R Townsend1,2, A Manji2, J Allotey3,4, Aep Heazell5,6, L Jorgensen7, L A Magee8, B W Mol9, Kie Snell10, R D Riley10, J Sandall11, Gcs Smith12, M Patel13, B Thilaganathan1,2, P von Dadelszen8, S Thangaratinam3,4, A Khalil1,2. 1. Molecular and Clinical Sciences Research Institute, St George's, University of London and St George's University Hospitals NHS Foundation Trust, London, UK. 2. Fetal Medicine Unit, St George's University Hospitals NHS Foundation Trust, London, UK. 3. Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK. 4. Pragmatic Clinical Trials Unit, Barts and the London, School of Medicine and Dentistry, Queen Mary University of London, London, UK. 5. Saint Mary's Hospital, Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, UK. 6. Faculty of Biology, Medicine and Health, Maternal and Fetal Health Research Centre, School of Medical Sciences, University of Manchester, Manchester, UK. 7. Katie's Team, East London, UK. 8. School of Life Course Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK. 9. Department of Obstetrics and Gynaecology, School of Medicine, Monash University, Melbourne, Australia. 10. Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, UK. 11. Department of Women and Children's Health, School of Life Course Sciences, Faculty of Life Sciences & Medicine, King's College London, St Thomas' Hospital, London, UK. 12. Department of Obstetrics and Gynaecology, NIHR Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, UK. 13. Sands (Stillbirth and Neonatal Death Society), London, UK.
Abstract
BACKGROUND: Stillbirth prevention is an international priority - risk prediction models could individualise care and reduce unnecessary intervention, but their use requires evaluation. OBJECTIVES: To identify risk prediction models for stillbirth, and assess their potential accuracy and clinical benefit in practice. SEARCH STRATEGY: MEDLINE, Embase, DH-DATA and AMED databases were searched from inception to June 2019 using terms relevant to stillbirth, perinatal mortality and prediction models. The search was compliant with Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. SELECTION CRITERIA: Studies developing and/or validating prediction models for risk of stillbirth developed for application during pregnancy. DATA COLLECTION AND ANALYSIS: Study screening and data extraction were conducted in duplicate, using the CHARMS checklist. Risk of bias was appraised using the PROBAST tool. RESULTS: The search identified 2751 citations. Fourteen studies reporting development of 69 models were included. Variables consistently included were: ethnicity, body mass index, uterine artery Doppler, pregnancy-associated plasma protein and placental growth factor. For almost all models there were significant concerns about risk of bias. Apparent model performance (i.e. in the development dataset) was highest in models developed for use later in pregnancy and including maternal characteristics, and ultrasound and biochemical variables, but few were internally validated and none were externally validated. CONCLUSIONS: Almost all models identified were at high risk of bias. There are first-trimester models of possible clinical benefit in early risk stratification; these require validation and clinical evaluation. There were few later pregnancy models but, if validated, these could be most relevant to individualised discussions around timing of birth. TWEETABLE ABSTRACT: Prediction models using maternal factors, blood tests and ultrasound could individualise stillbirth prevention, but existing models are at high risk of bias.
BACKGROUND:Stillbirth prevention is an international priority - risk prediction models could individualise care and reduce unnecessary intervention, but their use requires evaluation. OBJECTIVES: To identify risk prediction models for stillbirth, and assess their potential accuracy and clinical benefit in practice. SEARCH STRATEGY: MEDLINE, Embase, DH-DATA and AMED databases were searched from inception to June 2019 using terms relevant to stillbirth, perinatal mortality and prediction models. The search was compliant with Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. SELECTION CRITERIA: Studies developing and/or validating prediction models for risk of stillbirth developed for application during pregnancy. DATA COLLECTION AND ANALYSIS: Study screening and data extraction were conducted in duplicate, using the CHARMS checklist. Risk of bias was appraised using the PROBAST tool. RESULTS: The search identified 2751 citations. Fourteen studies reporting development of 69 models were included. Variables consistently included were: ethnicity, body mass index, uterine artery Doppler, pregnancy-associated plasma protein and placental growth factor. For almost all models there were significant concerns about risk of bias. Apparent model performance (i.e. in the development dataset) was highest in models developed for use later in pregnancy and including maternal characteristics, and ultrasound and biochemical variables, but few were internally validated and none were externally validated. CONCLUSIONS: Almost all models identified were at high risk of bias. There are first-trimester models of possible clinical benefit in early risk stratification; these require validation and clinical evaluation. There were few later pregnancy models but, if validated, these could be most relevant to individualised discussions around timing of birth. TWEETABLE ABSTRACT: Prediction models using maternal factors, blood tests and ultrasound could individualise stillbirth prevention, but existing models are at high risk of bias.