Preeclampsia prediction with blood pressure measurements: A global external validation of the ALSPAC models
Issued Date
2022-12-01
Resource Type
ISSN
22107789
eISSN
22107797
Scopus ID
2-s2.0-85138811971
Pubmed ID
36179538
Journal Title
Pregnancy Hypertension
Volume
30
Start Page
124
End Page
129
Rights Holder(s)
SCOPUS
Bibliographic Citation
Pregnancy Hypertension Vol.30 (2022) , 124-129
Suggested Citation
de Kat A.C., Hirst J.E., Woodward M., Barros F.C., Barsosio H.C., Berkley J.A., Carvalho M., Cheikh Ismail L., McGready R., Norris S.A., Nosten F., Ohuma E., Tshivuila-Matala C.O.O., Stones W., Staines Urias E., Clara Restrepo-Mendez M., Lambert A., Munim S., Winsey A., Papageorghiou A.T., Bhutta Z.A., Villar J., Kennedy S.H., Peters S.A.E. Preeclampsia prediction with blood pressure measurements: A global external validation of the ALSPAC models. Pregnancy Hypertension Vol.30 (2022) , 124-129. 129. doi:10.1016/j.preghy.2022.09.005 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/85251
Title
Preeclampsia prediction with blood pressure measurements: A global external validation of the ALSPAC models
Author(s)
de Kat A.C.
Hirst J.E.
Woodward M.
Barros F.C.
Barsosio H.C.
Berkley J.A.
Carvalho M.
Cheikh Ismail L.
McGready R.
Norris S.A.
Nosten F.
Ohuma E.
Tshivuila-Matala C.O.O.
Stones W.
Staines Urias E.
Clara Restrepo-Mendez M.
Lambert A.
Munim S.
Winsey A.
Papageorghiou A.T.
Bhutta Z.A.
Villar J.
Kennedy S.H.
Peters S.A.E.
Hirst J.E.
Woodward M.
Barros F.C.
Barsosio H.C.
Berkley J.A.
Carvalho M.
Cheikh Ismail L.
McGready R.
Norris S.A.
Nosten F.
Ohuma E.
Tshivuila-Matala C.O.O.
Stones W.
Staines Urias E.
Clara Restrepo-Mendez M.
Lambert A.
Munim S.
Winsey A.
Papageorghiou A.T.
Bhutta Z.A.
Villar J.
Kennedy S.H.
Peters S.A.E.
Author's Affiliation
Faculty of Tropical Medicine, Mahidol University
University of Sharjah
The Aga Khan University
University Medical Center Utrecht
London School of Hygiene & Tropical Medicine
Hospital for Sick Children University of Toronto
Green Templeton College
Universidade Catolica de Pelotas
Imperial College Faculty of Medicine
University of the Witwatersrand, Johannesburg
Nuffield Department of Medicine
University of Oxford Medical Sciences Division
KEMRI-Coast Centre for Geographical Medicine and Research
University of Sharjah
The Aga Khan University
University Medical Center Utrecht
London School of Hygiene & Tropical Medicine
Hospital for Sick Children University of Toronto
Green Templeton College
Universidade Catolica de Pelotas
Imperial College Faculty of Medicine
University of the Witwatersrand, Johannesburg
Nuffield Department of Medicine
University of Oxford Medical Sciences Division
KEMRI-Coast Centre for Geographical Medicine and Research
Other Contributor(s)
Abstract
Objective: The prediction of preeclampsia in pregnancy has resulted in a plethora of prognostic models. Yet, very few make it past the development stage and most fail to influence clinical practice. The timely identification of high-risk pregnant women could deliver a tailored antenatal care regimen, particularly in low-resource settings. This study externally validated and calibrated previously published models that predicted the risk of preeclampsia, based on blood pressure (BP) at multiple time points in pregnancy, in a geographically diverse population. Methods: The prospective INTERBIO-21st Fetal Study included 3,391 singleton pregnancies from Brazil, Kenya, Pakistan, South Africa, Thailand and the UK, 2012–2018. Preeclampsia prediction was based on baseline characteristics, BP and deviation from the expected BP trajectory at multiple time points in pregnancy. The prediction rules from the Avon Longitudinal Study of Parents and Children (ALSPAC) were implemented in the INTERBIO-21st cohort. Results: Model discrimination was similar to the development cohort. Performance was best with baseline characteristics and a BP measurement at 34 weeks’ gestation (AUC 0.85, 95 % CI 0.80–0.90). The ALSPAC models largely overestimated the true risk of preeclampsia incidence in the INTERBIO-21st cohort. Conclusions: After recalibration, these prediction models could potentially serve as a risk stratifying tool to help identify women who might benefit from increased surveillance during pregnancy.