Skip to main content
PMC home page
Medline Book to support NIHPA logoLink to Medline Book to support NIHPA
. 2024 Aug;28(47):1–119. doi: 10.3310/DABW4814

Development and validation of prediction models for fetal growth restriction and birthweight: an individual participant data meta-analysis.

John Allotey, Lucinda Archer, Dyuti Coomar, Kym Ie Snell, Melanie Smuk, Lucy Oakey, Sadia Haqnawaz, Ana Pilar Betrán, Lucy C Chappell, Wessel Ganzevoort, Sanne Gordijn, Asma Khalil, Ben W Mol, Rachel K Morris, Jenny Myers, Aris T Papageorghiou, Basky Thilaganathan, Fabricio Da Silva Costa, Fabio Facchinetti, Arri Coomarasamy, Akihide Ohkuchi, Anne Eskild, Javier Arenas Ramírez, Alberto Galindo, Ignacio Herraiz, Federico Prefumo, Shigeru Saito, Line Sletner, Jose Guilherme Cecatti, Rinat Gabbay-Benziv, Francois Goffinet, Ahmet A Baschat, Renato T Souza, Fionnuala Mone, Diane Farrar, Seppo Heinonen, Kjell Å Salvesen, Luc Jm Smits, Sohinee Bhattacharya, Chie Nagata, Satoru Takeda, Marleen Mhj van Gelder, Dewi Anggraini, SeonAe Yeo, Jane West, Javier Zamora, Hema Mistry, Richard D Riley, Shakila Thangaratinam
PMCID: PMC11404361  PMID: 39252507

Abstract

BACKGROUND

Fetal growth restriction is associated with perinatal morbidity and mortality. Early identification of women having at-risk fetuses can reduce perinatal adverse outcomes.

OBJECTIVES

To assess the predictive performance of existing models predicting fetal growth restriction and birthweight, and if needed, to develop and validate new multivariable models using individual participant data.

DESIGN

Individual participant data meta-analyses of cohorts in International Prediction of Pregnancy Complications network, decision curve analysis and health economics analysis.

PARTICIPANTS

Pregnant women at booking. External validation of existing models (9 cohorts, 441,415 pregnancies); International Prediction of Pregnancy Complications model development and validation (4 cohorts, 237,228 pregnancies).

PREDICTORS

Maternal clinical characteristics, biochemical and ultrasound markers.

PRIMARY OUTCOMES

fetal growth restriction defined as birthweight <10th centile adjusted for gestational age and with stillbirth, neonatal death or delivery before 32 weeks' gestation birthweight.

ANALYSIS

First, we externally validated existing models using individual participant data meta-analysis. If needed, we developed and validated new International Prediction of Pregnancy Complications models using random-intercept regression models with backward elimination for variable selection and undertook internal-external cross-validation. We estimated the study-specific performance (c-statistic, calibration slope, calibration-in-the-large) for each model and pooled using random-effects meta-analysis. Heterogeneity was quantified using τ2 and 95% prediction intervals. We assessed the clinical utility of the fetal growth restriction model using decision curve analysis, and health economics analysis based on National Institute for Health and Care Excellence 2008 model.

RESULTS

Of the 119 published models, one birthweight model (Poon) could be validated. None reported fetal growth restriction using our definition. Across all cohorts, the Poon model had good summary calibration slope of 0.93 (95% confidence interval 0.90 to 0.96) with slight overfitting, and underpredicted birthweight by 90.4 g on average (95% confidence interval 37.9 g to 142.9 g). The newly developed International Prediction of Pregnancy Complications-fetal growth restriction model included maternal age, height, parity, smoking status, ethnicity, and any history of hypertension, pre-eclampsia, previous stillbirth or small for gestational age baby and gestational age at delivery. This allowed predictions conditional on a range of assumed gestational ages at delivery. The pooled apparent c-statistic and calibration were 0.96 (95% confidence interval 0.51 to 1.0), and 0.95 (95% confidence interval 0.67 to 1.23), respectively. The model showed positive net benefit for predicted probability thresholds between 1% and 90%. In addition to the predictors in the International Prediction of Pregnancy Complications-fetal growth restriction model, the International Prediction of Pregnancy Complications-birthweight model included maternal weight, history of diabetes and mode of conception. Average calibration slope across cohorts in the internal-external cross-validation was 1.00 (95% confidence interval 0.78 to 1.23) with no evidence of overfitting. Birthweight was underestimated by 9.7 g on average (95% confidence interval -154.3 g to 173.8 g).

LIMITATIONS

We could not externally validate most of the published models due to variations in the definitions of outcomes. Internal-external cross-validation of our International Prediction of Pregnancy Complications-fetal growth restriction model was limited by the paucity of events in the included cohorts. The economic evaluation using the published National Institute for Health and Care Excellence 2008 model may not reflect current practice, and full economic evaluation was not possible due to paucity of data.

FUTURE WORK

International Prediction of Pregnancy Complications models' performance needs to be assessed in routine practice, and their impact on decision-making and clinical outcomes needs evaluation.

CONCLUSION

The International Prediction of Pregnancy Complications-fetal growth restriction and International Prediction of Pregnancy Complications-birthweight models accurately predict fetal growth restriction and birthweight for various assumed gestational ages at delivery. These can be used to stratify the risk status at booking, plan monitoring and management.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42019135045.

FUNDING

This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 17/148/07) and is published in full in Health Technology Assessment; Vol. 28, No. 14. See the NIHR Funding and Awards website for further award information.

Plain language summary

One in ten babies is born small for their age. A third of such small babies are considered to be ‘growth-restricted’ as they have complications such as dying in the womb (stillbirth) or after birth (newborn death), cerebral palsy, or needing long stays in hospital. When growth restriction is suspected in fetuses, they are closely monitored and often delivered early to avoid complications. Hence, it is important that we identify growth-restricted babies early to plan care. Our goal was to provide personalised and accurate estimates of the mother’s chances of having a growth-restricted baby and predict the baby’s weight if delivered at various time points in pregnancy. To do so, first we tested how accurate existing risk calculators (‘prediction models’) were in predicting growth restriction and birthweight. We then developed new risk-calculators and studied their clinical and economic benefits. We did so by accessing the data from individual pregnant women and their babies in our large database library (International Prediction of Pregnancy Complications). Published risk-calculators had various definitions of growth restriction and none predicted the chances of having a growth-restricted baby using our definition. One predicted baby’s birthweight. This risk-calculator performed well, but underpredicted the birthweight by up to 143 g. We developed two new risk-calculators to predict growth-restricted babies (International Prediction of Pregnancy Complications-fetal growth restriction) and birthweight (International Prediction of Pregnancy Complications-birthweight). Both calculators accurately predicted the chances of the baby being born with growth restriction, and its birthweight. The birthweight was underpredicted by <9.7 g. The calculators performed well in both mothers predicted to be low and high risk. Further research is needed to determine the impact of using these calculators in practice, and challenges to implementing them in practice. Both International Prediction of Pregnancy Complications-fetal growth restriction and International Prediction of Pregnancy Complications-birthweight risk calculators will inform healthcare professionals and empower parents make informed decisions on monitoring and timing of delivery.

Full text of this article can be found in Bookshelf.

References

  1. Mamelle N, Cochet V, Claris O. Definition of fetal growth restriction according to constitutional growth potential. Biol Neonate 2001;80(4):277–85. doi: 10.1159/000047157. [DOI] [PubMed]
  2. Divon MY, Haglund B, Nisell H, Otterblad PO, Westgren M. Fetal and neonatal mortality in the postterm pregnancy: the impact of gestational age and fetal growth restriction. Am J Obstet Gynecol 1998;178(4):726–31. doi: 10.1016/s0002-9378(98)70482-x. [DOI] [PubMed]
  3. Barker DJ, Osmond C, Golding J, Kuh D, Wadsworth ME. Growth in utero, blood pressure in childhood and adult life, and mortality from cardiovascular disease. BMJ 1989;298(6673):564–7. doi: 10.1136/bmj.298.6673.564. [DOI] [PMC free article] [PubMed]
  4. von Beckerath AK, Kollmann M, Rotky-Fast C, Karpf E, Lang U, Klaritsch P. Perinatal complications and long-term neurodevelopmental outcome of infants with intrauterine growth restriction. Am J Obstet Gynecol 2013;208(2):130 e1–6. doi: 10.1016/j.ajog.2012.11.014. [DOI] [PubMed]
  5. Wilcox AJ, Cortese M, McConnaughey DR, Moster D, Basso O. The limits of small-for-gestational-age as a high-risk category. Eur J Epidemiol 2021;36(10):985–91. doi: 10.1007/s10654-021-00810-z. [DOI] [PubMed]
  6. Gardosi J, Madurasinghe V, Williams M, Malik A, Francis A. Maternal and fetal risk factors for stillbirth: population based study. BMJ 2013;346:f108. doi: 10.1136/bmj.f108. [DOI] [PMC free article] [PubMed]
  7. Vayssiere C, Sentilhes L, Ego A, Bernard C, Cambourieu D, Flamant C, et al. Fetal growth restriction and intra-uterine growth restriction: guidelines for clinical practice from the French College of Gynaecologists and Obstetricians. Eur J Obstet Gynecol Reprod Biol 2015;193:10–8. doi: 10.1016/j.ejogrb.2015.06.021. [DOI] [PubMed]
  8. Saving Babies’ Lives Version Two: A Care Bundle for Reducing Perinatal Mortality. URL: www.england.nhs.uk/wp-content/uploads/2019/03/Saving-Babies-Lives-Care-Bundle-Version-Two-Updated-Final-Version.pdf (accessed 15 October 2020).
  9. Dall’Asta A, Brunelli V, Prefumo F, Frusca T, Lees CC. Early onset fetal growth restriction. Matern Health Neonatol Perinatol 2017;3:2. doi: 10.1186/s40748-016-0041-x. [DOI] [PMC free article] [PubMed]
  10. Hepburn M, Rosenberg K. An audit of the detection and management of small-for-gestational age babies. Br J Obstet Gynaecol 1986;93(3):212–6. doi: 10.1111/j.1471-0528.1986.tb07895.x. [DOI] [PubMed]
  11. Royal College of Obstetricians and Gynaecologists. The Investigation and Management of the Small–for–Gestational–Age Fetus – RCOG Green-top Guideline No. 31: 2nd Edition. February 2013. Minor revisions – January 2014. URL: www.rcog.org.uk/globalassets/documents/guidelines/gtg_31.pdf (accessed 29 June 2022).
  12. American College of Obstetricians, Gynecologists Committee on Practice Bulletins – Obstetrics SfM-FMPC. Fetal growth restriction: ACOG practice bulletin, number 227. Obstet Gynecol 2021;137(2):e16–28. doi: 10.1097/AOG.0000000000004251. [DOI] [PubMed]
  13. Lausman A, Kingdom J, Gagnon R, et al. Intrauterine growth restriction: screening, diagnosis, and management. J Obstet Gynaecol Can 2013;35(8):741–8. doi: 10.1016/S1701-2163(15)30865-3. [DOI] [PubMed]
  14. The Royal Australian and New Zealand College of Obstetricians and Gynaecologists. Screening in Early Pregnancy for Adverse Perinatal Outcomes. Prenatal Screening for Adverse Pregnancy Outcomes. C-Obs 61 (July 2015). URL: https://ranzcog.edu.au/wp-content/uploads/2022/05/Screening-in-Early-Pregnancy-for-Adverse-Perinatal-Outcomes.pdf (accessed 29 June 2022).
  15. Lees CC, Stampalija T, Baschat A, et al. ISUOG Practice Guidelines: diagnosis and management of small-for-gestational-age fetus and fetal growth restriction. Ultrasound Obstet Gynecol 2020;56(2):298–312. doi: 10.1002/uog.22134. [DOI] [PubMed]
  16. Melamed N, Baschat A, Yinon Y, et al. FIGO (International Federation of Gynecology and Obstetrics) initiative on fetal growth: best practice advice for screening, diagnosis, and management of fetal growth restriction. Int J Gynaecol Obstet 2021;152(Suppl. 1):3–57. doi: 10.1002/ijgo.13522. [DOI] [PMC free article] [PubMed]
  17. Bricker L, Medley N, Pratt JJ. Routine ultrasound in late pregnancy (after 24 weeks’ gestation). Cochrane Database Syst Rev 2015;(6):CD001451. doi: 10.1002/14651858.CD001451.pub4. [DOI] [PMC free article] [PubMed]
  18. 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(10008):2089–97. doi: 10.1016/S0140-6736(15)00131-2. [DOI] [PMC free article] [PubMed]
  19. Monier I, Blondel B, Ego A, Kaminiski M, Goffinet F, Zeitlin J. Poor effectiveness of antenatal detection of fetal growth restriction and consequences for obstetric management and neonatal outcomes: a French national study. BJOG 2015;122(4):518–27. doi: 10.1111/1471-0528.13148. [DOI] [PubMed]
  20. Henrichs J, Verfaille V, Jellema P, et al. Effectiveness of routine third trimester ultrasonography to reduce adverse perinatal outcomes in low risk pregnancy (the IRIS study): nationwide, pragmatic, multicentre, stepped wedge cluster randomised trial. BMJ 2019;367:l5517. doi: 10.1136/bmj.l5517. [DOI] [PMC free article] [PubMed]
  21. National Institute for Health and Care Excellence (NICE). Antenatal Care for Uncomplicated Pregnancies. Clinical Guideline [CG62]. London: NICE; 2008. URL: https://www.nice.org.uk/guidance/cg62 (accessed 21 March 2018).
  22. Morriss RK. Prediction and Prevention of Fetal Growth Restriction and Compromise of Fetal Wellbeing. Systematic Reviews and Meta-Analyses with Model Based Economic Evaluation. A thesis submitted to the University of Birmingham for the degree of Doctor of Philosophy. College of Medical and Dental Sciences, The University of Birmingham; September 2010, Volume I. URL: http://etheses.bham.ac.uk/1319/1/Morris11PhD.pdf (accessed 5 May 2018).
  23. Morris RK, Cnossen JS, Langejans M, 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. doi: 10.1186/1471-2393-8-33. [DOI] [PMC free article] [PubMed]
  24. Morris RK, Khan KS, Coomarasamy A, Robson SC, Kleijnen J. The value of predicting restriction of fetal growth and compromise of its wellbeing: systematic quantitative overviews (meta-analysis) of test accuracy literature. BMC Pregnancy Childbirth 2007;7:3. doi: 10.1186/1471-2393-7-3. [DOI] [PMC free article] [PubMed]
  25. Morris RK, Bilagi A, Devani P, Kilby MD. Association of serum PAPP-A levels in first trimester with small for gestational age and adverse pregnancy outcomes: systematic review and meta-analysis. Prenat Diagn 2017;37(3):253–65. doi: 10.1002/pd.5001. [DOI] [PubMed]
  26. Kleinrouweler CE, Cheong-See FM, Collins GS, et al. Prognostic models in obstetrics: available, but far from applicable. Am J Obstet Gynecol 2016 Jan;214(1):79–90.e36. https://doi.org/10.1016/j.ajog.2015.06.013. Epub 2015 Jun 10. PMID: 26070707. doi: 10.1016/j.ajog.2015.06.013. [DOI] [PubMed]
  27. Moons KG, Altman DG, Reitsma JB, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med 2015;162(1):W1–73. doi: 10.7326/M14-0698. [DOI] [PubMed]
  28. World Health Organization (WHO). Children’s Environmental Health Indicators. Intrauterine Growth Retardation in Newborn Children. URL: www.who.int/ceh/indicators/iugrnewborn.pdf (accessed 5 March 2018).
  29. Zhang X, Platt RW, Cnattingius S, Joseph KS, Kramer MS. The use of customised versus population-based birthweight standards in predicting perinatal mortality. BJOG 2007;114(4):474–7. doi: 10.1111/j.1471-0528.2007.01273.x. [DOI] [PubMed]
  30. McCowan LM, Harding JE, Stewart AW. Customized birthweight centiles predict SGA pregnancies with perinatal morbidity. BJOG 2005;112(8):1026–33. doi: 10.1111/j.1471-0528.2005.00656.x. [DOI] [PubMed]
  31. Riley RD, Ensor J, Snell KI. 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]
  32. 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(18):3158–80. doi: 10.1002/sim.5732. [DOI] [PubMed]
  33. Debray TP, Riley RD, Rovers MM, Reitsma JB, Moons KG; Cochrane IPDM-aMg. Individual participant data (IPD) meta-analyses of diagnostic and prognostic modeling studies: guidance on their use. PLOS Med 2015;12(10):e1001886. doi: 10.1371/journal.pmed.1001886. [DOI] [PMC free article] [PubMed]
  34. Riley RD, Lambert PC, Abo-Zaid G. Meta-analysis of individual participant data: rationale, conduct, and reporting. BMJ 2010;340:c221. doi: 10.1136/bmj.c221. [DOI] [PubMed]
  35. Riley RD, Tierney J, Stewart LA, editors. Individual Participant Data Meta-Analysis: A Handbook for Healthcare Research. Chichester: Wiley; 2021.
  36. Allotey J, Snell KI, Smuk M, et al. Validation and development of models using clinical, biochemical and ultrasound markers for predicting pre-eclampsia: an individual participant data meta-analysis. Health Technol Assess 2020;24(72):1–252. doi: 10.3310/hta24720. [DOI] [PMC free article] [PubMed]
  37. Gordijn SJ, Beune IM, Thilaganathan B, et al. Consensus definition of fetal growth restriction: a Delphi procedure. Ultrasound Obstet Gynecol 2016;48(3):333–9. doi: 10.1002/uog.15884. [DOI] [PubMed]
  38. Sovio U, Smith GCS. The effect of customization and use of a fetal growth standard on the association between birthweight percentile and adverse perinatal outcome. Am J Obstet Gynecol 2018;218(2S):S738–S44. doi: 10.1016/j.ajog.2017.11.563. [DOI] [PubMed]
  39. Altman DG, Vergouwe Y, Royston P, Moons KG. Prognosis and prognostic research: validating a prognostic model. BMJ 2009;338:b605. doi: 10.1136/bmj.b605. [DOI] [PubMed]
  40. Moons KG, Royston P, Vergouwe Y, Grobbee DE, Altman DG. Prognosis and prognostic research: what, why, and how? BMJ 2009;338:b375. doi: 10.1136/bmj.b375. [DOI] [PubMed]
  41. Royston P, Moons KG, Altman DG, Vergouwe Y. Prognosis and prognostic research: developing a prognostic model. BMJ 2009;338:b604. doi: 10.1136/bmj.b604. [DOI] [PubMed]
  42. Riley RD, van der Windt D, Croft P, et al., editors. Prognosis Research in Healthcare: Concepts, Methods and Impact. Oxford, UK: Oxford University Press; 2019.
  43. Stewart LA, Clarke M, Rovers M, et al. Preferred Reporting Items for Systematic Review and Meta-Analyses of individual participant data: the PRISMA-IPD Statement. JAMA 2015;313(16):1657–65. doi: 10.1001/jama.2015.3656. [DOI] [PubMed]
  44. Allotey J, Snell KIE, Chan C, 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. doi: 10.1186/s41512-017-0016-z. [DOI] [PMC free article] [PubMed]
  45. Snell KIE, Allotey J, Smuk M, et al. External validation of prognostic models predicting pre-eclampsia: individual participant data meta-analysis. BMC Med 2020;18(1):302. doi: 10.1186/s12916-020-01766-9. [DOI] [PMC free article] [PubMed]
  46. Townsend R, Sileo F, Stocker L, et al. Variation in outcome reporting in randomized controlled trials of interventions for prevention and treatment of fetal growth restriction. Ultrasound Obstet Gynecol 2019;53(5):598–608. doi: 10.1002/uog.20189. [DOI] [PubMed]
  47. Wolff RF, Moons KGM, Riley RD, et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med 2019;170(1):51–8. doi: 10.7326/M18-1376. [DOI] [PubMed]
  48. Cheong-See F, Allotey J, Marlin N, et al. Prediction models in obstetrics: understanding the treatment paradox and potential solutions to the threat it poses. BJOG 2016;123(7):1060–4. doi: 10.1111/1471-0528.13859. [DOI] [PubMed]
  49. Poon LC, Tan MY, Yerlikaya G, Syngelaki A, Nicolaides KH. Birth weight in live births and stillbirths. Ultrasound Obstet Gynecol 2016;48(5):602–6. doi: 10.1002/uog.17287. [DOI] [PubMed]
  50. Papageorghiou AT, Ohuma EO, Altman DG, et al. International standards for fetal growth based on serial ultrasound measurements: the Fetal Growth Longitudinal Study of the INTERGROWTH-21st Project. Lancet 2014;384(9946):869–79. doi: 10.1016/S0140-6736(14)61490-2. [DOI] [PubMed]
  51. de Onis M, Garza C, Victora CG, Onyango AW, Frongillo EA, Martines J. The WHO Multicentre Growth Reference Study: planning, study design, and methodology. Food Nutr Bull 2004;25(1 Suppl.):S15–26. doi: 10.1177/15648265040251S103. [DOI] [PubMed]
  52. Heazell AEP, Hayes DJL, Whitworth M, Takwoingi Y, Bayliss SE, Davenport C. Diagnostic accuracy of biochemical tests of placental function versus ultrasound assessment of fetal size for stillbirth and small-for-gestational-age infants. Cochrane Database Syst Rev 2019 May 14;5(5):CD012245. https://doi.org/10.1002/14651858.CD012245.pub2. PMID: 31087568; PMCID: PMC6515632. doi: 10.1002/14651858.CD012245.pub2. [DOI] [PMC free article] [PubMed]
  53. Riley RD, Debray TPA, Collins GS, et al. Minimum sample size for external validation of a clinical prediction model with a binary outcome. Stat Med 2022 Mar 30;41(7):1280–1295. https://doi.org/10.1002/sim.9275. Epub 2021 Dec 16. PMID: 34915593. doi: 10.1002/sim.9275. [DOI] [PubMed]
  54. Collins GS, Ogundimu EO, Altman DG. Sample size considerations for the external validation of a multivariable prognostic model: a resampling study. Stat Med 2016;35(2):214–26. doi: 10.1002/sim.6787. [DOI] [PMC free article] [PubMed]
  55. Archer L, Snell KIE, Ensor J, Hudda MT, Collins GS, Riley RD. Minimum sample size for external validation of a clinical prediction model with a continuous outcome. Stat Med 2021;40(1):133–46. doi: 10.1002/sim.8766. [DOI] [PubMed]
  56. Riley RD, Snell KI, Ensor J, et al. Minimum sample size for developing a multivariable prediction model: PART II – binary and time-to-event outcomes. Stat Med 2019;38(7):1276–96. doi: 10.1002/sim.7992. [DOI] [PMC free article] [PubMed]
  57. Roberts LA, Ling HZ, Poon LC, Nicolaides KH, Kametas NA. Maternal hemodynamics, fetal biometry and Doppler indices in pregnancies followed up for suspected fetal growth restriction. Ultrasound Obstet Gynecol 2018;52(4):507–14. doi: 10.1002/uog.19067. [DOI] [PubMed]
  58. Riley RD, Snell KIE, Ensor J, et al. Minimum sample size for developing a multivariable prediction model: Part I – Continuous outcomes. Stat Med 2019;38(7):1262–75. doi: 10.1002/sim.7993. [DOI] [PubMed]
  59. Poon LC, Karagiannis G, Staboulidou I, Shafiei A, Nicolaides KH. Reference range of birth weight with gestation and first-trimester prediction of small-for-gestation neonates. Prenat Diagn 2011;31(1):58–65. doi: 10.1002/pd.2520. [DOI] [PubMed]
  60. White IR, Royston P, Wood AM. Multiple imputation using chained equations: issues and guidance for practice. Stat Med 2011;30(4):377–99. doi: 10.1002/sim.4067. [DOI] [PubMed]
  61. Little RJA, Rubin DB. Statistical Analysis with Missing Data. 2nd edn. New York: John Wiley; 2002.
  62. Steyerberg EW. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. New York, NY: Springer; 2009.
  63. Steyerberg EW, Vickers AJ, Cook NR, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 2010;21(1):128–38. doi: 10.1097/EDE.0b013e3181c30fb2. [DOI] [PMC free article] [PubMed]
  64. 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(4):614–32. doi: 10.1002/bimj.201400004. [DOI] [PMC free article] [PubMed]
  65. Burke DL, Ensor J, Riley RD. Meta-analysis using individual participant data: one-stage and two-stage approaches, and why they may differ. Stat Med 2017;36(5):855–75. doi: 10.1002/sim.7141. [DOI] [PMC free article] [PubMed]
  66. Debray TP, Damen JA, Snell KI, et al. A guide to systematic review and meta-analysis of prediction model performance. BMJ 2017;356:i6460. doi: 10.1136/bmj.i6460. [DOI] [PubMed]
  67. Riley RD, Higgins JP, Deeks JJ. Interpretation of random effects meta-analyses. BMJ 2011;342:d549. doi: 10.1136/bmj.d549. [DOI] [PubMed]
  68. 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(11):3505–22. doi: 10.1177/0962280217705678. [DOI] [PMC free article] [PubMed]
  69. Rover C, Knapp G, Friede T. Hartung-Knapp-Sidik-Jonkman approach and its modification for random-effects meta-analysis with few studies. BMC Med Res Methodol 2015;15:99. doi: 10.1186/s12874-015-0091-1. [DOI] [PMC free article] [PubMed]
  70. Higgins JP, Thompson SG, Spiegelhalter DJ. A re-evaluation of random-effects meta-analysis. J R Stat Soc Ser A Stat Soc 2009;172(1):137–59. doi: 10.1111/j.1467-985X.2008.00552.x. [DOI] [PMC free article] [PubMed]
  71. Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making 2006;26(6):565–74. doi: 10.1177/0272989X06295361. [DOI] [PMC free article] [PubMed]
  72. 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. doi: 10.1136/bmj.i6. [DOI] [PMC free article] [PubMed]
  73. 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(11):1841–63. doi: 10.1002/sim.6451. [DOI] [PubMed]
  74. Van Houwelingen JC, Le Cessie S. Predictive value of statistical models. Stat Med 1990;9(11):1303–25. doi: 10.1002/sim.4780091109. [DOI] [PubMed]
  75. Fraser A, Macdonald-Wallis C, Tilling K, et al. Cohort Profile: the Avon Longitudinal Study of Parents and Children: ALSPAC mothers cohort. Int J Epidemiol 2013;42(1):97–110. doi: 10.1093/ije/dys066. [DOI] [PMC free article] [PubMed]
  76. Ontario’s Better Outcomes Registry & Network (BORN). URL: www.bornontario.ca/en/about-born/ (accessed 14 March 2019).
  77. Japan Society of Obstetrics and Gynecology. URL: www.jsog.or.jp/modules/en/index.php?content_id=1 (accessed 14 March 2019).
  78. Carter J, Seed PT, Tribe RM, et al. Saliva progesterone for prediction of spontaneous preterm birth: The POPPY study. Pregnancy Outcome Poster Abstracts. BJOG 2017;124:122–54.
  79. Al-Amin A, Rolnik DL, Black C, et al. Accuracy of second trimester prediction of preterm preeclampsia by three different screening algorithms. Aust N Z J Obstet Gynaecol 2018;58(2):192–6. doi: 10.1111/ajo.12689. [DOI] [PubMed]
  80. Allen RE, Zamora J, Arroyo-Manzano D, et al. External validation of preexisting first trimester preeclampsia prediction models. Eur J Obstet Gynecol Reprod Biol 2017;217:119–25. doi: 10.1016/j.ejogrb.2017.08.031. [DOI] [PubMed]
  81. Andersen LB, Dechend R, Jorgensen JS, et al. Prediction of preeclampsia with angiogenic biomarkers. Results from the prospective Odense Child Cohort. Hypertens Pregnancy 2016;35(3):405–19. doi: 10.3109/10641955.2016.1167219. [DOI] [PubMed]
  82. 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(7):635–9. doi: 10.1046/j.1469-0705.2000.00288.x. [DOI] [PubMed]
  83. 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. Clínica e Investigación en Ginecología y Obstetricia 2003;30(6):178–84.
  84. Askie LM, Duley L, Henderson-Smart DJ, Stewart LA. Antiplatelet agents for prevention of pre-eclampsia: a meta-analysis of individual patient data. Lancet 2007;369(9575):1791–8. doi: 10.1016/S0140-6736(07)60712-0. [DOI] [PubMed]
  85. Audibert F, Boucoiran I, An N, et al. Screening for preeclampsia using first-trimester serum markers and uterine artery Doppler in nulliparous women. Am J Obstet Gynecol 2010;203(4):383 e1–8. doi: 10.1016/j.ajog.2010.06.014. [DOI] [PubMed]
  86. 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(2):389–94. doi: 10.1093/ije/dyv356. [DOI] [PubMed]
  87. 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(5):514.e1–7. doi: 10.1016/j.ajog.2014.04.018. [DOI] [PubMed]
  88. Brown MA, Mackenzie C, Dunsmuir W, et al. Can we predict recurrence of pre-eclampsia or gestational hypertension? BJOG 2007;114(8):984–93. doi: 10.1111/j.1471-0528.2007.01376.x. [DOI] [PubMed]
  89. Cameroni I, Roncaglia N, Crippa I, 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(3):421–2.
  90. Caradeux J, Serra R, Nien JK, et al. First trimester prediction of early onset preeclampsia using demographic, clinical, and sonographic data: a cohort study. Prenat Diagn 2013;33(8):732–6. doi: 10.1002/pd.4113. [DOI] [PubMed]
  91. Carbillon L. The imbalance of circulating angiogenic/antiangiogenic factors is mild or absent in obese women destined to develop preeclampsia. Hypertens Pregnancy 2014;33(4):524. doi: 10.3109/10641955.2013.872252. [DOI] [PubMed]
  92. Caritis S, Sibai B, Hauth J, 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(11):701–5. doi: 10.1056/NEJM199803123381101. [DOI] [PubMed]
  93. Chappell LC, Seed PT, Briley AL, et al. Effect of antioxidants on the occurrence of pre-eclampsia in women at increased risk: a randomised trial. Lancet 1999;354(9181):810–6. doi: 10.1016/S0140-6736(99)80010-5. [DOI] [PubMed]
  94. Chiswick C, Reynolds RM, Denison F, 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(10):778–86. doi: 10.1016/S2213-8587(15)00219-3. [DOI] [PMC free article] [PubMed]
  95. 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(8):1467–73. doi: 10.3109/14767058.2011.643326. [DOI] [PubMed]
  96. Facchinetti F, Marozio L, Frusca T, et al. Maternal thrombophilia and the risk of recurrence of preeclampsia. Am J Obstet Gynecol 2009;200(1):46.e1–5. doi: 10.1016/j.ajog.2008.07.032. [DOI] [PubMed]
  97. Figueiró-Filho EA, Oliveira VM, 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(1):40–6. doi: 10.1590/s0100-72032012000100008. [DOI] [PubMed]
  98. Giguere Y, Masse J, Theriault S, et al. Screening for pre-eclampsia early in pregnancy: performance of a multivariable model combining clinical characteristics and biochemical markers. BJOG 2015;122(3):402–10. doi: 10.1111/1471-0528.13050. [DOI] [PubMed]
  99. Girchenko P, Lahti M, Tuovinen S, et al. Cohort Profile: prediction and prevention of preeclampsia and intrauterine growth restriction (PREDO) study. Int J Epidemiol 2017;46(5):1380–1g. doi: 10.1093/ije/dyw154. [DOI] [PubMed]
  100. 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 beta-hCG. Prenat Diagn 2010;30(12-13):1138–42. doi: 10.1002/pd.2627. [DOI] [PMC free article] [PubMed]
  101. Goffinet F, Aboulker D, Paris-Llado J, et al. 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(5):510–8. doi: 10.1111/j.1471-0528.2001.00116.x. [DOI] [PubMed]
  102. Gurgel Alves JA, Praciano de Sousa PC, Bezerra Maia EHMS, Kane SC, da Silva Costa F. First-trimester maternal ophthalmic artery Doppler analysis for prediction of pre-eclampsia. Ultrasound Obstet Gynecol 2014;44(4):411–8. doi: 10.1002/uog.13338. [DOI] [PubMed]
  103. Holzman C, Bullen B, Fisher R, Paneth N, Reuss L; Prematurity Study G. Pregnancy outcomes and community health: the POUCH study of preterm delivery. Paediatr Perinat Epidemiol 2001;15(Suppl. 2):136–58. doi: 10.1046/j.1365-3016.2001.00014.x. [DOI] [PubMed]
  104. 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(5):471–7. doi: 10.1002/pd.2505. [DOI] [PubMed]
  105. Jaaskelainen T, Heinonen S, Hamalainen E, et al. Angiogenic profile in the Finnish Genetics of Pre-Eclampsia Consortium (FINNPEC) cohort. Pregnancy Hypertens 2018;14:252–9. doi: 10.1016/j.preghy.2018.03.004. [DOI] [PubMed]
  106. Jaddoe VW, van Duijn CM, Franco OH, et al. The Generation R Study: design and cohort update 2012. Eur J Epidemiol 2012;27(9):739–56. doi: 10.1007/s10654-012-9735-1. [DOI] [PubMed]
  107. Jenum AK, Sletner L, Voldner N, 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(5 Suppl.):60–70. doi: 10.1177/1403494810378921. [DOI] [PubMed]
  108. Olsen J, Melbye M, Olsen SF, et al. The Danish National Birth Cohort – its background, structure and aim. Scand J Public Health 2001;29:300–7. doi: 10.1177/14034948010290040201. [DOI] [PubMed]
  109. 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(5):1123–8. doi: 10.1161/01.HYP.0000186328.90667.95. [DOI] [PubMed]
  110. 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(5):589–95. doi: 10.1111/j.1471-0528.2010.02842.x. [DOI] [PubMed]
  111. 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(5):e62140. doi: 10.1371/journal.pone.0062140. [DOI] [PMC free article] [PubMed]
  112. Llurba E, Carreras E, Gratacos E, 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. doi: 10.1155/2009/275613. [DOI] [PMC free article] [PubMed]
  113. Lykke JA, Paidas MJ, Langhoff-Roos J. Recurring complications in second pregnancy. Obstet Gynecol 2009;113(6):1217–24. doi: 10.1097/AOG.0b013e3181a66f2d. [DOI] [PubMed]
  114. Magnus P, Irgens LM, Haug K, et al. Cohort profile: the Norwegian Mother and Child Cohort Study (MoBa). Int J Epidemiol 2006;35(5):1146–50. doi: 10.1093/ije/dyl170. [DOI] [PubMed]
  115. Makrides M, Gibson RA, McPhee AJ, et al. Effect of DHA supplementation during pregnancy on maternal depression and neurodevelopment of young children: a randomized controlled trial. JAMA 2010;304(15):1675–83. doi: 10.1001/jama.2010.1507. [DOI] [PubMed]
  116. 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;3(169):501–8. doi: 10.1016/0002-9378(93)90608-l. [DOI] [PubMed]
  117. 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(5):1375–81. doi: 10.1007/s00404-011-2161-x. [DOI] [PubMed]
  118. Mone F, Mulcahy C, McParland 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. doi: 10.1016/j.cct.2016.07.003. [DOI] [PubMed]
  119. North RA, McCowan LM, Dekker GA, et al. Clinical risk prediction for pre-eclampsia in nulliparous women: development of model in international prospective cohort. BMJ 2011;342:d1875. doi: 10.1136/bmj.d1875. [DOI] [PMC free article] [PubMed]
  120. Odibo AO, Zhong Y, Goetzinger KR, et al. First-trimester placental protein 13, PAPP-A, uterine artery Doppler and maternal characteristics in the prediction of pre-eclampsia. Placenta 2011;32(8):598–602. doi: 10.1016/j.placenta.2011.05.006. [DOI] [PMC free article] [PubMed]
  121. 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(2):171–8. doi: 10.1046/j.1469-0705.2000.00192.x. [DOI] [PubMed]
  122. Poston L, Bell R, Croker H, et al. Effect of a behavioural intervention in obese pregnant women (the UPBEAT study): a multicentre, randomised controlled trial. Lancet Diabetes Endocrinol 2015;3(10):767–77. doi: 10.1016/S2213-8587(15)00227-2. [DOI] [PubMed]
  123. Poston L, Briley AL, Seed PT, Kelly FJ, Shennan AH. Vitamin C and vitamin E in pregnant women at risk for pre-eclampsia (VIP trial): randomised placebo-controlled trial. Lancet 2006;367(9517):1145–54. doi: 10.1016/S0140-6736(06)68433-X. [DOI] [PubMed]
  124. 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(12):1271–5. doi: 10.1080/00016340802460347. [DOI] [PubMed]
  125. Rang S, van Montfrans GA, Wolf H. Serial hemodynamic measurement in normal pregnancy, preeclampsia, and intrauterine growth restriction. Am J Obstet Gynecol 2008;198(5):519.e1–9. doi: 10.1016/j.ajog.2007.11.014. [DOI] [PubMed]
  126. Rocha RS, Alves JAG, Maia EHMSB, 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(7):843–9. doi: 10.1515/jpm-2016-0418. [DOI] [PubMed]
  127. Rocha RS, Gurgel Alves JA, Bezerra Maia EHMS, et al. Comparison of three algorithms for prediction preeclampsia in the first trimester of pregnancy. Pregnancy Hypertens 2017;10:113–7. doi: 10.1016/j.preghy.2017.07.146. [DOI] [PubMed]
  128. Rumbold AR, Crowther CA, Haslam RR, Dekker GA, Robinson JS, Group AS. Vitamins C and E and the risks of preeclampsia and perinatal complications. N Engl J Med 2006;354(17):1796–806. doi: 10.1056/NEJMoa054186. [DOI] [PubMed]
  129. 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. doi: 10.1186/1477-7827-6-55. [DOI] [PMC free article] [PubMed]
  130. Savitri AI, Zuithoff P, Browne JL, et al. Does pre-pregnancy BMI determine blood pressure during pregnancy? A prospective cohort study. BMJ Open 2016;6(8):e011626. doi: 10.1136/bmjopen-2016-011626. [DOI] [PMC free article] [PubMed]
  131. Sergio F, Maria Clara D, Gabriella F, et al. Prophylaxis of recurrent preeclampsia: low-molecular-weight heparin plus low-dose aspirin versus low-dose aspirin alone. Hypertens Pregnancy 2006;25(2):115–27. doi: 10.1080/10641950600745517. [DOI] [PubMed]
  132. Sibai BM, Caritis SN, Thom E, 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(17):1213–8. doi: 10.1056/NEJM199310213291701. [DOI] [PubMed]
  133. Skrastad RB, Hov GG, Blaas HG, Romundstad PR, Salvesen KA. Risk assessment for preeclampsia in nulliparous women at 11–13 weeks gestational age: prospective evaluation of two algorithms. BJOG 2015;122(13):1781–8. doi: 10.1111/1471-0528.13194. [DOI] [PubMed]
  134. 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(1):33–9. doi: 10.1016/j.ejogrb.2004.11.015. [DOI] [PubMed]
  135. 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(3):301–7. doi: 10.1002/uog.14640. [DOI] [PubMed]
  136. 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(1):41–5. doi: 10.1002/ajmg.a.20512. [DOI] [PubMed]
  137. Van Der Linden EL, Browne JL, Vissers KM, et al. Maternal body mass index and adverse pregnancy outcomes: a Ghanaian cohort study. Obesity (Silver Spring) 2016;24(1):215–22. doi: 10.1002/oby.21210. [DOI] [PubMed]
  138. van Kuijk SM, Delahaije DH, Dirksen CD, et al. External validation of a model for periconceptional prediction of recurrent early-onset preeclampsia. Hypertens Pregnancy 2014;33(3):265–76. doi: 10.3109/10641955.2013.872253. [DOI] [PubMed]
  139. van Kuijk SM, Nijdam ME, Janssen KJ, et al. A model for preconceptional prediction of recurrent early-onset preeclampsia: derivation and internal validation. Reprod Sci (Thousand Oaks, Calif) 2011;18(11):1154–9. doi: 10.1177/1933719111410708. [DOI] [PubMed]
  140. van Oostwaard MF, Langenveld J, Bijloo R, et al. Prediction of recurrence of hypertensive disorders of pregnancy between 34 and 37 weeks of gestation: a retrospective cohort study. BJOG 2012;119(7):840–7. doi: 10.1111/j.1471-0528.2012.03312.x. [DOI] [PubMed]
  141. Van Oostwaard MF, Langenveld J, Schuit E, et al. Prediction of recurrence of hypertensive disorders of pregnancy in the term period, a retrospective cohort study. Pregnancy Hypertens 2014;4(3):194–202. doi: 10.1016/j.preghy.2014.04.001. [DOI] [PubMed]
  142. 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(3):239.e1–6. doi: 10.1016/j.ajog.2006.10.909. [DOI] [PubMed]
  143. Verlohren S, Galindo A, Schlembach D, 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(2):161 e1–e11. doi: 10.1016/j.ajog.2009.09.016. [DOI] [PubMed]
  144. Verlohren S, Herraiz I, Lapaire O, 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(1):58.e1–8. doi: 10.1016/j.ajog.2011.07.037. [DOI] [PubMed]
  145. Vinter CA, Jensen DM, Ovesen P, Beck-Nielsen H, Jorgensen JS. The LiP (Lifestyle in Pregnancy) study: a randomized controlled trial of lifestyle intervention in 360 obese pregnant women. Diabetes Care 2011;34(12):2502–7. doi: 10.2337/dc11-1150. [DOI] [PMC free article] [PubMed]
  146. 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(1):127–34. doi: 10.1097/HJH.0b013e32833266fc. [DOI] [PubMed]
  147. Widmer M, Cuesta C, Khan KS, 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(4):330–8. doi: 10.1016/j.preghy.2015.09.004. [DOI] [PubMed]
  148. Wright E, Audette MC, Ye XY, et al. Maternal vascular malperfusion and adverse perinatal outcomes in low-risk nulliparous women. Obstet Gynecol 2017;130(5):1112–20. doi: 10.1097/AOG.0000000000002264. [DOI] [PubMed]
  149. Wright J, Small N, Raynor P, et al. Cohort Profile: the Born in Bradford multi-ethnic family cohort study. Int J Epidemiol 2013;42(4):978–91. doi: 10.1093/ije/dys112. [DOI] [PubMed]
  150. Zhang J, Troendle JF, Levine RJ. Risks of hypertensive disorders in the second pregnancy. Paediatr Perinat Epidemiol 2001;15(3):226–31. doi: 10.1046/j.1365-3016.2001.00347.x. [DOI] [PubMed]
  151. Coomarasamy A, Williams H, Truchanowicz E, et al. A randomized trial of progesterone in women with recurrent miscarriages. N Engl J Med 2015;373(22):2141–8. doi: 10.1056/NEJMoa1504927. [DOI] [PubMed]
  152. Coomarasamy A, Devall AJ, Cheed V, et al. A randomized trial of progesterone in women with bleeding in early pregnancy. N Engl J Med 2019;380(19):1815–24. doi: 10.1056/NEJMoa1813730. [DOI] [PubMed]
  153. Souza RT, Cecatti JG, Costa ML, et al. Planning, implementing, and running a multicentre preterm birth study with Biobank resources in Brazil: the Preterm SAMBA Study. Biomed Res Int 2019;2019:5476350. doi: 10.1155/2019/5476350. [DOI] [PMC free article] [PubMed]
  154. Hawkins TL, Roberts JM, Mangos GJ, Davis GK, Roberts LM, Brown MA. Plasma uric acid remains a marker of poor outcome in hypertensive pregnancy: a retrospective cohort study. BJOG 2012;119(4):484–92. doi: 10.1111/j.1471-0528.2011.03232.x. [DOI] [PubMed]
  155. Pilalis A, Souka AP, Antsaklis P, et al. Screening for pre-eclampsia and small for gestational age fetuses at the 11–14 weeks scan by uterine artery Dopplers. Acta Obstet Gynecol Scand 2007;86(5):530–4. doi: 10.1080/00016340601155056. [DOI] [PubMed]
  156. Dhillon-Smith RK, Middleton LJ, Sunner KK, et al. Levothyroxine in women with thyroid peroxidase antibodies before conception. N Engl J Med 2019;380(14):1316–25. doi: 10.1056/NEJMoa1812537. [DOI] [PubMed]
  157. Gabbay-Benziv R, Aviram A, Bardin R, et al. Prediction of small for gestational age: accuracy of different sonographic fetal weight estimation formulas. Fetal Diagn Ther 2016;40(3):205–13. doi: 10.1159/000443881. [DOI] [PubMed]
  158. Anggraini D, Abdollahian M, Marion K. Foetal weight prediction models at a given gestational age in the absence of ultrasound facilities: application in Indonesia. BMC Pregnancy Childbirth 2018;18(1):436. doi: 10.1186/s12884-018-2047-z. [DOI] [PMC free article] [PubMed]
  159. Meertens LJE, Scheepers HC, De Vries RG, et al. External validation study of first trimester obstetric prediction models (expect study I): research protocol and population characteristics. JMIR Res Protoc 2017;6(10):e203. doi: 10.2196/resprot.7837. [DOI] [PMC free article] [PubMed]
  160. Crovetto F, Triunfo S, Crispi F, et al. First-trimester screening with specific algorithms for early- and late-onset fetal growth restriction. Ultrasound Obstet Gynecol 2016;48(3):340–8. doi: 10.1002/uog.15879. [DOI] [PubMed]
  161. H Al Wattar B, Dodds J, Placzek A, et al. Mediterranean-style diet in pregnant women with metabolic risk factors (ESTEEM): a pragmatic multicentre randomised trial. PLOS Med 2019;16(7):e1002857-e. doi: 10.1371/journal.pmed.1002857. [DOI] [PMC free article] [PubMed]
  162. Souza JP, Gulmezoglu A, Lumbiganon P, et al. Caesarean section without medical indications is associated with an increased risk of adverse short-term maternal outcomes: the 2004–2008 WHO Global Survey on Maternal and Perinatal Health. BMC Med 2010;8:71. doi: 10.1186/1741-7015-8-71. [DOI] [PMC free article] [PubMed]
  163. Souza JP, Gulmezoglu AM, Carroli G, Lumbiganon P, Qureshi Z, Group WR. The world health organization multicountry survey on maternal and newborn health: study protocol. BMC Health Serv Res 2011;11:286. doi: 10.1186/1472-6963-11-286. [DOI] [PMC free article] [PubMed]
  164. Zhang J, Troendle J, Reddy UM, et al. Contemporary cesarean delivery practice in the United States. Am J Obstet Gynecol 2010;203(4):326 e1–e10. doi: 10.1016/j.ajog.2010.06.058. [DOI] [PMC free article] [PubMed]
  165. Weiner CP, Sabbagha RE, Vaisrub N, Socol ML. Ultrasonic fetal weight prediction: role of head circumference and femur length. Obstet Gynecol 1985;65(6):812–7. [PubMed]
  166. Liu CM, Chang SD, Cheng PJ. Prediction of fetal birthweight in Taiwanese women with pre-eclampsia and gestational hypertension using an equation based on maternal characteristics. J Obstet Gynaecol Res 2008;34(4):480–6. doi: 10.1111/j.1447-0756.2008.00813.x. [DOI] [PubMed]
  167. Papastefanou I, Souka AP, Eleftheriades M, Pilalis A, Chrelias C, Kassanos D. Predicting fetal growth deviation in parous women: combining the birth weight of the previous pregnancy and third trimester ultrasound scan. J Perinat Med 2015;43(4):485–92. doi: 10.1515/jpm-2013-0308. [DOI] [PubMed]
  168. Sharp A, Jackson R, Cornforth C, et al. A prediction model for short-term neonatal outcomes in severe early-onset fetal growth restriction. Eur J Obstet Gynecol Reprod Biol 2019;241:109–18. doi: 10.1016/j.ejogrb.2019.08.007. [DOI] [PubMed]
  169. National Collaborating Centre for Women’s and Children’s Health (UK). Antenatal Care: Routine Care for the Healthy Pregnant Woman. London: RCOG Press; 2008 Mar. PMID: 21370514. URL: www.ncbi.nlm.nih.gov/books/NBK51886/ (accessed 12 July 2021). [PubMed]
  170. National Institute for Health and Care Excellence (NICE). Guide to the Processes of Technology Appraisal. URL: www.nice.org.uk/Media/Default/About/what-we-do/NICE-guidance/NICE-technology-appraisals/technology-appraisal-processes-guide-apr-2018.pdf (accessed 19 November 2021). [PubMed]
  171. NHS Digital, ‘NHS Maternity Statistics, England 2018-19 [PAS] – NHS Digital’. NHS Maternity Statistics. 2019. URL: https://digital.nhs.uk/data-and-information/publications/statistical/nhs-maternity-statistics/2018-19 (accessed 13 May 2021).
  172. Department of Health, Reference Costs 2017/18, Dep. Heal., 2017.
  173. Netten A, Dennett J, Knight J. Unit Costs of Health and Social Care 1998. 1998. URL: www.rics.org/uk/knowledge/bcis/about-bcis/rebuilding/bcis-house-rebuilding-cost-index/ (accessed 12 May 2021).
  174. Vieira MC, Relph S, Copas A, et al. The DESiGN trial (DEtection of Small for Gestational age Neonate), evaluating the effect of the Growth Assessment Protocol (GAP): study protocol for a randomised controlled trial. Trials 2019;20(1):154. doi: 10.1186/s13063-019-3242-6. [DOI] [PMC free article] [PubMed]
  175. Pay AS, Wiik J, Backe B, Jacobsson B, Strandell A, Klovning A. Symphysis-fundus height measurement to predict small-for-gestational-age status at birth: a systematic review. BMC Pregnancy Childbirth 2015;15:22. doi: 10.1186/s12884-015-0461-z. [DOI] [PMC free article] [PubMed]
  176. Haragan AF, Hulsey TC, Hawk AF, Newman RB, Chang EY. Diagnostic accuracy of fundal height and handheld ultrasound-measured abdominal circumference to screen for fetal growth abnormalities. Am J Obstet Gynecol 2015;212(6):820.e1–8. doi: 10.1016/j.ajog.2015.03.042. [DOI] [PMC free article] [PubMed]
  177. Unit Costs of Health and Social Care 2006. PSSRU. URL: www.pssru.ac.uk/project-pages/unit-costs/unit-costs-2006/ (accessed 13 May 2021).
  178. Resource impact of NICE guidance | Into practice | What we do | About | NICE. URL: www.nice.org.uk/about/what-we-do/into-practice/resource-impact-assessment (accessed 24 May 2021).
  179. 3.5.7. What Are the Steps in Implementing an Impact Assessment? Marketlinks. URL: www.marketlinks.org/good-practice-center/value-chain-wiki/what-are-steps-implementing-impact-assessment (accessed 24 May 2021).
  180. Rubin DB. Multiple Imputation for Nonresponse in Surveys. New York: Wiley; 1987.
  181. McCowan LM, Thompson JM, Taylor RS, et al. Prediction of small for gestational age infants in healthy nulliparous women using clinical and ultrasound risk factors combined with early pregnancy biomarkers. PLOS ONE 2017;12(1):e0169311. doi: 10.1371/journal.pone.0169311. [DOI] [PMC free article] [PubMed]
  182. Wilson ECF, Wastlund D, Moraitis AA, Smith GCS. Late pregnancy ultrasound to screen for and manage potential birth complications in nulliparous women: a cost-effectiveness and value of information analysis. Value Health 2021;24(4):513–21. doi: 10.1016/j.jval.2020.11.005. [DOI] [PubMed]
  183. Smith GC, Moraitis AA, Wastlund D, et al. Universal late pregnancy ultrasound screening to predict adverse outcomes in nulliparous women: a systematic review and cost-effectiveness analysis. Health Technol Assess 2021;25(15):1–190. doi: 10.3310/hta25150. [DOI] [PMC free article] [PubMed]
  184. Khalil A, Gordijn SJ, Beune IM, et al. Essential variables for reporting research studies on fetal growth restriction: a Delphi consensus. Ultrasound Obstet Gynecol 2019;53(5):609–14. doi: 10.1002/uog.19196. [DOI] [PubMed]
  185. Siontis GC, Tzoulaki I, Castaldi PJ, Ioannidis JP. External validation of new risk prediction models is infrequent and reveals worse prognostic discrimination. J Clin Epidemiol 2015;68(1):25–34. doi: 10.1016/j.jclinepi.2014.09.007. [DOI] [PubMed]
  186. Ramspek CL, Jager KJ, Dekker FW, Zoccali C, van Diepen M. External validation of prognostic models: what, why, how, when and where? Clin Kidney J 2021;14(1):49–58. doi: 10.1093/ckj/sfaa188. [DOI] [PMC free article] [PubMed]
  187. Riley RD, Tierney JF, Stewart LA. Individual Participant Data Meta-analysis: A Handbook for Healthcare Research. Hoboken: Wiley; 2021.
  188. Husereau D, Drummond M, Augustovski F, et al. Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) statement: updated reporting guidance for health economic evaluations. BMJ 2022;376:e067975. doi: 10.1136/bmj-2021-067975. [DOI] [PMC free article] [PubMed]
  189. Philips Z, Ginnelly L, Sculpher M, et al. Review of guidelines for good practice in decision-analytic modelling in health technology assessment. Health Technol Assess 2004;8(36):iii–iv, ix–xi, 1–158. doi: 10.3310/hta8360. [DOI] [PubMed]
  190. Velauthar L, Plana MN, Kalidindi M, et al. First-trimester uterine artery Doppler and adverse pregnancy outcome: a meta-analysis involving 55,974 women. Ultrasound Obstet Gynecol 2014;43(5):500–7. doi: 10.1002/uog.13275. [DOI] [PubMed]
  191. Sibai BM. Management of late preterm and early-term pregnancies complicated by mild gestational hypertension/pre-eclampsia. Semin Perinatol 2011;35(5):292–6. doi: 10.1053/j.semperi.2011.05.010. [DOI] [PubMed]
  192. Shah A, Faundes A, Machoki M, et al. Methodological considerations in implementing the WHO Global Survey for Monitoring Maternal and Perinatal Health. Bull World Health Organ 2008;86(2):126–31. doi: 10.2471/BLT.06.039842. [DOI] [PMC free article] [PubMed]
  193. Souza JP; WHO Multicountry Survey on Maternal and Newborn Health Research Network. The World Health Organization Multicountry Survey on Maternal and Newborn Health project at a glance: the power of collaboration. BJOG 2014;121 (Suppl. 1):v–viii. doi: 10.1111/1471-0528.12690. [DOI] [PubMed]
  194. Eunice Kennedy Shriver National Institute of Child Health and Human Development. NICHD DASH Consortium of Safe Labor (CSL) Study Page. URL: https://dash.nichd.nih.gov/study/2331 (accessed 25 July 2018).
  195. Souza RT, Costa ML, Mayrink J, et al. Clinical and epidemiological factors associated with spontaneous preterm birth: a multicentre cohort of low risk nulliparous women. Sci Rep 2020;10(1):855. doi: 10.1038/s41598-020-57810-4. [DOI] [PMC free article] [PubMed]

RESOURCES