gold standard.

OBJECTIVE

This study aimed to compare the ability of 4 commonly used nomograms (Hadlock, International Fetal and Newborn Growth Consortium for the 21st Century, Eunice Kennedy Shriver National Institute of Child Health and Human Development-unified standard, and World Health Organization fetal growth charts) and 1 institution-specific reference to predict small for gestational age and poor neonatal outcomes.

STUDY DESIGN

This was a retrospective cohort study of all nonanomalous singleton pregnancies undergoing ultrasound at ≥20 weeks of gestation between 2013 and 2020 and delivering at a single academic center. Using random selection methods, the study sample was restricted to 1 pregnancy per patient and 1 ultrasound per pregnancy completed at ≥22 weeks of gestation. Fetal biometry data were used to calculate estimated fetal weight and percentiles according to the aforementioned 5 nomograms. Maternal and neonatal data were extracted from electronic medical records. Logistic regression was used to estimate the association between estimated fetal weight of <10th and <3rd percentiles compared with estimated fetal weight of 10th to 90th percentile as the reference group for small for gestational age and the neonatal composite outcomes (perinatal mortality, hypoxic-ischemic encephalopathy or seizures, respiratory morbidity, intraventricular hemorrhage, necrotizing enterocolitis, hyperbilirubinemia or hypoglycemia requiring neonatal intensive care unit admission, and retinopathy of prematurity). Receiver operating characteristic curve contrast estimation (primary analysis) and test characteristics were calculated for all nomograms and the prediction of small for gestational age and the neonatal composite outcomes. We restricted the sample to ultrasounds performed within 28 days of delivery; moreover, similar analyses were completed to assess the prediction of small for gestational age and neonatal composite outcomes.

RESULTS

Among 10,045 participants, the proportion of fetuses classified as <10th percentile varied across nomograms from 4.9% to 9.7%. Fetuses with an estimated fetal weight of <10th percentile had an increased risk of small for gestational age (odds ratio, 9.9 [95% confidence interval, 8.5-11.5] to 12.8 [95% confidence interval, 10.9-15.0]). In addition, the estimated fetal weight of <10th and <3rd percentile was associated with increased risk of the neonatal composite outcome (odds ratio, 2.4 [95% confidence interval, 2.0-2.8] to 3.5 [95% confidence interval, 2.9-4.3] and 5.7 [95% confidence interval, 4.5-7.2] to 8.8 [95% confidence interval, 6.6-11.8], respectively). The prediction of small for gestational age with an estimated fetal weight of <10th percentile had a positive likelihood ratio of 6.3 to 8.5 and an area under the curve of 0.62 to 0.67. Similarly, the prediction of the neonatal composite outcome with an estimated fetal weight of <10th percentile had a positive likelihood ratio of 2.1 to 3.1 and an area under the curve of 0.55 to 0.57. When analyses were restricted to ultrasound within 4 weeks of delivery, among fetuses with an estimated fetal weight of <10th percentile, the risk of small for gestational age increased across all nomograms (odds ratio, 16.7 [95% confidence interval, 12.6-22.3] to 25.1 [95% confidence interval, 17.0-37.0]), and prediction improved (positive likelihood ratio, 8.3-15.0; area under the curve, 0.69-0.75). Similarly, the risk of neonatal composite outcome increased (odds ratio, 3.2 [95% confidence interval, 2.4-4.2] to 5.2 [95% confidence interval, 3.8-7.2]), and prediction marginally improved (positive likelihood ratio, 2.4-4.1; area under the curve, 0.60-0.62). Importantly, the risk of both being small for gestational age and having the neonatal composite outcome further increased (odds ratio, 21.4 [95% confidence interval, 13.6-33.6] to 28.7 (95% confidence interval, 18.6-44.3]), and the prediction of concurrent small for gestational age and neonatal composite outcome greatly improved (positive likelihood ratio, 6.0-10.0; area under the curve, 0.80-0.83).

CONCLUSION

In this large cohort, Hadlock, recent fetal growth nomograms, and a local population-derived fetal growth reference performed comparably in the prediction of small for gestational age and neonatal composite outcomes.

Identifying risk of stillbirth using machine learning

BACKGROUND

Previous predictive models using logistic regression for stillbirth do not leverage the advanced and nuanced techniques involved in sophisticated machine learning methods, such as modeling nonlinear relationships between outcomes.

OBJECTIVE

This study aimed to create and refine machine learning models for predicting stillbirth using data available before viability (22-24 weeks) and throughout pregnancy, as well as demographic, medical, and prenatal visit data, including ultrasound and fetal genetics.

STUDY DESIGN

This is a secondary analysis of the Stillbirth Collaborative Research Network, which included data from pregnancies resulting in stillborn and live-born infants delivered at 59 hospitals in 5 diverse regions across the United States from 2006 to 2009. The primary aim was the creation of a model for predicting stillbirth using data available before viability. Secondary aims included refining models with variables available throughout pregnancy and determining variable importance.

RESULTS

Among 3000 live births and 982 stillbirths, 101 variables of interest were identified. Of the models incorporating data available before viability, the random forests model had 85.1% accuracy (area under the curve) and high sensitivity (88.6%), specificity (85.3%), positive predictive value (85.3%), and negative predictive value (84.8%). A random forests model using data collected throughout pregnancy resulted in accuracy of 85.0%; this model had 92.2% sensitivity, 77.9% specificity, 84.7% positive predictive value, and 88.3% negative predictive value. Important variables in the previability model included previous stillbirth, minority race, gestational age at the earliest prenatal visit and ultrasound, and second-trimester serum screening.

CONCLUSION

Applying advanced machine learning techniques to a comprehensive database of stillbirths and live births with unique and clinically relevant variables resulted in an algorithm that could accurately identify 85% of pregnancies that would result in stillbirth, before they reached viability. Once validated in representative databases reflective of the US birthing population and then prospectively, these models may provide effective risk stratification and clinical decision-making support to better identify and monitor those at risk of stillbirth.

External validation of Fetal Medicine Foundation competing-risks model for midgestation prediction of small-for-gestational-age neonates in Spanish population

OBJECTIVE

To examine the external validity of the new Fetal Medicine Foundation (FMF) competing-risks model for prediction in midgestation of small-for-gestational-age (SGA) neonates.

METHODS

This was a single-center prospective cohort study of 25 484 women with a singleton pregnancy undergoing routine ultrasound examination at 19 + 0 to 23 + 6 weeks' gestation. The FMF competing-risks model for the prediction of SGA combining maternal factors and midgestation estimated fetal weight by ultrasound scan (EFW) and uterine artery pulsatility index (UtA-PI) was used to calculate risks for different cut-offs of birth-weight percentile and gestational age at delivery. The predictive performance was evaluated in terms of discrimination and calibration.

RESULTS

The validation cohort was significantly different in composition compared with the FMF cohort in which the model was developed. In the validation cohort, at a 10% false-positive rate (FPR), maternal factors, EFW and UtA-PI yielded detection rates of 69.6%, 38.7% and 31.7% for SGA < 10th percentile with delivery at < 32, < 37 and ≥ 37 weeks' gestation, respectively. The respective values for SGA < 3rd percentile were 75.7%, 48.2% and 38.1%. Detection rates in the validation cohort were similar to those reported in the FMF study for SGA with delivery at < 32 weeks but lower for SGA with delivery at < 37 and ≥ 37 weeks. Predictive performance in the validation cohort was similar to that reported in a subgroup of the FMF cohort consisting of nulliparous and Caucasian women. Detection rates in the validation cohort at a 15% FPR were 77.4%, 50.0% and 41.5% for SGA < 10th percentile with delivery at < 32, < 37 and ≥ 37 weeks, respectively, which were similar to the respective values reported in the FMF study at a 10% FPR. The model had satisfactory calibration.

CONCLUSION

The new competing-risks model for midgestation prediction of SGA developed by the FMF performs well in a large independent Spanish population. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

Prediction of stillbirth low resource setting in Northern Uganda

BACKGROUND

Women of Afro-Caribbean and Asian origin are more at risk of stillbirths. However, there are limited tools built for risk-prediction models for stillbirth within sub-Saharan Africa. Therefore, we examined the predictors for stillbirth in low resource setting in Northern Uganda.

METHODS

Prospective cohort study at St. Mary's hospital Lacor in Northern Uganda. Using Yamane's 1967 formula for calculating sample size for cohort studies using finite population size, the required sample size was 379 mothers. We doubled the number (to > 758) to cater for loss to follow up, miscarriages, and clients opting out of the study during the follow-up period. Recruited 1,285 pregnant mothers at 16-24 weeks, excluded those with lethal congenital anomalies diagnosed on ultrasound. Their history, physical findings, blood tests and uterine artery Doppler indices were taken, and the mothers were encouraged to continue with routine prenatal care until the time for delivery. While in the delivery ward, they were followed up in labour until delivery by the research team. The primary outcome was stillbirth 24 + weeks with no signs of life. Built models in RStudio. Since the data was imbalanced with low stillbirth rate, used ROSE package to over-sample stillbirths and under-sample live-births to balance the data. We cross-validated the models with the ROSE-derived data using K (10)-fold cross-validation and obtained the area under curve (AUC) with accuracy, sensitivity and specificity.

RESULTS

The incidence of stillbirth was 2.5%. Predictors of stillbirth were history of abortion (aOR = 3.07, 95% CI 1.11-8.05, p = 0.0243), bilateral end-diastolic notch (aOR = 3.51, 95% CI 1.13-9.92, p = 0.0209), personal history of preeclampsia (aOR = 5.18, 95% CI 0.60-30.66, p = 0.0916), and haemoglobin 9.5 - 12.1 g/dL (aOR = 0.33, 95% CI 0.11-0.93, p = 0.0375). The models' AUC was 75.0% with 68.1% accuracy, 69.1% sensitivity and 67.1% specificity.

CONCLUSION

Risk factors for stillbirth include history of abortion and bilateral end-diastolic notch, while haemoglobin of 9.5-12.1 g/dL is protective.

A Review of Roles of Uterine Artery Doppler in Pregnancy Complications

The invasion of trophoblasts into the uterine decidua and decidual vessels is critical for the formation of placenta. The defects of placentation are related to the etiologies of preeclampsia (PE), fetal growth restriction (FGR), and small-for-gestational age (SGA) neonates. It is possible to predict significant vascular events during pregnancy through uterine artery Doppler (UAD). From the implantation stage to the end of pregnancy, detecting changes in uterine and placental blood vessels can provide a favorable diagnostic instrument for pregnancy complications. This review aims to collect literature about the roles of UAD in pregnancy complications. We consider all relevant articles in English from January 1, 1983 to October 30, 2021. Predicting pregnancy complications in advance allows practitioners to carry out timely interventions to avoid or lessen the harm to mothers and neonates. Administering low-dose aspirin daily before 16 weeks of pregnancy can significantly reduce the incidence of pregnancy complications. From early pregnancy to late pregnancy, UAD can combine with other maternal factors, biochemical indicators, and fetal measurement data to identify high-risk population. The identification of high-risk groups can also lessen maternal mortality. Besides, through moderate risk stratification, stringent monitoring for high-risk pregnant women can be implemented, decreasing the incidence of adversities.

Second-trimester contingent screening for small-for-gestational-age neonate

OBJECTIVES

First, to investigate the additive value of second-trimester placental growth factor (PlGF) for the prediction of a small-for-gestational-age (SGA) neonate. Second, to examine second-trimester contingent screening strategies.

METHODS

This was a prospective observational study in women with singleton pregnancy undergoing routine ultrasound examination at 19-24 weeks' gestation. We used the competing-risks model for prediction of SGA. The parameters for the prior model and the likelihoods for estimated fetal weight (EFW) and uterine artery pulsatility index (UtA-PI) were those presented in previous studies. A folded-plane regression model was fitted in the dataset of this study to describe the likelihood of PlGF. We compared the prediction of screening by maternal risk factors against the prediction provided by a combination of maternal risk factors, EFW, UtA-PI and PlGF. We also examined the additive value of PlGF in a policy that uses maternal risk factors, EFW and UtA-PI.

RESULTS

The study population included 40 241 singleton pregnancies. Overall, the prediction of SGA improved with increasing degree of prematurity, with increasing severity of smallness and in the presence of coexisting pre-eclampsia. The combination of maternal risk factors, EFW, UtA-PI and PlGF improved significantly the prediction provided by maternal risk factors alone for all the examined cut-offs of birth weight and gestational age at delivery. Screening by a combination of maternal risk factors and serum PlGF improved the prediction of SGA when compared to screening by maternal risk factors alone. However, the incremental improvement in prediction was decreased when PlGF was added to screening by a combination of maternal risk factors, EFW and UtA-PI. If first-line screening for a SGA neonate with birth weight < 10^th percentile delivered at < 37 weeks' gestation was by maternal risk factors and EFW, the same detection rate of 90%, at an overall false-positive rate (FPR) of 50%, as that achieved by screening with maternal risk factors, EFW, UtA-PI and PlGF in the whole population can be achieved by reserving measurements of UtA-PI and PlGF for only 80% of the population. Similarly, in screening for a SGA neonate with birth weight < 10^th percentile delivered at < 30 weeks, the same detection rate of 90%, at an overall FPR of 14%, as that achieved by screening with maternal risk factors, EFW, UtA-PI and PlGF in the whole population can be achieved by reserving measurements of UtA-PI and PlGF for only 70% of the population. The additive value of PlGF in reducing the FPR to about 10% with a simultaneous detection rate of 90% for a SGA neonate with birth weight < 3^rd percentile born < 30 weeks, is gained by measuring PlGF in only 50% of the population when first-line screening is by maternal factors, EFW and UtA-PI.

CONCLUSIONS

The combination of maternal risk factors, EFW, UtA-PI and PlGF provides effective second-trimester prediction of SGA. Serum PlGF is useful for predicting a SGA neonate with birth weight < 3^rd percentile born < 30 weeks after an inclusive assessment by maternal risk factors and biophysical markers. Similar detection rates and FPRs can be achieved by application of contingent screening strategies. © 2021 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.

Development and validation of model for prediction of placental dysfunction-related stillbirth from maternal factors, fetal weight and uterine artery Doppler at mid-gestation

OBJECTIVE

To examine the performance of a model combining maternal risk factors, uterine artery pulsatility index (UtA-PI) and estimated fetal weight (EFW) at 19-24 weeks' gestation, for predicting all antepartum stillbirths and those due to impaired placentation, in a training dataset used for development of the model and in a validation dataset.

METHODS

The data for this study were derived from prospective screening for adverse obstetric outcome in women with singleton pregnancy attending for routine pregnancy care at 19 + 0 to 24 + 6 weeks' gestation. The study population was divided into a training dataset used to develop prediction models for placental dysfunction-related antepartum stillbirth and a validation dataset to which the models were then applied. Multivariable logistic regression analysis was used to develop a model based on a combination of maternal risk factors, EFW Z-score and UtA-PI multiples of the normal median. We examined the predictive performance of the model by, first, the ability of the model to discriminate between the stillbirth and live-birth groups, using the area under the receiver-operating-characteristics curve (AUC) and the detection rate (DR) at a fixed false-positive rate (FPR) of 10%, and, second, calibration by measurements of calibration slope and intercept.

RESULTS

The study population of 131 514 pregnancies included 131 037 live births and 477 (0.36%) stillbirths. There are four main findings of this study. First, 92.5% (441/477) of stillbirths were antepartum and 7.5% (36/477) were intrapartum, and 59.2% (261/441) of antepartum stillbirths were observed in association with placental dysfunction and 40.8% (180/441) were unexplained or due to other causes. Second, placental dysfunction accounted for 80.1% (161/201) of antepartum stillbirths at < 32 weeks' gestation, 54.2% (52/96) at 32 + 0 to 36 + 6 weeks and 33.3% (48/144) at ≥ 37 weeks. Third, the risk of placental dysfunction-related antepartum stillbirth increased with increasing maternal weight and decreasing maternal height, was 3-fold higher in black than in white women, was 5.5-fold higher in parous women with previous stillbirth than in those with previous live birth, and was increased in smokers, in women with chronic hypertension and in parous women with a previous pregnancy complicated by pre-eclampsia and/or birth of a small-for-gestational-age baby. Fourth, in screening for placental dysfunction-related antepartum stillbirth by a combination of maternal risk factors, EFW and UtA-PI in the validation dataset, the DR at a 10% FPR was 62.3% (95% CI, 57.2-67.4%) and the AUC was 0.838 (95% CI, 0.799-0.878); these results were consistent with those in the dataset used for developing the algorithm and demonstrate high discrimination between affected and unaffected pregnancies. Similarly, the calibration slope was 1.029 and the intercept was -0.009, demonstrating good agreement between the predicted risk and observed incidence of placental dysfunction-related antepartum stillbirth. The performance of screening was better for placental dysfunction-related antepartum stillbirth at < 37 weeks' gestation compared to at term (DR at a 10% FPR, 69.8% vs 29.2%).

CONCLUSIONS

Screening at mid-gestation by a combination of maternal risk factors, EFW and UtA-PI can predict a high proportion of placental dysfunction-related stillbirths and, in particular, those that occur preterm. Such screening provides poor prediction of unexplained stillbirth or stillbirth due to other causes. © 2021 International Society of Ultrasound in Obstetrics and Gynecology.

Predictive performance for placental dysfunction related stillbirth of the competing risks model for small for gestational age fetuses

OBJECTIVES

First, to examine the predictive performance for placental dysfunction related stillbirths of the competing risks model for small for gestational age (SGA) fetuses based on a combination of maternal risk factors, estimated fetal weight (EFW) and uterine artery pulsatility index (UtA-PI); and second, to compare the performance of this model to that of stillbirth-specific model utilizing the same biomarkers and to the Royal College of Obstetricians and Gynecologists (RCOG) guideline for the investigation and management of the SGA fetus.

DESIGN

Prospective observational study.

SETTING

Two UK maternity hospitals.

POPULATION

131,514 women with singleton pregnancies attending for routine ultrasound examination at 19-24 weeks' gestation.

METHODS

The predictive performance for stillbirth achieved by three models was compared. Main outcome measure Placental dysfunction related stillbirth.

RESULTS

At 10% false positive rate, the competing risks model predicted 59%, 66% and 71% of placental dysfunction related stillbirths, at any gestation, at <37 weeks and at <32 weeks, respectively, which were similar to the respective figures of 62%, 70% and 73% for the stillbirth-specific model. At a screen positive rate of 21.8 %, as defined by the RCOG guideline, the competing risks model predicted 71%, 76% and 79% of placental dysfunction related stillbirths at any gestation, at <37 weeks and at <32 weeks, respectively, and the respective figures for the RCOG guideline were 40%, 44% and 42%.

CONCLUSION

The predictive performance for placental dysfunction related stillbirths by the competing risks model for SGA was similar to the stillbirth-specific model and superior to the RCOG guideline.

Competing risks model for prediction of small-for-gestational-age neonates from biophysical markers at 19 to 24 weeks' gestation

BACKGROUND

Antenatal identification of women at high risk to deliver small-for-gestational-age neonates may improve the management of the condition. The traditional but ineffective methods for small-for-gestational-age screening are the use of risk scoring systems based on maternal demographic characteristics and medical history and the measurement of the symphysial-fundal height. Another approach is to use logistic regression models that have higher performance and provide patient-specific risks for different prespecified cutoffs of birthweight percentile and gestational age at delivery. However, such models have led to an arbitrary dichotomization of the condition; different models for different small-for-gestational-age definitions are required and adding new biomarkers or examining other cutoffs requires refitting of the whole model. An alternative approach for the prediction of small-for-gestational-age neonates is to consider small for gestational age as a spectrum disorder whose severity is continuously reflected in both the gestational age at delivery and z score in birthweight for gestational age.

OBJECTIVE

This study aimed to develop a new competing risks model for the prediction of small-for-gestational-age neonates based on a combination of maternal demographic characteristics and medical history with sonographic estimated fetal weight, uterine artery pulsatility index, and mean arterial pressure at 19 to 24 weeks' gestation.

STUDY DESIGN

This was a prospective observational study of 96,678 women with singleton pregnancies undergoing routine ultrasound examination at 19 to 24 weeks' gestation, which included recording of estimated fetal weight, uterine artery pulsatility index, and mean arterial pressure. The competing risks model for small for gestational age is based on a previous joint distribution of gestational age at delivery and birthweight z score, according to maternal demographic characteristics and medical history. The likelihoods of the estimated fetal weight, uterine artery pulsatility index, and mean arterial pressure were fitted conditionally to both gestational age at delivery and birthweight z score and modified the previous distribution, according to the Bayes theorem, to obtain an individualized posterior distribution for gestational age at delivery and birthweight z score and therefore patient-specific risks for any desired cutoffs for birthweight z score and gestational age at delivery. The model was internally validated by randomly dividing the data into a training data set, to obtain the parameters of the model, and a test data set, to evaluate the model. The discrimination and calibration of the model were also examined.

RESULTS

The estimated fetal weight was described using a regression model with an interaction term between gestational age at delivery and birthweight z score. Folded plane regression models were fitted for uterine artery pulsatility index and mean arterial pressure. The prediction of small for gestational age by maternal factors was improved by adding biomarkers for increasing degree of prematurity, higher severity of smallness, and coexistence of preeclampsia. Screening by maternal factors with estimated fetal weight, uterine artery pulsatility index, and mean arterial pressure, predicted 41%, 56%, and 70% of small-for-gestational-age neonates with birthweights of <10th percentile delivered at ≥37, <37, and <32 weeks' gestation, at a 10% false-positive rate. The respective rates for a birthweight of <3rd percentile were 47%, 65%, and 77%. The rates in the presence of preeclampsia were 41%, 72%, and 91% for small-for-gestational-age neonates with birthweights of <10th percentile and 50%, 75%, and 92% for small-for-gestational-age neonates with birthweights of <3rd percentile. Overall, the model was well calibrated. The detection rates and calibration indices were similar in the training and test data sets, demonstrating the internal validity of the model.

CONCLUSION

The performance of screening for small-for-gestational-age neonates by a competing risks model that combines maternal factors with estimated fetal weight, uterine artery pulsatility index, and mean arterial pressure was superior to that of screening by maternal characteristics and medical history alone.

Competing-risks model for prediction of small-for-gestational-age neonate from estimated fetal weight at 19-24 weeks' gestation

OBJECTIVE

To develop further a new competing-risks model for the prediction of a small-for-gestational-age (SGA) neonate, by including second-trimester ultrasonographic estimated fetal weight (EFW).

METHODS

This was a prospective observational study in 96 678 women with singleton pregnancy undergoing routine ultrasound examination at 19-24 weeks' gestation. All pregnancies had ultrasound biometry assessment, and EFW was calculated according to the Hadlock formula. We refitted in this large dataset a previously described competing-risks model for the joint distribution of gestational age (GA) at delivery and birth-weight Z-score, according to maternal demographic characteristics and medical history, to obtain the prior distribution. The continuous likelihood of the EFW was fitted conditionally to GA at delivery and birth-weight Z-score and modified the prior distribution, according to Bayes' theorem, to obtain individualized distributions for GA at delivery and birth-weight Z-score and therefore patient-specific risks for any cut-offs for GA at delivery and birth-weight Z-score. We assessed the discriminative ability of the model for predicting SGA with, without or independently of pre-eclampsia occurrence. A calibration study was carried out. Performance of screening was evaluated for SGA defined according to the Fetal Medicine Foundation birth-weight charts.

RESULTS

The distribution of EFW, conditional to both GA at delivery and birth-weight Z-score, was best described by a regression model. For earlier gestations, the association between EFW and birth weight was steeper. The prediction of SGA by maternal factors and EFW improved for increasing degree of prematurity and greater severity of smallness but not for coexistence of pre-eclampsia. Screening by maternal factors predicted 31%, 34% and 39% of SGA neonates with birth weight < 10^th percentile delivered at ≥ 37, < 37 and < 30 weeks' gestation, respectively, at a 10% false-positive rate, and, after addition of EFW, these rates increased to 38%, 43% and 59%, respectively; the respective rates for birth weight < 3^rd percentile were 43%, 50% and 64%. The addition of EFW improved the calibration of the model.

CONCLUSION

In the competing-risks model for prediction of SGA, the performance of screening by maternal characteristics and medical history is improved by the addition of second-trimester EFW. © 2021 International Society of Ultrasound in Obstetrics and Gynecology.

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.

Prediction of stillbirth: an umbrella review of evaluation of prognostic variables

BACKGROUND

Stillbirth accounts for over 2 million deaths a year worldwide and rates remains stubbornly high. Multivariable prediction models may be key to individualised monitoring, intervention or early birth in pregnancy to prevent stillbirth.

OBJECTIVES

To collate and evaluate systematic reviews of factors associated with stillbirth in order to identify variables relevant to prediction model development.

SEARCH STRATEGY

MEDLINE, Embase, DARE and Cochrane Library databases and reference lists were searched up to November 2019.

SELECTION CRITERIA

We included systematic reviews of association of individual variables with stillbirth without language restriction.

DATA COLLECTION AND ANALYSIS

Abstract screening and data extraction were conducted in duplicate. Methodological quality was assessed using AMSTAR and QUIPS criteria. The evidence supporting association with each variable was graded.

RESULTS

The search identified 1198 citations. Sixty-nine systematic reviews reporting 64 variables were included. The most frequently reported were maternal age (n = 5), body mass index (n = 6) and maternal diabetes (n = 5). Uterine artery Doppler appeared to have the best performance of any single test for stillbirth. The strongest evidence of association was for nulliparity and pre-existing hypertension.

CONCLUSION

We have identified variables relevant to the development of prediction models for stillbirth. Age, parity and prior adverse pregnancy outcomes had a more convincing association than the best performing tests, which were PAPP-A, PlGF and UtAD. The evidence was limited by high heterogeneity and lack of data on intervention bias.

TWEETABLE ABSTRACT

Review shows key predictors for use in developing models predicting stillbirth include age, prior pregnancy outcome and PAPP-A, PLGF and Uterine artery Doppler.

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.

Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980-2015

Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric history, lack of validation, and restriction to a single classifier (logistic regression). Consequently, predictive performance remains low, and risk quantification has not been adopted into antenatal practice. The study population consisted of all births to women in Western Australia from 1980 to 2015, excluding terminations. After all exclusions there were 947,025 livebirths and 5,788 stillbirths. Predictive models for stillbirth were developed using multiple machine learning classifiers: regularised logistic regression, decision trees based on classification and regression trees, random forest, extreme gradient boosting (XGBoost), and a multilayer perceptron neural network. We applied 10-fold cross-validation using independent data not used to develop the models. Predictors included maternal socio-demographic characteristics, chronic medical conditions, obstetric complications and family history in both the current and previous pregnancy. In this cohort, 66% of stillbirths were observed for multiparous women. The best performing classifier (XGBoost) predicted 45% (95% CI: 43%, 46%) of stillbirths for all women and 45% (95% CI: 43%, 47%) of stillbirths after the inclusion of previous pregnancy history. Almost half of stillbirths could be potentially identified antenatally based on a combination of current pregnancy complications, congenital anomalies, maternal characteristics, and medical history. Greatest sensitivity is achieved with addition of current pregnancy complications. Ensemble classifiers offered marginal improvement for prediction compared to logistic regression.

Placental MRI and its application to fetal intervention

OBJECTIVE

Magnetic resonance imaging (MRI) of placental invasion has been part of clinical practice for many years. The possibility of being better able to assess placental vascularization and function using MRI has multiple potential applications. This review summarises up-to-date research on placental function using different MRI modalities.

METHOD

We discuss how combinations of these MRI techniques have much to contribute to fetal conditions amenable for therapy such as singletons at high risk for fetal growth restriction (FGR) and monochorionic twin pregnancies for planning surgery and counselling for selective growth restriction and transfusion conditions.

RESULTS

The whole placenta can easily be visualized on MRI, with a clear boundary against the amniotic fluid, and a less clear placental-uterine boundary. Contrasts such as diffusion weighted imaging, relaxometry, blood oxygenation level dependent MRI and flow and metabolite measurement by dynamic contrast enhanced MRI, arterial spin labeling, or spectroscopic techniques are contributing to our wider understanding of placental function.

CONCLUSION

The future of placental MRI is exciting, with the increasing availability of multiple contrasts and new models that will boost the capability of MRI to measure oxygen saturation and placental exchange, enabling examination of placental function in complicated pregnancies.

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.

Bemiparin as a Prophylaxis After an Unexplained Stillbirth: Open-Label Interventional Prospective Study

Stillbirth is a devastating event to the parents, relatives, friends, and families. The role of anticoagulants in the prevention of unexplained stillbirths is uncertain. An open-label interventional prospective cohort study was conducted on 144 women with a history of unexplained stillbirths. The intervention group had a high umbilical artery resistance index (RI) and received bemiparin. The nonintervention group had a normal RI and did not receive any intervention. We measured the adjusted odds ratio (OR) and 95% confidence interval (CI) of the main outcome for these variables using logistic regression analysis. Fresh stillbirth and early neonatal death rates were lower (P = .005, OR = 11.949 and 95% CI = 2.099-68.014) and newborn weight was higher (P = .015, OR = 0.048, 95% CI = 0.004-0.549) in the group that received bemiparin. Bemiparin is effective in decreasing the rate of stillbirth in women with a history of previous unexplained stillbirths.

A stillbirth calculator: Development and internal validation of a clinical prediction model to quantify stillbirth risk

Objective

To generate a clinical prediction tool for stillbirth that combines maternal risk factors to provide an evidence based approach for the identification of women who will benefit most from antenatal testing for stillbirth prevention.

Design

Retrospective cohort study

Setting

Midwestern United States quaternary referral center

Population

Singleton pregnancies undergoing second trimester anatomic survey from 1999–2009. Pregnancies with incomplete follow-up were excluded.

Methods

Candidate predictors were identified from the literature and univariate analysis. Backward stepwise logistic regression with statistical comparison of model discrimination, calibration and clinical performance was used to generate final models for the prediction of stillbirth. Internal validation was performed using bootstrapping with 1,000 repetitions. A stillbirth risk calculator and stillbirth risk score were developed for the prediction of stillbirth at or beyond 32 weeks excluding fetal anomalies and aneuploidy. Statistical and clinical cut-points were identified and the tools compared using the Integrated Discrimination Improvement.

Main outcome measures

Antepartum stillbirth

Results

64,173 women met inclusion criteria. The final stillbirth risk calculator and score included maternal age, black race, nulliparity, body mass index, smoking, chronic hypertension and pre-gestational diabetes. The stillbirth calculator and simple risk score demonstrated modest discrimination but clinically significant performance with no difference in overall performance between the tools [(AUC 0.66 95% CI 0.60–0.72) and (AUC 0.64 95% CI 0.58–0.70), (p = 0.25)].

Conclusion

A stillbirth risk score was developed incorporating maternal risk factors easily ascertained during prenatal care to determine an individual woman’s risk for stillbirth and provide an evidenced based approach to the initiation of antenatal testing for the prediction and prevention of stillbirth.

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

OBJECTIVES

To investigate whether the addition of maternal serum placental growth factor (PlGF) measured at 19-24 weeks' gestation improves the performance of screening for stillbirth that is achieved by a combination of maternal factors, fetal biometry and uterine artery pulsatility index (UtA-PI) and to evaluate the performance of screening with this model for all stillbirths and those due to impaired placentation and unexplained or other causes.

METHODS

This was a prospective screening study of 70 003 singleton pregnancies including 268 stillbirths, carried out in two phases. The first phase included prospective measurement of UtA-PI and fetal biometry, which were available in all cases. The second phase included prospective measurement of maternal serum PlGF, which was available for 9870 live births and 86 antepartum stillbirths. The values of PlGF obtained from this screening study were simulated in the remaining cases based on bivariate Gaussian distributions, defined by the mean and standard deviations. Multivariable logistic regression analysis was used to determine whether the addition of maternal serum PlGF improved the performance of screening that was achieved by a combination of maternal factors, fetal biometry and UtA-PI.

RESULTS

Significant contribution to the prediction of stillbirth was provided by maternal factor-derived a-priori risk, multiples of the median values of PlGF, UtA-PI and fetal biometry Z-scores. A model combining these variables predicted 58% of all stillbirths and 84% of those due to impaired placentation, at a false-positive rate of 10%. Within the impaired-placentation group, the detection rate of stillbirth < 32 weeks' gestation was higher than that of stillbirth ≥ 37 weeks (97% vs 61%; P < 0.01).

CONCLUSIONS

A high proportion of stillbirths due to impaired placentation can be identified effectively in the second trimester of pregnancy using a combination of maternal factors, fetal biometry, uterine artery Doppler and maternal serum PlGF. Copyright © 2016 ISUOG. Published by John Wiley & Sons Ltd.