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Townsend R, Sileo FG, Allotey J, Dodds J, Heazell A, Jorgensen L, Kim VB, Magee L, Mol B, Sandall J, Smith G, Thilaganathan B, von Dadelszen P, Thangaratinam S, Khalil A. Prediction of stillbirth: an umbrella review of evaluation of prognostic variables. BJOG 2020; 128:238-250. [PMID: 32931648 DOI: 10.1111/1471-0528.16510] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/17/2020] [Indexed: 12/23/2022]
Abstract
BACKGROUND Stillbirth accounts for over 2 million deaths a year worldwide and rates remains stubbornly high. Multivariable prediction models may be key to individualised monitoring, intervention or early birth in pregnancy to prevent stillbirth. OBJECTIVES To collate and evaluate systematic reviews of factors associated with stillbirth in order to identify variables relevant to prediction model development. SEARCH STRATEGY MEDLINE, Embase, DARE and Cochrane Library databases and reference lists were searched up to November 2019. SELECTION CRITERIA We included systematic reviews of association of individual variables with stillbirth without language restriction. DATA COLLECTION AND ANALYSIS Abstract screening and data extraction were conducted in duplicate. Methodological quality was assessed using AMSTAR and QUIPS criteria. The evidence supporting association with each variable was graded. RESULTS The search identified 1198 citations. Sixty-nine systematic reviews reporting 64 variables were included. The most frequently reported were maternal age (n = 5), body mass index (n = 6) and maternal diabetes (n = 5). Uterine artery Doppler appeared to have the best performance of any single test for stillbirth. The strongest evidence of association was for nulliparity and pre-existing hypertension. CONCLUSION We have identified variables relevant to the development of prediction models for stillbirth. Age, parity and prior adverse pregnancy outcomes had a more convincing association than the best performing tests, which were PAPP-A, PlGF and UtAD. The evidence was limited by high heterogeneity and lack of data on intervention bias. TWEETABLE ABSTRACT Review shows key predictors for use in developing models predicting stillbirth include age, prior pregnancy outcome and PAPP-A, PLGF and Uterine artery Doppler.
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Affiliation(s)
- R Townsend
- Molecular and Clinical Sciences Research Institute, St George's, University of London and St George's University Hospitals NHS Foundation Trust, London, UK.,Fetal Medicine Unit, St George's University Hospitals NHS Foundation Trust, London, UK
| | - F G Sileo
- Molecular and Clinical Sciences Research Institute, St George's, University of London and St George's University Hospitals NHS Foundation Trust, London, UK.,Fetal Medicine Unit, St George's University Hospitals NHS Foundation Trust, London, UK
| | - J Allotey
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK.,Pragmatic Clinical Trials Unit, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - J Dodds
- Pragmatic Clinical Trials Unit, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK.,Centre for Women's Health, Institute of Population Health Sciences, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - A Heazell
- St Mary's Hospital, Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, UK.,Faculty of Biology, Medicine and Health, Maternal and Fetal Health Research Centre, School of Medical Sciences, University of Manchester, Manchester, UK
| | | | - V B Kim
- The Robinson Institute, University of Adelaide, Adelaide, SA, Australia
| | - L Magee
- Faculty of Life Sciences and Medicine, School of Life Course Sciences, King's College London, London, UK
| | - B Mol
- Department of Obstetrics and Gynaecology, School of Medicine, Monash University, Melbourne, Vic., Australia
| | - J Sandall
- Health Service and Population Research Department, Centre for Implementation Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Department of Women and Children's Health, Faculty of Life Sciences & Medicine, School of Life Course Sciences, King's College London, St Thomas' Hospital, London, UK
| | - Gcs Smith
- Department of Obstetrics and Gynaecology, University of Cambridge, NIHR Cambridge Biomedical Research Centre, Cambridge, UK.,Department of Physiology, Development and Neuroscience, Centre for Trophoblast Research (CTR), University of Cambridge, Cambridge, UK
| | - B Thilaganathan
- Molecular and Clinical Sciences Research Institute, St George's, University of London and St George's University Hospitals NHS Foundation Trust, London, UK.,Fetal Medicine Unit, St George's University Hospitals NHS Foundation Trust, London, UK
| | - P von Dadelszen
- Faculty of Life Sciences and Medicine, School of Life Course Sciences, King's College London, London, UK
| | - S Thangaratinam
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK.,Pragmatic Clinical Trials Unit, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - A Khalil
- Molecular and Clinical Sciences Research Institute, St George's, University of London and St George's University Hospitals NHS Foundation Trust, London, UK.,Fetal Medicine Unit, St George's University Hospitals NHS Foundation Trust, London, UK
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