External Validation and Clinical Usefulness of First Trimester Prediction Models for the Risk of Preeclampsia: A Prospective Cohort Study

First-Trimester Prediction Models Based on Maternal Characteristics for Adverse Pregnancy Outcomes: A Systematic Review and Meta-Analysis

BACKGROUND

Early risk stratification can facilitate timely interventions for adverse pregnancy outcomes, including preeclampsia (PE), small-for-gestational-age neonates (SGA), spontaneous preterm birth (sPTB) and gestational diabetes mellitus (GDM).

OBJECTIVES

To perform a systematic review and meta-analysis of first-trimester prediction models for adverse pregnancy outcomes.

SEARCH STRATEGY

The PubMed database was searched until 6 June 2024.

SELECTION CRITERIA

First-trimester prediction models based on maternal characteristics were included. Articles reporting on prediction models that comprised biochemical or ultrasound markers were excluded.

DATA COLLECTION AND ANALYSIS

Two authors identified articles, extracted data and assessed risk of bias and applicability using PROBAST.

MAIN RESULTS

A total of 77 articles were included, comprising 30 developed models for PE, 15 for SGA, 11 for sPTB and 35 for GDM. Discriminatory performance in terms of median area under the curve (AUC) of these models was 0.75 [IQR 0.69-0.78] for PE models, 0.62 [0.60-0.71] for SGA models of nulliparous women, 0.74 [0.72-0.74] for SGA models of multiparous women, 0.65 [0.61-0.67] for sPTB models of nulliparous women, 0.71 [0.68-0.74] for sPTB models of multiparous women and 0.71 [0.67-0.76] for GDM models. Internal validation was performed in 40/91 (43.9%) of the models. Model calibration was reported in 21/91 (23.1%) models. External validation was performed a total of 96 times in 45/91 (49.5%) of the models. High risk of bias was observed in 94.5% of the developed models and in 58.3% of the external validations.

CONCLUSIONS

Multiple first-trimester prediction models are available, but almost all suffer from high risk of bias, and internal and external validations were often not performed. Hence, methodological quality improvement and assessment of the clinical utility are needed.

Preconception lipid profile and the subsequent risk of pregnancy disorders characterized by uteroplacental dysfunction in a multi-ethnic population: the linked HELIUS-PERINED study

BACKGROUND

Unfavorable lipid profile is associated with pregnancy disorders characterized by uteroplacental dysfunction, including hypertensive disorders of pregnancy, preterm birth and fetal growth restriction. None of current tools used to predict the risk of pregnancy complications include lipid levels.

OBJECTIVE(S)

In this study, we examined the association of preconception lipid profile with pregnancy disorders characterized by uteroplacental dysfunction in a multi-ethnic population, aiming to improve the identification of women at high risk for uteroplacental dysfunction using current prediction models.

STUDY DESIGN

We conducted a linkage study combining lipid profile collected in the multi-ethnic HELIUS study (Amsterdam, 2011-2015), linked with national perinatal registry data on pregnancy complications after inclusion until 2019. We included 1177 women of Dutch, South-Asian Surinamese, African Surinamese, Ghanaian, Turkish, and Moroccan origin. Associations were studied using Poisson regression. The discriminative ability was assessed for different pregnancy complications of significantly associated lipid parameters when added to commonly used prediction tools for preeclampsia.

RESULTS

Preconception triglyceride level was associated with prevalence of hypertensive disorders of pregnancy (e^triglyceride level (mmol/L) adjusted prevalence ratio 1.07, 95% CI 1.00 to 1.14). Age-adjusted prevalence of hypertensive disorders of pregnancy was also higher among women with high LDL-C level, high TC/HDL-C or ≥4 adverse lipid parameters, but most of these findings were not statistically significant when adjusted for demographic, lifestyle and medical characteristics. Addition of triglyceride level and other lipid parameters to the NICE guideline criteria and to the EXPECT prediction tool did not improve discriminative ability for hypertensive disorders of pregnancy, preterm birth or fetal growth restriction.

CONCLUSION(S)

Lipid profile did not aid in the identification of women at high risk for pregnancy disorders characterized by uteroplacental dysfunction. Further studies are needed to improve preconception prediction models for hypertensive disorders of pregnancy and other pregnancy disorders characterized by uteroplacental dysfunction using biomarkers or other easily available measurements.

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.

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.

Analysis of Predictive Information From Biomarkers Added to Clinical Models of Preeclampsia: Consideration of PAPP-A2, Activin A, and sFlt-1:PlGF Ratio

BACKGROUND

Preeclampsia remains a major cause of maternal and fetal adverse outcomes in pregnancy; however, accurate and universally acceptable predictive tools remain elusive. We investigated whether a panel of biomarkers could improve risk prediction for preeclampsia when measured at various pregnancy time points.

METHODS

In this prospective cohort study, 192 women with first-trimester high-risk singleton pregnancies were consecutively recruited from tertiary obstetrics clinics in Montréal, Canada. Clinical information (height, pre-pregnancy weight, personal and family medical history, medication use) was collected at baseline. Blood pressure was measured and blood samples collected at each trimester to quantify soluble Fms-like tyrosine kinase 1 (sFlt-1), placental growth factor (PlGF), pregnancy-associated plasma protein A2 (PAPP-A2), PAPP-A, activin A, inhibin A, follistatin, and glycosylated fibronectin. A random-effects hierarchic logistic regression model was used to relate change in biomarker levels to incidence of preeclampsia.

RESULTS

When added to a clinical model composed of maternal age, pre-pregnancy body mass index, race, and mean arterial pressure, a positive third-trimester result for both PAPP-A2 and activin A had a better positive predictive value than the sFlt-1:PlGF ratio added to the clinical model (91.67% [95% confidence interval (CI) 78.57%-100%] vs 66.67% [57.14%-100%]), while maintaining a comparable high negative predictive value (97.69% [95% CI 95.34%-100%] vs 96.00% [92.19%-99.21%]).

CONCLUSIONS

Whereas the third-trimester sFlt-1:PlGF ratio can predict short-term absence of preeclampsia, PAPP-A2 and activin A had both high positive and negative predictive values and therefore could serve as biomarkers to predict the occurrence (and absence) of preeclampsia; these findings will be validated in future studies.

Pre-pregnancy body mass index combined with peripheral blood PLGF, DCN, LDH, and UA in a risk prediction model for pre-eclampsia

Objective

This study analyzes the levels of peripheral blood placental growth factor (PLGF), body mass index (BMI), decorin (DCN), lactate dehydrogenase (LDH), uric acid (UA), and clinical indicators of patients with preeclampsia (PE), and establishes a predictive risk model of PE, which can provide a reference for early and effective prediction of PE.

Methods

81 cases of pregnant women with PE who had regular prenatal checkups and delivered in Jinshan Branch of Shanghai Sixth People's Hospital from June 2020 to December 2022 were analyzed, and 92 pregnant women with normal pregnancies who had their antenatal checkups and delivered at the hospital during the same period were selected as the control group. Clinical data and peripheral blood levels of PLGF, DCN, LDH, and UA were recorded, and the two groups were subjected to univariate screening and multifactorial logistic regression analysis. Based on the screening results, the diagnostic efficacy of PE was evaluated using the receiver operating characteristic (ROC) curve. Risk prediction nomogram model was constructed using R language. The Bootstrap method (self-sampling method) was used to validate and produce calibration plots; the decision curve analysis (DCA) was used to assess the clinical benefit rate of the model.

Results

There were statistically significant differences in age, pre-pregnancy BMI, gestational weight gain, history of PE or family history, family history of hypertension, gestational diabetes mellitus, and history of renal disease between the two groups (P < 0.05). The results of multifactorial binary logistic stepwise regression revealed that peripheral blood levels of PLGF, DCN, LDH, UA, and pre-pregnancy BMI were independent influences on the occurrence of PE (P < 0.05). The area under the curve of PLGF, DCN, LDH, UA levels and pre-pregnancy BMI in the detection of PE was 0.952, with a sensitivity of 0.901 and a specificity of 0.913, which is better than a single clinical diagnostic indicator. The results of multifactor analysis were constructed as a nomogram model, and the mean absolute error of the calibration curve of the modeling set was 0.023, suggesting that the predictive probability of the model was generally compatible with the actual value. DCA showed the predictive model had a high net benefit in the range of 5% to 85%, suggesting that the model has clinical utility value.

Conclusion

The occurrence of PE is related to the peripheral blood levels of PLGF, DCN, LDH, UA and pre-pregnancy BMI, and the combination of these indexes has a better clinical diagnostic value than a single index. The nomogram model constructed by using the above indicators can be used for the prediction of PE and has high predictive efficacy.

Arterial stiffness for the early prediction of pre-eclampsia compared with blood pressure, uterine artery Doppler and angiogenic biomarkers: a prospective cohort study

OBJECTIVE

Our aim was to evaluate the ability of arterial stiffness parameters to predict pre-eclampsia early compared with peripheral blood pressure, uterine artery Doppler and established angiogenic biomarkers.

DESIGN

Prospective cohort study.

SETTING

Tertiary care antenatal clinics in Montreal, Canada.

POPULATION

Women with singleton high-risk pregnancies.

METHODS

In the first trimester, arterial stiffness was measured by applanation tonometry, along with peripheral blood pressure and serum/plasma angiogenic biomarkers; uterine artery Doppler was measured in the second trimester. The predictive ability of different metrics was assessed through multivariate logistic regression.

MAIN OUTCOME MEASURES

Arterial stiffness (carotid-femoral pulse wave velocity, carotid-radial pulse wave velocity) and wave reflection (augmentation index, reflected wave start time), peripheral blood pressure, ultrasound indices of velocimetry and circulating angiogenic biomarker concentrations.

RESULTS

In this prospective study, among 191 high-risk pregnant women, 14 (7.3%) developed pre-eclampsia. A first-trimester 1 m/s increase in carotid-femoral pulse wave velocity was associated with 64% increased odds (P < 0.05), and a 1-millisecond increase in time to wave reflection with 11% decreased odds for pre-eclampsia (P < 0.01). The area under the curve of arterial stiffness, blood pressure, ultrasound indices and angiogenic biomarkers was 0.83 (95% confidence interval [CI] 0.74-0.92), 0.71 (95% CI 0.57-0.86), 0.58 (95% CI 0.39-0.77), and 0.64 (95% CI 0.44-0.83), respectively. With a 5% false-positive rate, blood pressure had a sensitivity of 14% for pre-eclampsia and arterial stiffness a sensitivity of 36%.

CONCLUSIONS

Arterial stiffness predicted pre-eclampsia earlier and with greater ability than blood pressure, ultrasound indices or angiogenic biomarkers.

Target product profiles for novel medicines to prevent and treat preeclampsia: An expert consensus

BACKGROUND

Preeclampsia and eclampsia are a leading cause of global maternal and newborn mortality. Currently, there are few effective medicines that can prevent or treat preeclampsia. Target Product Profiles (TPPs) are important tools for driving new product development by specifying upfront the characteristics that new products should take. Considering the lack of investment and innovation around new medicines for obstetric conditions, we aimed to develop two new TPPs for medicines to prevent and treat preeclampsia.

METHODS AND FINDINGS

We used a multi-methods approach comprised of a literature review, stakeholder interviews, online survey, and public consultation. Following an initial literature review, diverse stakeholders (clinical practice, research, academia, international organizations, funders, consumer representatives) were invited for in-depth interviews and an online international survey, as well as public consultation on draft TPPs. The level of stakeholder agreement with TPPs was assessed, and findings from interviews were synthesised to inform the final TPPs. We performed 23 stakeholder interviews and received 46 survey responses. A high level of agreement was observed in survey results, with 89% of TPP variables reaching consensus (75% agree or strongly agree). Points of discussion were raised around the target population for preeclampsia prevention and treatment, as well as the acceptability of cold-chain storage and routes of administration.

CONCLUSION

There is consensus within the maternal health research community on the parameters that new medicines for preeclampsia prevention and treatment must achieve to meet real-world health needs. These TPPs provide necessary guidance to spur interest, innovation and investment in the development of new medicines to prevent and treat preeclampsia.

Associations Between Late-Onset Preeclampsia and the Use of Calcium-Based Antacids and Proton Pump Inhibitors During Pregnancy: A Prospective Cohort Study

Purpose

Preeclampsia is a leading cause of maternal morbidity and mortality. Calcium-based antacids and proton pump inhibitors (PPIs) are commonly used during pregnancy to treat symptoms of gastroesophageal reflux disease. Both have been hypothesized to reduce the risk of preeclampsia. We determined associations of calcium-based antacid and PPI use during pregnancy with late-onset preeclampsia (≥34 weeks of gestation), taking into account dosage and timing of use.

Patients and Methods

We included 9058 pregnant women participating in the PRIDE Study (2012–2019) or The Dutch Pregnancy Drug Register (2014–2019), two prospective cohorts in The Netherlands. Data were collected through web-based questionnaires and obstetric records. We estimated risk ratios (RRs) for late-onset preeclampsia for any use and trajectories of calcium-based antacid and PPI use before gestational day 238, and hazard ratios (HRs) for time-varying exposures after gestational day 237.

Results

Late-onset preeclampsia was diagnosed in 2.6% of pregnancies. Any use of calcium-based antacids (RR 1.2 [95% CI 0.9–1.6]) or PPIs (RR 1.4 [95% CI 0.8–2.4]) before gestational day 238 was not associated with late-onset preeclampsia. Use of low-dose calcium-based antacids in gestational weeks 0–16 (<1 g/day; RR 1.8 [95% CI 1.1–2.9]) and any use of PPIs in gestational weeks 17–33 (RR 1.6 [95% CI 1.0–2.8]) seemed to increase risks of late-onset preeclampsia. We did not observe associations between late-onset preeclampsia and use of calcium-based antacids (HR 1.0 [95% CI 0.6–1.5]) and PPIs (HR 1.4 [95% CI 0.7–2.9]) after gestational day 237.

Conclusion

In this prospective cohort study, use of calcium-based antacids and PPIs during pregnancy was not found to reduce the risk of late-onset preeclampsia.

Assessment of first-trimester utero-placental vascular morphology by 3D power Doppler ultrasound image analysis using a skeletonization algorithm: the Rotterdam Periconception Cohort

STUDY QUESTION

Can three-dimensional (3D) Power Doppler (PD) ultrasound and a skeletonization algorithm be used to assess first-trimester development of the utero-placental vascular morphology?

SUMMARY ANSWER

The application of 3D PD ultrasonography and a skeletonization algorithm facilitates morphologic assessment of utero-placental vascular development in the first trimester and reveals less advanced vascular morphologic development in pregnancies with placenta-related complications than in pregnancies without placenta-related complications.

WHAT IS KNOWN ALREADY

Suboptimal development of the utero-placental vasculature is one of the main contributors to the periconceptional origin of placenta-related complications. The nature and attribution of aberrant vascular structure and branching patterns remain unclear, as validated markers monitoring first-trimester utero-placental vascular morphologic development are lacking.

STUDY DESIGN, SIZE, DURATION

In this prospective observational cohort, 214 ongoing pregnancies were included before 10 weeks gestational age (GA) at a tertiary hospital between January 2017 and July 2018, as a subcohort of the ongoing Rotterdam Periconception Cohort study.

PARTICIPANTS/MATERIALS, SETTING, METHODS

By combining 3D PD ultrasonography and virtual reality, utero-placental vascular volume (uPVV) measurements were obtained at 7, 9 and 11 weeks GA. A skeletonization algorithm was applied to the uPVV measurements to generate the utero-placental vascular skeleton (uPVS), a network-like structure containing morphologic characteristics of the vasculature. Quantification of vascular morphology was performed by assigning a morphologic characteristic to each voxel in the uPVS (end-, vessel-, bifurcation- or crossing-point) and calculating total vascular network length. A Mann–Whitney U test was performed to investigate differences in morphologic development of the first-trimester utero-placental vasculature between pregnancies with and without placenta-related complications. Linear mixed models were used to estimate trajectories of the morphologic characteristics in the first trimester.

MAIN RESULTS AND THE ROLE OF CHANCE

All morphologic characteristics of the utero-placental vasculature increased significantly in the first trimester (P < 0.005). In pregnancies with placenta-related complications (n = 54), utero-placental vascular branching was significantly less advanced at 9 weeks GA (vessel points P = 0.040, bifurcation points P = 0.050, crossing points P = 0.020, total network length P = 0.023). Morphologic growth trajectories remained similar after adjustment for parity, conception mode, foetal sex and occurrence of placenta-related complications.

LIMITATIONS, REASONS FOR CAUTION

The tertiary setting of this prospective observational study provides high internal, but possibly limited external, validity. Extrapolation of the study’s findings should therefore be addressed with caution.

WIDER IMPLICATIONS OF THE FINDINGS

The uPVS enables assessment of morphologic development of the first-trimester utero-placental vasculature. Further investigation of this innovative methodology needs to determine its added value for the assessment of (patho-) physiological utero-placental vascular development.

STUDY FUNDING/COMPETING INTEREST(S)

This research was funded by the Department of Obstetrics and Gynecology of the Erasmus MC, University Medical Centre, Rotterdam, The Netherlands. There are no conflicts of interest.

TRIAL REGISTRATION NUMBER

Registered at the Dutch Trial Register (NTR6854).

The Tommy's Clinical Decision Tool, a device for reducing the clinical impact of placental dysfunction and preterm birth: protocol for a mixed-methods early implementation evaluation study

Background

Disparities in stillbirth and preterm birth persist even after correction for ethnicity and social deprivation, demonstrating that there is wide geographical variation in the quality of care. To address this inequity, Tommy’s National Centre for Maternity Improvement developed the Tommy’s Clinical Decision Tool, which aims to support the provision of “the right care at the right time”, personalising risk assessment and care according to best evidence. This web-based clinical decision tool assesses the risk of preterm birth and placental dysfunction more accurately than current methods, and recommends best evidenced-based care pathways in a format accessible to both women and healthcare professionals. It also provides links to reliable sources of pregnancy information for women. The aim of this study is to evaluate implementation of Tommy’s Clinical Decision Tool in four early-adopter UK maternity services, to inform wider scale-up.

Methods

The Tommy’s Clinical Decision Tool has been developed involving maternity service users and healthcare professionals in partnership. This mixed-methods study will evaluate: maternity service user and provider acceptability and experience; barriers and facilitators to implementation; reach (whether particular groups are excluded and why), fidelity (degree to which the intervention is delivered as intended), and unintended consequences. Data will be gathered over 25 months through interviews, focus groups, questionnaires and through the Tommy’s Clinical Decision Tool itself. The NASSS framework (Non-adoption or Abandonment of technology by individuals and difficulties achieving Scale-up, Spread and Sustainability) will inform data analysis.

Discussion

This paper describes the intervention, Tommy’s Clinical Decision Tool, according to TiDIER guidelines, and the protocol for the early adopter implementation evaluation study. Findings will inform future scale up.

Trial registration

This study was prospectively registered on the ISRCTN registry no. 13498237, on 31^st January 2022.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12884-022-04867-w.

First trimester preeclampsia screening and prediction

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

Validation and development of models using clinical, biochemical and ultrasound markers for predicting pre-eclampsia: an individual participant data meta-analysis

BACKGROUND

Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk is needed to plan management.

OBJECTIVES

To assess the performance of existing pre-eclampsia prediction models and to develop and validate models for pre-eclampsia using individual participant data meta-analysis. We also estimated the prognostic value of individual markers.

DESIGN

This was an individual participant data meta-analysis of cohort studies.

SETTING

Source data from secondary and tertiary care.

PREDICTORS

We identified predictors from systematic reviews, and prioritised for importance in an international survey.

PRIMARY OUTCOMES

Early-onset (delivery at < 34 weeks' gestation), late-onset (delivery at ≥ 34 weeks' gestation) and any-onset pre-eclampsia.

ANALYSIS

We externally validated existing prediction models in UK cohorts and reported their performance in terms of discrimination and calibration. We developed and validated 12 new models based on clinical characteristics, clinical characteristics and biochemical markers, and clinical characteristics and ultrasound markers in the first and second trimesters. We summarised the data set-specific performance of each model using a random-effects meta-analysis. Discrimination was considered promising for C-statistics of ≥ 0.7, and calibration was considered good if the slope was near 1 and calibration-in-the-large was near 0. Heterogeneity was quantified using I ² and τ². A decision curve analysis was undertaken to determine the clinical utility (net benefit) of the models. We reported the unadjusted prognostic value of individual predictors for pre-eclampsia as odds ratios with 95% confidence and prediction intervals.

RESULTS

The International Prediction of Pregnancy Complications network comprised 78 studies (3,570,993 singleton pregnancies) identified from systematic reviews of tests to predict pre-eclampsia. Twenty-four of the 131 published prediction models could be validated in 11 UK cohorts. Summary C-statistics were between 0.6 and 0.7 for most models, and calibration was generally poor owing to large between-study heterogeneity, suggesting model overfitting. The clinical utility of the models varied between showing net harm to showing minimal or no net benefit. The average discrimination for IPPIC models ranged between 0.68 and 0.83. This was highest for the second-trimester clinical characteristics and biochemical markers model to predict early-onset pre-eclampsia, and lowest for the first-trimester clinical characteristics models to predict any pre-eclampsia. Calibration performance was heterogeneous across studies. Net benefit was observed for International Prediction of Pregnancy Complications first and second-trimester clinical characteristics and clinical characteristics and biochemical markers models predicting any pre-eclampsia, when validated in singleton nulliparous women managed in the UK NHS. History of hypertension, parity, smoking, mode of conception, placental growth factor and uterine artery pulsatility index had the strongest unadjusted associations with pre-eclampsia.

LIMITATIONS

Variations in study population characteristics, type of predictors reported, too few events in some validation cohorts and the type of measurements contributed to heterogeneity in performance of the International Prediction of Pregnancy Complications models. Some published models were not validated because model predictors were unavailable in the individual participant data.

CONCLUSION

For models that could be validated, predictive performance was generally poor across data sets. Although the International Prediction of Pregnancy Complications models show good predictive performance on average, and in the singleton nulliparous population, heterogeneity in calibration performance is likely across settings.

FUTURE WORK

Recalibration of model parameters within populations may improve calibration performance. Additional strong predictors need to be identified to improve model performance and consistency. Validation, including examination of calibration heterogeneity, is required for the models we could not validate.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42015029349.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 24, No. 72. See the NIHR Journals Library website for further project information.

Prediction of preeclampsia risk in first time pregnant women: Metabolite biomarkers for a clinical test

Preeclampsia remains a leading cause of maternal and perinatal morbidity and mortality. Accurate prediction of preeclampsia risk would enable more effective, risk-based prenatal care pathways. Current risk assessment algorithms depend on clinical risk factors largely unavailable for first-time pregnant women. Delivering accurate preeclampsia risk assessment to this cohort of women, therefore requires for novel biomarkers. Here, we evaluated the relevance of metabolite biomarker candidates for their selection into a prototype rapid, quantitative Liquid Chromatography-tandem Mass Spectrometry (LC-MS/MS) based clinical screening assay. First, a library of targeted LC-MS/MS assays for metabolite biomarker candidates was developed, using a medium-throughput translational metabolomics workflow, to verify biomarker potential in the Screening-for-Pregnancy-Endpoints (SCOPE, European branch) study. A variable pre-selection step was followed by the development of multivariable prediction models for pre-defined clinical use cases, i.e., prediction of preterm preeclampsia risk and of any preeclampsia risk. Within a large set of metabolite biomarker candidates, we confirmed the potential of dilinoleoyl-glycerol and heptadecanoyl-2-hydroxy-sn-glycero-3-phosphocholine to effectively complement Placental Growth Factor, an established preeclampsia biomarker, for the prediction of preeclampsia risk in first-time pregnancies without overt risk factors. These metabolites will be considered for integration in a prototype rapid, quantitative LC-MS/MS assay, and subsequent validation in an independent cohort.

External validation of prognostic models predicting pre-eclampsia: individual participant data meta-analysis

BACKGROUND

Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk during pregnancy is required to plan management. Although there are many published prediction models for pre-eclampsia, few have been validated in external data. Our objective was to externally validate published prediction models for pre-eclampsia using individual participant data (IPD) from UK studies, to evaluate whether any of the models can accurately predict the condition when used within the UK healthcare setting.

METHODS

IPD from 11 UK cohort studies (217,415 pregnant women) within the International Prediction of Pregnancy Complications (IPPIC) pre-eclampsia network contributed to external validation of published prediction models, identified by systematic review. Cohorts that measured all predictor variables in at least one of the identified models and reported pre-eclampsia as an outcome were included for validation. We reported the model predictive performance as discrimination (C-statistic), calibration (calibration plots, calibration slope, calibration-in-the-large), and net benefit. Performance measures were estimated separately in each available study and then, where possible, combined across studies in a random-effects meta-analysis.

RESULTS

Of 131 published models, 67 provided the full model equation and 24 could be validated in 11 UK cohorts. Most of the models showed modest discrimination with summary C-statistics between 0.6 and 0.7. The calibration of the predicted compared to observed risk was generally poor for most models with observed calibration slopes less than 1, indicating that predictions were generally too extreme, although confidence intervals were wide. There was large between-study heterogeneity in each model's calibration-in-the-large, suggesting poor calibration of the predicted overall risk across populations. In a subset of models, the net benefit of using the models to inform clinical decisions appeared small and limited to probability thresholds between 5 and 7%.

CONCLUSIONS

The evaluated models had modest predictive performance, with key limitations such as poor calibration (likely due to overfitting in the original development datasets), substantial heterogeneity, and small net benefit across settings. The evidence to support the use of these prediction models for pre-eclampsia in clinical decision-making is limited. Any models that we could not validate should be examined in terms of their predictive performance, net benefit, and heterogeneity across multiple UK settings before consideration for use in practice.

TRIAL REGISTRATION

PROSPERO ID: CRD42015029349 .

Impact on perinatal health and cost-effectiveness of risk-based care in obstetrics: a before-after study

BACKGROUND

Obstetric health care relies on an adequate antepartum risk selection. Most guidelines used for risk stratification, however, do not assess absolute risks. In 2017, a prediction tool was implemented in a Dutch region. This tool combines first trimester prediction models with obstetric care paths tailored to the individual risk profile, enabling risk-based care.

OBJECTIVE

To assess impact and cost-effectiveness of risk-based care compared to care-as-usual in a general population.

METHODS

A before-after study was conducted using 2 multicenter prospective cohorts. The first cohort (2013-2015) received care-as-usual; the second cohort (2017-2018) received risk-based care. Health outcomes were (1) a composite of adverse perinatal outcomes and (2) maternal quality-adjusted life-years. Costs were estimated using a health care perspective from conception to 6 weeks after the due date. Mean costs per woman, cost differences between the 2 groups, and incremental cost effectiveness ratios were calculated. Sensitivity analyses were performed to evaluate the robustness of the findings.

RESULTS

In total 3425 women were included. In nulliparous women there was a significant reduction of perinatal adverse outcomes among the risk-based care group (adjusted odds ratio, 0.56; 95% confidence interval, 0.32-0.94), but not in multiparous women. Mean costs per pregnant woman were significantly lower for risk-based care (mean difference, -€2766; 95% confidence interval, -€3700 to -€1825). No differences in maternal quality of life, adjusted for baseline health, were observed.

CONCLUSION

In the Netherlands, risk-based care in nulliparous women was associated with improved perinatal outcomes as compared to care-as-usual. Furthermore, risk-based care was cost-effective compared to care-as-usual and resulted in lower health care costs.

Low-dose-aspirin usage among women with an increased preeclampsia risk: A prospective cohort study

INTRODUCTION

Low-dose aspirin (LDA) prophylaxis has been shown to reduce women's preeclampsia risk. Evidence regarding LDA adherence rates of pregnant women is based almost exclusively on clinical trials, giving a potentially biased picture. Moreover, these studies do not report on determinants of adherence. Since 2017, obstetric healthcare professionals in a Dutch region have assessed women's preeclampsia risk by means of a prediction tool and counseled those with an above-population average risk on LDA as a prophylactic measure.

MATERIAL AND METHODS

From 2017 to 2018, 865 women were recruited in multiple centers and prospectively followed using web-based surveys (Expect Study II). Rates and determinants of LDA usage among women with an increased preeclampsia risk in daily practice were assessed. Results were compared with findings in a similar cohort from a care-as-usual setting lacking risk-based counseling (Expect Study I, n = 2614). Netherlands Trial Register NTR4143.

RESULTS

In total, 306 women had a predicted increased preeclampsia risk. LDA usage was higher for women receiving risk-based care than care-as-usual (29.4% vs 1.5%, odds ratio 19.1, 95% confidence interval 11.2-32.5). Daily LDA usage was positively correlated with both predicted risk and women's concerns regarding preeclampsia. Most reported reasons for non- or incomplete use were unawareness of LDA as a preventive intervention, concerns about potential adverse effects and doubts regarding the benefits.

CONCLUSIONS

Risk-based counseling was associated with a higher prevalence of LDA usage, but general usage rates were low. Future research regarding potential factors improving the usage of LDA during pregnancy is necessary.

Implementing a Preeclampsia Prediction Model in Obstetrics: Cutoff Determination and Health Care Professionals' Adherence

Background. Despite improved management, preeclampsia remains an important cause of maternal and neonatal mortality and morbidity. Low-dose aspirin (LDA) lowers the risk of preeclampsia. Although several guidelines recommend LDA prophylaxis in women at increased risk, they disagree about the definition of high risk. Recently, an externally validated prediction model for preeclampsia was implemented in a Dutch region combined with risk-based obstetric care paths. Objectives. To demonstrate the selection of a risk threshold and to evaluate the adherence of obstetric health care professionals to the prediction tool. Study Design. Using a survey (n = 136) and structured meetings among health care professionals, possible cutoff values at which LDA should be discussed were proposed. The prediction model, with chosen cutoff and corresponding risk-based care paths, was embedded in an online tool. Subsequently, a prospective multicenter cohort study (n = 850) was performed to analyze the adherence of health care professionals. Patient questionnaires, linked to the individual risk profiles calculated by the online tool, were used to evaluate adherence. Results. Health care professionals agreed upon employing a tool with a high detection rate (cutoff: 3.0%; sensitivity 75%, specificity 64%) followed by shared decision between patients and health care professionals on LDA prophylaxis. Of the 850 enrolled women, 364 women had an increased risk of preeclampsia. LDA was discussed with 273 of these women, resulting in an 81% adherence rate. Conclusion. Consensus regarding a suitable risk cutoff threshold was reached. The adherence to this recommendation was 81%, indicating adequate implementation.

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

Objective

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

Design and setting

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

Methods

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

Results

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

Conclusion

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