Kym I E Snell, John Allotey, Melanie Smuk, Richard Hooper, Claire Chan, Asif Ahmed, Lucy C Chappell, Peter Von Dadelszen, Marcus Green, Louise Kenny, Asma Khalil, Khalid S Khan, Ben W Mol, Jenny Myers, Lucilla Poston, Basky Thilaganathan, Anne C Staff, Gordon C S Smith, Wessel Ganzevoort, Hannele Laivuori, Anthony O Odibo, Javier Arenas Ramírez, John Kingdom, George Daskalakis, Diane Farrar, Ahmet A 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 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, Seon Ae Yeo, Joyce L Browne, Karel G M Moons, Richard D Riley, Shakila Thangaratinam
BACKGROUND: Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk during pregnancy is required to plan management. Although there are many published prediction models for pre-eclampsia, few have been validated in external data. Our objective was to externally validate published prediction models for pre-eclampsia using individual participant data (IPD) from UK studies, to evaluate whether any of the models can accurately predict the condition when used within the UK healthcare setting...
November 2, 2020: BMC Medicine