Performance of first-trimester combined screening for preterm pre-eclampsia: findings from cohort of 10 110 pregnancies in Spain

Pregnancy-associated plasma protein A (PAPP-A) as a first trimester serum biomarker for preeclampsia screening: a systematic review and meta-analysis

OBJECTIVE

The aim of this study is to systematically examine the role of the pregnancy-associated plasma protein A (PAPP-A) serum biomarker in the first trimester screening of preeclampsia (PE).

MATERIALS AND METHODS

A systematic search of the literature was conducted on PubMed via Medline, and Cochrane Library up to 8 November 2022, for prospective studies evaluating PAPP-A serum levels in first trimester pregnant women as a screening biomarker for PE. Eligible were all prospectively designed case-control or cohort studies, published in English. Two investigators independently examined the studies and the studies' characteristics were extracted. Newcastle-Ottawa Scale (NOS) for case-control and cohort studies were applied to assess the risk of bias. For the quantitative analysis of the studies, a meta-analysis was also performed.

RESULTS

A total of 22 studies including 33,651 pregnant women were assessed, of whom, 2001 were diagnosed with PE. A meta-analysis was performed, showing that PAPP-A levels in the first trimester were significantly lower in early onset preeclamptic women (MD: -0.24, 95% CI: -0.37, -0.11, p = .0002), late onset (MD: -0.15, 95% CI: -0.25, -0.05, p = .03), and total preeclamptic cases (MD = -0.17, 95% CI = -0.23, -0.11, p < .00001) when compared with controls.

CONCLUSIONS

Our results suggest that PAPP-A can be a promising predictor in early screening for PE; hence, women at risk can be diagnosed early in their pregnancy stage and benefit from individualized PE treatment before it progresses.

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

PAPP-A has limited value in first-trimester screening for PE, whereas PlGF adds significantly to the detection rate of preterm PE at 10-14 weeks' gestation. © 2025 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Continuous Risk Assessment of Late and Term Preeclampsia Throughout Pregnancy: A Retrospective Cohort Study

Background and Objectives: To evaluate the diagnostic accuracy of widely available biomarkers longitudinally measured throughout pregnancy to predict all and term (delivery at ≥37 weeks) preeclampsia (PE). Materials and Methods: This is a longitudinal retrospective study performed at Hospital Universitario de Torrejón (Madrid, Spain) and Shterev Hospital (Sofia, Bulgaria) between August 2017 and December 2022. All pregnant women with singleton pregnancies and non-malformed live fetuses attending their routine ultrasound examination and first-trimester screening for preterm PE at 11 + 0 to 13 + 6 weeks' gestation at the participating centers were invited to participate in a larger study for the prediction of pregnancy complications. The dataset was divided into two subsets to develop and validate a joint model of time-to-event outcome and longitudinal data, and to evaluate how the area under the receiving operating characteristic curve (AUROC) evolved with time. Results: 4056 pregnancies were included in the training set (59 all PE, 40 term PE) and 944 in the validation set (23 all PE, 20 term PE). For the joint model and all PE, the AUROC was 0.84 (95% CI 0.73 to 0.94) and the detection rate (DR) for a 10% screening positive rate (SPR) was 56.5 (95% CI 34.5 to 76.8). For term PE, AUROC was 0.80 (95% CI 0.69 to 0.91), and DR for a 10% SPR was 55.0 (95% CI 31.5 to 76.9). The AUROC using only information from the first trimester was 0.50 (95% CI 0.37 to 0.64) and it increased to 0.84 (0.73 to 0.94) when using all information available. Conclusions: Routinely measuring MAP and UtA-PI throughout pregnancy may improve the predictive prediction power for all and term-PE.

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

BACKGROUND

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

OBJECTIVE

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

STUDY DESIGN

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

RESULTS

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

CONCLUSION

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

Comparison of different methods of first-trimester screening for preterm pre-eclampsia: cohort study

OBJECTIVE

To compare the predictive performance of three different mathematical models for first-trimester screening of pre-eclampsia (PE), which combine maternal risk factors with mean arterial pressure (MAP), uterine artery pulsatility index (UtA-PI) and serum placental growth factor (PlGF), and two risk-scoring systems.

METHODS

This was a prospective cohort study performed in eight fetal medicine units in five different regions of Spain between September 2017 and December 2019. All pregnant women with singleton pregnancy and a non-malformed live fetus attending their routine ultrasound examination at 11 + 0 to 13 + 6 weeks' gestation were invited to participate in the study. Maternal characteristics and medical history were recorded and measurements of MAP, UtA-PI, serum PlGF and pregnancy-associated plasma protein-A (PAPP-A) were converted into multiples of the median (MoM). Risks for term PE, preterm PE (< 37 weeks' gestation) and early PE (< 34 weeks' gestation) were calculated according to the FMF competing-risks model, the Crovetto et al. logistic regression model and the Serra et al. Gaussian model. PE classification was also performed based on the recommendations of the National Institute for Health and Care Excellence (NICE) and the American College of Obstetricians and Gynecologists (ACOG). We estimated detection rates (DR) with their 95% CIs at a fixed 10% screen-positive rate (SPR), as well as the area under the receiver-operating-characteristics curve (AUC) for preterm PE, early PE and all PE for the three mathematical models. For the scoring systems, we calculated DR and SPR. Risk calibration was also assessed.

RESULTS

The study population comprised 10 110 singleton pregnancies, including 32 (0.3%) that developed early PE, 72 (0.7%) that developed preterm PE and 230 (2.3%) with any PE. At a fixed 10% SPR, the FMF, Crovetto et al. and Serra et al. models detected 82.7% (95% CI, 69.6-95.8%), 73.8% (95% CI, 58.7-88.9%) and 79.8% (95% CI, 66.1-93.5%) of early PE; 72.7% (95% CI, 62.9-82.6%), 69.2% (95% CI, 58.8-79.6%) and 74.1% (95% CI, 64.2-83.9%) of preterm PE; and 55.1% (95% CI, 48.8-61.4%), 47.1% (95% CI, 40.6-53.5%) and 53.9% (95% CI, 47.4-60.4%) of all PE, respectively. The best correlation between predicted and observed cases was achieved by the FMF model, with an AUC of 0.911 (95% CI, 0.879-0.943), a slope of 0.983 (95% CI, 0.846-1.120) and an intercept of 0.154 (95% CI, -0.091 to 0.397). The NICE criteria identified 46.7% (95% CI, 35.3-58.0%) of preterm PE at 11% SPR and ACOG criteria identified 65.9% (95% CI, 55.4-76.4%) of preterm PE at 33.8% SPR.

CONCLUSIONS

The best performance of screening for preterm PE is achieved by mathematical models that combine maternal factors with MAP, UtA-PI and PlGF, as compared to risk-scoring systems such as those of NICE and ACOG. While all three algorithms show similar results in terms of overall prediction, the FMF model showed the best performance at an individual level. © 2024 International Society of Ultrasound in Obstetrics and Gynecology.

Association between Perinatal Outcomes and Maternal Risk Factors: A Cohort Study

Background and Objectives: The aim of this study was to analyze the association between maternal risk factors, such as age, body mass index (BMI), and cigarette smoking, and perinatal outcomes. Materials and Methods: We conducted a retrospective analysis based on prospectively collected data at Hospital Universitario de Torrejón (Madrid, Spain) between September 2017 and December 2019. All pregnant women with singleton pregnancies and non-malformed live fetuses attending their routine ultrasound examination at 11+0 to 13+6 weeks' gestation were invited to participate. The association between preeclampsia, preterm birth, gestational diabetes mellitus (GDM), small-for-gestational-age (SGA) or fetal-growth-restricted (FGR) neonates, and type of delivery and maternal age, BMI, and cigarette smoking was studied. Logistic mixed models were used to analyze the data. Results: A total of 1921 patients were included in the analysis. Women who were ≥40 years old had a significantly higher risk of having GDM (odds ratio (OR) 1.61, 95% confidence interval (CI) 1.08 to 2.36) and SGA neonates (OR 1.54, 95% CI 1.00 to 2.37). Women with a BMI < 18 had an increased rate of giving birth to SGA and FGR neonates (OR 3.28, 95% CI 1.51 to 7.05, and OR 3.73, 95% CI 1.54 to 8.37, respectively), whereas women with a BMI ≥ 35 had a higher risk of GDM (OR 3.10, 95% CI 1.95 to 4.89). Smoking increased the risk of having SGA and FGR neonates (OR 1.83, 95% CI 1.36 to 2.46, and OR 1.91, 95% CI 1.29 to 2.78). Conclusions: Advanced maternal age, low or high BMI, and smoking status are significant risk factors for pregnancy complications. Both clinicians and society should concentrate their efforts on addressing these factors to enhance reproductive health.

Performance of the first-trimester Fetal Medicine Foundation competing risks model for preeclampsia prediction: an external validation study in Brazil

BACKGROUND

The current version of the Fetal Medicine Foundation competing risks model for preeclampsia prediction has not been previously validated in Brazil.

OBJECTIVE

This study aimed (1) to validate the Fetal Medicine Foundation combined algorithm for the prediction of preterm preeclampsia in the Brazilian population and (2) to describe the accuracy and calibration of the Fetal Medicine Foundation algorithm when considering the prophylactic use of aspirin by clinical criteria.

STUDY DESIGN

This was a cohort study, including consecutive singleton pregnancies undergoing preeclampsia screening at 11 to 14 weeks of gestation, examining maternal characteristics, medical history, and biophysical markers between October 2010 and December 2018 in a university hospital in Brazil. Risks were calculated using the 2018 version of the algorithm available on the Fetal Medicine Foundation website, and cases were classified as low or high risk using a cutoff of 1/100 to evaluate predictive performance. Expected and observed cases with preeclampsia according to the Fetal Medicine Foundation-estimated risk range (≥1 in 10; 1 in 11 to 1 in 50; 1 in 51 to 1 in 100; 1 in 101 to 1 in 150; and <1 in 150) were compared. After identifying high-risk pregnant women who used aspirin, the treatment effect of 62% reduction in preterm preeclampsia identified in the Combined Multimarker Screening and Randomized Patient Treatment with Aspirin for Evidence-Based Preeclampsia Prevention trial was used to evaluate the predictive performance adjusted for the effect of aspirin. The number of potentially unpreventable cases in the group without aspirin use was estimated.

RESULTS

Among 2749 pregnancies, preterm preeclampsia occurred in 84 (3.1%). With a risk cutoff of 1/100, the screen-positive rate was 25.8%. The detection rate was 71.4%, with a false positive rate of 24.4%. The area under the curve was 0.818 (95% confidence interval, 0.773-0.863). In the risk range ≥1/10, there is an agreement between the number of expected cases and the number of observed cases, and in the other ranges, the predicted risk was lower than the observed rates. Accounting for the effect of aspirin resulted in an increase in detection rate and positive predictive values and a slight decrease in the false positive rate. With 27 cases of preterm preeclampsia in the high-risk group without aspirin use, we estimated that 16 of these cases of preterm preeclampsia would have been avoided if this group had received prophylaxis.

CONCLUSION

In a high-prevalence setting, the Fetal Medicine Foundation algorithm can identify women who are more likely to develop preterm preeclampsia. Not accounting for the effect of aspirin underestimates the screening performance.

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.

First-trimester prediction of preterm pre-eclampsia and prophylaxis by aspirin: Effect on spontaneous and iatrogenic preterm birth

OBJECTIVE

To report the predictive performance for preterm birth (PTB) of the Fetal Medicine Foundation (FMF) triple test and National Institute for health and Care Excellence (NICE) guidelines used to screen for pre-eclampsia and examine the impact of aspirin in the prevention of PTB.

DESIGN

Secondary analysis of data from the SPREE study and the ASPRE trial.

SETTING

Multicentre studies.

POPULATION

In SPREE, women with singleton pregnancies had screening for preterm pre-eclampsia at 11-13 weeks of gestation by the FMF method and NICE guidelines. There were 16 451 pregnancies that resulted in delivery at ≥24 weeks of gestation and these data were used to derive the predictive performance for PTB of the two methods of screening. The results from the ASPRE trial were used to examine the effect of aspirin in the prevention of PTB in the population from SPREE.

METHODS

Comparison of performance of FMF method and NICE guidelines for pre-eclampsia in the prediction of PTB and use of aspirin in prevention of PTB.

MAIN OUTCOME MEASURE

Spontaneous PTB (sPTB), iatrogenic PTB for pre-eclampsia (iPTB-PE) and iatrogenic PTB for reasons other than pre-eclampsia (iPTB-noPE).

RESULTS

Estimated incidence rates of sPTB, iPTB-PE and iPTB-noPE were 3.4%, 0.8% and 1.6%, respectively. The corresponding detection rates were 17%, 82% and 25% for the triple test and 12%, 39% and 19% for NICE guidelines, using the same overall screen positive rate of 10.2%. The estimated proportions prevented by aspirin were 14%, 65% and 0%, respectively.

CONCLUSION

Prediction of sPTB and iPTB-noPE by the triple test was poor and poorer by the NICE guidelines. Neither sPTB nor iPTB-noPE was reduced substantially by aspirin.

Validation of machine-learning model for first-trimester prediction of pre-eclampsia using cohort from PREVAL study

OBJECTIVE

Effective first-trimester screening for pre-eclampsia (PE) can be achieved using a competing-risks model that combines risk factors from the maternal history with multiples of the median (MoM) values of biomarkers. A new model using artificial intelligence through machine-learning methods has been shown to achieve similar screening performance without the need for conversion of raw data of biomarkers into MoM. This study aimed to investigate whether this model can be used across populations without specific adaptations.

METHODS

Previously, a machine-learning model derived with the use of a fully connected neural network for first-trimester prediction of early (< 34 weeks), preterm (< 37 weeks) and all PE was developed and tested in a cohort of pregnant women in the UK. The model was based on maternal risk factors and mean arterial blood pressure (MAP), uterine artery pulsatility index (UtA-PI), placental growth factor (PlGF) and pregnancy-associated plasma protein-A (PAPP-A). In this study, the model was applied to a dataset of 10 110 singleton pregnancies examined in Spain who participated in the first-trimester PE validation (PREVAL) study, in which first-trimester screening for PE was carried out using the Fetal Medicine Foundation (FMF) competing-risks model. The performance of screening was assessed by examining the area under the receiver-operating-characteristics curve (AUC) and detection rate (DR) at a 10% screen-positive rate (SPR). These indices were compared with those derived from the application of the FMF competing-risks model. The performance of screening was poor if no adjustment was made for the analyzer used to measure PlGF, which was different in the UK and Spain. Therefore, adjustment for the analyzer used was performed using simple linear regression.

RESULTS

The DRs at 10% SPR for early, preterm and all PE with the machine-learning model were 84.4% (95% CI, 67.2-94.7%), 77.8% (95% CI, 66.4-86.7%) and 55.7% (95% CI, 49.0-62.2%), respectively, with the corresponding AUCs of 0.920 (95% CI, 0.864-0.975), 0.913 (95% CI, 0.882-0.944) and 0.846 (95% CI, 0.820-0.872). This performance was achieved with the use of three of the biomarkers (MAP, UtA-PI and PlGF); inclusion of PAPP-A did not provide significant improvement in DR. The machine-learning model had similar performance to that achieved by the FMF competing-risks model (DR at 10% SPR, 82.7% (95% CI, 69.6-95.8%) for early PE, 72.7% (95% CI, 62.9-82.6%) for preterm PE and 55.1% (95% CI, 48.8-61.4%) for all PE) without requiring specific adaptations to the population.

CONCLUSIONS

A machine-learning model for first-trimester prediction of PE based on a neural network provides effective screening for PE that can be applied in different populations. However, before doing so, it is essential to make adjustments for the analyzer used for biochemical testing. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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