Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 30

The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.

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

Allotey, John, Laivuori, Hannele, Snell, Kym IE, Smuk, Melanie, Hooper, Richard, Chan, Claire L, Ahmed, Asif, Chappell, Lucy C, von Dadelszen, Peter, Dodds, Julie, Green, Marcus, Kenny, Louise, Khalil, Asma, Khan, Khalid S, Mol, Ben W, Myers, Jenny, Poston, Lucilla, Thilaganathan, Basky, Staff, Anne C, Smith, Gordon CS, Ganzevoort, Wessel, Odibo, Anthony O, Ramírez, Javier A, Kingdom, John, Daskalakis, George, Farrar, Diane, Baschat, Ahmet A, Seed, Paul T, Prefumo, Federico, da Silva Costa, Fabricio, Groen, Henk, Audibert, Francois, Masse, Jacques, Skråstad, Ragnhild B, Salvesen, Kjell Å, Haavaldsen, Camilla, Nagata, Chie, Rumbold, Alice R, Heinonen, Seppo, Askie, Lisa M, Smits, Luc JM, Vinter, Christina A, Magnus, Per M, Eero, Kajantie, Villa, Pia M, Jenum, Anne K, Andersen, Louise B, Norman, Jane E, Ohkuchi, Akihide, Eskild, Anne, Bhattacharya, Sohinee, McAuliffe, Fionnuala M, Galindo, Alberto, Herraiz, Ignacio, Carbillon, Lionel, Klipstein-Grobusch, Kerstin, Yeo, SeonAe, Teede, Helena J, Browne, Joyce L, Moons, Karel GM, Riley, Richard D, Thangaratinam, Shakila·2020·Health Technology Assessment
DOI 10.3310/hta24720
Open this paper in CoChat — free account

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 I 2 and τ 2. 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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3310/hta24720

Cites

24 of 24 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 24 references

Research works printed but not linked: 221

  1. Brown, 2000, The detection, investigation and management of hypertension in pregnancy: executive summary, Aust N Z J Obstet Gynaecol, 40, 133, 10.1111/j.1479-828x.2000.tb01136.x · DOI
  2. Sibai, 2005, Pre-eclampsia, Lancet, 365, 785, 10.1016/S0140-6736(05)17987-2 · DOI
  3. Sibai, 2003, Diagnosis and management of gestational hypertension and preeclampsia, Obstet Gynecol, 102, 181, 10.1097/00006250-200307000-00033 · DOI
  4. Tranquilli, 2014, The classification, diagnosis and management of the hypertensive disorders of pregnancy: a revised statement from the ISSHP, Pregnancy Hypertens, 4, 97, 10.1016/j.preghy.2014.02.001 · DOI
  5. Staff, 2013, Redefining preeclampsia using placenta-derived biomarkers, Hypertension, 61, 932, 10.1161/HYPERTENSIONAHA.111.00250 · DOI
  6. Duley, 2009, The global impact of pre-eclampsia and eclampsia, Semin Perinatol, 33, 130, 10.1053/j.semperi.2009.02.010 · DOI · Paper page
  7. Gardosi, 2005, Classification of stillbirth by relevant condition at death (ReCoDe): population based cohort study, BMJ, 331, 1113, 10.1136/bmj.38629.587639.7C · DOI
  8. Iams, 1998, 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, 178, 1035, 10.1016/S0002-9378(98)70544-7 · DOI
  9. Davies, 2016, Preeclampsia and preterm delivery: a population-based case–control study, Hypertens Pregnancy, 35, 510, 10.1080/10641955.2016.1190846 · DOI
  10. Tang, 1997, Critical care in obstetrical patients: an eight-year review, Chin Med J, 110, 936
  11. Carty, 2010, Preeclampsia and future maternal health, J Hypertens, 28, 1349, 10.1097/HJH.0b013e32833a39d0 · DOI
  12. Thoulass, 2016, Hypertensive disorders of pregnancy and adult offspring cardiometabolic outcomes: a systematic review of the literature and meta-analysis, J Epidemiol Community Health, 70, 414, 10.1136/jech-2015-205483 · DOI
  13. Davis, 2012, Cardiovascular risk factors in children and young adults born to preeclamptic pregnancies: a systematic review, Pediatrics, 129, e1552, 10.1542/peds.2011-3093 · DOI
  14. Tuovinen, 2013, 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, 208, 200e1, 10.1016/j.ajog.2012.12.017 · DOI
  15. Tuovinen, 2010, Depressive symptoms in adulthood and intrauterine exposure to pre-eclampsia: the Helsinki Birth Cohort Study, BJOG, 117, 1236, 10.1111/j.1471-0528.2010.02634.x · DOI
  16. Crispi, 2006, 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, 195, 201, 10.1016/j.ajog.2006.01.014 · DOI
  17. Crispi, 2008, Predictive value of angiogenic factors and uterine artery Doppler for early- versus late-onset pre-eclampsia and intrauterine growth restriction, Ultrasound Obstet Gynecol, 31, 303, 10.1002/uog.5184 · DOI
  18. Valensise, 2008, Early and late preeclampsia: two different maternal hemodynamic states in the latent phase of the disease, Hypertension, 52, 873, 10.1161/HYPERTENSIONAHA.108.117358 · DOI
  19. Redman, 2014, IFPA Senior Award Lecture: making sense of pre-eclampsia – two placental causes of preeclampsia?, Placenta, S20, 10.1016/j.placenta.2013.12.008 · DOI
  20. MacKay, 2001, Pregnancy-related mortality from preeclampsia and eclampsia, Obstet Gynecol, 97, 533, 10.1097/00006250-200104000-00011 · DOI
  21. von Dadelszen, 2011, Prediction of adverse maternal outcomes in pre-eclampsia: development and validation of the fullPIERS model, Lancet, 377, 219, 10.1016/S0140-6736(10)61351-7 · DOI
  22. von Dadelszen, 2003, Subclassification of preeclampsia, Hypertens Pregnancy, 22, 143, 10.1081/PRG-120021060 · DOI
  23. Lisonkova, 2013, Incidence of preeclampsia: risk factors and outcomes associated with early- versus late-onset disease, Am J Obstet Gynecol, 209, 544e1, 10.1016/j.ajog.2013.08.019 · DOI
  24. Murphy, 2000, Mortality and morbidity associated with early-onset preeclampsia, Hypertens Pregnancy, 19, 221, 10.1081/PRG-100100138 · DOI
  25. Sibai, 1993, Maternal morbidity and mortality in 442 pregnancies with hemolysis, elevated liver enzymes, and low platelets (HELLP syndrome), Am J Obstet Gynecol, 169, 1000, 10.1016/0002-9378(93)90043-I · DOI
  26. Churchill, 2002, Interventionist versus expectant care for severe pre-eclampsia before term, Cochrane Database Syst Rev, 3, 10.1002/14651858.CD003106 · DOI
  27. Mangham, 2009, The cost of preterm birth throughout childhood in England and Wales, Pediatrics, 123, e312, 10.1542/peds.2008-1827 · DOI
  28. Douglas, 1994, Eclampsia in the United Kingdom, BMJ, 309, 1395, 10.1136/bmj.309.6966.1395 · DOI
  29. Sibai, 2011, Management of late preterm and early-term pregnancies complicated by mild gestational hypertension/pre-eclampsia, Semin Perinatol, 35, 292, 10.1053/j.semperi.2011.05.010 · DOI
  30. Bujold, 2010, Prevention of preeclampsia and intrauterine growth restriction with aspirin started in early pregnancy: a meta-analysis, Obstet Gynecol, 116, 402, 10.1097/AOG.0b013e3181e9322a · DOI
  31. Townsend, 2019, Prediction of pre-eclampsia: review of reviews, Ultrasound Obstet Gynecol, 54, 16, 10.1002/uog.20117 · DOI
  32. Allen, 2014, Abnormal blood biomarkers in early pregnancy are associated with preeclampsia: a meta-analysis, Eur J Obstet Gynecol Reprod Biol, 182, 194, 10.1016/j.ejogrb.2014.09.027 · DOI
  33. Velauthar, 2014, First-trimester uterine artery Doppler and adverse pregnancy outcome: a meta-analysis involving 55,974 women, Ultrasound Obstet Gynecol, 43, 500, 10.1002/uog.13275 · DOI
  34. Conde-Agudelo, 2004, World Health Organization systematic review of screening tests for preeclampsia, Obstet Gynecol, 104, 1367, 10.1097/01.AOG.0000147599.47713.5d · DOI
  35. Giguère, 2010, Combining biochemical and ultrasonographic markers in predicting preeclampsia: a systematic review, Clin Chem, 56, 361, 10.1373/clinchem.2009.134080 · DOI
  36. North, 2011, Clinical risk prediction for pre-eclampsia in nulliparous women: development of model in international prospective cohort, BMJ, 342, d1875, 10.1136/bmj.d1875 · DOI
  37. Kleinrouweler, 2016, Prognostic models in obstetrics: available, but far from applicable, Am J Obstet Gynecol, 214, 79, 10.1016/j.ajog.2015.06.013 · DOI
  38. Meertens, 2019, External validation and clinical usefulness of first trimester prediction models for the risk of preeclampsia: a prospective cohort study, Fetal Diagn Ther, 45, 381, 10.1159/000490385 · DOI
  39. Farina, 2011, Prospective evaluation of ultrasound and biochemical-based multivariable models for the prediction of late pre-eclampsia, Prenat Diagn, 31, 1147, 10.1002/pd.2849 · DOI
  40. Herraiz, 2009, Application of a first-trimester prediction model for pre-eclampsia based on uterine arteries and maternal history in high-risk pregnancies, Prenat Diagn, 29, 1123, 10.1002/pd.2383 · DOI
  41. Debray, 2013, A framework for developing, implementing, and evaluating clinical prediction models in an individual participant data meta-analysis, Stat Med, 32, 3158, 10.1002/sim.5732 · DOI
  42. Debray, 2015, Meta-analysis Methods group. Individual participant data (IPD) meta-analyses of diagnostic and prognostic modelling studies: guidance on their use, PLOS Med, 12, e1001886, 10.1371/journal.pmed.1001886 · DOI
  43. Allotey, 2017, 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, 1, 16, 10.1186/s41512-017-0016-z · DOI
  44. Snell, 2020, External validation of prognostic models predicting pre-eclampsia: individual participant data meta-analysis, BMC Med, 18, 302, 10.1186/s12916-020-01766-9 · DOI
  45. Altman, 2009, Prognosis and prognostic research: validating a prognostic model, BMJ, 338, b605, 10.1136/bmj.b605 · DOI · Paper page
  46. Moons, 2009, Prognosis and prognostic research: what, why, and how?, BMJ, 338, b375, 10.1136/bmj.b375 · DOI · Paper page
  47. 2015, Accuracy of Clinical Characteristics, Biochemical and Ultrasound Markers in the Prediction of Pre-eclampsia: an Individual Participant Data (IPD) Meta-analysis
  48. Gardosi, 2009, The value of customised centiles in assessing perinatal mortality risk associated with parity and maternal size, BJOG, 116, 1356, 10.1111/j.1471-0528.2009.02245.x · DOI
  49. Ahmed, 2011, New insights into the etiology of preeclampsia: identification of key elusive factors for the vascular complications, Thromb Res, 127, 72, 10.1016/S0049-3848(11)70020-2 · DOI
  50. Bloomenthal, 2002, The effect of factor V Leiden carriage on maternal and fetal health, CMAJ, 167, 48
  51. Cnossen, 2008, Use of uterine artery Doppler ultrasonography to predict pre-eclampsia and intrauterine growth restriction: a systematic review and bivariable meta-analysis, CMAJ, 178, 701, 10.1503/cmaj.070430 · DOI · Paper page
  52. Gallos, 2013, Pre-eclampsia is associated with, and preceded by, hypertriglyceridaemia: a meta-analysis, BJOG, 120, 1321, 10.1111/1471-0528.12375 · DOI
  53. Kleinrouweler, 2012, 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, 119, 778, 10.1111/j.1471-0528.2012.03311.x · DOI
  54. Leeflang, 2007, Accuracy of fibronectin tests for the prediction of pre-eclampsia: a systematic review, Eur J Obstet Gynecol Reprod Biol, 133, 12, 10.1016/j.ejogrb.2007.01.003 · DOI
  55. Meads, 2008, Methods of prediction and prevention of pre-eclampsia: systematic reviews of accuracy and effectiveness literature with economic modelling, Health Technol Assess, 12, 10.3310/hta12060 · DOI · Paper page
  56. Morris, 2008, Serum screening with Down’s syndrome markers to predict pre-eclampsia and small for gestational age: systematic review and meta-analysis, BMC Pregnancy Childbirth, 8, 33, 10.1186/1471-2393-8-33 · DOI
  57. Steegers, 2010, Pre-eclampsia, Lancet, 376, 631, 10.1016/S0140-6736(10)60279-6 · DOI · Paper page
  58. Thangaratinam, 2011, Prediction and primary prevention of pre-eclampsia, Best Pract Res Clin Obstet Gynaecol, 25, 419, 10.1016/j.bpobgyn.2011.02.008 · DOI
  59. van der Tuuk, 2011, 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, 51, 339, 10.1111/j.1479-828X.2011.01311.x · DOI
  60. von Dadelszen, 2012, 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, 34, 917, 10.1016/S1701-2163(16)35405-6 · DOI
  61. American College of Obstetricians and Gynecologists; Task Force on Hypertension in Pregnancy, 2013, Hypertension in pregnancy. Report of the American College of Obstetricians and Gynecologists’ Task Force on Hypertension in Pregnancy, Obstet Gynecol, 122, 1122
  62. von Dadelszen, 2013, 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, 3, 199, 10.1016/j.preghy.2013.06.002 · DOI
  63. GONet: The Global Obstetrics Network
  64. Myatt, 2014, Strategy for standardization of preeclampsia research study design, Hypertension, 63, 1293, 10.1161/HYPERTENSIONAHA.113.02664 · DOI
  65. Gardosi, 2018, Customised Centile Calculator. GROW version 8.0.4
  66. Resche-Rigon, 2018, Multiple imputation by chained equations for systematically and sporadically missing multilevel data, Stat Methods Med Res, 27, 1634, 10.1177/0962280216666564 · DOI
  67. Jolani, 2015, Imputation of systematically missing predictors in an individual participant data meta-analysis: a generalized approach using MICE, Stat Med, 34, 1841, 10.1002/sim.6451 · DOI · Paper page
  68. Meng, 1994, Multiple-imputation inferences with uncongenial sources of input, Statist Sci, 9, 538, 10.1214/ss/1177010269 · DOI
  69. Little, 2002, Statistical Analysis with Missing Data, 10.1002/9781119013563 · DOI · Paper page
  70. Hosmer, 2000, Applied Logistic Regression, 10.1002/0471722146 · DOI · Paper page
  71. Rubin, 1987, Multiple Imputation for Nonresponse in Surveys, 10.1002/9780470316696 · DOI · Paper page
  72. Snell, 2018, 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, 27, 3505, 10.1177/0962280217705678 · DOI
  73. Snell, 2016, Multivariate meta-analysis of individual participant data helped externally validate the performance and implementation of a prediction model, J Clin Epidemiol, 69, 40, 10.1016/j.jclinepi.2015.05.009 · DOI
  74. Vickers, 2006, Decision curve analysis: a novel method for evaluating prediction models, Med Decis Making, 26, 565, 10.1177/0272989X06295361 · DOI · Paper page
  75. Vickers, 2016, Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests, BMJ, 352, i6, 10.1136/bmj.i6 · DOI · Paper page
  76. Quartagno, Multilevel Joint Modelling Multiple Imputation: Package ‘jomo’, Version 2.6–6, License GPL-2
  77. Quartagno, 2016, Multiple imputation for IPD meta-analysis: allowing for heterogeneity and studies with missing covariates, Stat Med, 35, 2938, 10.1002/sim.6837 · DOI
  78. Sauerbrei, 1999, The use of resampling methods to simplify regression models in medical statistics, J Royal Stat Soc, 48, 313, 10.1111/1467-9876.00155 · DOI
  79. Heinze, 2018, Variable selection – a review and recommendations for the practicing statistician, Biom J, 60, 431, 10.1002/bimj.201700067 · DOI
  80. Wood, 2015, The estimation and use of predictions for the assessment of model performance using large samples with multiply imputed data, Biom J, 57, 614, 10.1002/bimj.201400004 · DOI · Paper page
  81. Royston, 2004, Construction and validation of a prognostic model across several studies, with an application in superficial bladder cancer, Stat Med, 23, 907, 10.1002/sim.1691 · DOI
  82. Harrell, 2015, Regression Modeling Strategies, with Applications to Linear Models, Logistic Regression, and Survival Analysis, 10.1007/978-3-319-19425-7 · DOI · Paper page
  83. Steyerberg, 2009, Clinical Prediction Models. A Practical Approach to Development, Validation, and Updating
  84. Riley, 2019, Prognosis Research in Healthcare: Concepts, Methods and Impact, 10.1093/med/9780198796619.001.0001 · DOI
  85. Fraser, 2013, Cohort profile: the Avon Longitudinal Study of Parents and Children: ALSPAC mothers cohort, Int J Epidemiol, 42, 97, 10.1093/ije/dys066 · DOI · Paper page
  86. Better Outcomes Registry & Network (BORN) Ontario
  87. Carter, 2017, Saliva progesterone for prediction of spontaneous preterm birth: the POPPY study. Pregnancy Outcome Poster Abstracts, BJOG, 124, 122, 10.1111/1471-0528.14589 · DOI
  88. Al-Amin, 2018, Accuracy of second trimester prediction of preterm preeclampsia by three different screening algorithms, Aust N Z J Obstet Gynaecol, 58, 192, 10.1111/ajo.12689 · DOI
  89. Allen, 2017, External validation of preexisting first trimester preeclampsia prediction models, Eur J Obstet Gynecol Reprod Biol, 217, 119, 10.1016/j.ejogrb.2017.08.031 · DOI
  90. Andersen, 2016, Prediction of preeclampsia with angiogenic biomarkers. Results from the prospective Odense Child Cohort, Hypertens Pregnancy, 35, 405, 10.3109/10641955.2016.1167219 · DOI
  91. Antsaklis, 2000, 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, 16, 635, 10.1046/j.1469-0705.2000.00288.x · DOI
  92. Arenas, 2003, Cribado con doppler de las arterias uterinas para la predicción de complicaciones de la gestación, Clínicae Investigación en Ginecología y Obstetricia, 30, 178, 10.1016/S0210-573X(03)77255-4 · DOI
  93. Askie, 2007, Antiplatelet agents for prevention of pre-eclampsia: a meta-analysis of individual patient data, Lancet, 369, 1791, 10.1016/S0140-6736(07)60712-0 · DOI
  94. Audibert, 2010, Screening for preeclampsia using first-trimester serum markers and uterine artery Doppler in nulliparous women, Am J Obstet Gynecol, 203, 383e1, 10.1016/j.ajog.2010.06.014 · DOI
  95. Ayorinde, 2016, Data resource profile: the Aberdeen Maternity and Neonatal Databank (AMND), Int J Epidemiol, 45, 389, 10.1093/ije/dyv356 · DOI
  96. Baschat, 2014, Prediction of preeclampsia utilizing the first trimester screening examination, Am J Obstet Gynecol, 211, 514e1, 10.1016/j.ajog.2014.04.018 · DOI
  97. Brown, 2007, Can we predict recurrence of pre-eclampsia or gestational hypertension?, BJOG, 114, 984, 10.1111/j.1471-0528.2007.01376.x · DOI
  98. Cameroni, 2008, P32.05: Uterine artery Doppler in a risk population: what’s its role in the prediction of severe pregnancy complications?, Ultrasound Obstet Gynecol, 32, 421, 10.1002/uog.5992 · DOI
  99. Caradeux, 2013, First trimester prediction of early onset preeclampsia using demographic, clinical, and sonographic data: a cohort study, Prenat Diagn, 33, 732, 10.1002/pd.4113 · DOI
  100. Carbillon, 2014, The imbalance of circulating angiogenic/antiangiogenic factors is mild or absent in obese women destined to develop preeclampsia, Hypertens Pregnancy, 33, 524, 10.3109/10641955.2013.872252 · DOI
  101. Caritis, 1998, 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, 338, 701, 10.1056/NEJM199803123381101 · DOI
  102. Chappell, 1999, Effect of antioxidants on the occurrence of pre-eclampsia in women at increased risk: a randomised trial, Lancet, 354, 810, 10.1016/S0140-6736(99)80010-5 · DOI
  103. Conserva, 2012, Recurrence and severity of abnormal pregnancy outcome in patients treated by low-molecular-weight heparin: a prospective pilot study, J Matern Fetal Neonatal Med, 25, 1467, 10.3109/14767058.2011.643326 · DOI
  104. Facchinetti, 2009, Maternal thrombophilia and the risk of recurrence of preeclampsia, Am J Obstet Gynecol, 200, 46e1, 10.1016/j.ajog.2008.07.032 · DOI
  105. Figueiró-Filho, 2012, Marcadores séricos de trombofilias hereditárias e anticorpos antifosfolípides em gestantes com antecedentes de pré-eclâmpsia grave, Revista Brasileira de Ginecologia e Obstetrícia, 34, 40, 10.1590/S0100-72032012000100008 · DOI
  106. Giguère, 2015, Screening for pre-eclampsia early in pregnancy: performance of a multivariable model combining clinical characteristics and biochemical markers, BJOG, 122, 402, 10.1111/1471-0528.13050 · DOI
  107. Girchenko, 2017, Cohort profile: prediction and prevention of preeclampsia and intrauterine growth restriction (PREDO) study, Int J Epidemiol, 46, 1380, 10.1093/ije/dyw154 · DOI
  108. Goetzinger, 2010, 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, 30, 1138, 10.1002/pd.2627 · DOI
  109. Goffinet, 2001, 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, 108, 510, 10.1111/j.1471-0528.2001.00116.x · DOI
  110. Gurgel Alves, 2014, First-trimester maternal ophthalmic artery Doppler analysis for prediction of pre-eclampsia, Ultrasound Obstet Gynecol, 44, 411, 10.1002/uog.13338 · DOI
  111. Holzman, 2001, Pregnancy outcomes and community health: the POUCH study of preterm delivery, Paediatr Perinat Epidemiol, 15, 136, 10.1046/j.1365-3016.2001.00014.x · DOI
  112. Huang, 2010, 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, 30, 471, 10.1002/pd.2505 · DOI
  113. Jääskeläinen, 2018, Angiogenic profile in the Finnish Genetics of Pre-Eclampsia Consortium (FINNPEC) cohort, Pregnancy Hypertens, 14, 252, 10.1016/j.preghy.2018.03.004 · DOI
  114. Jenum, 2010, 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, 38, 60, 10.1177/1403494810378921 · DOI
  115. Olsen, 2001, The Danish National Birth Cohort – its background, structure and aim, Scand J Public Health, 29, 300, 10.1177/14034948010290040201 · DOI
  116. Khan, 2005, Changes in endothelial function precede the clinical disease in women in whom preeclampsia develops, Hypertension, 46, 1123, 10.1161/01.HYP.0000186328.90667.95 · DOI
  117. Langenveld, 2011, Recurrence risk and prediction of a delivery under 34 weeks of gestation after a history of a severe hypertensive disorder, BJOG, 118, 589, 10.1111/j.1471-0528.2010.02842.x · DOI
  118. Lecarpentier, 2013, Risk factors of superimposed preeclampsia in women with essential chronic hypertension treated before pregnancy, PLOS ONE, 8, e62140, 10.1371/journal.pone.0062140 · DOI
  119. Llurba, 2009, 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, 275613, 10.1155/2009/275613 · DOI
  120. Lykke, 2009, Recurring complications in second pregnancy, Obstet Gynecol, 113, 1217, 10.1097/AOG.0b013e3181a66f2d · DOI
  121. Magnus, 2006, Cohort profile: the Norwegian Mother and Child cohort study (MoBa), Int J Epidemiol, 35, 1146, 10.1093/ije/dyl170 · DOI
  122. Massé, 1993, A prospective study of several potential biologic markers for early prediction of the development of preeclampsia, Am J Obstet Gynecol, 169, 501, 10.1016/0002-9378(93)90608-L · DOI
  123. Mbah, 2012, Previous cesarean section, gestational age at first delivery and subsequent risk of pre-eclampsia in obese mothers, Arch Gynecol Obstet, 285, 1375, 10.1007/s00404-011-2161-x · DOI
  124. Mone, 2016, 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, 49, 143, 10.1016/j.cct.2016.07.003 · DOI
  125. Odibo, 2011, First-trimester placental protein 13, PAPP-A, uterine artery Doppler and maternal characteristics in the prediction of pre-eclampsia, Placenta, 32, 598, 10.1016/j.placenta.2011.05.006 · DOI
  126. Ohkuchi, 2000, 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, 16, 171, 10.1046/j.1469-0705.2000.00192.x · DOI
  127. Poston, 2006, 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, 367, 1145, 10.1016/S0140-6736(06)68433-X · DOI · Paper page
  128. Prefumo, 2008, First trimester uterine artery Doppler in women with previous pre-eclampsia, Acta Obstet Gynecol Scand, 87, 1271, 10.1080/00016340802460347 · DOI
  129. Rang, 2008, Serial hemodynamic measurement in normal pregnancy, preeclampsia, and intrauterine growth restriction, Am J Obstet Gynecol, 198, 519e1, 10.1016/j.ajog.2007.11.014 · DOI
  130. Rocha, 2017, Simple approach based on maternal characteristics and mean arterial pressure for the prediction of preeclampsia in the first trimester of pregnancy, J Perinat Med, 45, 843, 10.1515/jpm-2016-0418 · DOI
  131. Rocha, 2017, Comparison of three algorithms for prediction preeclampsia in the first trimester of pregnancy, Pregnancy Hypertens, 10, 113, 10.1016/j.preghy.2017.07.146 · DOI
  132. Rumbold, 2006, Vitamins C and E and the risks of preeclampsia and perinatal complications, N Engl J Med, 354, 1796, 10.1056/NEJMoa054186 · DOI
  133. Salim, 2008, 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, 6, 55, 10.1186/1477-7827-6-55 · DOI
  134. Savitri, 2016, Does pre-pregnancy BMI determine blood pressure during pregnancy? A prospective cohort study, BMJ Open, 6, e011626, 10.1136/bmjopen-2016-011626 · DOI
  135. Ferrazzani, 2006, Prophylaxis of recurrent preeclampsia: low-molecular-weight heparin plus low-dose aspirin versus low-dose aspirin alone, Hypertens Pregnancy, 25, 115, 10.1080/10641950600745517 · DOI
  136. Sibai, 1993, 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, 329, 1213, 10.1056/NEJM199310213291701 · DOI
  137. Skråstad, 2015, Risk assessment for preeclampsia in nulliparous women at 11–13 weeks gestational age: prospective evaluation of two algorithms, BJOG, 122, 1781, 10.1111/1471-0528.13194 · DOI
  138. Sovio, 2015, 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, 386, 2089, 10.1016/S0140-6736(15)00131-2 · DOI
  139. Staff, 2005, Circulating concentrations of sFlt1 (soluble fms-like tyrosine kinase 1) in fetal and maternal serum during pre-eclampsia, Eur J Obstet Gynecol Reprod Biol, 122, 33, 10.1016/j.ejogrb.2004.11.015 · DOI
  140. Stirrup, 2015, Fetal growth reference ranges in twin pregnancy: analysis of the Southwest Thames Obstetric Research Collaborative (STORK) multiple pregnancy cohort, Ultrasound Obstet Gynecol, 45, 301, 10.1002/uog.14640 · DOI
  141. Trogstad, 2004, Recurrence risk of preeclampsia in twin and singleton pregnancies, Am J Med Genet A, 126A, 41, 10.1002/ajmg.a.20512 · DOI
  142. Van Der Linden, 2016, Maternal body mass index and adverse pregnancy outcomes: a Ghanaian cohort study, Obesity, 24, 215, 10.1002/oby.21210 · DOI
  143. van Kuijk, 2014, External validation of a model for periconceptional prediction of recurrent early-onset preeclampsia, Hypertens Pregnancy, 33, 265, 10.3109/10641955.2013.872253 · DOI
  144. van Kuijk, 2011, A model for preconceptional prediction of recurrent early-onset preeclampsia: derivation and internal validation, Reprod Sci, 18, 1154, 10.1177/1933719111410708 · DOI
  145. van Oostwaard, 2012, Prediction of recurrence of hypertensive disorders of pregnancy between 34 and 37 weeks of gestation: a retrospective cohort study, BJOG, 119, 840, 10.1111/j.1471-0528.2012.03312.x · DOI
  146. van Oostwaard, 2014, Prediction of recurrence of hypertensive disorders of pregnancy in the term period, a retrospective cohort study, Pregnancy Hypertens, 4, 194, 10.1016/j.preghy.2014.04.001 · DOI
  147. Vatten, 2007, Changes in circulating level of angiogenic factors from the first to second trimester as predictors of preeclampsia, Am J Obstet Gynecol, 196, 239e1, 10.1016/j.ajog.2006.10.909 · DOI
  148. Verlohren, 2010, An automated method for the determination of the sFlt-1/PIGF ratio in the assessment of preeclampsia, Am J Obstet Gynecol, 202, 161e1, 10.1016/j.ajog.2009.09.016 · DOI
  149. Verlohren, 2012, The sFlt-1/PlGF ratio in different types of hypertensive pregnancy disorders and its prognostic potential in preeclamptic patients, Am J Obstet Gynecol, 206, 58e1, 10.1016/j.ajog.2011.07.037 · DOI · Paper page
  150. Vinter, 2011, The LiP (Lifestyle in Pregnancy) study: a randomized controlled trial of lifestyle intervention in 360 obese pregnant women, Diabetes Care, 34, 2502, 10.2337/dc11-1150 · DOI
  151. Vollebregt, 2010, Limited accuracy of the hyperbaric index, ambulatory blood pressure and sphygmomanometry measurements in predicting gestational hypertension and preeclampsia, J Hypertens, 28, 127, 10.1097/HJH.0b013e32833266fc · DOI
  152. Widmer, 2015, Accuracy of angiogenic biomarkers at ≤ 20 weeks’ gestation in predicting the risk of pre-eclampsia: a WHO multicentre study, Pregnancy Hypertens, 5, 330, 10.1016/j.preghy.2015.09.004 · DOI
  153. Wright, 2017, Maternal vascular malperfusion and adverse perinatal outcomes in low-risk nulliparous women, Obstet Gynecol, 130, 1112, 10.1097/AOG.0000000000002264 · DOI
  154. Wright, 2013, Cohort Profile: the Born in Bradford multi-ethnic family cohort study, Int J Epidemiol, 42, 978, 10.1093/ije/dys112 · DOI
  155. Cnossen, 2007, Accuracy of body mass index in predicting pre-eclampsia: bivariate meta-analysis, BJOG, 114, 1477, 10.1111/j.1471-0528.2007.01483.x · DOI
  156. O’Brien, 2003, Maternal body mass index and the risk of preeclampsia: a systematic overview, Epidemiology, 14, 368, 10.1097/00001648-200305000-00020 · DOI
  157. Duckitt, 2005, Risk factors for pre-eclampsia at antenatal booking: systematic review of controlled studies, BMJ, 330, 565, 10.1136/bmj.38380.674340.E0 · DOI · Paper page
  158. Alpoim, 2013, Preeclampsia and ABO blood groups: a systematic review and meta-analysis, Mol Biol Rep, 40, 2253, 10.1007/s11033-012-2288-2 · DOI
  159. England, 2007, Smoking and risk of preeclampsia: a systematic review, Front Biosci, 12, 2471, 10.2741/2248 · DOI
  160. Rebelo, 2013, C-reactive protein and later preeclampsia: systematic review and meta-analysis taking into account the weight status, J Hypertens, 31, 16, 10.1097/HJH.0b013e32835b0556 · DOI
  161. Luo, 2007, The effects and mechanisms of primiparity on the risk of pre-eclampsia: a systematic review, Paediatr Perinat Epidemiol, 21, 36, 10.1111/j.1365-3016.2007.00836.x · DOI
  162. Cnossen, 2008, Accuracy of mean arterial pressure and blood pressure measurements in predicting pre-eclampsia: systematic review and meta-analysis, BMJ, 336, 1117, 10.1136/bmj.39540.522049.BE · DOI
  163. Sgolastra, 2013, Relationship between periodontitis and pre-eclampsia: a meta-analysis, PLOS ONE, 8, e71387, 10.1371/journal.pone.0071387 · DOI
  164. Kunnen, 2010, Periodontal disease and pre-eclampsia: a systematic review, J Clin Periodontol, 37, 1075, 10.1111/j.1600-051X.2010.01636.x · DOI
  165. Morris, 2012, 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, 345, e4342, 10.1136/bmj.e4342 · DOI
  166. Sanchez-Ramos, 2013, The protein-to-creatinine ratio for the prediction of significant proteinuria in patients at risk for preeclampsia: a meta-analysis, Ann Clin Lab Sci, 43, 211
  167. Wolf, 2014, Leisure time physical activity and the risk of pre-eclampsia: a systematic review, Matern Child Health J, 18, 899, 10.1007/s10995-013-1316-8 · DOI
  168. Palmer, 2013, Work activities and risk of prematurity, low birth weight and pre-eclampsia: an updated review with meta-analysis, Occup Environ Med, 70, 213, 10.1136/oemed-2012-101032 · DOI
  169. Bonzini, 2007, Risk of prematurity, low birthweight and pre-eclampsia in relation to working hours and physical activities: a systematic review, Occup Environ Med, 64, 228, 10.1136/oem.2006.026872 · DOI
  170. Cnossen, 2006, Accuracy of serum uric acid determination in predicting pre-eclampsia: a systematic review, Acta Obstet Gynecol Scand, 85, 519, 10.1080/00016340500342037 · DOI
  171. Chien, 2000, How useful is uterine artery Doppler flow velocimetry in the prediction of pre-eclampsia, intrauterine growth retardation and perinatal death? An overview, BJOG, 107, 196, 10.1111/j.1471-0528.2000.tb11690.x · DOI · Paper page
  172. Kleinrouweler, 2013, 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, 42, 257, 10.1002/uog.12435 · DOI
  173. Pedrosa, 2011, Screening for pre-eclampsia: a systematic review of tests combining uterine artery Doppler with other markers, J Perinat Med, 39, 619, 10.1515/JPM.2011.077 · DOI
  174. Kosmas, 2003, Association of Leiden mutation in factor V gene with hypertension in pregnancy and pre-eclampsia: a meta-analysis, J Hypertens, 21, 1221, 10.1097/00004872-200307000-00002 · DOI
  175. Dudding, 2008, Factor V Leiden is associated with pre-eclampsia but not with fetal growth restriction: a genetic association study and meta-analysis, J Thromb Haemost, 6, 1869, 10.1111/j.1538-7836.2008.03134.x · DOI
  176. Rodger, 2010, 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, 7, e1000292, 10.1371/journal.pmed.1000292 · DOI
  177. Xia, 2012, Meta-analysis of the methylenetetrahydrofolate reductase C677T polymorphism and susceptibility to pre-eclampsia, Hypertens Res, 35, 1129, 10.1038/hr.2012.117 · DOI
  178. Kosmas, 2004, Association of C677T polymorphism in the methylenetetrahydrofolate reductase gene with hypertension in pregnancy and pre-eclampsia: a meta-analysis, J Hypertens, 22, 1655, 10.1097/00004872-200409000-00004 · DOI
  179. Zusterzeel, 2000, Methylenetetrahydrofolate reductase polymorphisms in preeclampsia and the HELLP syndrome, Hypertens Pregnancy, 19, 299, 10.1081/PRG-100101991 · DOI
  180. Li, 2014, Polymorphisms and plasma level of transforming growth factor-Beta 1 and risk for preeclampsia: a systematic review, PLOS ONE, 9, e97230, 10.1371/journal.pone.0097230 · DOI
  181. Wang, 2013, Methylenetetrahydrofolate reductase (MTHFR) gene C677T polymorphism and risk of preeclampsia: an updated meta-analysis based on 51 studies, Arch Med Res, 44, 159, 10.1016/j.arcmed.2013.01.011 · DOI
  182. Widmer, 2007, Mapping the theories of preeclampsia and the role of angiogenic factors: a systematic review, Obstet Gynecol, 109, 168, 10.1097/01.AOG.0000249609.04831.7c · DOI
  183. Jacobs, 2011, Levels of soluble fms-like tyrosine kinase one in first trimester and outcomes of pregnancy: a systematic review, Reprod Biol Endocrinol, 9, 77, 10.1186/1477-7827-9-77 · DOI
  184. Huppertz, 2013, Placental protein 13 (PP13): a new biological target shifting individualized risk assessment to personalized drug design combating pre-eclampsia, Hum Reprod Update, 19, 391, 10.1093/humupd/dmt003 · DOI
  185. Schneuer, 2012, First trimester screening of maternal placental protein 13 for predicting preeclampsia and small for gestational age: in-house study and systematic review, Placenta, 33, 735, 10.1016/j.placenta.2012.05.012 · DOI
  186. Lau, 2013, Tumour necrosis factor-alpha, interleukin-6, and interleukin-10 levels are altered in preeclampsia: a systematic review and meta-analysis, Am J Reprod Immunol, 70, 412, 10.1111/aji.12138 · DOI
  187. Tabesh, 2013, Maternal vitamin D status and risk of pre-eclampsia: a systematic review and meta-analysis, J Clin Endocrinol Metab, 98, 3165, 10.1210/jc.2013-1257 · DOI
  188. Morgan, 2013, Association of plasminogen activator inhibitor-type 1 (–675 4G/5G) polymorphism with pre-eclampsia: systematic review, PLOS ONE, 8, e56907, 10.1371/journal.pone.0056907 · DOI
  189. Dai, 2013, The polymorphism for endothelial nitric oxide synthase gene, the level of nitric oxide and the risk for pre-eclampsia: a meta-analysis, Gene, 519, 187, 10.1016/j.gene.2013.01.004 · DOI
  190. Chen, 2012, Angiotensin-converting enzyme insertion/deletion polymorphism and risk of pregnancy hypertensive disorders: a meta-analysis, J Renin Angiotensin Aldosterone Syst, 13, 184, 10.1177/1470320311427755 · DOI
  191. Qi, 2013, Endothelial nitric oxide synthase gene polymorphisms and risk of preeclampsia, Am J Perinatol, 30, 795, 10.1055/s-0032-1333406 · DOI
  192. Zhao, 2013, 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, 19, 136, 10.1093/molehr/gas056 · DOI
  193. Zhao, 2012, Association of maternal AGTR1 polymorphisms and preeclampsia: a systematic review and meta-analysis, J Matern Fetal Neonatal Med, 25, 2676, 10.3109/14767058.2012.708370 · DOI
  194. Zhong, 2012, Meta analysis of angiotensin-converting enzyme I/D polymorphism as a risk factor for preeclampsia in Chinese women, Genet Mol Res, 11, 2268, 10.4238/2012.May.21.1 · DOI
  195. Ni, 2012, AGT M235T polymorphism contributes to risk of preeclampsia: evidence from a meta-analysis, J Renin Angiotensin Aldosterone Syst, 13, 379, 10.1177/1470320312440903 · DOI
  196. Hui, 2012, Combinations of maternal serum markers to predict preeclampsia, small for gestational age, and stillbirth: a systematic review, J Obstet Gynaecol Can, 34, 142, 10.1016/S1701-2163(16)35157-X · DOI
  197. Giguère, 2011, Combining biochemical and ultrasonographic markers in predicting preeclampsia: a systematic review, Ann Biol Clin, 69, 257, 10.1684/abc.2011.0572 · DOI
  198. Abou-Nassar, 2011, The association between antiphospholipid antibodies and placenta mediated complications: a systematic review and meta-analysis, Thromb Res, 128, 77, 10.1016/j.thromres.2011.02.006 · DOI
  199. do Prado, 2010, Association of anticardiolipin antibodies with preeclampsia: a systematic review and meta-analysis, Obstet Gynecol, 116, 1433, 10.1097/AOG.0b013e3181fe02ec · DOI
  200. Gupta, 2009, Lipid peroxidation and antioxidant status in preeclampsia: a systematic review, Obstet Gynecol Surv, 64, 750, 10.1097/OGX.0b013e3181bea0ac · DOI

200 of 221 shown

Cited by

See all 30

Metadata

Authors
Allotey, J., Laivuori, H., Snell, K. I., Smuk, M., Hooper, R., Chan, C. L., Ahmed, A., Chappell, L. C., von Dadelszen, P., Dodds, J., Green, M., Kenny, L., Khalil, A., Khan, K. S., Mol, B. W., Myers, J., Poston, L., Thilaganathan, B., Staff, A. C., Smith, G. C., Ganzevoort, W., Odibo, A. O., Ramírez, J. A., Kingdom, J., Daskalakis, G., Farrar, D., Baschat, A. A., Seed, P. T., Prefumo, F., da Silva Costa, F., Groen, H., Audibert, F., Masse, J., Skråstad, R. B., Salvesen, K. Å., Haavaldsen, C., Nagata, C., Rumbold, A. R., Heinonen, S., Askie, L. M., Smits, L. J., Vinter, C. A., Magnus, P. M., Eero, K., Villa, P. M., Jenum, A. K., Andersen, L. B., Norman, J. E., Ohkuchi, A., Eskild, A., Bhattacharya, S., McAuliffe, F. M., Galindo, A., Herraiz, I., Carbillon, L., Klipstein-Grobusch, K., Yeo, S., Teede, H. J., Browne, J. L., Moons, K. G., Riley, R. D., & Thangaratinam, S.
Affiliations
  • Barts Research Centre for Women’s Health (BARC), Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
  • Medical and Clinical Genetics, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
  • Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, UK
  • Pragmatic Clinical Trials Unit, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
  • Aston Medical Research Institute, Aston Medical School, Aston University, Birmingham, UK
  • Department of Women & Children’s Health, School of Life Course Sciences, Faculty of Life Sciences & Medicine, King’s College London, London, UK
  • Action on Pre-eclampsia (APEC), Evesham, UK
  • Vice Chancellor’s Office, Faculty of Health & Life Sciences, University of Liverpool, Liverpool, UK
  • Fetal Medicine Unit, St George’s University Hospitals NHS Foundation Trust and Molecular and Clinical Sciences Research Institute, St George’s University of London, London, UK
  • Department of Obstetrics and Gynaecology, Monash University, Monash Medical Centre, Clayton, VIC, Australia
  • Maternal and Fetal Health Research Centre, Manchester Academic Health Science Centre, University of Manchester, Manchester University NHS Foundation Trust, Manchester, UK
  • Division of Obstetrics and Gynaecology, Oslo University Hospital, Oslo, Norway
  • Department of Obstetrics and Gynaecology, NIHR Biomedical Research Centre, University of Cambridge, Cambridge, UK
  • Department of Obstetrics, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
  • University of South Florida, Tampa, FL, USA
  • Department of Obstetrics and Gynaecology, University Hospital de Cabueñes, Gijón, Spain
  • Maternal-Fetal Medicine Division, Department of Obstetrics and Gynaecology, Mount Sinai Hospital, University of Toronto, Toronto, ON, Canada
  • Department of Obstetrics and Gynaecology, National and Kapodistrian University of Athens, Alexandra Hospital, Athens, Greece
  • Bradford Institute for Health Research, Bradford Teaching Hospitals, Bradford, UK
  • Johns Hopkins Center for Fetal Therapy, Department of Gynecology & Obstetrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA
  • Department of Obstetrics and Gynaecology, University of Brescia, Brescia, Italy
  • Department of Gynaecology and Obstetrics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
  • Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
  • Department of Obstetrics and Gynaecology, CHU Sainte-Justine, Université de Montréal, Montréal, QC, Canada
  • Department of Molecular Biology, Medical Biochemistry and Pathology, Université Laval, Québec City, QC, Canada
  • Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
  • Department of Laboratory Medicine, Children’s and Women’s Health, Norwegian University of Science and Technology, Trondheim, Norway
  • Department of Obstetrics and Gynacology, Akershus University Hospital, Lørenskog, Norway
  • Department of Education for Clinical Research, National Center for Child Health and Development, Tokyo, Japan
  • South Australian Health and Medical Research Institute, Adelaide, SA, Australia
  • Department of Obstetrics and Gynaecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
  • NHMRC Clinical Trials Centre, University of Sydney, NSW, Australia
  • Care and Public Health Research Institute, Maastricht University Medical Centre, Maastricht, the Netherlands
  • Department of Gynaecology and Obstetrics, Odense University Hospital, University of Southern Denmark, Odense, Denmark
  • Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
  • Finnish Institute for Health and Welfare, Helsinki, Finland
  • General Practice Research Unit (AFE), Department of General Practice, Institute of Health and Society, University of Oslo, Oslo, Norway
  • Institute for Clinical Research, University of Southern Denmark, Odense, Denmark
  • MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, UK
  • Department of Obstetrics and Gynecology, School of Medicine, Jichi Medical University, Shimotsuke-shi, Tochigi, Japan
  • Obstetrics & Gynaecology, Institute of Applied Health Sciences, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK
  • UCD Perinatal Research Centre, UCD School of Medicine, University College Dublin, National Maternity Hospital, Dublin, Ireland
  • Fetal Medicine Unit, Maternal and Child Health and Development Research Network (SAMID), Department of Obstetrics and Gynaecology, Hospital Universitario Cruces, Instituto de Investigación Hospital, Barakaldo, Spain
  • Department of Obstetrics and Gynaecology, Hospital Universitario 12 de Octubre, Madrid, Spain
  • Department of Obstetrics and Gynecology, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France
  • Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, the Netherlands
  • School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
  • Monash Partners Academic Health Sciences Centre, Monash University and Monash Health, Melbourne, VIC, Australia
ORCID
Venue
Health Technology Assessment
Publisher
National Institute for Health and Care Research
Published
2020-12
Volume
24
Issue
72
Pages
1-252
ISSN
  • 1366-5278
  • 2046-4924
DOI
10.3310/hta24720
Cited by
30
References
24
Language
en
Licence
www.nationalarchives.gov.uk/doc/non-commercial-government-licence/non-commercial-government-licence.htm (unspecified)
Funders
Health Technology Assessment programme (14/158/02)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Validation and development of models using clinical, biochemical and ultrasound markers for predicting pre-eclampsia: an individual participant data meta-analysis. https://app.cochat.ai/papers/10.3310/hta24720
The paper
Allotey, J., Laivuori, H., Snell, K. I., Smuk, M., Hooper, R., Chan, C. L., Ahmed, A., Chappell, L. C., von Dadelszen, P., Dodds, J., Green, M., Kenny, L., Khalil, A., Khan, K. S., Mol, B. W., Myers, J., Poston, L., Thilaganathan, B., Staff, A. C., . . . Thangaratinam, S. (2020). Validation and development of models using clinical, biochemical and ultrasound markers for predicting pre-eclampsia: an individual participant data meta-analysis. Health Technology Assessment. https://doi.org/10.3310/hta24720

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.