Cost-utility of a first-trimester screening strategy versus the standard of care for nulliparous women to prevent pre-term pre-eclampsia in Belgium

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 screening for pre-eclampsia and targeted aspirin prophylaxis: a cost-effectiveness cohort study

OBJECTIVE

Investigate cost-effectiveness of first trimester pre-eclampsia screening using the Fetal Medicine Foundation (FMF) algorithm and targeted aspirin prophylaxis in comparison with standard care.

DESIGN

Retrospective observational study.

SETTING

London tertiary hospital.

POPULATION

5957 pregnancies screened for pre-eclampsia using the National Institute for Health and Care Excellence (NICE) method.

METHODS

Differences in pregnancy outcomes between those who developed pre-eclampsia, term pre-eclampsia and preterm pre-eclampsia were compared by the Kruskal-Wallis and Chi-square tests. The FMF algorithm was applied retrospectively to the cohort. A decision analytic model was used to estimate costs and outcomes for pregnancies screened using NICE and those screened using the FMF algorithm. The decision point probabilities were calculated using the included cohort.

MAIN OUTCOME MEASURES

Incremental healthcare costs and QALY gained per pregnancy screened.

RESULTS

Of 5957 pregnancies, 12.8% and 15.9% were screen-positive for development of pre-eclampsia using the NICE and FMF methods, respectively. Of those who were screen-positive by NICE recommendations, aspirin was not prescribed in 25%. Across the three groups, namely, pregnancies without pre-eclampsia, term pre-eclampsia and preterm pre-eclampsia there was a statistically significant trend in rates of emergency caesarean (respectively 21%, 43% and 71.4%; P < 0.001), admission to neonatal intensive care unit (NICU) (5.9%, 9.4%, 41%; P < 0.001) and length of stay in NICU. The FMF algorithm was associated with seven fewer cases of preterm pre-eclampsia, cost saving of £9.06 and QALY gain of 0.00006/pregnancy screened.

CONCLUSIONS

Using a conservative approach, application of the FMF algorithm achieved clinical benefit and an economic cost saving.

Cost-effectiveness analysis of a first-trimester screening test for preterm preeclampsia in the Netherlands

OBJECTIVES

The risk of preterm preeclampsia (PT PE) can significantly be reduced by starting acetylsalicylic acid ≤ 16 weeks of gestational age. First trimester predictive models based on maternal risk factors to effectively start this therapy lacked sufficient power, but recent studies showed that these models can be improved by including test results of biochemical and/or -physical markers. To investigate whether testing a biochemical marker in the first trimester is cost-effective in the Netherlands, a cost-effectiveness analysis was performed in this study.

STUDY DESIGN

The outcome of this study was expressed as an incremental cost-effectiveness ratio (ICER) with as effect prevented PT PE cases. To evaluate the impact of each model parameter and to determine model uncertainties, both univariate and probabilistic sensitivity analyses were performed.

RESULTS

When compared to the baseline strategy, the test strategy is estimated to save almost 4 million euros per year on a national scale and at the same time this would prevent an additional 228 PT PE cases. The sensitivity analyses showed that the major drivers of the result are the costs to monitor a high-risk pregnancy and the specificity and that most of the model simulations were in the southeast quadrant: cost saving and more prevented complications.

CONCLUSIONS

This study showed that a first-trimester test strategy to screen for PT PE in the first trimester is potentially cost-effective in the Dutch healthcare setting. The fact that the specificity is a major driver of the ICER indicates the importance for a (new) screening model to correctly classify low-risk pregnancies.

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

BACKGROUND

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

OBJECTIVE

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

STUDY DESIGN

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

RESULTS

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

CONCLUSION

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

Cell-free DNA methylome analysis for early preeclampsia prediction

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

What helps define outcomes in persistent uninterpretable non-invasive prenatal testing: Maternal factors, fetal fraction or quality scores?

OBJECTIVES

To assess maternal characteristics and comorbidities in patients with persistent uninterpretable non-invasive prenatal testing (NIPT) and to evaluate the association with adverse pregnancy outcome in a general risk population.

METHODS

A retrospective cohort study (July 2017-December 2020) was conducted of patients with persistent uninterpretable NIPT samples. Maternal characteristics and pregnancy outcomes were compared with the general Belgian obstetric population.

RESULTS

Of the 148 patients with persistent uninterpretable NIPT, 37 cases were due to a low fetal fraction (LFF) and 111 due to a low quality score (LQS). Both groups (LFF, LQS) showed more obesity (60.6%, 42.4%), multiple pregnancies (18.9%, 4.5%) and more obstetrical complications. In the LQS group, a high rate of maternal auto-immune disorders (30.6%) was seen and hypertensive complications (17.6%), preterm birth (17.6%) and neonatal intensive care unit (NICU) admission (22%) were significantly increased. In the LFF group hypertensive complications (21.6%), gestational diabetes (20.6%), preterm birth (27%), SGA (25.6%), major congenital malformations (11.4%), c-section rate (51.4%) and NICU admission (34.9%) were significantly increased. Chromosomal abnormalities were not increased in both groups.

CONCLUSIONS

Patients with persistent uninterpretable NIPT have significantly more maternal obesity, comorbidities and adverse pregnancy outcome than the general population and should receive high-risk pregnancy care. Distinguishing between LFF and LQS optimizes counseling because maternal characteristics and pregnancy outcome differ between these groups.

Which first-trimester risk assessment method for preeclampsia is most suitable? A model-based impact study

BACKGROUND

Low-dose aspirin treatment reduces the risk of preeclampsia among high-risk pregnant women. Internationally, several first-trimester risk-calculation methods are applied.

OBJECTIVE

This study aimed to assess the costs and benefits of different first-trimester preeclampsia risk estimation algorithms: EXPECT (an algorithmic prediction model based on maternal characteristics), National Institute for Health and Care Excellence (a checklist of risk factors), and the Fetal Medicine Foundation (a prediction model using additional uterine artery Doppler measurement and laboratory testing) models, coupled with low-dose aspirin treatment, in comparison with no risk assessment.

STUDY DESIGN

We constructed a decision analytical model estimating the number of cases of preeclampsia with each strategy and the costs of risk assessment for preeclampsia and early aspirin treatment, expressed in euros (€) in a hypothetical population of 100,000 women. We performed 1-way sensitivity analyses to assess the impact of adherence rates on model outcomes.

RESULTS

Application of the EXPECT, National Institute for Health and Care Excellence, and Fetal Medicine Foundation models results in respectively 1.98%, 2.55%, and 1.90% of the women developing preeclampsia, as opposed to 3.00% of women in the case of no risk assessment. Overall, the net financial benefits of the EXPECT, National Institute for Health and Care Excellence, and Fetal Medicine Foundation models relative to no risk assessment are €144, €43, and €38 per patient, respectively. The respective percentages of women receiving aspirin treatment are 18.6%, 10.2%, and 6.0% for the 3 risk assessment methods.

CONCLUSION

The EXPECT and Fetal Medicine Foundation model are comparable with regard to numbers of prevented preeclampsia cases, and both are superior to the National Institute for Health and Care Excellence model and to no risk assessment. EXPECT is less resource-demanding and results in the highest cost savings, but also requires the highest number of women to be treated with aspirin. When deciding which strategy is preferable, cost savings and easier use have to be weighed against the degree of overtreatment, although low-dose aspirin has no clear disadvantages during pregnancy.

Low-dose aspirin to prevent preeclampsia and growth restriction in nulliparous women identified by uterine artery Doppler as at high risk of preeclampsia: A double blinded randomized placebo-controlled trial

INTRODUCTION

This trial evaluates whether daily low-dose aspirin initiated before 16 weeks of gestation can reduce preeclampsia and fetal growth restriction in nulliparous women identified by first-trimester uterine artery Dopplers as at high risk of preeclampsia.

METHODS

This randomized, blinded, placebo-controlled, parallel-group trial took place in 17 French obstetric departments providing antenatal care. Pregnant nulliparous women aged ≥ 18 years with a singleton pregnancy at a gestational age < 16 weeks of gestation with a lowest pulsatility index ≥ 1.7 or a bilateral protodiastolic notching for both uterine arteries on an ultrasound performed between 11+0 and 13+6 weeks by a certified sonographer were randomized at a 1:1 ratio to 160 mg of low-dose aspirin or to placebo to be taken daily from inclusion to their 34th week of gestation. The main outcome was preeclampsia or a birthweight ≤ 5th percentile. Other outcomes included preeclampsia, severe preeclampsia, preterm preeclampsia, preterm delivery before 34 weeks, mode of delivery, type of anesthesia, birthweight ≤ 5th percentile and perinatal death.

RESULTS

The trial was interrupted due to recruiting difficulties. Between June 2012 and June 2016, 1104 women were randomized, two withdrew consent, and two had terminations of pregnancies. Preeclampsia or a birthweight ≤ 5th percentile occurred in 88 (16.0%) women in the low-dose aspirin group and in 79 (14.4%) in the placebo group (proportion difference 1.6 [-2.6; 5.9] p = 0.45). The two groups did not differ significantly for the secondary outcomes.

CONCLUSION

Low-dose aspirin was not associated with a lower rate of either preeclampsia or birthweight ≤ 5th percentile in women identified by their first-trimester uterine artery Doppler as at high risk of preeclampsia.

TRIAL REGISTRATION

(NCT0172946).

Reviewing Accuracy of First Trimester Screening for Preeclampsia Using Maternal Factors and Biomarkers

Preeclampsia is a common and important complication of pregnancy, one with potentially significant morbidity and even mortality to both mother and baby. Identifying those at high risk of developing the condition is helpful as there is evidence that the incidence of preeclampsia can be reduced with low dose aspirin taken in pregnancy. Accurately predicting the risk of preeclampsia allows for more targeted aspirin prophylaxis and a greater opportunity for early detection of maternal and/or fetal complications associated with impaired placentation through a schedule of enhanced antenatal surveillance. Traditional preeclampsia prediction models use maternal characteristics and risk factors and have been shown to be of low predictive value. Multiparametric screening tests combine patient characteristics with serum biomarkers and ultrasound Doppler indices and have been shown to be more effective at detecting those at high risk of preeclampsia - more specifically, early-onset preeclampsia (onset of preeclampsia <34 weeks' gestation). Multiparametric screening has now been validated in different populations. The true cost effectiveness of a multiparametric screening model for preeclampsia screening is not yet fully known and will vary depending on the clinical setting. Despite the growing body of evidence for its improved detection rates, first trimester preeclampsia screening using multiparametric models is not widely implemented and is not part of the recommendations for antenatal screening from most international bodies. The International Federation of Gynecology and Obstetrics has advised universal preeclampsia screening using maternal risk factors and biomarkers and has strongly encouraged its promotion worldwide. Various barriers to implementation must be considered such as the immediate cost of equipment and training, the need for audit and quality control, and the expected benefit to the population. Low to middle income settings may require a pragmatic approach to the implementation of multiparametric screening given limited resources.

Screening for preeclampsia in twin pregnancies

Twin pregnancies are an important risk factor for preeclampsia, a hypertensive disorder of pregnancy that is associated with a significant risk of maternal and perinatal morbidity. Given the burden of preeclampsia, the identification of women at high risk in early pregnancy is essential to allow for preventive strategies and close monitoring. In singleton pregnancies, the risk factors for preeclampsia are well established, and a combined first-trimester prediction model has been shown to adequately predict preterm disease. Furthermore, intervention with low-dose aspirin at 150 mg/day in those identified as high-risk reduces the rate of preterm preeclampsia by 62%. In contrast, risk factors for preeclampsia in twin pregnancies are less established, the proposed screening models have shown poor performance with high false-positive rates, and the benefit of aspirin for the prevention of preeclampsia is not clearly demonstrated. In this review, we examine the literature assessing prediction and prevention of preeclampsia in twin pregnancies.

Enhancing the value of the sFlt-1/PlGF ratio for the prediction of preeclampsia: Cost analysis from the Belgian healthcare payers' perspective

OBJECTIVE

To evaluate the economic impact of introducing the soluble fms-like tyrosine kinase (sFlt-1) to placental growth factor (PlGF) ratio test into clinical practice in Belgium for the prediction of preeclampsia (PE).

STUDY DESIGN

We developed a one-year time-horizon decision tree model to evaluate the short-term costs associated with the introduction of the sFlt-1/PlGF test for guiding the management of women with suspected PE from the Belgian public healthcare payers' perspective. The model estimated the costs associated with the diagnosis and management of PE in pregnant women managed in either a test scenario, in which the sFlt-1/PlGF test is used in addition to current clinical practice, or a no test scenario, in which clinical decisions are based on current practice alone. Test characteristics were derived from PROGNOSIS, a non-interventional study in women presenting with clinical suspicion of PE. Unit costs were obtained from Belgian-specific sources. The main model outcome was the total cost per patient.

RESULTS

Introduction of the sFlt-1/PlGF ratio test is expected to result in a cost saving of €712 per patient compared with the no test scenario. These savings are generated mainly due to a reduction in unnecessary hospitalizations.

CONCLUSIONS

The sFlt-1/PlGF test is projected to result in substantial cost savings for the Belgian public healthcare payers through reduction of unnecessary hospitalization of women with clinical suspicion of PE that ultimately do not develop the condition. The test also has the potential to ensure that women at high risk of developing PE are identified and appropriately managed.