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Keywords:

  • Biomarkers;
  • placental growth factor;
  • pre-eclampsia;
  • soluble endoglin;
  • soluble fms-like tyrosine kinase-1;
  • vascular endothelial growth factor

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Disclosure of interests
  8. Contribution to authorship
  9. Details of ethics approval
  10. Funding
  11. Acknowledgement
  12. References
  13. Supporting Information

Please cite this paper as: Kleinrouweler C, Wiegerinck M, Ris-Stalpers C, Bossuyt P, van der Post J, von Dadelszen P, Mol B, Pajkrt E, for the EBM CONNECT Collaboration. Accuracy of circulating placental growth factor, vascular endothelial growth factor, soluble fms-like tyrosine kinase 1 and soluble endoglin in the prediction of pre-eclampsia: a systematic review and meta-analysis. BJOG 2012;119:778–787.

Background  Biomarkers have been proposed for identification of women at increased risk of developing pre-eclampsia.

Objectives  To investigate the capacity of circulating placental growth factor (PlGF), vascular endothelial growth factor (VEGF), soluble fms-like tyrosine kinase-1 (sFLT1) and soluble endoglin (sENG) to predict pre-eclampsia.

Search strategy  Medline and Embase through October 2010 and reference lists of reviews, without constraints.

Selection criteria  We included original publications on testing of PlGF, VEGF, sFLT1 and sENG in serum or plasma of pregnant women at <30 weeks of gestation and before clinical onset of pre-eclampsia.

Data collection and analysis  Two reviewers independently identified eligible studies, extracted descriptive and test accuracy data and assessed methodological quality. Summary estimates of discriminatory performance were obtained.

Main results  We included 34 studies. Concentrations of PlGF (27 studies) and VEGF (three studies) were lower in women who developed pre-eclampsia: standardised mean differences (SMD) −0.56 (95% CI −0.77 to −0.35) and −1.25 (95% CI −2.73 to 0.23). Concentrations of sFLT1 (19 studies) and sENG (ten studies) were higher: SMD 0.48 (95% CI 0.21–0.75) and SMD 0.54 (95% CI 0.24–0.84). The summary diagnostic odds ratios were: PlGF 9.0 (95% CI 5.6–14.5), sFLT1 6.6 (95% CI 3.1–13.7), sENG 4.2 (95% CI 2.4–7.2), which correspond to sensitivities of 32%, 26% and 18%, respectively, for a 5% false-positive rate.

Author’s conclusions  PlGF, sFLT1 and sENG showed modest but significantly different concentrations before 30 weeks of gestation in women who developed pre-eclampsia. Test accuracies of all four markers, however, are too poor for accurate prediction of pre-eclampsia in clinical practice.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Disclosure of interests
  8. Contribution to authorship
  9. Details of ethics approval
  10. Funding
  11. Acknowledgement
  12. References
  13. Supporting Information

Pre-eclampsia, a multisystem disorder of pregnancy defined as hypertension and proteinuria, is a major obstetric problem. It contributes substantially to maternal and perinatal morbidity and mortality worldwide, especially in developing countries.1

A singular problem in the management of pre-eclampsia is that by the time symptoms occur the only definitive treatment of the underlying disorder is delivery, often preterm. Expectant management of preterm pre-eclampsia focuses on safely prolonging pregnancy through intensive monitoring to prevent maternal and fetal complications.1

Screening for pregnant women to identify those at risk of developing pre-eclampsia should substantially improve the quality, focus, resource use and efficacy of antenatal care, with the prospect of improving maternal and perinatal outcomes.2,3 Moreover, preventive treatment such as aspirin would probably be more beneficial when started in early pregnancy.4

The pathogenesis of pre-eclampsia, especially that of early onset, begins at the time of trophoblast invasion and remodelling of the spiral arteries during the first 12 weeks of pregnancy.1 Inadequate placentation and subsequent hypoxia are thought to be followed by an increased release of the placenta-produced anti-angiogenic factor soluble fms-like tyrosine kinase-1 (sFLT1) into the maternal circulation.5 The sFLT1 binds to the angiogenic proteins placental growth factor (PlGF) and vascular endothelial growth factor (VEGF), thereby blocking their actions through the plasma-membrane-bound form of the receptor, that contains the tyrosine kinase domain essential to its biological activity.6–9 Soluble endoglin (sENG), the extracellular domain of the co-receptor endoglin, impairs binding of transforming growth factor-β1 to cell surface receptors and decreases endothelial nitric oxide signalling, hence inhibiting angiogenesis and promoting vascular dysfunction.5 Dysfunction of the maternal endothelium ultimately results in the clinical syndrome of pre-eclampsia.10,11

A number of studies have been performed to investigate circulating levels of these factors in pre-eclamptic pregnancies and to compare them with uncomplicated pregnancies. A recent Health Technology Assessment report that investigated the accuracy of predictive tests for pre-eclampsia and their possible cost-effectiveness expressed the need for systematic reviews on new tests not considered in the report, including biomarkers.12 In a systematic review from 2007, sFLT1 levels were found to be elevated and PlGF levels to be reduced in pre-eclamptic pregnancies, but absolute levels varied markedly between studies.13 Because the number of studies on sFLT1 and PlGF has increased substantially since that review and the evidence on VEGF and sENG has not been summarised previously, we undertook a systematic review of the literature on the accuracy of the biomarkers PlGF, VEGF, sFLT1 and sENG in the prediction of pre-eclampsia.

Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Disclosure of interests
  8. Contribution to authorship
  9. Details of ethics approval
  10. Funding
  11. Acknowledgement
  12. References
  13. Supporting Information

Data sources

We performed an electronic search on 26 October 2010 in Medline (from 1948) and Embase (from 1980) without language or publication date restrictions to identify all articles reporting on the prediction of pre-eclampsia using one or more of the markers PlGF, VEGF, sFLT1 and sENG. The electronic search strategy was based on MeSH terms and keywords related to pre-eclampsia and to each of the four markers, combined with methodological filters, allowing efficient identification of studies on diagnostic and prognostic tests (see Appendix S1). Reference lists of review articles13 and eligible primary studies were checked to identify cited articles not captured by the electronic search.

This systematic review and meta-analysis was conducted according to the Meta-analysis Of Observational Studies in Epidemiology (MOOSE) guidelines.14

Eligibility criteria

Eligible studies were those that reported on testing of PlGF, VEGF, sFLT1 or sENG in serum or plasma of pregnant women with blood sampling before clinical onset of pre-eclampsia and before 30 weeks of gestation. To be included, studies should describe the occurrence of pre-eclampsia conditional on the test result in such a way that 2 × 2 classification tables could be (re-)constructed, or should describe the test results conditional on the occurrence of pre-eclampsia as means and standard deviations in pregnancies before pre-eclampsia and uncomplicated pregnancies.

Study selection

Studies were selected in a staged process. First, two reviewers (EK and MW) independently scrutinised titles and abstracts of all retrieved references to select potentially eligible articles. Full text papers of references selected by at least one reviewer were obtained. Second, both reviewers independently examined these full text papers to see whether they met the inclusion and exclusion criteria. In case of multiple publications of one dataset we included only the most recent or most complete paper. Disagreements about inclusion were resolved by consensus or by consulting a third reviewer (BM). We did not contact authors for further information.

Data extraction

For each included study, data on clinical characteristics of the women (age, obstetric history), characteristics of the index test (marker, medium, test kit and manufacturer), reference standard and test accuracy were extracted independently by two experienced reviewers (EK and MW) using standardised data extraction forms.

Quality assessment

Both reviewers assessed the methodological quality of the included studies using the quality assessment of diagnostic accuracy studies (QUADAS) criteria.15 In addition, we assessed whether the study participants had received preventive treatment. Acceptable reference standards for pre-eclampsia were persistent high systolic (≥140 mmHg) or diastolic (≥90 mmHg) blood pressure and proteinuria (≥0.3 g/24 hours or a dipstick result of ≥1+, equivalent to 30 mg/dl in a single urine sample or spot urine protein/creatinine ratio ≥30 mg protein/mmol creatinine) of new onset after 20 weeks of gestation, according to the International Society for the Study of Hypertension in Pregnancy criteria.16

Data synthesis

For the studies with marker concentration reported as a continuous variable, we assessed the differences in marker concentration between women who did and did not develop pre-eclampsia and expressed the results in standardised mean differences. For uniform presentation in the tables, all reported marker concentrations were converted into pg/ml, as this is the most frequently reported unit. For pooling of the results, we used an inverse-variance weighted random effect approach in Review Manager 5.0.17

Results of studies reporting sensitivities and specificities were plotted in receiver operating characteristics spaces with summary receiver operating characteristics curves that correspond to summary diagnostic odds ratios. Analogous to the odds ratio for expressing the strength of association between exposure and disease, the diagnostic odds ratio can be applied to express the strength of the association between test result and disease. The diagnostic odds ratio describes the odds of positive test results in patients with disease compared with the odds of positive test results in those without disease. Higher diagnostic odds ratios represent higher test accuracies: a test with a sensitivity and specificity of 90% has a diagnostic odds ratio of 81. A diagnostic odds ratio value of 1 indicates that a test does not discriminate between women with the disease and those without the disease. Values <1 point to improper test interpretation with more negative tests among the diseased.18,19 We pooled the diagnostic odds ratios with a random effects model using the DerSimonian Laird method in Meta-DiSc 1.4 software.20

Sensitivity analyses were performed to assess the impact of gestational age at testing, study design and study population, selection of controls in case–control studies, type of test kit used and commercial funding on the pooled estimates.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Disclosure of interests
  8. Contribution to authorship
  9. Details of ethics approval
  10. Funding
  11. Acknowledgement
  12. References
  13. Supporting Information

The searches in Medline and Embase provided a total of 4072 citations after removing duplicates. Of these, 3968 studies had to be discarded after reviewing titles and abstracts. The full text of the remaining 104 papers was examined in more detail. Of these, 70 studies did not meet the inclusion criteria as described. These studies are listed in the Appendix S2. Thirty-four studies met the inclusion criteria and were included in this systematic review.21–54 No eligible unpublished studies on the subject were identified (Figure 1).

image

Figure 1.  Flow diagram of identification and selection of studies of PlGF, VEGF, sFLT1 and sENG for prediction of pre-eclampsia, for inclusion in this systematic review.

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There were 27 studies reporting on PlGF, three studies on VEGF, 19 studies on sFLT1 and ten studies on sENG. Fifteen studies reported on one marker, 13 on two, and six on three different markers. The most common combination was PlGF and sFLT1. The set contained 12 cohort studies and 22 case–control studies. Markers had been evaluated from 5 to 28 weeks of gestation. Study sample size ranged from 29 to 3098 women. Several studies investigated testing of markers at multiple gestational ages, mostly in the same women. Some studies reported data on pre-eclamptic pregnancies for subgroups only, for example in women with clinical onset of pre-eclampsia before and after 32, 34 or 37 weeks of gestation. We provide an overview of all studies in Tables S1–S7.

Quality

Figure 2 summarises the results of the quality assessment. All studies met the following QUADAS quality criteria: use of an acceptable and independent reference test, appropriate time period between tests, avoidance of partial and differential verification bias, withdrawals explained and relevant clinical information available. More than 70% of studies also met the items for representative spectrum of patients, selection criteria described, blinding of the reference test, and reporting of uninterpretable results (and instrument variation and free of commercial funding).

image

Figure 2.  Summary of quality assessment.

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Studies scored poorly in terms of adequately describing details of the reference test and, more importantly, the index test: only four studies (three of the 27 on PlGF and one of the three on VEGF) accurately described whether the instrument measured the free subset or total pool of the marker. Four studies reported on the application of preventive treatment (aspirin, calcium, vitamins C and E). For PlGF, five out of 15 studies reporting sensitivity and specificity did not report corresponding cut-off points. For sFLT1 and sENG, these were one of eight and none of five studies, respectively.

PlGF

A total of 27 studies reported concentrations of PlGF in women who developed pre-eclampsia and in women with uncomplicated pregnancies. In women who developed pre-eclampsia, concentrations of PlGF were significantly lower: standardised mean difference (SMD) −0.56 (95% CI −0.78 to −0.35). In studies that performed the test before 16 weeks of gestation, the difference was less apparent (SMD −0.51, 95% CI −0.95 to −0.07) than in studies that tested from 19 weeks onward (SMD −0.85, 95% CI −1.52 to −0.18) but greater than in studies that tested in the overlap of both periods, 6–26 weeks (SMD −0.45, 95% CI −0.69 to −0.22), see Appendix S3A.

We identified 15 eligible studies that estimated the sensitivity and specificity of PlGF testing (see Appendix S4A). The summary diagnostic odds ratio was 9.0 (95% CI 5.6–14.5). This corresponds to both sensitivities and specificities of 0.75, or a sensitivity of 0.32 for a 5% false-positive rate.

VEGF

There were three studies reporting on VEGF testing. Women who would develop pre-eclampsia had lower concentrations of VEGF, but this was not significant: SMD −1.25 (95% CI −2.73 to 0.23) (see Appendix S3B). We did not identify any studies reporting sensitivity and specificity of VEGF testing.

sFLT1

Concentrations of sFLT1 were reported in 19 studies. These were higher in pregnancies before pre-eclampsia than in uncomplicated pregnancies: SMD 0.48 (95% CI 0.21–0.74). Here also, differences were most pronounced in studies that tested women ≥19 weeks of gestation (SMD 0.85, 95% CI 0.36–1.33) compared with ≤16 weeks of gestation (SMD 0.60, 95% CI −0.11 to 1.31) or 7–26 weeks (SMD 0.24, 95% CI 0.05–0.43) (see Appendix S3C).

We identified eight studies that estimated the sensitivity and specificity of sFLT1 testing (see Appendix S4B). The summary diagnostic odds ratio for these studies was 6.6 (95% CI 3.1–13.7). This corresponds to a 0.72 sensitivity and specificity, or a sensitivity of 0.26 for a 5% false-positive rate.

sENG

In the ten studies that reported concentrations of sENG, higher concentrations were found in pregnancies before pre-eclampsia: SMD 0.54 (95% CI 0.24–0.84). In studies testing before 16 weeks of gestation, these differences were smaller (SMD 0.18, 95% CI −0.02 to 0.38) compared with studies testing between 7 and 26 weeks of gestation (SMD 0.70, 95% CI 0.30–1.10) (see Appendix S3D).

We identified four studies that reported sensitivity and specificity of sENG testing (see Appendix S4C). The summary diagnostic odds ratio was 4.2 (95% CI 2.4–7.2). This corresponds to a sensitivity and specificity of 0.67, or a sensitivity of 0.18 for a 5% false-positive rate.

Highly significant between-study heterogeneity was recorded for all markers, with I2 statistics of 84% (PlGF), 96% (VEGF), 93% (sFLT1) and 91% (sENG). Sensitivity analyses taking into account differences in gestational age at testing (see Appendix S3), study design and selection of study population, and selection of eligible controls in case–control studies (see Appendix S5) did not explain this heterogeneity. In a sensitivity analysis with only studies that used the most frequently used ELISA kits from R&D Systems (Abingdon, UK), I2 statistics decreased to 80% for PlGF (15/18 studies, SMD −0.60, 95% CI −0.84 to −0.30) and 70% for sFLT1 (14/17 studies, SMD 0.33, 95% CI 0.16–0.49) but remained 91% for sENG (eight of nine studies, SMD 0.42, 95% CI 0.08–0.76). All studies on VEGF used R&D Systems assays. We could not find any evidence that commercial participation in a study influenced the predictive ability of the markers.

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Disclosure of interests
  8. Contribution to authorship
  9. Details of ethics approval
  10. Funding
  11. Acknowledgement
  12. References
  13. Supporting Information

This systematic review provides an overview of the discriminatory performance and predictive capacity of the pro-angiogenic and anti-angiogenic biomarkers PlGF, VEGF, sFLT1 and sENG for pre-eclampsia before 30 weeks of gestation. We included studies that investigated differences in marker concentration between women who developed pre-eclampsia and those who remained healthy throughout pregnancy and studies that provided details on the accuracy of such tests. In 34 such studies with overall good quality, we found that mean concentrations of PlGF were modestly but significantly lower before pre-eclampsia and concentrations of sFLT1 and sENG were higher. In studies that compared the timing of testing (≤16 weeks, ≥19 weeks or both), differences between the groups were largest from 19 weeks of gestation onwards and significant for all three markers, whereas for sFLT1 and sENG, the differences in marker concentration were not significantly different ≤16 weeks of gestation. However, the test accuracy of the markers PlGF, sFLT1 and sENG in terms of sensitivity and specificity was too poor for accurate identification of pre-eclampsia cases in clinical practice, and we recommend that these markers should not be used alone for the prediction of pre-eclampsia.

Some methodological aspects of this study require comment. This systematic review revealed a considerable amount of heterogeneity between studies, which is reflected in the high values of I2 in the meta-analyses. We propose several mechanisms through which this might have occurred. First, eligible studies reported on testing in dissimilar and overlapping periods of gestational age, which hampered the distinction between first-trimester and second-trimester assessments, and concentrations of the markers are known to change with gestational age as the result of biological variability.55,56 In addition, the majority of studies—24 of 27 for PlGF and two out of three for VEGF—did not report exactly what was measured by the test kit, i.e. whether it concerned the total circulating level of the marker, the free fraction or a subset of either one of these. Also, different ELISA test kits were used. A combination of these factors may have resulted in varying levels of the same circulating factor, even in similar populations and at similar gestational ages, which makes it possible to express the results only as SMD.

Although we maintained high quality standards for the conduct of this systematic review and meta-analysis, we realise that the results and their implications are only as good as the source of the data. In the absence of a sufficient number of well-designed marker evaluation studies, we had to include studies that differed in design: nested or matched case–control studies as well as cohort studies, with widely ranging sample sizes. Furthermore, selection of the study population (at lower or higher risk) in cohort studies and selection of eligible controls (completely healthy or without pre-eclampsia) in case–control studies differed between studies. Despite these likely sources of heterogeneity, sensitivity analyses did not point to a single most important factor. Other factors that may partly explain the heterogeneity are the endpoint of disease and the definition of pre-eclampsia used in the original studies, although we minimised this by only including studies that defined pre-eclampsia according to the International Society for the Study of Hypertension in Pregnancy criteria,16 and we found that almost all studies used ‘all pre-eclampsia’ as the endpoint of disease. Because only a few studies have provided results for subgroups with onset of disease before or after a certain (varying) gestational age, we did not perform sensitivity analyses taking this factor into account. The endpoint of disease and selection criteria used in the original studies can be found in Tables S1–S7.

We identified several studies that reported the results in medians and ranges or multiples of the medians, which cannot be compared directly with the included studies, so we were not able to include all existing evidence in this paper.

We also investigated the test accuracy of the markers PlGF, sFLT1 and sENG in terms of their sensitivity and specificity. Because of different or unreported positivity thresholds, a meta-analysis of sensitivities and specificities was inappropriate because this would ignore threshold differences and underestimate diagnostic performance. Instead we used the diagnostic odds ratio as a single measure of test performance. A disadvantage of this method is that it does not result in unique summary estimates of sensitivity and specificity: summary estimates of one value can only be obtained by specifying the value of the other.18,19 Hence, for each of the biomarkers, we reported two possible estimates of sensitivity and specificity that corresponded to the pooled diagnostic odds ratios: one in which sensitivity and specificity were equal, and the other in which there was a false-positive rate of 5% (or a specificity of 95%), which is common in screening studies.

Identifying women at risk for pre-eclampsia remains an important aspect of antenatal care57 because it can not only contribute to the development and evaluation of preventive treatments, but can also guide the structure of antenatal care. A recent meta-analysis showed that aspirin started at 16 weeks or earlier in pregnancy was associated with a significant and greater reduction in pre-eclampsia in women at moderate or high risk than aspirin started after 16 weeks of gestation.4 In addition, early estimation of patient-specific risks for pregnancy complications could shift antenatal care from a series of routine visits for everyone to an approach in which care, in terms of schedule and content, is given to those who need it and others are reassured at an early stage. Women identified as being at high risk can have close surveillance, but the great majority of women are safe with a substantially reduced number of visits.58

Our findings that concentrations of PlGF, sFLT1 and sENG are significantly different before the onset of pre-eclampsia and that these differences are greatest from 19 weeks of gestation onwards would be unfavourable in the clinical situation because it would not allow for early intervention. Moreover, using these markers as a discriminative test proved to be more difficult and had only limited accuracy.

In addition, all pre-eclampsia cases do not appear to have the same origin.1 While early-onset pre-eclampsia is most closely associated with inadequate placentation and may well be associated with alterations in angiogenic balance, as suggested by this review, term pre-eclampsia is most commonly associated with normal placental development and likely to be predicted by factors associated with long-term cardiovascular risk, such as obesity, diabetes and chronic hypertension.59 However, most studies included in this review did not describe subgroups of early-onset and late-onset pre-eclampsia and we did not make this distinction in our analyses.

It is unlikely that any single biomarker or related pairing of biomarkers will be able to predict all forms of pre-eclampsia. An effective test is likely to require the assessment of the various pathways that lead to the clinical phenotype both remote from and at term. The large number of relatively small studies that we identified is of concern in this area of research. There are as yet no rules for the early registration of cohort studies on prediction models similar to the registration of randomised clinical trials. As a consequence, there is the chance of many small studies in which multiple potential markers will be tested. The predictive capacities of the markers in these types of studies tend to be overestimated, whereas larger studies will generally report less extreme results.

Although we found that the accuracy of the biomarkers PlGF, sFLT1 and sENG is too poor to allow their routine use for prediction of pre-eclampsia, they might be useful when incorporated in multivariable prediction models. The recent SCOPE study showed that the ability of clinical characteristics to predict pre-eclampsia in healthy nulliparous women is modest. Clinical characteristics included age, mean arterial blood pressure, body mass index, family history of pre-eclampsia, family history of coronary heart disease, maternal birthweight, vaginal bleeding for at least 5 days, previous single miscarriage with the same partner, time to conception, intake of fruit, cigarette smoking and alcohol use in the first trimester. To improve overall accuracy and detection of cases, the clinical algorithm will require the addition of predictors. The addition of 19–21-week uterine artery Doppler indices did not improve the predictive performance, but the authors suggest instead that biomarkers could be added to the model and if externally validated, such models could provide a personalised clinical risk profile.60 This is supported by some of the included studies and other cohort studies that have shown that combinations of patient factors and (multiple) biomarkers result in better prediction,21,22,49,61 although these models have to be externally validated as well. In contrast, the bulk of relatively small studies, such as we found in this meta-analysis, are unlikely to clarify this matter.

Before a biomarker can be implemented in clinical practice, its clinical validity and clinical utility have to be evaluated. Ultimately, it is the influence on patient outcome provided by the test that matters. In the decision as to whether these markers should be used, one should not only be informed about the test accuracy, but also about its ability to influence medical decision making; for example the intensity of monitoring or application of preventive treatment, and the resulting change in patient outcome. Currently, we are only at the start of this process. No randomised trials have been conducted that compare prenatal care including biomarker testing to standard prenatal care. Future studies should also consider the cost-effectiveness of such a screening strategy as a whole. Prior tests with high costs, such as biomarker testing, would need substantially improved sensitivities to be able to improve cost-effectiveness, but such levels of sensitivity have rarely been achieved.12 Realising that most studies on biomarkers use scarce public resources, we may consider a global, collaborative approach to evaluate the role of biomarkers in the prediction of pre-eclampsia.

Disclosure of interests

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Disclosure of interests
  8. Contribution to authorship
  9. Details of ethics approval
  10. Funding
  11. Acknowledgement
  12. References
  13. Supporting Information

PD receives salary support from the Canadian Institutes for Health Research, Michael Smith Foundation for Health Research, and the Child and Family Research Institute. He also reported receiving salary support for consultancy and lectures from Alere International and for expert testimony from the Canadian Medical Protective Agency.

Contribution to authorship

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Disclosure of interests
  8. Contribution to authorship
  9. Details of ethics approval
  10. Funding
  11. Acknowledgement
  12. References
  13. Supporting Information

BWM conceived the study, supervised the analyses and critically revised the manuscript for important intellectual content. EK and MW carried out the literature search and drafted the manuscript. EK performed the analyses. PB advised on and supervised the analyses and critically revised the manuscript for important intellectual content. CRS, EP, JP and PD assisted in data interpretation and critically revised the manuscript for important intellectual content. All authors approved the final version of the manuscript that was submitted.

Funding

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Disclosure of interests
  8. Contribution to authorship
  9. Details of ethics approval
  10. Funding
  11. Acknowledgement
  12. References
  13. Supporting Information

EK is supported by a PhD Scholarship from the AMC Graduate School. PD receives salary support from the Canadian Institutes for Health Research, Michael Smith Foundation for Health Research, and the Child and Family Research Institute. The authors received funding from the European Union made available to the EBM-CONNECT Collaboration through its Seventh Framework Programme, Marie Curie Actions, International Staff Exchange Scheme (Proposal number: 101377; Grant Agreement number: 247613); EBM-CONNECT Canadian Collaborators received funding from the Canadian Institutes of Health Research. The EBM-CONNECT (Evidence-Based Medicine COllaboratioN: NEtwork for systematic reviews and guideline development researCh and dissemination) Collaboration (in alphabetical order by country) includes: L Mignini, Centro Rosarino de Estudios Perinatales, Argentina; P von Dadelszen, L Magee, D Sawchuck, University of British Columbia, Canada; E Gao, Shanghai Institute of Planned Parenthood Research, China; BW Mol, K Oude Rengerink, Academic Medical Center, the Netherlands; J Zamora, Ramon y Cajal, Spain; C Fox, J Daniels, University of Birmingham, UK; and KS Khan, S Thangaratinam, C Meads, Barts and the London School of Medicine, Queen Mary University of London, UK. No funders were involved in the design and conduct of the study; collection, management, analysis and interpretation of the data; or in the preparation, review or approval of the manuscript.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Disclosure of interests
  8. Contribution to authorship
  9. Details of ethics approval
  10. Funding
  11. Acknowledgement
  12. References
  13. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Disclosure of interests
  8. Contribution to authorship
  9. Details of ethics approval
  10. Funding
  11. Acknowledgement
  12. References
  13. Supporting Information

Tables S1–S7. Study characteristics, characteristics of women and test results of all 34 included studies, grouped by biomarker and presentation of results.

Appendix S1. Search strategy for MEDLINE and EMBASE.

Appendix S2. References of studies excluded after screening of full text, as described in Figure 1.

Appendix S3. Forest plots showing standardised mean differences in marker concentration between women who would develop pre-eclampsia and women with uncomplicated pregnancies in all studies reporting data as continuous variables. Negative values indicate lower concentrations; positive values indicate higher concentrations before pre-eclampsia. (A) PlGF; (B) VEGF; (C) sFLT1; and (D) sENG.

Appendix S4. Results of studies on PlGF (A), sFLT1 (B) and sENG (C) reporting sensitivity and specificity of the test, plotted in receiver operating characteristics (ROC) spaces with summary ROC curves. Area of circles is proportional to study sample size.

Appendix S5. Sensitivity analyses that are not reported in the paper.

FilenameFormatSizeDescription
BJO_3311_sm_AppendixS1.pdf75KSupporting info item
BJO_3311_sm_AppendixS2.pdf38KSupporting info item
BJO_3311_sm_AppendixS3.pdf596KSupporting info item
BJO_3311_sm_AppendixS4.pdf97KSupporting info item
BJO_3311_sm_AppendixS5.pdf659KSupporting info item
BJO_3311_sm_TableS1.pdf92KSupporting info item
BJO_3311_sm_TableS2.pdf86KSupporting info item
BJO_3311_sm_TableS3.pdf43KSupporting info item
BJO_3311_sm_TableS4.pdf105KSupporting info item
BJO_3311_sm_TableS5.pdf61KSupporting info item
BJO_3311_sm_TableS6.pdf69KSupporting info item
BJO_3311_sm_TableS7.pdf22KSupporting info item

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