Linda J E Meertens1, Hubertina C J Scheepers2, Sander M J van Kuijk3, Robert Aardenburg4, Ivo M A van Dooren5, Josje Langenveld4, Annemieke M van Wijck6, Iris Zwaan7, Marc E A Spaanderman2, Luc J M Smits8. 1. Department of Epidemiology, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands, linda.meertens@maastrichtuniversity.nl. 2. Department of Obstetrics and Gynaecology, School for Oncology and Developmental Biology (GROW), Maastricht University Medical Center, Maastricht, The Netherlands. 3. Department of Clinical Epidemiology and Medical Technology Assessment (KEMTA), Maastricht University Medical Center, Maastricht, The Netherlands. 4. Department of Obstetrics and Gynaecology, Zuyderland Medical Center, Heerlen, The Netherlands. 5. Department of Obstetrics and Gynaecology, Sint Jans Gasthuis Weert, Weert, The Netherlands. 6. Department of Obstetrics and Gynaecology, VieCuri Medical Center, Venlo, The Netherlands. 7. Department of Obstetrics and Gynaecology, Laurentius Hospital, Roermond, The Netherlands. 8. Department of Epidemiology, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands.
Abstract
INTRODUCTION: This study assessed the external validity of all published first trimester prediction models for the risk of preeclampsia (PE) based on routinely collected maternal predictors. Moreover, the potential utility of the best-performing models in clinical practice was evaluated. MATERIAL AND METHODS: Ten prediction models were systematically selected from the literature. We performed a multicenter prospective cohort study in the Netherlands between July 1, 2013, and December 31, 2015. Eligible pregnant women completed a web-based questionnaire before 16 weeks' gestation. The outcome PE was established using postpartum questionnaires and medical records. Predictive performance of each model was assessed by means of discrimination (c-statistic) and a calibration plot. Clinical usefulness was evaluated by means of decision curve analysis and by calculating the potential impact at different risk thresholds. RESULTS: The validation cohort contained 2,614 women of whom 76 developed PE (2.9%). Five models showed moderate discriminative performance with c-statistics ranging from 0.73 to 0.77. Adequate calibration was obtained after refitting. The best models were clinically useful over a small range of predicted probabilities. DISCUSSION: Five of the ten included first trimester prediction models for PE showed moderate predictive performance. The best models may provide more benefit compared to risk selection as used in current guidelines.
INTRODUCTION: This study assessed the external validity of all published first trimester prediction models for the risk of preeclampsia (PE) based on routinely collected maternal predictors. Moreover, the potential utility of the best-performing models in clinical practice was evaluated. MATERIAL AND METHODS: Ten prediction models were systematically selected from the literature. We performed a multicenter prospective cohort study in the Netherlands between July 1, 2013, and December 31, 2015. Eligible pregnant women completed a web-based questionnaire before 16 weeks' gestation. The outcome PE was established using postpartum questionnaires and medical records. Predictive performance of each model was assessed by means of discrimination (c-statistic) and a calibration plot. Clinical usefulness was evaluated by means of decision curve analysis and by calculating the potential impact at different risk thresholds. RESULTS: The validation cohort contained 2,614 women of whom 76 developed PE (2.9%). Five models showed moderate discriminative performance with c-statistics ranging from 0.73 to 0.77. Adequate calibration was obtained after refitting. The best models were clinically useful over a small range of predicted probabilities. DISCUSSION: Five of the ten included first trimester prediction models for PE showed moderate predictive performance. The best models may provide more benefit compared to risk selection as used in current guidelines.
Authors: John Allotey; Kym Ie Snell; Melanie Smuk; Richard Hooper; Claire L Chan; Asif Ahmed; Lucy C Chappell; Peter von Dadelszen; Julie Dodds; Marcus Green; Louise Kenny; Asma Khalil; Khalid S Khan; Ben W Mol; Jenny Myers; Lucilla Poston; Basky Thilaganathan; Anne C Staff; Gordon Cs Smith; Wessel Ganzevoort; Hannele Laivuori; Anthony O Odibo; Javier A 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 Jm 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 Gm Moons; Richard D Riley; Shakila Thangaratinam Journal: Health Technol Assess Date: 2020-12 Impact factor: 4.014
Authors: 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 Journal: BMC Med Date: 2020-11-02 Impact factor: 8.775
Authors: Louise C Kenny; Grégoire Thomas; Lucilla Poston; Jenny E Myers; Nigel A B Simpson; Fergus P McCarthy; Leslie W Brown; Alison E Bond; Robin Tuytten; Philip N Baker Journal: PLoS One Date: 2020-12-28 Impact factor: 3.240
Authors: Pim van Montfort; Luc J M Smits; Ivo M A van Dooren; Stéphanie M P Lemmens; Maartje Zelis; Iris M Zwaan; Marc E A Spaanderman; Hubertina C J Scheepers Journal: Med Decis Making Date: 2019-12-02 Impact factor: 2.583