Can risk prediction models help us individualise stillbirth prevention? A systematic review and critical appraisal of published risk models

R Townsend^{1

2}, A Manji², J Allotey^{3

4}, Aep Heazell^{5

6}, L Jorgensen⁷, L A Magee⁸, B W Mol⁹, Kie Snell¹⁰, R D Riley¹⁰, J Sandall¹¹, Gcs Smith¹², M Patel¹³, B Thilaganathan^{1

2}, P von Dadelszen⁸, S Thangaratinam^{3

4}, A Khalil^{1

2}

Affiliations

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.

PMID: 32894620
DOI: 10.1111/1471-0528.16487

Can risk prediction models help us individualise stillbirth prevention? A systematic review and critical appraisal of published risk models

R Townsend et al. BJOG. 2021 Jan.

. 2021 Jan;128(2):214-224.

doi: 10.1111/1471-0528.16487. Epub 2020 Oct 13.

Authors

Affiliations

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.

PMID: 32894620
DOI: 10.1111/1471-0528.16487

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.

Keywords: Epidemiology; fetal medicine; model; perinatal; prediction; serum screening; stillbirth; systematic reviews.

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Comment in

Improved stillbirth risk stratification, an urgent global need.
Page JM. Page JM. BJOG. 2021 Jan;128(2):225. doi: 10.1111/1471-0528.16548. Epub 2020 Oct 27. BJOG. 2021. PMID: 33006825 No abstract available.

References

1. Kleinrouweler CE, Cheong-See F, Collins G, Kwee A, Thangaratinam S, Khan KS, et al. Prognostic models in obstetrics: available, but far from applicable. Am J Obstet Gynecol 2016;214:79-90.
1. Blencowe H, Cousens S, Jassir FB, Say L, Chou D, Mathers C, et al. National, regional, and worldwide estimates of stillbirth rates in 2015, with trends from 2000: a systematic analysis. Lancet Glob Heal 2016;4:e98-e108.
1. Widdows K, Roberts S, Camacho E, Heazell A. Evaluation of the implementation of the Saving Babies’ Lives Care Bundle in early adopter NHS Trusts in England. Manchester, UK; 2018.
1. Moons KGM, De GJAH, Bouwmeester W, Vergouwe Y, Mallett S. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: The CHARMS checklist. PLoS Med 2014;11:e1001744.
1. Wollf R, Whiting P, Mallett S, Riley R, Westwood M, Kleijnen J, et al. PROBAST: a risk of bias tool for prediction modelling studies. Cochrane Colloqium. Vienna; 2015.

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Can risk prediction models help us individualise stillbirth prevention? A systematic review and critical appraisal of published risk models

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Can risk prediction models help us individualise stillbirth prevention? A systematic review and critical appraisal of published risk models

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Medical