Accuracy of clinical characteristics biochemical and ultrasound markers in predicting pre-eclampsia: External validation and development of prediction models using an Individual Participant Data (IPD) meta-analysis

Research output: Contribution to journalArticle

Authors

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

Colleges, School and Institutes

External organisations

  • Queen Mary University of London
  • Barts and The London Queen Mary's School of Medicine and Dentistry
  • Academic Medical Centre, University of Amsterdam
  • Aberdeen Maternity Hospital
  • UNIVERSITY COLLEGE DUBLIN
  • University Medical Center Utrecht
  • Research Institute of Primary Care and Health Sciences
  • Keele University

Details

Original languageEnglish
JournalHealth Technology Assessment
Publication statusAccepted/In press - 24 Mar 2020

Keywords

  • Prediction model, Prognostic model, Validation, Pre-eclampsia, Individual Participant Data, IPD