External validation of prognostic models to predict stillbirth using International Prediction of Pregnancy Complications (IPPIC) Network database: individual participant data meta-analysis.
external validation
individual participant data
intrauterine death
prediction model
stillbirth
Journal
Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology
ISSN: 1469-0705
Titre abrégé: Ultrasound Obstet Gynecol
Pays: England
ID NLM: 9108340
Informations de publication
Date de publication:
02 2022
02 2022
Historique:
revised:
30
06
2021
received:
20
04
2021
accepted:
02
08
2021
pubmed:
19
8
2021
medline:
1
3
2022
entrez:
18
8
2021
Statut:
ppublish
Résumé
Stillbirth is a potentially preventable complication of pregnancy. Identifying women at high risk of stillbirth can guide decisions on the need for closer surveillance and timing of delivery in order to prevent fetal death. Prognostic models have been developed to predict the risk of stillbirth, but none has yet been validated externally. In this study, we externally validated published prediction models for stillbirth using individual participant data (IPD) meta-analysis to assess their predictive performance. MEDLINE, EMBASE, DH-DATA and AMED databases were searched from inception to December 2020 to identify studies reporting stillbirth prediction models. Studies that developed or updated prediction models for stillbirth for use at any time during pregnancy were included. IPD from cohorts within the International Prediction of Pregnancy Complications (IPPIC) Network were used to validate externally the identified prediction models whose individual variables were available in the IPD. The risk of bias of the models and cohorts was assessed using the Prediction study Risk Of Bias ASsessment Tool (PROBAST). The discriminative performance of the models was evaluated using the C-statistic, and calibration was assessed using calibration plots, calibration slope and calibration-in-the-large. Performance measures were estimated separately in each cohort, as well as summarized across cohorts using random-effects meta-analysis. Clinical utility was assessed using net benefit. Seventeen studies reporting the development of 40 prognostic models for stillbirth were identified. None of the models had been previously validated externally, and the full model equation was reported for only one-fifth (20%, 8/40) of the models. External validation was possible for three of these models, using IPD from 19 cohorts (491 201 pregnant women) within the IPPIC Network database. Based on evaluation of the model development studies, all three models had an overall high risk of bias, according to PROBAST. In the IPD meta-analysis, the models had summary C-statistics ranging from 0.53 to 0.65 and summary calibration slopes ranging from 0.40 to 0.88, with risk predictions that were generally too extreme compared with the observed risks. The models had little to no clinical utility, as assessed by net benefit. However, there remained uncertainty in the performance of some models due to small available sample sizes. The three validated stillbirth prediction models showed generally poor and uncertain predictive performance in new data, with limited evidence to support their clinical application. The findings suggest methodological shortcomings in their development, including overfitting. Further research is needed to further validate these and other models, identify stronger prognostic factors and develop more robust prediction models. © 2021 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.
Types de publication
Journal Article
Validation Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
209-219Subventions
Organisme : Medical Research Council
ID : MR/L002477/1
Pays : United Kingdom
Investigateurs
A Coomarasamy
(A)
A Kwong
(A)
A I Savitri
(AI)
K Å Salvesen
(KÅ)
S Bhattacharya
(S)
C S P M Uiterwaal
(CSPM)
A C Staff
(AC)
L B Andersen
(LB)
E L Olive
(EL)
C Redman
(C)
L Sletner
(L)
G Daskalakis
(G)
M Macleod
(M)
B Thilaganathan
(B)
M Abdollahain
(M)
J A Ramírez
(JA)
J Massé
(J)
A Khalil
(A)
F Audibert
(F)
P M Magnus
(PM)
A K Jenum
(AK)
A Baschat
(A)
A Ohkuchi
(A)
F M McAuliffe
(FM)
J West
(J)
L M Askie
(LM)
F Mone
(F)
D Farrar
(D)
P A Zimmerman
(PA)
L J M Smits
(LJM)
C Riddell
(C)
J C Kingdom
(JC)
J van de Post
(J)
S E Illanes
(SE)
C Holzman
(C)
S M J van Kuijk
(SMJ)
L Carbillon
(L)
P M Villa
(PM)
A Eskild
(A)
L Chappell
(L)
F Prefumo
(F)
L Velauthar
(L)
P Seed
(P)
M van Oostwaard
(M)
S Verlohren
(S)
L Poston
(L)
E Ferrazzi
(E)
C A Vinter
(CA)
C Nagata
(C)
M Brown
(M)
K C Vollebregt
(KC)
S Takeda
(S)
J Langenveld
(J)
M Widmer
(M)
S Saito
(S)
C Haavaldsen
(C)
G Carroli
(G)
J Olsen
(J)
H Wolf
(H)
N Zavaleta
(N)
I Eisensee
(I)
P Vergani
(P)
P Lumbiganon
(P)
M Makrides
(M)
F Facchinetti
(F)
E Sequeira
(E)
R Gibson
(R)
S Ferrazzani
(S)
T Frusca
(T)
J E Norman
(JE)
E A Figueiró-Filho
(EA)
O Lapaire
(O)
H Laivuori
(H)
J A Lykke
(JA)
A Conde-Agudelo
(A)
A Galindo
(A)
A Mbah
(A)
A P Betran
(AP)
I Herraiz
(I)
L Trogstad
(L)
G G S Smith
(GGS)
E A P Steegers
(EAP)
R Salim
(R)
T Huang
(T)
A Adank
(A)
J Zhang
(J)
W S Meschino
(WS)
J L Browne
(JL)
R E Allen
(RE)
F Da Silva Costa
(FDS)
K Klipstein-Grobusch
(K)
C A Crowther
(CA)
J S Jørgensen
(JS)
J-C Forest
(JC)
A R Rumbold
(AR)
B W Mol
(BW)
Y Giguère
(Y)
L C Kenny
(LC)
W Ganzevoort
(W)
A O Odibo
(AO)
J Myers
(J)
S A Yeo
(SA)
F Goffinet
(F)
L McCowan
(L)
E Pajkrt
(E)
H J Teede
(HJ)
B G Haddad
(BG)
G Dekker
(G)
E C Kleinrouweler
(EC)
É LeCarpentier
(É)
C T Roberts
(CT)
H Groen
(H)
R B Skråstad
(RB)
S Heinonen
(S)
K Eero
(K)
D Anggraini
(D)
A Souka
(A)
J G Cecatti
(JG)
I Monterio
(I)
A Pillalis
(A)
R Souza
(R)
L A Hawkins
(LA)
R Gabbay-Benziv
(R)
F Crovetto
(F)
F Figuera
(F)
L Jorgensen
(L)
J Dodds
(J)
M Patel
(M)
A Aviram
(A)
A Papageorghiou
(A)
K Khan
(K)
Informations de copyright
© 2021 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.
Références
Flenady V, Wojcieszek AM, Middleton P, Ellwood D, Erwich JJ, Coory M, Khong TY, Silver RM, Smith GCS, Boyle FM, Lawn JE, Blencowe H, Leisher SH, Gross MM, Horey D, Farrales L, Bloomfield F, McCowan L, Brown SJ, Joseph KS, Zeitlin J, Reinebrant HE, Ravaldi C, Vannacci A, Cassidy J, Cassidy P, Farquhar C, Wallace E, Siassakos D, Heazell AEP, Storey C, Sadler L, Petersen S, Frøen JF, Goldenberg RL. Stillbirths: recall to action in high-income countries. Lancet 2016; 387: 691-702.
Flenady V, Koopmans L, Middleton P, Frøen JF, Smith GC, Gibbons K, Coory M, Gordon A, Ellwood D, McIntyre HD, Fretts R, Ezzati M. Major risk factors for stillbirth in high-income countries: a systematic review and meta-analysis. Lancet 2011; 377: 1331-1340.
Draper ES, Gallimore ID, Kurinczuk JJ, Smith PW, Boby T, Smith LK, Manktelow BN, on behalf of the MBRRACE-UK Collaboration. MBRRACE-UK Perinatal Mortality Surveillance Report, UK Perinatal Deaths for Births from January to December 2016. Leicester: The Infant Mortality and Morbidity Studies, Department of Health Sciences, University of Leicester. 2018. https://www.npeu.ox.ac.uk/assets/downloads/mbrrace-uk/reports/MBRRACE-UK%20Perinatal%20Surveillance%20Full%20Report%20for%202016%20-%20June%202018.pdf.
Euro-Peristat Project. European Perinatal Health Report. Core indicators of the health and care of pregnant women and babies in Europe in 2015. November 2018. https://www.europeristat.com/images/EPHR2015_Euro-Peristat.pdf.
ONS. Vital statistics in the UK: births, deaths and marriages - 2018 update, Office of National Statistics, London, England. https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/livebirths/bulletins/birthsummarytablesenglandandwales/2017.
Heazell AE, Whitworth MK, Whitcombe J, Glover SW, Bevan C, Brewin J, Calderwood C, Canter A, Jessop F, Johnson G, Martin I, Metcalf L. Research priorities for stillbirth: process overview and results from UK Stillbirth Priority Setting Partnership. Ultrasound Obstet Gynecol 2015; 46: 641-647.
Sexton J, Coory M, Kumar S, Smith G, Gordon A, Chambers G, Pereira G, Raynes-Greenow C, Hilder L, Middleton P, Bowman A, Lieske S, Warrilow K, Morris J, Ellwood D, Flenady V. Protocol for the development and validation of a risk prediction model for stillbirths from 35 weeks gestation in Australia. Diagn Progn Res 2020; 4: 21.
Townsend R, Manji A, Allotey J, Heazell A, Jorgensen L, Magee LA, Mol BW, Snell K, Riley RD, Sandall J, Smith G, Patel M, Thilaganathan B, von Dadelszen P, Thangaratinam S, Khalil A. Can risk prediction models help us individualise stillbirth prevention? A systematic review and critical appraisal of published risk models. BJOG 2021; 128: 214-224.
Riley RD, Ensor J, Snell KI, Debray TP, Altman DG, Moons KG, Collins GS. External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges. BMJ 2016; 353: i3140.
Debray TP, Riley RD, Rovers MM, Reitsma JB, Moons KG; Cochrane IPD Meta-analysis Methods group. Individual participant data (IPD) meta-analyses of diagnostic and prognostic modeling studies: guidance on their use. PLoS Med 2015; 12: e1001886.
Debray TPA, Moons KGM, Ahmed I, Koffijberg H, Riley RD. A framework for developing, implementing, and evaluating clinical prediction models in an individual participant data meta-analysis. Stat Med 2013; 32: 3158-3180.
Debray TP, Vergouwe Y, Koffijberg H, Nieboer D, Steyerberg EW, Moons KG. A new framework to enhance the interpretation of external validation studies of clinical prediction models. J Clin Epidemiol 2015; 68: 279-289.
Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement. Ann Intern Med 2015; 162: 55-63.
Wolff RF, Moons KGM, Riley RD, Whiting PF, Westwood M, Collins GS, Reitsma JB, Kleijnen J, Mallett S, Groupdagger P. PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies. Ann Intern Med 2019; 170: 51-58.
Allotey J, Snell KIE, Chan C, Hooper R, Dodds J, Rogozinska E, Khan KS, Poston L, Kenny L, Myers J, Thilaganathan B, Chappell L, Mol BW, Von Dadelszen P, Ahmed A, Green M, Poon L, Khalil A, Moons KGM, Riley RD, Thangaratinam S; IPPIC Collaborative Network. 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.
Snell KIE, Allotey J, Smuk M, Hooper R, Chan C, Ahmed A, Chappell LC, Von Dadelszen P, Green M, Kenny L, Khalil A, Khan KS, Mol BW, Myers J, Poston L, Thilaganathan B, Staff AC, Smith GCS, Ganzevoort W, Laivuori H, Odibo AO, Arenas Ramírez J, Kingdom J, Daskalakis G, Farrar D, Baschat AA, Seed PT, Prefumo F, da Silva Costa F, Groen H, Audibert F, Masse J, Skråstad RB, Salvesen KÅ, Haavaldsen C, Nagata C, Rumbold AR, Heinonen S, Askie LM, Smits LJM, Vinter CA, Magnus P, Eero K, Villa PM, Jenum AK, Andersen LB, Norman JE, Ohkuchi A, Eskild A, Bhattacharya S, McAuliffe FM, Galindo A, Herraiz I, Carbillon L, Klipstein-Grobusch K, Yeo SA, Browne JL, Moons KGM, Riley RD, Thangaratinam S; IPPIC Collaborative Network. External validation of prognostic models predicting pre-eclampsia: individual participant data meta-analysis. BMC Med 2020; 18: 302.
Allotey J, Snell KI, Smuk M, Hooper R, Chan CL, Ahmed A, Chappell LC, von Dadelszen P, Dodds J, Green M, Kenny L, Khalil A, Khan KS, Mol BW, Myers J, Poston L, Thilaganathan B, Staff AC, Smith GC, Ganzevoort W, Laivuori H, Odibo AO, Ramírez JA, Kingdom J, Daskalakis G, Farrar D, Baschat AA, Seed PT, Prefumo F, da Silva Costa F, Groen H, Audibert F, Masse J, Skråstad RB, Salvesen KÅ, Haavaldsen C, Nagata C, Rumbold AR, Heinonen S, Askie LM, Smits LJ, Vinter CA, Magnus PM, Eero K, Villa PM, Jenum AK, Andersen LB, Norman JE, Ohkuchi A, Eskild A, Bhattacharya S, McAuliffe FM, Galindo A, Herraiz I, Carbillon L, Klipstein-Grobusch K, Yeo S, Teede HJ, Browne JL, Moons KG, Riley RD, Thangaratinam S. 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: 1-252.
Resche-Rigon M, White IR. Multiple imputation by chained equations for systematically and sporadically missing multilevel data. Stat Methods Med Res 2018; 27: 1634-1649.
Jolani S, Debray TP, Koffijberg H, van Buuren S, Moons KG. Imputation of systematically missing predictors in an individual participant data meta-analysis: a generalized approach using MICE. Stat Med 2015; 34: 1841-1863.
Rubin DB. Multiple Imputation for Nonresponse in Surveys. 1987. https://onlinelibrary.wiley.com/doi/book/10.1002/9780470316696.
Altman DG, Vergouwe Y, Royston P, Moons KG. Prognosis and prognostic research: validating a prognostic model. BMJ 2009; 338: b605.
Hosmer DW, Lemeshow, S. Assessing the Fit of the Model. In Applied Logistic Regression (2nd edn). Wiley; New York, NY, 2000; 143-202.
Hartung J, Knapp G. A refined method for the meta-analysis of controlled clinical trials with binary outcome. Stat Med 2001; 20: 3875-3889.
Langan D, Higgins JPT, Jackson D, Bowden J, Veroniki AA, Kontopantelis E, Viechtbauer W, Simmonds M. A comparison of heterogeneity variance estimators in simulated random-effects meta-analyses. Res Synth Methods 2019; 10: 83-98.
Higgins JPT, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ 2003; 327: 557-560.
Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making 2006; 26: 565-574.
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.
Smith GC, Yu CK, Papageorghiou AT, Cacho AM, Nicolaides KH; Fetal Medicine Foundation Second Trimester Screening Group. Maternal uterine artery Doppler flow velocimetry and the risk of stillbirth. Obstet Gynecol 2007; 109: 144-1451.
Yerlikaya G, Akolekar R, McPherson K, Syngelaki A, Nicolaides KH. Prediction of stillbirth from maternal demographic and pregnancy characteristics. Ultrasound Obstet Gynecol 2016; 48: 607-612.
Trudell AS, Tuuli MG, Colditz GA, Macones GA, Odibo AO. A stillbirth calculator: Development and internal validation of a clinical prediction model to quantify stillbirth risk. PloS One 2017; 12: e0173461.
Kleinrouweler CE, Cheong-See Mrcog FM, Collins GS, Kwee A, Thangaratinam S, Khan KS, Mol BW, Pajkrt E, Moons KG, Schuit E. Prognostic models in obstetrics: available, but far from applicable. Am J Obstet Gynecol 2016; 214: 79-90.e36.
White IR, Royston P, Wood AM. Multiple imputation using chained equations: Issues and guidance for practice. Stat Med 2011; 30: 377-399.
Moons KG, Altman DG, Reitsma JB, Ioannidis JP, Macaskill P, Steyerberg EW, Vickers AJ, Ransohoff DF, Collins GS. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med 2015; 162: W1-73.
Moons KG, Kengne AP, Grobbee DE, Royston P, Vergouwe Y, Altman DG, Woodward M. Risk prediction models: II. External validation, model updating, and impact assessment. Heart 2012; 98: 691-698.
Kayode GA, Grobbee DE, Amoakoh-Coleman M, Adeleke IT, Ansah E, de Groot JA, Klipstein-Grobusch K. Predicting stillbirth in a low resource setting. BMC Pregnancy Childbirth 2016; 16: 274.
Aupont JE, Akolekar R, Illian A, Neonakis S, Nicolaides KH. Prediction of stillbirth from placental growth factor at 19-24 weeks. Ultrasound Obstet Gynecol 2016; 48: 631-635.
Riley RD, Ensor J, Snell KIE, Harrell FE Jr, Martin GP, Reitsma JB, Moons KGM, Collins G, van Smeden M. Calculating the sample size required for developing a clinical prediction model. BMJ 2020; 368: m441.
Riley RD, Snell KI, Ensor J, Burke DL, Harrell FE Jr, Moons KG, Collins GS. Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes. Stat Med 2019; 38: 1276-1296.
Saving Babies' Lives Version Two: A care bundle for reducing perinatal mortality. NHS England, 2019. https://www.england.nhs.uk/wp-content/uploads/2019/03/Saving-Babies-Lives-Care-Bundle-Version-Two-Updated-Final-Version.pdf.
Riley RD, van der Windt D, Croft P, Moons KGM. Prognosis Research in Healthcare: Concepts, Methods and Impact. Oxford University Press, Oxford, UK; 2019.
Townsend R, Sileo FG, Allotey J, Dodds J, Heazell A, Jorgensen L, Kim VB, Magee L, Mol B, Sandall J, Smith G, Thilaganathan B, von Dadelszen P, Thangaratinam S, Khalil A. Prediction of stillbirth: an umbrella review of evaluation of prognostic variables. BJOG 2021; 128: 238-250.
Stirrup OT, Khalil A, D'Antonio F, Thilaganathan B; Southwest Thames Obstetric Research C. Fetal growth reference ranges in twin pregnancy: analysis of the Southwest Thames Obstetric Research Collaborative (STORK) multiple pregnancy cohort. Ultrasound Obstet Gynecol 2015; 45: 301-317.
Mone F, Mulcahy C, McParland P, Stanton A, Culliton M, Downey P, McCormack D, Tully E, Dicker P, Breathnach F, Malone FD, McAuliffe FM. 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: 143-148.
Sovio U, White IR, Dacey A, Pasupathy D, Smith GCS. Screening for fetal growth restriction with universal third trimester ultrasonography in nulliparous women in the Pregnancy Outcome Prediction (POP) study: a prospective cohort study. Lancet 2015; 386: 2089-2097.
Allen RE, Zamora J, Arroyo-Manzano D, Velauthar L, Allotey J, Thangaratinam S, Aquilina J. External validation of preexisting first trimester preeclampsia prediction models. Eur J Obstet Gynecol Reprod Biol 2017; 217: 119-125.
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: 1138-1142.
Japan Society of Obstetrics and Gynecology (JSOG). http://www.jsog.or.jp/modules/en/index.php?content_id=1.
Jenum AK, Sletner L, Voldner N, Vangen S, Mørkrid K, Andersen LF, Nakstad B, Skrivarhaug T, Rognerud-Jensen OH, Roald B, Birkeland KI. 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.
North RA, McCowan LM, Dekker GA, Poston L, Chan EH, Stewart AW, Black MA, Taylor RS, Walker JJ, Baker PN, Kenny LC. Clinical risk prediction for pre-eclampsia in nulliparous women: development of model in international prospective cohort. BMJ 2011; 342: d1875.
Fraser A, Macdonald-Wallis C, Tilling K, Boyd A, Golding J, Davey Smith G, Henderson J, Macleod J, Molloy L, Ness A, Ring S, Nelson SM, Lawlor DA. Cohort Profile: the Avon Longitudinal Study of Parents and Children: ALSPAC mothers cohort. Int J Epidemiol 2013; 42: 97-110.
Antsaklis A, Daskalakis G, Tzortzis E, Michalas S. The effect of gestational age and placental location on the prediction of pre-eclampsia by uterine artery Doppler velocimetry in low-risk nulliparous women. Ultrasound Obstet Gynecol 2000; 16: 635-639.
Widmer M, Cuesta C, Khan KS, Conde-Agudelo A, Carroli G, Fusey S, Karumanchi SA, Lapaire O, Lumbiganon P, Sequeira E, Zavaleta N, Frusca T, Gülmezoglu AM, Lindheimer MD. Accuracy of angiogenic biomarkers at 20 weeks' gestation in predicting the risk of pre-eclampsia: A WHO multicentre study. Pregnancy Hypertens 2015; 5: 330-338.
Andersen LB, Dechend R, Jorgensen JS, Luef BM, Nielsen J, Barington T, Christesen HT. Prediction of preeclampsia with angiogenic biomarkers. Results from the prospective Odense Child Cohort. Hypertens Pregnancy 2016; 35: 405-419.
Sibai BM. Management of late preterm and early-term pregnancies complicated by mild gestational hypertension/pre-eclampsia. Semin Perinatol 2011; 35: 292-296.
Sibai BM, Caritis SN, Thom E, Klebanoff M, McNellis D, Rocco L, Paul RH, Romero R, Witter F, Rosen M, Depp R; The National Institute of Child Health and Human Development Network of Maternal-Fetal Medicine Units. Prevention of preeclampsia with low-dose aspirin in healthy, nulliparous pregnant women. N Engl J Med 1993; 329: 1213-1218.
Holzman C, Bullen B, Fisher R, Paneth N, Reuss L; Prematurity Study Group. Pregnancy outcomes and community health: the POUCH study of preterm delivery. Paediatr Perinat Epidemiol 2001; 15 (Suppl 2): 136-158.
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: 1796-1806.
Savitri AI, Zuithoff P, Browne JL, Amelia D, Baharuddin M, Grobbee DE, Uiterwaal CS. Does pre-pregnancy BMI determine blood pressure during pregnancy? A prospective cohort study. BMJ Open 2016; 6: e011626.
van Oostwaard MF, Langenveld J, Bijloo R, Wong KM, Scholten I, Loix S, Hukkelhoven CW, Vergouwe Y, Papatsonis DN, Mol BW, Ganzevoort W. Prediction of recurrence of hypertensive disorders of pregnancy between 34 and 37 weeks of gestation: a retrospective cohort study. BJOG 2012; 119: 840-847.
Van Oostwaard MF, Langenveld J, Schuit E, Wigny K, Van Susante H, Beune I, Ramaekers R, Papatsonis DN, Mol BW, Ganzevoort W. Prediction of recurrence of hypertensive disorders of pregnancy in the term period, a retrospective cohort study. Pregnancy Hypertens 2014; 4: 194-202.