Skip to main content
PMC home page
Medline Book to support NIHPA logoLink to Medline Book to support NIHPA
. 2020 Dec;24(72):1–252. doi: 10.3310/hta24720

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

John Allotey, Kym Ie Snell, Melanie Smuk, Richard Hooper, Claire L Chan, Asif Ahmed, Lucy C Chappell, Peter von Dadelszen, Julie Dodds, Marcus Green, Louise Kenny, Asma Khalil, Khalid S Khan, Ben W Mol, Jenny Myers, Lucilla Poston, Basky Thilaganathan, Anne C Staff, Gordon Cs Smith, Wessel Ganzevoort, Hannele Laivuori, Anthony O Odibo, Javier A Ramírez, John Kingdom, George Daskalakis, Diane Farrar, Ahmet A Baschat, Paul T Seed, Federico Prefumo, Fabricio da Silva Costa, Henk Groen, Francois Audibert, Jacques Masse, Ragnhild B Skråstad, Kjell Å Salvesen, Camilla Haavaldsen, Chie Nagata, Alice R Rumbold, Seppo Heinonen, Lisa M Askie, Luc Jm Smits, Christina A Vinter, Per M Magnus, Kajantie Eero, Pia M Villa, Anne K Jenum, Louise B Andersen, Jane E Norman, Akihide Ohkuchi, Anne Eskild, Sohinee Bhattacharya, Fionnuala M McAuliffe, Alberto Galindo, Ignacio Herraiz, Lionel Carbillon, Kerstin Klipstein-Grobusch, SeonAe Yeo, Helena J Teede, Joyce L Browne, Karel Gm Moons, Richard D Riley, Shakila Thangaratinam
PMCID: PMC7780127  PMID: 33336645

Abstract

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 I2 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.

Plain language summary

WHAT IS THE PROBLEM?

Pre-eclampsia, a condition in pregnancy that results in raised blood pressure and protein in the urine, is a major cause of complications for the mother and baby.

WHAT IS NEEDED?

A way of accurately identifying women at high risk of pre-eclampsia to allow clinicians to start preventative interventions such as administering aspirin or frequently monitoring women during pregnancy.

WHERE ARE THE RESEARCH GAPS?

Although over 100 tools (models) have been reported worldwide to predict pre-eclampsia, to date their performance in women managed in the UK NHS is unknown.

WHAT DID WE PLAN TO DO?

We planned to comprehensively identify all published models that predict the risk of pre-eclampsia occurring at any time during pregnancy and to assess if this prediction is accurate in the UK population. If the existing models did not perform satisfactorily, we aimed to develop new prediction models.

WHAT DID WE FIND?

We formed the International Prediction of Pregnancy Complications network, which provided data from a large number of studies (78 studies, 25 countries, 125 researchers, 3,570,993 singleton pregnancies). We were able to assess the performance of 24 out of the 131 models published to predict pre-eclampsia in 11 UK data sets. The models did not accurately predict the risk of pre-eclampsia across all UK data sets, and their performance varied within individual data sets. We developed new prediction models that showed promising performance on average across all data sets, but their ability to correctly identify women who develop pre-eclampsia varied between populations. The models were more clinically useful when used in the care of first-time mothers pregnant with one child, compared to a strategy of treating them all as if they were at high-risk of pre-eclampsia.

WHAT DOES THIS MEAN?

Before using the International Prediction of Pregnancy Complications models in various populations, they need to be adjusted for characteristics of the particular population and the setting of application.

Full text of this article can be found in Bookshelf.

References

  1. Brown MA, Hague WM, Higgins J, Lowe S, McCowan L, Oats J, et al. The detection, investigation and management of hypertension in pregnancy: executive summary. Aust N Z J Obstet Gynaecol 2000;40:133–8. https://doi.org/10.1111/j.1479-828x.2000.tb01136.x doi: 10.1111/j.1479-828x.2000.tb01136.x. [DOI] [PubMed]
  2. Sibai B, Dekker G, Kupferminc M. Pre-eclampsia. Lancet 2005;365:785–99. https://doi.org/10.1016/S0140-6736(05)17987-2 doi: 10.1016/S0140-6736(05)17987-2. [DOI] [PubMed]
  3. Sibai BM. Diagnosis and management of gestational hypertension and preeclampsia. Obstet Gynecol 2003;102:181–92. https://doi.org/10.1097/00006250-200307000-00033 doi: 10.1097/00006250-200307000-00033. [DOI] [PubMed]
  4. Tranquilli AL, Dekker G, Magee L, Roberts J, Sibai BM, Steyn W, et al. The classification, diagnosis and management of the hypertensive disorders of pregnancy: a revised statement from the ISSHP. Pregnancy Hypertens 2014;4:97–104. https://doi.org/10.1016/j.preghy.2014.02.001 doi: 10.1016/j.preghy.2014.02.001. [DOI] [PubMed]
  5. Staff AC, Benton SJ, von Dadelszen P, Roberts JM, Taylor RN, Powers RW, et al. Redefining preeclampsia using placenta-derived biomarkers. Hypertension 2013;61:932–42. https://doi.org/10.1161/HYPERTENSIONAHA.111.00250 doi: 10.1161/HYPERTENSIONAHA.111.00250. [DOI] [PubMed]
  6. Duley L. The global impact of pre-eclampsia and eclampsia. Semin Perinatol 2009;33:130–7. https://doi.org/10.1053/j.semperi.2009.02.010 doi: 10.1053/j.semperi.2009.02.010. [DOI] [PubMed]
  7. Gardosi J, Kady SM, McGeown P, Francis A, Tonks A. Classification of stillbirth by relevant condition at death (ReCoDe): population based cohort study. BMJ 2005;331:1113–17. https://doi.org/10.1136/bmj.38629.587639.7C doi: 10.1136/bmj.38629.587639.7C. [DOI] [PMC free article] [PubMed]
  8. Iams JD, Goldenberg RL, Mercer BM, Moawad A, Thom E, Meis PJ, et al. The Preterm Prediction Study: recurrence risk of spontaneous preterm birth. National Institute of Child Health and Human Development Maternal-Fetal Medicine Units Network. Am J Obstet Gynecol 1998;178:1035–40. https://doi.org/10.1016/S0002-9378(98)70544-7 doi: 10.1016/S0002-9378(98)70544-7. [DOI] [PubMed]
  9. Davies EL, Bell JS, Bhattacharya S. Preeclampsia and preterm delivery: a population-based case–control study. Hypertens Pregnancy 2016;35:510–19. https://doi.org/10.1080/10641955.2016.1190846 doi: 10.1080/10641955.2016.1190846. [DOI] [PubMed]
  10. Tang LC, Kwok AC, Wong AY, Lee YY, Sun KO, So AP. Critical care in obstetrical patients: an eight-year review. Chin Med J 1997;110:936–41. [PubMed]
  11. Khan KS, Wojdyla D, Say L, Gülmezoglu AM, Van Look PF. WHO analysis of causes of maternal death: a systematic review. Lancet 2006;367:1066–74. https://doi.org/10.1016/S0140-6736(06)68397-9 doi: 10.1016/S0140-6736(06)68397-9. [DOI] [PubMed]
  12. World Health Organization. The World Health Report: 2005 – Make Every Mother and Child Count. Geneva: WHO; 2005. URL: www.who.int/whr/2005/whr2005_en.pdf (accessed 6 December 2018).
  13. Carty DM, Delles C, Dominiczak AF. Preeclampsia and future maternal health. J Hypertens 2010;28:1349–55. https://doi.org/10.1097/HJH.0b013e32833a39d0 doi: 10.1097/HJH.0b013e32833a39d0. [DOI] [PubMed]
  14. Bellamy L, Casas JP, Hingorani AD, Williams DJ. Pre-eclampsia and risk of cardiovascular disease and cancer in later life: systematic review and meta-analysis. BMJ 2007;335:974. https://doi.org/10.1136/bmj.39335.385301.BE doi: 10.1136/bmj.39335.385301.BE. [DOI] [PMC free article] [PubMed]
  15. Thoulass JC, Robertson L, Denadai L, Black C, Crilly M, Iversen L, et al. Hypertensive disorders of pregnancy and adult offspring cardiometabolic outcomes: a systematic review of the literature and meta-analysis. J Epidemiol Community Health 2016;70:414–22. https://doi.org/10.1136/jech-2015-205483 doi: 10.1136/jech-2015-205483. [DOI] [PubMed]
  16. Davis EF, Lazdam M, Lewandowski AJ, Worton SA, Kelly B, Kenworthy Y, et al. Cardiovascular risk factors in children and young adults born to preeclamptic pregnancies: a systematic review. Pediatrics 2012;129:e1552–61. https://doi.org/10.1542/peds.2011-3093 doi: 10.1542/peds.2011-3093. [DOI] [PubMed]
  17. Tuovinen S, Eriksson JG, Kajantie E, Lahti J, Pesonen AK, Heinonen K, et al. Maternal hypertensive disorders in pregnancy and self-reported cognitive impairment of the offspring 70 years later: the Helsinki Birth Cohort Study. Am J Obstet Gynecol 2013;208:200.e1–9. https://doi.org/10.1016/j.ajog.2012.12.017 doi: 10.1016/j.ajog.2012.12.017. [DOI] [PubMed]
  18. Tuovinen S, Räikkönen K, Kajantie E, Pesonen AK, Heinonen K, Osmond C, et al. Depressive symptoms in adulthood and intrauterine exposure to pre-eclampsia: the Helsinki Birth Cohort Study. BJOG 2010;117:1236–42. https://doi.org/10.1111/j.1471-0528.2010.02634.x doi: 10.1111/j.1471-0528.2010.02634.x. [DOI] [PubMed]
  19. Crispi F, Domínguez C, Llurba E, Martín-Gallán P, Cabero L, Gratacós E. Placental angiogenic growth factors and uterine artery Doppler findings for characterization of different subsets in preeclampsia and in isolated intrauterine growth restriction. Am J Obstet Gynecol 2006;195:201–7. https://doi.org/10.1016/j.ajog.2006.01.014 doi: 10.1016/j.ajog.2006.01.014. [DOI] [PubMed]
  20. Crispi F, Llurba E, Domínguez C, Martín-Gallán P, Cabero L, Gratacós E. Predictive value of angiogenic factors and uterine artery Doppler for early- versus late-onset pre-eclampsia and intrauterine growth restriction. Ultrasound Obstet Gynecol 2008;31:303–9. https://doi.org/10.1002/uog.5184 doi: 10.1002/uog.5184. [DOI] [PubMed]
  21. Valensise H, Vasapollo B, Gagliardi G, Novelli GP. Early and late preeclampsia: two different maternal hemodynamic states in the latent phase of the disease. Hypertension 2008;52:873–80. https://doi.org/10.1161/HYPERTENSIONAHA.108.117358 doi: 10.1161/HYPERTENSIONAHA.108.117358. [DOI] [PubMed]
  22. Redman CW, Sargent IL, Staff AC. IFPA Senior Award Lecture: making sense of pre-eclampsia – two placental causes of preeclampsia? Placenta 2014;(Suppl.):S20–5. https://doi.org/10.1016/j.placenta.2013.12.008 doi: 10.1016/j.placenta.2013.12.008. [DOI] [PubMed]
  23. MacKay AP, Berg CJ, Atrash HK. Pregnancy-related mortality from preeclampsia and eclampsia. Obstet Gynecol 2001;97:533–8. https://doi.org/10.1097/00006250-200104000-00011 doi: 10.1097/00006250-200104000-00011. [DOI] [PubMed]
  24. von Dadelszen P, Payne B, Li J, Ansermino JM, Broughton Pipkin F, Côté AM, et al. Prediction of adverse maternal outcomes in pre-eclampsia: development and validation of the fullPIERS model. Lancet 2011;377:219–27. https://doi.org/10.1016/S0140-6736(10)61351-7 doi: 10.1016/S0140-6736(10)61351-7. [DOI] [PubMed]
  25. von Dadelszen P, Magee LA, Roberts JM. Subclassification of preeclampsia. Hypertens Pregnancy 2003;22:143–8. https://doi.org/10.1081/PRG-120021060 doi: 10.1081/PRG-120021060. [DOI] [PubMed]
  26. Lisonkova S, Joseph KS. Incidence of preeclampsia: risk factors and outcomes associated with early- versus late-onset disease. Am J Obstet Gynecol 2013;209:544.e1–544.e12. https://doi.org/10.1016/j.ajog.2013.08.019 doi: 10.1016/j.ajog.2013.08.019. [DOI] [PubMed]
  27. Murphy DJ, Stirrat GM. Mortality and morbidity associated with early-onset preeclampsia. Hypertens Pregnancy 2000;19:221–31. https://doi.org/10.1081/PRG-100100138 doi: 10.1081/PRG-100100138. [DOI] [PubMed]
  28. Sibai BM, Ramadan MK, Usta I, Salama M, Mercer BM, Friedman SA. Maternal morbidity and mortality in 442 pregnancies with hemolysis, elevated liver enzymes, and low platelets (HELLP syndrome) Am J Obstet Gynecol 1993;169:1000–6. https://doi.org/10.1016/0002-9378(93)90043-I doi: 10.1016/0002-9378(93)90043-I. [DOI] [PubMed]
  29. Churchill D, Duley L. Interventionist versus expectant care for severe pre-eclampsia before term. Cochrane Database Syst Rev 2002;3:CD003106. https://doi.org/10.1002/14651858.CD003106 doi: 10.1002/14651858.CD003106. [DOI] [PubMed]
  30. Mangham LJ, Petrou S, Doyle LW, Draper ES, Marlow N. The cost of preterm birth throughout childhood in England and Wales. Pediatrics 2009;123:e312–27. https://doi.org/10.1542/peds.2008-1827 doi: 10.1542/peds.2008-1827. [DOI] [PubMed]
  31. Douglas KA, Redman CW. Eclampsia in the United Kingdom. BMJ 1994;309:1395–400. https://doi.org/10.1136/bmj.309.6966.1395 doi: 10.1136/bmj.309.6966.1395. [DOI] [PMC free article] [PubMed]
  32. Sibai BM. Management of late preterm and early-term pregnancies complicated by mild gestational hypertension/pre-eclampsia. Semin Perinatol 2011;35:292–6. https://doi.org/10.1053/j.semperi.2011.05.010 doi: 10.1053/j.semperi.2011.05.010. [DOI] [PubMed]
  33. Bujold E, Roberge S, Lacasse Y, Bureau M, Audibert F, Marcoux S, et al. Prevention of preeclampsia and intrauterine growth restriction with aspirin started in early pregnancy: a meta-analysis. Obstet Gynecol 2010;116:402–14. https://doi.org/10.1097/AOG.0b013e3181e9322a doi: 10.1097/AOG.0b013e3181e9322a. [DOI] [PubMed]
  34. Rolnik DL, Wright D, Poon LC, O’Gorman N, Syngelaki A, de Paco Matallana C, et al. Aspirin versus placebo in pregnancies at high risk for preterm preeclampsia. N Engl J Med 2017;377:613–22. https://doi.org/10.1056/NEJMoa1704559 doi: 10.1056/NEJMoa1704559. [DOI] [PubMed]
  35. National Institute for Health and Care Excellence. Hypertension in Pregnancy: The Management of Hypertensive Disorders During Pregnancy. Clinical Guideline 107. London: National Institute for Health and Care Excellence; 2010. URL: www.nice.org.uk/guidance/cg107 (accessed 31 March 2016).
  36. National Institute for Health and Care Excellence. Hypertension in Pregnancy: Diagnosis and Management. NICE Guideline 133. London: National Institute for Health and Care Excellence; 2019. URL: www.nice.org.uk/guidance/cg107
  37. Townsend R, Khalil A, Premakumar Y, Allotey J, Snell KIE, Chan C, et al. Prediction of pre-eclampsia: review of reviews. Ultrasound Obstet Gynecol 2019;54:16–27. https://doi.org/10.1002/uog.20117 doi: 10.1002/uog.20117. [DOI] [PubMed]
  38. Allen RE, Rogozinska E, Cleverly K, Aquilina J, Thangaratinam S. Abnormal blood biomarkers in early pregnancy are associated with preeclampsia: a meta-analysis. Eur J Obstet Gynecol Reprod Biol 2014;182:194–201. https://doi.org/10.1016/j.ejogrb.2014.09.027 doi: 10.1016/j.ejogrb.2014.09.027. [DOI] [PubMed]
  39. Velauthar L, Plana MN, Kalidindi M, Zamora J, Thilaganathan B, Illanes SE, et al. First-trimester uterine artery Doppler and adverse pregnancy outcome: a meta-analysis involving 55,974 women. Ultrasound Obstet Gynecol 2014;43:500–7. https://doi.org/10.1002/uog.13275 doi: 10.1002/uog.13275. [DOI] [PubMed]
  40. Conde-Agudelo A, Villar J, Lindheimer M. World Health Organization systematic review of screening tests for preeclampsia. Obstet Gynecol 2004;104:1367–91. https://doi.org/10.1097/01.AOG.0000147599.47713.5d doi: 10.1097/01.AOG.0000147599.47713.5d. [DOI] [PubMed]
  41. Giguère Y, Charland M, Bujold E, Bernard N, Grenier S, Rousseau F, et al. Combining biochemical and ultrasonographic markers in predicting preeclampsia: a systematic review. Clin Chem 2010;56:361–75. https://doi.org/10.1373/clinchem.2009.134080 doi: 10.1373/clinchem.2009.134080. [DOI] [PubMed]
  42. North RA, McCowan LM, Dekker GA, Poston L, Chan EH, Stewart AW, et al. Clinical risk prediction for pre-eclampsia in nulliparous women: development of model in international prospective cohort. BMJ 2011;342:d1875. https://doi.org/10.1136/bmj.d1875 doi: 10.1136/bmj.d1875. [DOI] [PMC free article] [PubMed]
  43. Kleinrouweler CE, Cheong-See FM, Collins GS, Kwee A, Thangaratinam S, Khan KS, et al. Prognostic models in obstetrics: available, but far from applicable. Am J Obstet Gynecol 2016;214:79–90. https://doi.org/10.1016/j.ajog.2015.06.013 doi: 10.1016/j.ajog.2015.06.013. [DOI] [PubMed]
  44. Meertens LJE, Scheepers HCJ, van Kuijk SMJ, Aardenburg R, van Dooren IMA, Langenveld J, et al. External validation and clinical usefulness of first trimester prediction models for the risk of preeclampsia: a prospective cohort study. Fetal Diagn Ther 2019;45:381–93. https://doi.org/10.1159/000490385 doi: 10.1159/000490385. [DOI] [PMC free article] [PubMed]
  45. Farina A, Rapacchia G, Freni Sterrantino A, Pula G, Morano D, Rizzo N. Prospective evaluation of ultrasound and biochemical-based multivariable models for the prediction of late pre-eclampsia. Prenat Diagn 2011;31:1147–52. https://doi.org/10.1002/pd.2849 doi: 10.1002/pd.2849. [DOI] [PubMed]
  46. Herraiz I, Arbués J, Camaño I, Gómez-Montes E, Grañeras A, Galindo A. Application of a first-trimester prediction model for pre-eclampsia based on uterine arteries and maternal history in high-risk pregnancies. Prenat Diagn 2009;29:1123–9. https://doi.org/10.1002/pd.2383 doi: 10.1002/pd.2383. [DOI] [PubMed]
  47. Debray TP, Moons KG, Ahmed I, Koffijberg H, Riley RD. A framework for developing, implementing, and evaluating clinical prediction models in an individual participant data meta-analysis. Stat Med 2013;32:3158–80. https://doi.org/10.1002/sim.5732 doi: 10.1002/sim.5732. [DOI] [PubMed]
  48. Riley RD, Ensor J, Snell KI, Debray TP, Altman DG, Moons KG, Collins GS. External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges. BMJ 2016;353:i3140. https://doi.org/10.1136/bmj.i3140 doi: 10.1136/bmj.i3140. [DOI] [PMC free article] [PubMed]
  49. Debray TP, Riley RD, Rovers MM, Reitsma JB, Moons KG, Cochrane IPD Meta-analysis Methods group. Individual participant data (IPD) meta-analyses of diagnostic and prognostic modelling studies: guidance on their use. PLOS Med 2015;12:e1001886. https://doi.org/10.1371/journal.pmed.1001886 doi: 10.1371/journal.pmed.1001886. [DOI] [PMC free article] [PubMed]
  50. Riley RD, Lambert PC, Abo-Zaid G. Meta-analysis of individual participant data: rationale, conduct, and reporting. BMJ 2010;340:c221. https://doi.org/10.1136/bmj.c221 doi: 10.1136/bmj.c221. [DOI] [PubMed]
  51. Allotey J, Snell KIE, Chan C, Hooper R, Dodds J, Rogozinska E, et al. External validation, update and development of prediction models for pre-eclampsia using an Individual Participant Data (IPD) meta-analysis: the International Prediction of Pregnancy Complication Network (IPPIC pre-eclampsia) protocol. Diagn Progn Res 2017;1:16. https://doi.org/10.1186/s41512-017-0016-z doi: 10.1186/s41512-017-0016-z. [DOI] [PMC free article] [PubMed]
  52. Snell KIE, Allotey J, Smuk M, Hooper R, Chan C, Ahmed A, et al. External validation of prognostic models predicting pre-eclampsia: individual participant data meta-analysis. BMC Med 2020;18:302. https://doi.org/10.1186/s12916-020-01766-9 doi: 10.1186/s12916-020-01766-9. [DOI] [PMC free article] [PubMed]
  53. Altman DG, Vergouwe Y, Royston P, Moons KG. Prognosis and prognostic research: validating a prognostic model. BMJ 2009;338:b605. https://doi.org/10.1136/bmj.b605 doi: 10.1136/bmj.b605. [DOI] [PubMed]
  54. Moons KG, Royston P, Vergouwe Y, Grobbee DE, Altman DG. Prognosis and prognostic research: what, why, and how? BMJ 2009;338:b375. https://doi.org/10.1136/bmj.b375 doi: 10.1136/bmj.b375. [DOI] [PubMed]
  55. Royston P, Moons KG, Altman DG, Vergouwe Y. Prognosis and prognostic research: developing a prognostic model. BMJ 2009;338:b604. https://doi.org/10.1136/bmj.b604 doi: 10.1136/bmj.b604. [DOI] [PubMed]
  56. Moons KG, Altman DG, Reitsma JB, Ioannidis JP, Macaskill P, Steyerberg EW, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med 2015;162:W1–73. https://doi.org/10.7326/M14-0698 doi: 10.7326/M14-0698. [DOI] [PubMed]
  57. Stewart LA, Clarke M, Rovers M, Riley RD, Simmonds M, Stewart G, Tierney JF, PRISMA-IPD Development Group. Preferred Reporting Items for Systematic Review and Meta-Analyses of individual participant data: the PRISMA-IPD statement. JAMA 2015;313:1657–65. https://doi.org/10.1001/jama.2015.3656 doi: 10.1001/jama.2015.3656. [DOI] [PubMed]
  58. Accuracy of Clinical Characteristics, Biochemical and Ultrasound Markers in the Prediction of Pre-eclampsia: an Individual Participant Data (IPD) Meta-analysis. PROSPERO CRD42015029349. 2015. URL: www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42015029349 (accessed 27 March 2017).
  59. Gardosi J, Clausson B, Francis A. The value of customised centiles in assessing perinatal mortality risk associated with parity and maternal size. BJOG 2009;116:1356–63. https://doi.org/10.1111/j.1471-0528.2009.02245.x doi: 10.1111/j.1471-0528.2009.02245.x. [DOI] [PubMed]
  60. Ahmed A. New insights into the etiology of preeclampsia: identification of key elusive factors for the vascular complications. Thromb Res 2011;127(Suppl. 3):72–5. https://doi.org/10.1016/S0049-3848(11)70020-2 doi: 10.1016/S0049-3848(11)70020-2. [DOI] [PubMed]
  61. Bloomenthal D, von Dadelszen P, Liston R, Magee L, Tsang P. The effect of factor V Leiden carriage on maternal and fetal health. CMAJ 2002;167:48–54. [PMC free article] [PubMed]
  62. Bramham K, Parnell B, Nelson-Piercy C, Seed PT, Poston L, Chappell LC. Chronic hypertension and pregnancy outcomes: systematic review and meta-analysis. BMJ 2014;348:g2301. https://doi.org/10.1136/bmj.g2301 doi: 10.1136/bmj.g2301. [DOI] [PMC free article] [PubMed]
  63. Cnossen JS, Morris RK, ter Riet G, Mol BW, van der Post JA, Coomarasamy A, et al. Use of uterine artery Doppler ultrasonography to predict pre-eclampsia and intrauterine growth restriction: a systematic review and bivariable meta-analysis. CMAJ 2008;178:701–11. https://doi.org/10.1503/cmaj.070430 doi: 10.1503/cmaj.070430. [DOI] [PMC free article] [PubMed]
  64. Gallos ID, Sivakumar K, Kilby MD, Coomarasamy A, Thangaratinam S, Vatish M. Pre-eclampsia is associated with, and preceded by, hypertriglyceridaemia: a meta-analysis. BJOG 2013;120:1321–32. https://doi.org/10.1111/1471-0528.12375 doi: 10.1111/1471-0528.12375. [DOI] [PubMed]
  65. Kleinrouweler CE, Wiegerinck MM, Ris-Stalpers C, Bossuyt PM, van der Post JA, von Dadelszen P, et al. 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;119:778–87. https://doi.org/10.1111/j.1471-0528.2012.03311.x doi: 10.1111/j.1471-0528.2012.03311.x. [DOI] [PubMed]
  66. Leeflang MM, Cnossen JS, van der Post JA, Mol BW, Khan KS, ter Riet G. Accuracy of fibronectin tests for the prediction of pre-eclampsia: a systematic review. Eur J Obstet Gynecol Reprod Biol 2007;133:12–19. https://doi.org/10.1016/j.ejogrb.2007.01.003 doi: 10.1016/j.ejogrb.2007.01.003. [DOI] [PubMed]
  67. Meads CA, Cnossen JS, Meher S, Juarez-Garcia A, ter Riet G, Duley L, et al. Methods of prediction and prevention of pre-eclampsia: systematic reviews of accuracy and effectiveness literature with economic modelling. Health Technol Assess 2008;12(6). https://doi.org/10.3310/hta12060 doi: 10.3310/hta12060. [DOI] [PubMed]
  68. Morris RK, Cnossen JS, Langejans M, Robson SC, Kleijnen J, Ter Riet G, et al. Serum screening with Down’s syndrome markers to predict pre-eclampsia and small for gestational age: systematic review and meta-analysis. BMC Pregnancy Childbirth 2008;8:33. https://doi.org/10.1186/1471-2393-8-33 doi: 10.1186/1471-2393-8-33. [DOI] [PMC free article] [PubMed]
  69. Steegers EA, von Dadelszen P, Duvekot JJ, Pijnenborg R. Pre-eclampsia. Lancet 2010;376:631–44. https://doi.org/10.1016/S0140-6736(10)60279-6 doi: 10.1016/S0140-6736(10)60279-6. [DOI] [PubMed]
  70. Thangaratinam S, Langenveld J, Mol BW, Khan KS. Prediction and primary prevention of pre-eclampsia. Best Pract Res Clin Obstet Gynaecol 2011;25:419–33. https://doi.org/10.1016/j.bpobgyn.2011.02.008 doi: 10.1016/j.bpobgyn.2011.02.008. [DOI] [PubMed]
  71. van der Tuuk K, Koopmans CM, Groen H, Aarnoudse JG, van den Berg PP, van Beek JJ, et al. Prediction of progression to a high risk situation in women with gestational hypertension or mild pre-eclampsia at term. Aust N Z J Obstet Gynaecol 2011;51:339–46. https://doi.org/10.1111/j.1479-828X.2011.01311.x doi: 10.1111/j.1479-828X.2011.01311.x. [DOI] [PubMed]
  72. von Dadelszen P, Firoz T, Donnay F, Gordon R, Justus Hofmeyr G, Lalani S, et al. Preeclampsia in low and middle income countries-health services lessons learned from the PRE-EMPT (PRE-Eclampsia-Eclampsia Monitoring, Prevention and Treatment) project. J Obstet Gynaecol Can 2012;34:917–26. https://doi.org/10.1016/S1701-2163(16)35405-6 doi: 10.1016/S1701-2163(16)35405-6. [DOI] [PubMed]
  73. American College of Obstetricians and Gynecologists; Task Force on Hypertension in Pregnancy. Hypertension in pregnancy. Report of the American College of Obstetricians and Gynecologists’ Task Force on Hypertension in Pregnancy. Obstet Gynecol 2013;122:1122–31. doi: 10.1097/01.AOG.0000437382.03963.88. [DOI] [PubMed]
  74. von Dadelszen P, Sawchuck D, Justus Hofmeyr G, Magee LA, Bracken H, Mathai M, et al. PRE-EMPT (PRE-eclampsia-Eclampsia Monitoring, Prevention and Treatment): a low and middle income country initiative to reduce the global burden of maternal, fetal and infant death and disease related to pre-eclampsia. Pregnancy Hypertens 2013;3:199–202. https://doi.org/10.1016/j.preghy.2013.06.002 doi: 10.1016/j.preghy.2013.06.002. [DOI] [PubMed]
  75. GONet: The Global Obstetrics Network. URL: www.globalobstetricsnetwork.org/ (accessed 14 March 2019).
  76. Myatt L, Redman CW, Staff AC, Hansson S, Wilson ML, Laivuori H, et al. Strategy for standardization of preeclampsia research study design. Hypertension 2014;63:1293–301. https://doi.org/10.1161/HYPERTENSIONAHA.113.02664 doi: 10.1161/HYPERTENSIONAHA.113.02664. [DOI] [PubMed]
  77. Gardosi J, Francis A. Customised Centile Calculator. GROW version 8.0.4. Gestation Network; 2018. URL: www.gestation.net (accessed 14 February 2018).
  78. Wolff RF, Moons KGM, Riley RD, Whiting PF, Westwood M, Collins GS, et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med 2019;170:51–8. https://doi.org/10.7326/M18-1376 doi: 10.7326/M18-1376. [DOI] [PubMed]
  79. Resche-Rigon M, White IR. Multiple imputation by chained equations for systematically and sporadically missing multilevel data. Stat Methods Med Res 2018;27:1634–49. https://doi.org/10.1177/0962280216666564 doi: 10.1177/0962280216666564. [DOI] [PMC free article] [PubMed]
  80. Jolani S, Debray TP, Koffijberg H, van Buuren S, Moons KG. Imputation of systematically missing predictors in an individual participant data meta-analysis: a generalized approach using MICE. Stat Med 2015;34:1841–63. https://doi.org/10.1002/sim.6451 doi: 10.1002/sim.6451. [DOI] [PubMed]
  81. Meng XL. Multiple-imputation inferences with uncongenial sources of input. Statist Sci 1994;9:538–58. https://doi.org/10.1214/ss/1177010269 doi: 10.1214/ss/1177010269. [DOI]
  82. Little RJA, Rubin DB. Statistical Analysis with Missing Data. 2nd edn. New York, NY: John Wiley; 2002. https://doi.org/10.1002/9781119013563 doi: 10.1002/9781119013563. [DOI]
  83. Hosmer DW, Lemeshow S. Applied Logistic Regression. 2nd edn. Hoboken, NJ: John Wiley & Sons; 2000. https://doi.org/10.1002/0471722146 doi: 10.1002/0471722146. [DOI]
  84. Rubin DB. Multiple Imputation for Nonresponse in Surveys. New York, NY: Wiley; 1987. https://doi.org/10.1002/9780470316696 doi: 10.1002/9780470316696. [DOI]
  85. Snell KI, Ensor J, Debray TP, Moons KG, Riley RD. Meta-analysis of prediction model performance across multiple studies: which scale helps ensure between-study normality for the C-statistic and calibration measures? Stat Methods Med Res 2018;27:3505–22. https://doi.org/10.1177/0962280217705678 doi: 10.1177/0962280217705678. [DOI] [PMC free article] [PubMed]
  86. Debray TP, Damen JA, Snell KI, Ensor J, Hooft L, Reitsma JB, et al. A guide to systematic review and meta-analysis of prediction model performance. BMJ 2017;356:i6460. https://doi.org/10.1136/bmj.i6460 doi: 10.1136/bmj.i6460. [DOI] [PubMed]
  87. Snell KI, Hua H, Debray TP, Ensor J, Look MP, Moons KG, Riley RD. Multivariate meta-analysis of individual participant data helped externally validate the performance and implementation of a prediction model. J Clin Epidemiol 2016;69:40–50. https://doi.org/10.1016/j.jclinepi.2015.05.009 doi: 10.1016/j.jclinepi.2015.05.009. [DOI] [PMC free article] [PubMed]
  88. Hartung J, Knapp G. A refined method for the meta-analysis of controlled clinical trials with binary outcome. Stat Med 2001;20:3875–89. https://doi.org/10.1002/sim.1009 doi: 10.1002/sim.1009. [DOI] [PubMed]
  89. Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making 2006;26:565–74. https://doi.org/10.1177/0272989X06295361 doi: 10.1177/0272989X06295361. [DOI] [PMC free article] [PubMed]
  90. Vickers AJ, Van Calster B, Steyerberg EW. Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests. BMJ 2016;352:i6. https://doi.org/10.1136/bmj.i6 doi: 10.1136/bmj.i6. [DOI] [PMC free article] [PubMed]
  91. Quartagno M, Carpenter J. Multilevel Joint Modelling Multiple Imputation: Package ‘jomo’, Version 2.6–6, License GPL-2. URL: https://cran.r-project.org/web/packages/jomo/jomo.pdf (accessed 3 May 2018).
  92. Quartagno M, Carpenter JR. Multiple imputation for IPD meta-analysis: allowing for heterogeneity and studies with missing covariates. Stat Med 2016;35:2938–54. https://doi.org/10.1002/sim.6837 doi: 10.1002/sim.6837. [DOI] [PMC free article] [PubMed]
  93. Sauerbrei W. The use of resampling methods to simplify regression models in medical statistics. J Royal Stat Soc 1999;48:313–29. https://doi.org/10.1111/1467-9876.00155 doi: 10.1111/1467-9876.00155. [DOI]
  94. Heinze G, Wallisch C, Dunkler D. Variable selection – a review and recommendations for the practicing statistician. Biom J 2018;60:431–49. https://doi.org/10.1002/bimj.201700067 doi: 10.1002/bimj.201700067. [DOI] [PMC free article] [PubMed]
  95. Wood AM, Royston P, White IR. The estimation and use of predictions for the assessment of model performance using large samples with multiply imputed data. Biom J 2015;57:614–32. https://doi.org/10.1002/bimj.201400004 doi: 10.1002/bimj.201400004. [DOI] [PMC free article] [PubMed]
  96. Royston P, Parmar MK, Sylvester R. Construction and validation of a prognostic model across several studies, with an application in superficial bladder cancer. Stat Med 2004;23:907–26. https://doi.org/10.1002/sim.1691 doi: 10.1002/sim.1691. [DOI] [PubMed]
  97. Harrell FE. Regression Modeling Strategies, with Applications to Linear Models, Logistic Regression, and Survival Analysis. 2nd edn. New York, NY: Springer; 2015. https://doi.org/10.1007/978-3-319-19425-7_10 doi: 10.1007/978-3-319-19425-7_10. [DOI]
  98. Steyerberg EW. Clinical Prediction Models. A Practical Approach to Development, Validation, and Updating. New York, NY: Springer; 2009.
  99. Riley RD, van der Windt D, Croft P, Moons KGM, editors. Prognosis Research in Healthcare: Concepts, Methods and Impact. Oxford: Oxford University Press; 2019. https://doi.org/10.1093/med/9780198796619.001.0001 doi: 10.1093/med/9780198796619.001.0001. [DOI]
  100. IntHout J, Ioannidis JP, Rovers MM, Goeman JJ. Plea for routinely presenting prediction intervals in meta-analysis. BMJ Open 2016;6:e010247. https://doi.org/10.1136/bmjopen-2015-010247 doi: 10.1136/bmjopen-2015-010247. [DOI] [PMC free article] [PubMed]
  101. IntHout J, Ioannidis JP, Borm GF. The Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis is straightforward and considerably outperforms the standard DerSimonian-Laird method. BMC Med Res Methodol 2014;14:25. https://doi.org/10.1186/1471-2288-14-25 doi: 10.1186/1471-2288-14-25. [DOI] [PMC free article] [PubMed]
  102. Knapp G, Hartung J. Improved tests for a random effects meta-regression with a single covariate. Stat Med 2003;22:2693–710. https://doi.org/10.1002/sim.1482 doi: 10.1002/sim.1482. [DOI] [PubMed]
  103. Fraser A, Macdonald-Wallis C, Tilling K, Boyd A, Golding J, Davey Smith G, et al. Cohort profile: the Avon Longitudinal Study of Parents and Children: ALSPAC mothers cohort. Int J Epidemiol 2013;42:97–110. https://doi.org/10.1093/ije/dys066 doi: 10.1093/ije/dys066. [DOI] [PMC free article] [PubMed]
  104. Better Outcomes Registry & Network (BORN) Ontario. URL: www.bornontario.ca/en/about-born/ (accessed 14 March 2019).
  105. Japan Society of Obstetrics and Gynecology. URL: www.jsog.or.jp/modules/en/index.php?content_id=1 (accessed 14 March 2019).
  106. Carter J, Seed PT, Tribe RM, David A, Lachelin G, Shennan AH, et al. Saliva progesterone for prediction of spontaneous preterm birth: the POPPY study. Pregnancy Outcome Poster Abstracts. BJOG 2017;124:122–54. https://doi.org/10.1111/1471-0528.14589 doi: 10.1111/1471-0528.14589. [DOI]
  107. Al-Amin A, Rolnik DL, Black C, White A, Stolarek C, Brennecke S, da Silva Costa F. Accuracy of second trimester prediction of preterm preeclampsia by three different screening algorithms. Aust N Z J Obstet Gynaecol 2018;58:192–6. https://doi.org/10.1111/ajo.12689 doi: 10.1111/ajo.12689. [DOI] [PubMed]
  108. Allen RE, Zamora J, Arroyo-Manzano D, Velauthar L, Allotey J, Thangaratinam S, Aquilina J. External validation of preexisting first trimester preeclampsia prediction models. Eur J Obstet Gynecol Reprod Biol 2017;217:119–25. https://doi.org/10.1016/j.ejogrb.2017.08.031 doi: 10.1016/j.ejogrb.2017.08.031. [DOI] [PubMed]
  109. Andersen LB, Dechend R, Jørgensen JS, Luef BM, Nielsen J, Barington T, Christesen HT. Prediction of preeclampsia with angiogenic biomarkers. Results from the prospective Odense Child Cohort. Hypertens Pregnancy 2016;35:405–19. https://doi.org/10.3109/10641955.2016.1167219 doi: 10.3109/10641955.2016.1167219. [DOI] [PubMed]
  110. Antsaklis A, Daskalakis G, Tzortzis E, Michalas S. The effect of gestational age and placental location on the prediction of pre-eclampsia by uterine artery Doppler velocimetry in low-risk nulliparous women. Ultrasound Obstet Gynecol 2000;16:635–9. https://doi.org/10.1046/j.1469-0705.2000.00288.x doi: 10.1046/j.1469-0705.2000.00288.x. [DOI] [PubMed]
  111. Arenas J, Fernández-iñarea J, Rodríguez-mon C, Duplá B, Díez E, González-garcía A. Cribado con doppler de las arterias uterinas para la predicción de complicaciones de la gestación. Clínicae Investigación en Ginecología y Obstetricia 2003;30:178–84. https://doi.org/10.1016/S0210-573X(03)77255-4 doi: 10.1016/S0210-573X(03)77255-4. [DOI]
  112. Askie LM, Duley L, Henderson-Smart DJ, Stewart LA, PARIS Collaborative Group. Antiplatelet agents for prevention of pre-eclampsia: a meta-analysis of individual patient data. Lancet 2007;369:1791–8. https://doi.org/10.1016/S0140-6736(07)60712-0 doi: 10.1016/S0140-6736(07)60712-0. [DOI] [PubMed]
  113. Audibert F, Boucoiran I, An N, Aleksandrov N, Delvin E, Bujold E, Rey E. Screening for preeclampsia using first-trimester serum markers and uterine artery Doppler in nulliparous women. Am J Obstet Gynecol 2010;203:383.e1–8. https://doi.org/10.1016/j.ajog.2010.06.014 doi: 10.1016/j.ajog.2010.06.014. [DOI] [PubMed]
  114. Ayorinde AA, Wilde K, Lemon J, Campbell D, Bhattacharya S. Data resource profile: the Aberdeen Maternity and Neonatal Databank (AMND). Int J Epidemiol 2016;45:389–94. https://doi.org/10.1093/ije/dyv356 doi: 10.1093/ije/dyv356. [DOI] [PubMed]
  115. Baschat AA, Magder LS, Doyle LE, Atlas RO, Jenkins CB, Blitzer MG. Prediction of preeclampsia utilizing the first trimester screening examination. Am J Obstet Gynecol 2014;211:514.e1–7. https://doi.org/10.1016/j.ajog.2014.04.018 doi: 10.1016/j.ajog.2014.04.018. [DOI] [PubMed]
  116. Brown MA, Mackenzie C, Dunsmuir W, Roberts L, Ikin K, Matthews J, et al. Can we predict recurrence of pre-eclampsia or gestational hypertension? BJOG 2007;114:984–93. https://doi.org/10.1111/j.1471-0528.2007.01376.x doi: 10.1111/j.1471-0528.2007.01376.x. [DOI] [PubMed]
  117. Cameroni I, Roncaglia N, Crippa I, Orsenigo F, Locatelli A, Vergani P, et al. P32.05: Uterine artery Doppler in a risk population: what’s its role in the prediction of severe pregnancy complications? Ultrasound Obstet Gynecol 2008;32:421–2. https://doi.org/10.1002/uog.5992 doi: 10.1002/uog.5992. [DOI]
  118. Caradeux J, Serra R, Nien JK, Pérez-Sepulveda A, Schepeler M, Guerra F, et al. First trimester prediction of early onset preeclampsia using demographic, clinical, and sonographic data: a cohort study. Prenat Diagn 2013;33:732–6. https://doi.org/10.1002/pd.4113 doi: 10.1002/pd.4113. [DOI] [PubMed]
  119. Carbillon L. The imbalance of circulating angiogenic/antiangiogenic factors is mild or absent in obese women destined to develop preeclampsia. Hypertens Pregnancy 2014;33:524. https://doi.org/10.3109/10641955.2013.872252 doi: 10.3109/10641955.2013.872252. [DOI] [PubMed]
  120. Caritis S, Sibai B, Hauth J, Lindheimer MD, Klebanoff M, Thom E, et al. Low-dose aspirin to prevent preeclampsia in women at high risk. National Institute of Child Health and Human Development Network of Maternal-Fetal Medicine Units. N Engl J Med 1998;338:701–5. https://doi.org/10.1056/NEJM199803123381101 doi: 10.1056/NEJM199803123381101. [DOI] [PubMed]
  121. Chappell LC, Seed PT, Briley AL, Kelly FJ, Lee R, Hunt BJ, et al. Effect of antioxidants on the occurrence of pre-eclampsia in women at increased risk: a randomised trial. Lancet 1999;354:810–16. https://doi.org/10.1016/S0140-6736(99)80010-5 doi: 10.1016/S0140-6736(99)80010-5. [DOI] [PubMed]
  122. Chiswick C, Reynolds RM, Denison F, Drake AJ, Forbes S, Newby DE, et al. Effect of metformin on maternal and fetal outcomes in obese pregnant women (EMPOWaR): a randomised, double-blind, placebo-controlled trial. Lancet Diabetes Endocrinol 2015;3:778–86. https://doi.org/10.1016/S2213-8587(15)00219-3 doi: 10.1016/S2213-8587(15)00219-3. [DOI] [PMC free article] [PubMed]
  123. Conserva V, Muggiasca M, Arrigoni L, Mantegazza V, Rossi E, Ferrazzi E. Recurrence and severity of abnormal pregnancy outcome in patients treated by low-molecular-weight heparin: a prospective pilot study. J Matern Fetal Neonatal Med 2012;25:1467–73. https://doi.org/10.3109/14767058.2011.643326 doi: 10.3109/14767058.2011.643326. [DOI] [PubMed]
  124. Facchinetti F, Marozio L, Frusca T, Grandone E, Venturini P, Tiscia GL, et al. Maternal thrombophilia and the risk of recurrence of preeclampsia. Am J Obstet Gynecol 2009;200:46.e1–5. https://doi.org/10.1016/j.ajog.2008.07.032 doi: 10.1016/j.ajog.2008.07.032. [DOI] [PubMed]
  125. Figueiró-Filho EA, Oliveira VMd, Coelho LR, Breda I. Marcadores séricos de trombofilias hereditárias e anticorpos antifosfolípides em gestantes com antecedentes de pré-eclâmpsia grave. Revista Brasileira de Ginecologia e Obstetrícia 2012;34:40–6. https://doi.org/10.1590/S0100-72032012000100008 doi: 10.1590/S0100-72032012000100008. [DOI] [PubMed]
  126. Giguère Y, Massé J, Thériault S, Bujold E, Lafond J, Rousseau F, Forest JC. Screening for pre-eclampsia early in pregnancy: performance of a multivariable model combining clinical characteristics and biochemical markers. BJOG 2015;122:402–10. https://doi.org/10.1111/1471-0528.13050 doi: 10.1111/1471-0528.13050. [DOI] [PubMed]
  127. Girchenko P, Lahti M, Tuovinen S, Savolainen K, Lahti J, Binder EB, et al. Cohort profile: prediction and prevention of preeclampsia and intrauterine growth restriction (PREDO) study. Int J Epidemiol 2017;46:1380–1381g. https://doi.org/10.1093/ije/dyw154 doi: 10.1093/ije/dyw154. [DOI] [PubMed]
  128. Goetzinger KR, Singla A, Gerkowicz S, Dicke JM, Gray DL, Odibo AO. Predicting the risk of pre-eclampsia between 11 and 13 weeks’ gestation by combining maternal characteristics and serum analytes, PAPP-A and free β-hCG. Prenat Diagn 2010;30:1138–42. https://doi.org/10.1002/pd.2627 doi: 10.1002/pd.2627. [DOI] [PMC free article] [PubMed]
  129. Goffinet F, Aboulker D, Paris-Llado J, Bucourt M, Uzan M, Papiernik E, Bréart G. Screening with a uterine Doppler in low risk pregnant women followed by low dose aspirin in women with abnormal results: a multicenter randomised controlled trial. BJOG 2001;108:510–18. https://doi.org/10.1111/j.1471-0528.2001.00116.x doi: 10.1111/j.1471-0528.2001.00116.x. [DOI] [PubMed]
  130. Gurgel Alves JA, Praciano de Sousa PC, Bezerra Maia e Holanda Moura S, Kane SC, da Silva Costa F. First-trimester maternal ophthalmic artery Doppler analysis for prediction of pre-eclampsia. Ultrasound Obstet Gynecol 2014;44:411–18. https://doi.org/10.1002/uog.13338 doi: 10.1002/uog.13338. [DOI] [PubMed]
  131. Holzman C, Bullen B, Fisher R, Paneth N, Reuss L, Prematurity Study Group. Pregnancy outcomes and community health: the POUCH study of preterm delivery. Paediatr Perinat Epidemiol 2001;15(Suppl. 2):136–58. https://doi.org/10.1046/j.1365-3016.2001.00014.x doi: 10.1046/j.1365-3016.2001.00014.x. [DOI] [PubMed]
  132. Huang T, Hoffman B, Meschino W, Kingdom J, Okun N. Prediction of adverse pregnancy outcomes by combinations of first and second trimester biochemistry markers used in the routine prenatal screening of Down syndrome. Prenat Diagn 2010;30:471–7. https://doi.org/10.1002/pd.2505 doi: 10.1002/pd.2505. [DOI] [PubMed]
  133. Jääskeläinen T, Heinonen S, Hämäläinen E, Pulkki K, Romppanen J, Laivuori H, FINNPEC. Angiogenic profile in the Finnish Genetics of Pre-Eclampsia Consortium (FINNPEC) cohort. Pregnancy Hypertens 2018;14:252–9. https://doi.org/10.1016/j.preghy.2018.03.004 doi: 10.1016/j.preghy.2018.03.004. [DOI] [PubMed]
  134. Jaddoe VW, van Duijn CM, Franco OH, van der Heijden AJ, van Iizendoorn MH, de Jongste JC, et al. The Generation R Study: design and cohort update 2012. Eur J Epidemiol 2012;27:739–56. https://doi.org/10.1007/s10654-012-9735-1 doi: 10.1007/s10654-012-9735-1. [DOI] [PubMed]
  135. Jenum AK, Sletner L, Voldner N, Vangen S, Mørkrid K, Andersen LF, et al. The STORK Groruddalen research programme: a population-based cohort study of gestational diabetes, physical activity, and obesity in pregnancy in a multiethnic population. Rationale, methods, study population, and participation rates. Scand J Public Health 2010;38(Suppl. 5):60–70. https://doi.org/10.1177/1403494810378921 doi: 10.1177/1403494810378921. [DOI] [PubMed]
  136. Olsen J, Melbye M, Olsen SF, Sørensen TI, Aaby P, Andersen AM, et al. The Danish National Birth Cohort – its background, structure and aim. Scand J Public Health 2001;29:300–7. https://doi.org/10.1177/14034948010290040201 doi: 10.1177/14034948010290040201. [DOI] [PubMed]
  137. Khan F, Belch JJ, MacLeod M, Mires G. Changes in endothelial function precede the clinical disease in women in whom preeclampsia develops. Hypertension 2005;46:1123–8. https://doi.org/10.1161/01.HYP.0000186328.90667.95 doi: 10.1161/01.HYP.0000186328.90667.95. [DOI] [PubMed]
  138. Langenveld J, Buttinger A, van der Post J, Wolf H, Mol BW, Ganzevoort W. Recurrence risk and prediction of a delivery under 34 weeks of gestation after a history of a severe hypertensive disorder. BJOG 2011;118:589–95. https://doi.org/10.1111/j.1471-0528.2010.02842.x doi: 10.1111/j.1471-0528.2010.02842.x. [DOI] [PubMed]
  139. Lecarpentier E, Tsatsaris V, Goffinet F, Cabrol D, Sibai B, Haddad B. Risk factors of superimposed preeclampsia in women with essential chronic hypertension treated before pregnancy. PLOS ONE 2013;8:e62140. https://doi.org/10.1371/journal.pone.0062140 doi: 10.1371/journal.pone.0062140. [DOI] [PMC free article] [PubMed]
  140. Llurba E, Carreras E, Gratacós E, Juan M, Astor J, Vives A, et al. Maternal history and uterine artery Doppler in the assessment of risk for development of early- and late-onset preeclampsia and intrauterine growth restriction. Obstet Gynecol Int 2009;2009:275613. https://doi.org/10.1155/2009/275613 doi: 10.1155/2009/275613. [DOI] [PMC free article] [PubMed]
  141. Lykke JA, Paidas MJ, Langhoff-Roos J. Recurring complications in second pregnancy. Obstet Gynecol 2009;113:1217–24. https://doi.org/10.1097/AOG.0b013e3181a66f2d doi: 10.1097/AOG.0b013e3181a66f2d. [DOI] [PubMed]
  142. Magnus P, Irgens LM, Haug K, Nystad W, Skjaerven R, Stoltenberg C, MoBa Study Group. Cohort profile: the Norwegian Mother and Child cohort study (MoBa). Int J Epidemiol 2006;35:1146–50. https://doi.org/10.1093/ije/dyl170 doi: 10.1093/ije/dyl170. [DOI] [PubMed]
  143. Makrides M, Gibson RA, McPhee AJ, Yelland L, Quinlivan J, Ryan P, DOMInO Investigative Team. Effect of DHA supplementation during pregnancy on maternal depression and neurodevelopment of young children: a randomized controlled trial. JAMA 2010;304:1675–83. https://doi.org/10.1001/jama.2010.1507 doi: 10.1001/jama.2010.1507. [DOI] [PubMed]
  144. Massé J, Forest JC, Moutquin JM, Marcoux S, Brideau NA, Bélanger M. A prospective study of several potential biologic markers for early prediction of the development of preeclampsia. Am J Obstet Gynecol 1993;169:501–8. https://doi.org/10.1016/0002-9378(93)90608-L doi: 10.1016/0002-9378(93)90608-L. [DOI] [PubMed]
  145. Mbah AK, Sharma PP, Alio AP, Fombo DW, Bruder K, Salihu HM. Previous cesarean section, gestational age at first delivery and subsequent risk of pre-eclampsia in obese mothers. Arch Gynecol Obstet 2012;285:1375–81. https://doi.org/10.1007/s00404-011-2161-x doi: 10.1007/s00404-011-2161-x. [DOI] [PubMed]
  146. Mone F, Mulcahy C, McParland P, Stanton A, Culliton M, Downey P, et al. An open-label randomized-controlled trial of low dose aspirin with an early screening test for pre-eclampsia and growth restriction (TEST): trial protocol. Contemp Clin Trials 2016;49:143–8. https://doi.org/10.1016/j.cct.2016.07.003 doi: 10.1016/j.cct.2016.07.003. [DOI] [PubMed]
  147. Odibo AO, Zhong Y, Goetzinger KR, Odibo L, Bick JL, Bower CR, Nelson DM. First-trimester placental protein 13, PAPP-A, uterine artery Doppler and maternal characteristics in the prediction of pre-eclampsia. Placenta 2011;32:598–602. https://doi.org/10.1016/j.placenta.2011.05.006 doi: 10.1016/j.placenta.2011.05.006. [DOI] [PMC free article] [PubMed]
  148. Ohkuchi A, Minakami H, Sato I, Mori H, Nakano T, Tateno M. Predicting the risk of pre-eclampsia and a small-for-gestational-age infant by quantitative assessment of the diastolic notch in uterine artery flow velocity waveforms in unselected women. Ultrasound Obstet Gynecol 2000;16:171–8. https://doi.org/10.1046/j.1469-0705.2000.00192.x doi: 10.1046/j.1469-0705.2000.00192.x. [DOI] [PubMed]
  149. Poston L, Bell R, Croker H, Flynn AC, Godfrey KM, Goff L, et al. Effect of a behavioural intervention in obese pregnant women (the UPBEAT study): a multicentre, randomised controlled trial. Lancet Diabetes Endocrinol 2015;3:767–77. https://doi.org/10.1016/S2213-8587(15)00227-2 doi: 10.1016/S2213-8587(15)00227-2. [DOI] [PubMed]
  150. Poston L, Briley AL, Seed PT, Kelly FJ, Shennan AH, Vitamins in Pre-eclampsia (VIP) Trial Consortium. Vitamin C and vitamin E in pregnant women at risk for pre-eclampsia (VIP trial): randomised placebo-controlled trial. Lancet 2006;367:1145–54. https://doi.org/10.1016/S0140-6736(06)68433-X doi: 10.1016/S0140-6736(06)68433-X. [DOI] [PubMed]
  151. Prefumo F, Fratelli N, Ganapathy R, Bhide A, Frusca T, Thilaganathan B. First trimester uterine artery Doppler in women with previous pre-eclampsia. Acta Obstet Gynecol Scand 2008;87:1271–5. https://doi.org/10.1080/00016340802460347 doi: 10.1080/00016340802460347. [DOI] [PubMed]
  152. Rang S, van Montfrans GA, Wolf H. Serial hemodynamic measurement in normal pregnancy, preeclampsia, and intrauterine growth restriction. Am J Obstet Gynecol 2008;198:519.e1–9. https://doi.org/10.1016/j.ajog.2007.11.014 doi: 10.1016/j.ajog.2007.11.014. [DOI] [PubMed]
  153. Rocha RS, Gurgel Alves JA, Bezerra Maia e Holanda Moura S, Araujo Júnior E, Peixoto AB, Santana EFM, et al. Simple approach based on maternal characteristics and mean arterial pressure for the prediction of preeclampsia in the first trimester of pregnancy. J Perinat Med 2017;45:843–9. https://doi.org/10.1515/jpm-2016-0418 doi: 10.1515/jpm-2016-0418. [DOI] [PubMed]
  154. Rocha RS, Gurgel Alves JA, Bezerra Maia e Holanda Moura S, Araujo Júnior E, Martins WP, Vasconcelos CTM, et al. Comparison of three algorithms for prediction preeclampsia in the first trimester of pregnancy. Pregnancy Hypertens 2017;10:113–17. https://doi.org/10.1016/j.preghy.2017.07.146 doi: 10.1016/j.preghy.2017.07.146. [DOI] [PubMed]
  155. Rumbold AR, Crowther CA, Haslam RR, Dekker GA, Robinson JS, ACTS Study Group. Vitamins C and E and the risks of preeclampsia and perinatal complications. N Engl J Med 2006;354:1796–806. https://doi.org/10.1056/NEJMoa054186 doi: 10.1056/NEJMoa054186. [DOI] [PubMed]
  156. Salim R, Czarnowicki T, Nachum Z, Shalev E. The impact of close surveillance on pregnancy outcome among women with a prior history of antepartum complications attributed to thrombosis: a cohort study. Reprod Biol Endocrinol 2008;6:55. https://doi.org/10.1186/1477-7827-6-55 doi: 10.1186/1477-7827-6-55. [DOI] [PMC free article] [PubMed]
  157. Savitri AI, Zuithoff P, Browne JL, Amelia D, Baharuddin M, Grobbee DE, Uiterwaal CS. Does pre-pregnancy BMI determine blood pressure during pregnancy? A prospective cohort study. BMJ Open 2016;6:e011626. https://doi.org/10.1136/bmjopen-2016-011626 doi: 10.1136/bmjopen-2016-011626. [DOI] [PMC free article] [PubMed]
  158. Ferrazzani S, D’Alessio MC, Fatigante G, Soreca G, De Carolis S, Paradisi G, et al. Prophylaxis of recurrent preeclampsia: low-molecular-weight heparin plus low-dose aspirin versus low-dose aspirin alone. Hypertens Pregnancy 2006;25:115–27. https://doi.org/10.1080/10641950600745517 doi: 10.1080/10641950600745517. [DOI] [PubMed]
  159. Sibai BM, Caritis SN, Thom E, Klebanoff M, McNellis D, Rocco L, et al. Prevention of preeclampsia with low-dose aspirin in healthy, nulliparous pregnant women. The National Institute of Child Health and Human Development Network of Maternal-Fetal Medicine Units. N Engl J Med 1993;329:1213–8. https://doi.org/10.1056/NEJM199310213291701 doi: 10.1056/NEJM199310213291701. [DOI] [PubMed]
  160. Skråstad RB, Hov GG, Blaas HG, Romundstad PR, Salvesen KÅ. Risk assessment for preeclampsia in nulliparous women at 11–13 weeks gestational age: prospective evaluation of two algorithms. BJOG 2015;122:1781–8. https://doi.org/10.1111/1471-0528.13194 doi: 10.1111/1471-0528.13194. [DOI] [PubMed]
  161. Sovio U, White IR, Dacey A, Pasupathy D, Smith GCS. Screening for fetal growth restriction with universal third trimester ultrasonography in nulliparous women in the Pregnancy Outcome Prediction (POP) study: a prospective cohort study. Lancet 2015;386:2089–97. https://doi.org/10.1016/S0140-6736(15)00131-2 doi: 10.1016/S0140-6736(15)00131-2. [DOI] [PMC free article] [PubMed]
  162. Staff AC, Braekke K, Harsem NK, Lyberg T, Holthe MR. Circulating concentrations of sFlt1 (soluble fms-like tyrosine kinase 1) in fetal and maternal serum during pre-eclampsia. Eur J Obstet Gynecol Reprod Biol 2005;122:33–9. https://doi.org/10.1016/j.ejogrb.2004.11.015 doi: 10.1016/j.ejogrb.2004.11.015. [DOI] [PubMed]
  163. Stirrup OT, Khalil A, D’Antonio F, Thilaganathan B, Southwest Thames Obstetric Research Collaborative (STORK). Fetal growth reference ranges in twin pregnancy: analysis of the Southwest Thames Obstetric Research Collaborative (STORK) multiple pregnancy cohort. Ultrasound Obstet Gynecol 2015;45:301–7. https://doi.org/10.1002/uog.14640 doi: 10.1002/uog.14640. [DOI] [PubMed]
  164. Trogstad L, Skrondal A, Stoltenberg C, Magnus P, Nesheim BI, Eskild A. Recurrence risk of preeclampsia in twin and singleton pregnancies. Am J Med Genet A 2004;126A:41–5. https://doi.org/10.1002/ajmg.a.20512 doi: 10.1002/ajmg.a.20512. [DOI] [PubMed]
  165. Van Der Linden EL, Browne JL, Vissers KM, Antwi E, Agyepong IA, Grobbee DE, Klipstein-Grobusch K. Maternal body mass index and adverse pregnancy outcomes: a Ghanaian cohort study. Obesity 2016;24:215–22. https://doi.org/10.1002/oby.21210 doi: 10.1002/oby.21210. [DOI] [PubMed]
  166. van Kuijk SM, Delahaije DH, Dirksen CD, Scheepers HC, Spaanderman ME, Ganzevoort W, et al. External validation of a model for periconceptional prediction of recurrent early-onset preeclampsia. Hypertens Pregnancy 2014;33:265–76. https://doi.org/10.3109/10641955.2013.872253 doi: 10.3109/10641955.2013.872253. [DOI] [PubMed]
  167. van Kuijk SM, Nijdam ME, Janssen KJ, Sep SJ, Peeters LL, Delahaije DH, et al. A model for preconceptional prediction of recurrent early-onset preeclampsia: derivation and internal validation. Reprod Sci 2011;18:1154–9. https://doi.org/10.1177/1933719111410708 doi: 10.1177/1933719111410708. [DOI] [PubMed]
  168. van Oostwaard MF, Langenveld J, Bijloo R, Wong KM, Scholten I, Loix S, et al. Prediction of recurrence of hypertensive disorders of pregnancy between 34 and 37 weeks of gestation: a retrospective cohort study. BJOG 2012;119:840–7. https://doi.org/10.1111/j.1471-0528.2012.03312.x doi: 10.1111/j.1471-0528.2012.03312.x. [DOI] [PubMed]
  169. van Oostwaard MF, Langenveld J, Schuit E, Wigny K, Van Susante H, Beune I, et al. Prediction of recurrence of hypertensive disorders of pregnancy in the term period, a retrospective cohort study. Pregnancy Hypertens 2014;4:194–202. https://doi.org/10.1016/j.preghy.2014.04.001 doi: 10.1016/j.preghy.2014.04.001. [DOI] [PubMed]
  170. Vatten LJ, Eskild A, Nilsen TI, Jeansson S, Jenum PA, Staff AC. Changes in circulating level of angiogenic factors from the first to second trimester as predictors of preeclampsia. Am J Obstet Gynecol 2007;196:239.e1–6. https://doi.org/10.1016/j.ajog.2006.10.909 doi: 10.1016/j.ajog.2006.10.909. [DOI] [PubMed]
  171. Verlohren S, Galindo A, Schlembach D, Zeisler H, Herraiz I, Moertl MG, et al. An automated method for the determination of the sFlt-1/PIGF ratio in the assessment of preeclampsia. Am J Obstet Gynecol 2010;202:161.e1–161.e11. https://doi.org/10.1016/j.ajog.2009.09.016 doi: 10.1016/j.ajog.2009.09.016. [DOI] [PubMed]
  172. Verlohren S, Herraiz I, Lapaire O, Schlembach D, Moertl M, Zeisler H, et al. The sFlt-1/PlGF ratio in different types of hypertensive pregnancy disorders and its prognostic potential in preeclamptic patients. Am J Obstet Gynecol 2012;206:58.e1–8. https://doi.org/10.1016/j.ajog.2011.07.037 doi: 10.1016/j.ajog.2011.07.037. [DOI] [PubMed]
  173. Vinter CA, Jensen DM, Ovesen P, Beck-Nielsen H, Jørgensen JS. The LiP (Lifestyle in Pregnancy) study: a randomized controlled trial of lifestyle intervention in 360 obese pregnant women. Diabetes Care 2011;34:2502–7. https://doi.org/10.2337/dc11-1150 doi: 10.2337/dc11-1150. [DOI] [PMC free article] [PubMed]
  174. Vollebregt KC, Gisolf J, Guelen I, Boer K, van Montfrans G, Wolf H. Limited accuracy of the hyperbaric index, ambulatory blood pressure and sphygmomanometry measurements in predicting gestational hypertension and preeclampsia. J Hypertens 2010;28:127–34. https://doi.org/10.1097/HJH.0b013e32833266fc doi: 10.1097/HJH.0b013e32833266fc. [DOI] [PubMed]
  175. Widmer M, Cuesta C, Khan KS, Conde-Agudelo A, Carroli G, Fusey S, et al. Accuracy of angiogenic biomarkers at ≤ 20 weeks’ gestation in predicting the risk of pre-eclampsia: a WHO multicentre study. Pregnancy Hypertens 2015;5:330–8. https://doi.org/10.1016/j.preghy.2015.09.004 doi: 10.1016/j.preghy.2015.09.004. [DOI] [PubMed]
  176. Wright E, Audette MC, Ye XY, Keating S, Hoffman B, Lye SJ, et al. Maternal vascular malperfusion and adverse perinatal outcomes in low-risk nulliparous women. Obstet Gynecol 2017;130:1112–20. https://doi.org/10.1097/AOG.0000000000002264 doi: 10.1097/AOG.0000000000002264. [DOI] [PubMed]
  177. Wright J, Small N, Raynor P, Tuffnell D, Bhopal R, Cameron N, et al. Cohort Profile: the Born in Bradford multi-ethnic family cohort study. Int J Epidemiol 2013;42:978–91. https://doi.org/10.1093/ije/dys112 doi: 10.1093/ije/dys112. [DOI] [PubMed]
  178. Zhang J, Troendle JF, Levine RJ. Risks of hypertensive disorders in the second pregnancy. Paediatr Perinat Epidemiol 2001;15:226–31. https://doi.org/10.1046/j.1365-3016.2001.00347.x doi: 10.1046/j.1365-3016.2001.00347.x. [DOI] [PubMed]
  179. Cnossen JS, Leeflang MM, de Haan EE, Mol BW, van der Post JA, Khan KS, ter Riet G. Accuracy of body mass index in predicting pre-eclampsia: bivariate meta-analysis. BJOG 2007;114:1477–85. https://doi.org/10.1111/j.1471-0528.2007.01483.x doi: 10.1111/j.1471-0528.2007.01483.x. [DOI] [PubMed]
  180. O’Brien TE, Ray JG, Chan WS. Maternal body mass index and the risk of preeclampsia: a systematic overview. Epidemiology 2003;14:368–74. https://doi.org/10.1097/00001648-200305000-00020 doi: 10.1097/00001648-200305000-00020. [DOI] [PubMed]
  181. Wang Z, Wang P, Liu H, He X, Zhang J, Yan H, et al. Maternal adiposity as an independent risk factor for pre-eclampsia: a meta-analysis of prospective cohort studies. Obes Rev 2013;14:508–21. https://doi.org/10.1111/obr.12025 doi: 10.1111/obr.12025. [DOI] [PubMed]
  182. Duckitt K, Harrington D. Risk factors for pre-eclampsia at antenatal booking: systematic review of controlled studies. BMJ 2005;330:565. https://doi.org/10.1136/bmj.38380.674340.E0 doi: 10.1136/bmj.38380.674340.E0. [DOI] [PMC free article] [PubMed]
  183. Alpoim PN, de Barros Pinheiro M, Junqueira DR, Freitas LG, das Graças Carvalho M, Fernandes AP, et al. Preeclampsia and ABO blood groups: a systematic review and meta-analysis. Mol Biol Rep 2013;40:2253–61. https://doi.org/10.1007/s11033-012-2288-2 doi: 10.1007/s11033-012-2288-2. [DOI] [PubMed]
  184. England L, Zhang J. Smoking and risk of preeclampsia: a systematic review. Front Biosci 2007;12:2471–83. https://doi.org/10.2741/2248 doi: 10.2741/2248. [DOI] [PubMed]
  185. Rebelo F, Schlüssel MM, Vaz JS, Franco-Sena AB, Pinto TJ, Bastos FI, et al. C-reactive protein and later preeclampsia: systematic review and meta-analysis taking into account the weight status. J Hypertens 2013;31:16–26. https://doi.org/10.1097/HJH.0b013e32835b0556 doi: 10.1097/HJH.0b013e32835b0556. [DOI] [PubMed]
  186. Luo ZC, An N, Xu HR, Larante A, Audibert F, Fraser WD. The effects and mechanisms of primiparity on the risk of pre-eclampsia: a systematic review. Paediatr Perinat Epidemiol 2007;21(Suppl. 1):36–45. https://doi.org/10.1111/j.1365-3016.2007.00836.x doi: 10.1111/j.1365-3016.2007.00836.x. [DOI] [PubMed]
  187. Cnossen JS, Vollebregt KC, de Vrieze N, ter Riet G, Mol BW, Franx A, et al. Accuracy of mean arterial pressure and blood pressure measurements in predicting pre-eclampsia: systematic review and meta-analysis. BMJ 2008;336:1117–20. https://doi.org/10.1136/bmj.39540.522049.BE doi: 10.1136/bmj.39540.522049.BE. [DOI] [PMC free article] [PubMed]
  188. Sgolastra F, Petrucci A, Severino M, Gatto R, Monaco A. Relationship between periodontitis and pre-eclampsia: a meta-analysis. PLOS ONE 2013;8:e71387. https://doi.org/10.1371/journal.pone.0071387 doi: 10.1371/journal.pone.0071387. [DOI] [PMC free article] [PubMed]
  189. Kunnen A, van Doormaal JJ, Abbas F, Aarnoudse JG, van Pampus MG, Faas MM. Periodontal disease and pre-eclampsia: a systematic review. J Clin Periodontol 2010;37:1075–87. https://doi.org/10.1111/j.1600-051X.2010.01636.x doi: 10.1111/j.1600-051X.2010.01636.x. [DOI] [PubMed]
  190. Morris RK, Riley RD, Doug M, Deeks JJ, Kilby MD. Diagnostic accuracy of spot urinary protein and albumin to creatinine ratios for detection of significant proteinuria or adverse pregnancy outcome in patients with suspected pre-eclampsia: systematic review and meta-analysis. BMJ 2012;345:e4342. https://doi.org/10.1136/bmj.e4342 doi: 10.1136/bmj.e4342. [DOI] [PMC free article] [PubMed]
  191. Sanchez-Ramos L, Gillen G, Zamora J, Stenyakina A, Kaunitz AM. The protein-to-creatinine ratio for the prediction of significant proteinuria in patients at risk for preeclampsia: a meta-analysis. Ann Clin Lab Sci 2013;43:211–20. [PubMed]
  192. Wolf HT, Owe KM, Juhl M, Hegaard HK. Leisure time physical activity and the risk of pre-eclampsia: a systematic review. Matern Child Health J 2014;18:899–910. https://doi.org/10.1007/s10995-013-1316-8 doi: 10.1007/s10995-013-1316-8. [DOI] [PubMed]
  193. Palmer KT, Bonzini M, Harris EC, Linaker C, Bonde JP. Work activities and risk of prematurity, low birth weight and pre-eclampsia: an updated review with meta-analysis. Occup Environ Med 2013;70:213–22. https://doi.org/10.1136/oemed-2012-101032 doi: 10.1136/oemed-2012-101032. [DOI] [PMC free article] [PubMed]
  194. Bonzini M, Coggon D, Palmer KT. Risk of prematurity, low birthweight and pre-eclampsia in relation to working hours and physical activities: a systematic review. Occup Environ Med 2007;64:228–43. https://doi.org/10.1136/oem.2006.026872 doi: 10.1136/oem.2006.026872. [DOI] [PMC free article] [PubMed]
  195. Cnossen JS, de Ruyter-Hanhijärvi H, van der Post JA, Mol BW, Khan KS, ter Riet G. Accuracy of serum uric acid determination in predicting pre-eclampsia: a systematic review. Acta Obstet Gynecol Scand 2006;85:519–25. https://doi.org/10.1080/00016340500342037 doi: 10.1080/00016340500342037. [DOI] [PubMed]
  196. Chien PF, Arnott N, Gordon A, Owen P, Khan KS. How useful is uterine artery Doppler flow velocimetry in the prediction of pre-eclampsia, intrauterine growth retardation and perinatal death? An overview. BJOG 2000;107:196–208. https://doi.org/10.1111/j.1471-0528.2000.tb11690.x doi: 10.1111/j.1471-0528.2000.tb11690.x. [DOI] [PubMed]
  197. Kleinrouweler CE, Bossuyt PM, Thilaganathan B, Vollebregt KC, Arenas Ramírez J, Ohkuchi A, et al. Value of adding second-trimester uterine artery Doppler to patient characteristics in identification of nulliparous women at increased risk for pre-eclampsia: an individual patient data meta-analysis. Ultrasound Obstet Gynecol 2013;42:257–67. https://doi.org/10.1002/uog.12435 doi: 10.1002/uog.12435. [DOI] [PubMed]
  198. Pedrosa AC, Matias A. Screening for pre-eclampsia: a systematic review of tests combining uterine artery Doppler with other markers. J Perinat Med 2011;39:619–35. https://doi.org/10.1515/JPM.2011.077 doi: 10.1515/JPM.2011.077. [DOI] [PubMed]
  199. Kosmas IP, Tatsioni A, Ioannidis JP. Association of Leiden mutation in factor V gene with hypertension in pregnancy and pre-eclampsia: a meta-analysis. J Hypertens 2003;21:1221–8. https://doi.org/10.1097/00004872-200307000-00002 doi: 10.1097/00004872-200307000-00002. [DOI] [PubMed]
  200. Dudding T, Heron J, Thakkinstian A, Nurk E, Golding J, Pembrey M, et al. Factor V Leiden is associated with pre-eclampsia but not with fetal growth restriction: a genetic association study and meta-analysis. J Thromb Haemost 2008;6:1869–75. https://doi.org/10.1111/j.1538-7836.2008.03134.x doi: 10.1111/j.1538-7836.2008.03134.x. [DOI] [PubMed]
  201. Rodger MA, Betancourt MT, Clark P, Lindqvist PG, Dizon-Townson D, Said J, et al. The association of factor V leiden and prothrombin gene mutation and placenta-mediated pregnancy complications: a systematic review and meta-analysis of prospective cohort studies. PLOS Med 2010;7:e1000292. https://doi.org/10.1371/journal.pmed.1000292 doi: 10.1371/journal.pmed.1000292. [DOI] [PMC free article] [PubMed]
  202. Xia XP, Chang WW, Cao YX. Meta-analysis of the methylenetetrahydrofolate reductase C677T polymorphism and susceptibility to pre-eclampsia. Hypertens Res 2012;35:1129–34. https://doi.org/10.1038/hr.2012.117 doi: 10.1038/hr.2012.117. [DOI] [PubMed]
  203. Kosmas IP, Tatsioni A, Ioannidis JP. Association of C677T polymorphism in the methylenetetrahydrofolate reductase gene with hypertension in pregnancy and pre-eclampsia: a meta-analysis. J Hypertens 2004;22:1655–62. https://doi.org/10.1097/00004872-200409000-00004 doi: 10.1097/00004872-200409000-00004. [DOI] [PubMed]
  204. Zusterzeel PL, Visser W, Blom HJ, Peters WH, Heil SG, Steegers EA. Methylenetetrahydrofolate reductase polymorphisms in preeclampsia and the HELLP syndrome. Hypertens Pregnancy 2000;19:299–307. https://doi.org/10.1081/PRG-100101991 doi: 10.1081/PRG-100101991. [DOI] [PubMed]
  205. Li X, Shen L, Tan H. Polymorphisms and plasma level of transforming growth factor-Beta 1 and risk for preeclampsia: a systematic review. PLOS ONE 2014;9:e97230. https://doi.org/10.1371/journal.pone.0097230 doi: 10.1371/journal.pone.0097230. [DOI] [PMC free article] [PubMed]
  206. Wang XM, Wu HY, Qiu XJ. Methylenetetrahydrofolate reductase (MTHFR) gene C677T polymorphism and risk of preeclampsia: an updated meta-analysis based on 51 studies. Arch Med Res 2013;44:159–68. https://doi.org/10.1016/j.arcmed.2013.01.011 doi: 10.1016/j.arcmed.2013.01.011. [DOI] [PubMed]
  207. Widmer M, Villar J, Benigni A, Conde-Agudelo A, Karumanchi SA, Lindheimer M. Mapping the theories of preeclampsia and the role of angiogenic factors: a systematic review. Obstet Gynecol 2007;109:168–80. https://doi.org/10.1097/01.AOG.0000249609.04831.7c doi: 10.1097/01.AOG.0000249609.04831.7c. [DOI] [PubMed]
  208. Jacobs M, Nassar N, Roberts CL, Hadfield R, Morris JM, Ashton AW. Levels of soluble fms-like tyrosine kinase one in first trimester and outcomes of pregnancy: a systematic review. Reprod Biol Endocrinol 2011;9:77. https://doi.org/10.1186/1477-7827-9-77 doi: 10.1186/1477-7827-9-77. [DOI] [PMC free article] [PubMed]
  209. Huppertz B, Meiri H, Gizurarson S, Osol G, Sammar M. Placental protein 13 (PP13): a new biological target shifting individualized risk assessment to personalized drug design combating pre-eclampsia. Hum Reprod Update 2013;19:391–405. https://doi.org/10.1093/humupd/dmt003 doi: 10.1093/humupd/dmt003. [DOI] [PubMed]
  210. Schneuer FJ, Nassar N, Khambalia AZ, Tasevski V, Guilbert C, Ashton AW, et al. First trimester screening of maternal placental protein 13 for predicting preeclampsia and small for gestational age: in-house study and systematic review. Placenta 2012;33:735–40. https://doi.org/10.1016/j.placenta.2012.05.012 doi: 10.1016/j.placenta.2012.05.012. [DOI] [PubMed]
  211. Lau SY, Guild SJ, Barrett CJ, Chen Q, McCowan L, Jordan V, Chamley LW. Tumour necrosis factor-alpha, interleukin-6, and interleukin-10 levels are altered in preeclampsia: a systematic review and meta-analysis. Am J Reprod Immunol 2013;70:412–27. https://doi.org/10.1111/aji.12138 doi: 10.1111/aji.12138. [DOI] [PubMed]
  212. Tabesh M, Salehi-Abargouei A, Tabesh M, Esmaillzadeh A. Maternal vitamin D status and risk of pre-eclampsia: a systematic review and meta-analysis. J Clin Endocrinol Metab 2013;98:3165–73. https://doi.org/10.1210/jc.2013-1257 doi: 10.1210/jc.2013-1257. [DOI] [PubMed]
  213. Morgan JA, Bombell S, McGuire W. Association of plasminogen activator inhibitor-type 1 (–675 4G/5G) polymorphism with pre-eclampsia: systematic review. PLOS ONE 2013;8:e56907. https://doi.org/10.1371/journal.pone.0056907 doi: 10.1371/journal.pone.0056907. [DOI] [PMC free article] [PubMed]
  214. Dai B, Liu T, Zhang B, Zhang X, Wang Z. The polymorphism for endothelial nitric oxide synthase gene, the level of nitric oxide and the risk for pre-eclampsia: a meta-analysis. Gene 2013;519:187–93. https://doi.org/10.1016/j.gene.2013.01.004 doi: 10.1016/j.gene.2013.01.004. [DOI] [PubMed]
  215. Chen Z, Xu F, Wei Y, Liu F, Qi H. Angiotensin-converting enzyme insertion/deletion polymorphism and risk of pregnancy hypertensive disorders: a meta-analysis. J Renin Angiotensin Aldosterone Syst 2012;13:184–95. https://doi.org/10.1177/1470320311427755 doi: 10.1177/1470320311427755. [DOI] [PubMed]
  216. Qi HP, Fraser WD, Luo ZC, Julien P, Audibert F, Wei SQ. Endothelial nitric oxide synthase gene polymorphisms and risk of preeclampsia. Am J Perinatol 2013;30:795–804. https://doi.org/10.1055/s-0032-1333406 doi: 10.1055/s-0032-1333406. [DOI] [PubMed]
  217. Zhao L, Bracken MB, Dewan AT, Chen S. Association between the SERPINE1 (PAI-1) 4G/5G insertion/deletion promoter polymorphism (rs1799889) and pre-eclampsia: a systematic review and meta-analysis. Mol Hum Reprod 2013;19:136–43. https://doi.org/10.1093/molehr/gas056 doi: 10.1093/molehr/gas056. [DOI] [PubMed]
  218. Zhao L, Dewan AT, Bracken MB. Association of maternal AGTR1 polymorphisms and preeclampsia: a systematic review and meta-analysis. J Matern Fetal Neonatal Med 2012;25:2676–80. https://doi.org/10.3109/14767058.2012.708370 doi: 10.3109/14767058.2012.708370. [DOI] [PMC free article] [PubMed]
  219. Zhong WG, Wang Y, Zhu H, Zhao X. Meta analysis of angiotensin-converting enzyme I/D polymorphism as a risk factor for preeclampsia in Chinese women. Genet Mol Res 2012;11:2268–76. https://doi.org/10.4238/2012.May.21.1 doi: 10.4238/2012.May.21.1. [DOI] [PubMed]
  220. Ni S, Zhang Y, Deng Y, Gong Y, Huang J, Bai Y, Zhou R. AGT M235T polymorphism contributes to risk of preeclampsia: evidence from a meta-analysis. J Renin Angiotensin Aldosterone Syst 2012;13:379–86. https://doi.org/10.1177/1470320312440903 doi: 10.1177/1470320312440903. [DOI] [PubMed]
  221. Hui D, Okun N, Murphy K, Kingdom J, Uleryk E, Shah PS. Combinations of maternal serum markers to predict preeclampsia, small for gestational age, and stillbirth: a systematic review. J Obstet Gynaecol Can 2012;34:142–53. https://doi.org/10.1016/S1701-2163(16)35157-X doi: 10.1016/S1701-2163(16)35157-X. [DOI] [PubMed]
  222. Giguère Y, Charland M, Bujold E, Bernard N, Grenier S, Rousseau F, et al. [Combining biochemical and ultrasonographic markers in predicting preeclampsia: a systematic review.] Ann Biol Clin 2011;69:257–71. https://doi.org/10.1684/abc.2011.0572 doi: 10.1684/abc.2011.0572. [DOI] [PubMed]
  223. Abou-Nassar K, Carrier M, Ramsay T, Rodger MA. The association between antiphospholipid antibodies and placenta mediated complications: a systematic review and meta-analysis. Thromb Res 2011;128:77–85. https://doi.org/10.1016/j.thromres.2011.02.006 doi: 10.1016/j.thromres.2011.02.006. [DOI] [PubMed]
  224. do Prado AD, Piovesan DM, Staub HL, Horta BL. Association of anticardiolipin antibodies with preeclampsia: a systematic review and meta-analysis. Obstet Gynecol 2010;116:1433–43. https://doi.org/10.1097/AOG.0b013e3181fe02ec doi: 10.1097/AOG.0b013e3181fe02ec. [DOI] [PubMed]
  225. Gupta S, Aziz N, Sekhon L, Agarwal R, Mansour G, Li J, Agarwal A. Lipid peroxidation and antioxidant status in preeclampsia: a systematic review. Obstet Gynecol Surv 2009;64:750–9. https://doi.org/10.1097/OGX.0b013e3181bea0ac doi: 10.1097/OGX.0b013e3181bea0ac. [DOI] [PubMed]
  226. Bombell S, McGuire W. Tumour necrosis factor (–308A) polymorphism in pre-eclampsia: meta-analysis of 16 case–control studies. Aust N Z J Obstet Gynaecol 2008;48:547–51. https://doi.org/10.1111/j.1479-828X.2008.00924.x doi: 10.1111/j.1479-828X.2008.00924.x. [DOI] [PubMed]
  227. Zafarmand MH, Nijdam ME, Franx A, Grobbee DE, Bots ML. The angiotensinogen gene M235T polymorphism and development of preeclampsia/eclampsia: a meta-analysis and meta-regression of observational studies. J Hypertens 2008;26:1726–34. https://doi.org/10.1097/HJH.0b013e3283009ca5 doi: 10.1097/HJH.0b013e3283009ca5. [DOI] [PubMed]
  228. Wiwanitkit V. Correlation between plasminogen activator inhibitor-1 4G/5G polymorphism and pre-eclampsia: an appraisal. Arch Gynecol Obstet 2006;273:322–4. https://doi.org/10.1007/s00404-005-0117-8 doi: 10.1007/s00404-005-0117-8. [DOI] [PubMed]
  229. Crovetto F, Figueras F, Triunfo S, Crispi F, Rodriguez-Sureda V, Dominguez C, et al. First trimester screening for early and late preeclampsia based on maternal characteristics, biophysical parameters, and angiogenic factors. Prenat Diagn 2015;35:183–91. https://doi.org/10.1002/pd.4519 doi: 10.1002/pd.4519. [DOI] [PubMed]
  230. Kuc S, Koster MP, Franx A, Schielen PC, Visser GH. Maternal characteristics, mean arterial pressure and serum markers in early prediction of preeclampsia. PLOS ONE 2013;8:e63546. https://doi.org/10.1371/journal.pone.0063546 doi: 10.1371/journal.pone.0063546. [DOI] [PMC free article] [PubMed]
  231. Plasencia W, Maiz N, Bonino S, Kaihura C, Nicolaides KH. Uterine artery Doppler at 11 + 0 to 13 + 6 weeks in the prediction of pre-eclampsia. Ultrasound Obstet Gynecol 2007;30:742–9. https://doi.org/10.1002/uog.5157 doi: 10.1002/uog.5157. [DOI] [PubMed]
  232. Poon LC, Kametas NA, Chelemen T, Leal A, Nicolaides KH. Maternal risk factors for hypertensive disorders in pregnancy: a multivariate approach. J Hum Hypertens 2010;24:104–10. https://doi.org/10.1038/jhh.2009.45 doi: 10.1038/jhh.2009.45. [DOI] [PubMed]
  233. Poon LC, Kametas NA, Pandeva I, Valencia C, Nicolaides KH. Mean arterial pressure at 11(+0) to 13(+6) weeks in the prediction of preeclampsia. Hypertension 2008;51:1027–33. https://doi.org/10.1161/HYPERTENSIONAHA.107.104646 doi: 10.1161/HYPERTENSIONAHA.107.104646. [DOI] [PubMed]
  234. Poon LC, Maiz N, Valencia C, Plasencia W, Nicolaides KH. First-trimester maternal serum pregnancy-associated plasma protein-A and pre-eclampsia. Ultrasound Obstet Gynecol 2009;33:23–33. https://doi.org/10.1002/uog.6280 doi: 10.1002/uog.6280. [DOI] [PubMed]
  235. Scazzocchio E, Figueras F, Crispi F, Meler E, Masoller N, Mula R, Gratacos E. Performance of a first-trimester screening of preeclampsia in a routine care low-risk setting. Am J Obstet Gynecol 2013;208:203.e1–203.e10. https://doi.org/10.1016/j.ajog.2012.12.016 doi: 10.1016/j.ajog.2012.12.016. [DOI] [PubMed]
  236. Wright D, Syngelaki A, Akolekar R, Poon LC, Nicolaides KH. Competing risks model in screening for preeclampsia by maternal characteristics and medical history. Am J Obstet Gynecol 2015;213:62.e1–62.e10. https://doi.org/10.1016/j.ajog.2015.02.018 doi: 10.1016/j.ajog.2015.02.018. [DOI] [PubMed]
  237. Yu CK, Smith GC, Papageorghiou AT, Cacho AM, Nicolaides KH, Fetal Medicine Foundation Second Trimester Screening Group. An integrated model for the prediction of pre-eclampsia using maternal factors and uterine artery Doppler velocimetry in unselected low-risk women. Am J Obstet Gynecol 2006;195:330. https://doi.org/10.1016/j.ajog.2006.06.010 doi: 10.1016/j.ajog.2006.06.010. [DOI] [PubMed]
  238. Yu CK, Smith GC, Papageorghiou AT, Cacho AM, Nicolaides KH, Fetal Medicine Foundation Second Trimester Screening Group. An integrated model for the prediction of preeclampsia using maternal factors and uterine artery Doppler velocimetry in unselected low-risk women. Am J Obstet Gynecol 2005;193:429–36. https://doi.org/10.1016/j.ajog.2004.12.014 doi: 10.1016/j.ajog.2004.12.014. [DOI] [PubMed]
  239. Albert A, Anderson JA. On the existence of maximum likelihood estimates in logistic regression models. Biometrika 1984;71:1. https://doi.org/10.1093/biomet/71.1.1 doi: 10.1093/biomet/71.1.1. [DOI]
  240. White IR, Royston P, Wood AM. Multiple imputation using chained equations: issues and guidance for practice. Stat Med 2011;30:377–99. https://doi.org/10.1002/sim.4067 doi: 10.1002/sim.4067. [DOI] [PubMed]
  241. Akolekar R, Zaragoza E, Poon LC, Pepes S, Nicolaides KH. Maternal serum placental growth factor at 11 + 0 to 13 + 6 weeks of gestation in the prediction of pre-eclampsia. Ultrasound Obstet Gynecol 2008;32:732–9. https://doi.org/10.1002/uog.6244 doi: 10.1002/uog.6244. [DOI] [PubMed]
  242. National Institute for Health and Care Excellence. Hypertension in Pregnancy: The Management of Hypertensive Disorders During Pregnancy. Clinical Guideline 107. London: National Institute for Health and Care Excellence; 2010. URL: www.nice.org.uk/guidance/cg107 (accessed 12 February 2015).
  243. O’Gorman N, Wright D, Syngelaki A, Akolekar R, Wright A, Poon LC, Nicolaides KH. Competing risks model in screening for preeclampsia by maternal factors and biomarkers at 11–13 weeks gestation. Am J Obstet Gynecol 2016;214:103.e1–103.e12. https://doi.org/10.1016/j.ajog.2015.08.034 doi: 10.1016/j.ajog.2015.08.034. [DOI] [PubMed]
  244. O’Gorman N, Wright D, Poon LC, Rolnik DL, Syngelaki A, de Alvarado M, et al. Multicenter screening for pre-eclampsia by maternal factors and biomarkers at 11–13 weeks’ gestation: comparison with NICE guidelines and ACOG recommendations. Ultrasound Obstet Gynecol 2017;49:756–60. https://doi.org/10.1002/uog.17455 doi: 10.1002/uog.17455. [DOI] [PubMed]
  245. Oliveira N, Magder LS, Blitzer MG, Baschat AA. First-trimester prediction of pre-eclampsia: external validity of algorithms in a prospectively enrolled cohort. Ultrasound Obstet Gynecol 2014;44:279–85. https://doi.org/10.1002/uog.13435 doi: 10.1002/uog.13435. [DOI] [PubMed]
  246. Park FJ, Leung CH, Poon LC, Williams PF, Rothwell SJ, Hyett JA. Clinical evaluation of a first trimester algorithm predicting the risk of hypertensive disease of pregnancy. Aust N Z J Obstet Gynaecol 2013;53:532–9. https://doi.org/10.1111/ajo.12126 doi: 10.1111/ajo.12126. [DOI] [PubMed]
  247. Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowski N, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 2010;21:128–38. https://doi.org/10.1097/EDE.0b013e3181c30fb2 doi: 10.1097/EDE.0b013e3181c30fb2. [DOI] [PMC free article] [PubMed]
  248. Wright D, Tan MY, O’Gorman N, Poon LC, Syngelaki A, Wright A, Nicolaides KH. Predictive performance of the competing risk model in screening for preeclampsia. Am J Obstet Gynecol 2019;220:199.e1–199.e13. https://doi.org/10.1016/j.ajog.2018.11.1087 doi: 10.1016/j.ajog.2018.11.1087. [DOI] [PubMed]
  249. Debray TP, Vergouwe Y, Koffijberg H, Nieboer D, Steyerberg EW, Moons KG. A new framework to enhance the interpretation of external validation studies of clinical prediction models. J Clin Epidemiol 2015;68:279–89. https://doi.org/10.1016/j.jclinepi.2014.06.018 doi: 10.1016/j.jclinepi.2014.06.018. [DOI] [PubMed]

RESOURCES