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

Townsend, R; Manji, A; Allotey, J; Heazell, A; Jorgensen, L; Magee, LA; Mol, BW; Snell, K; Riley, RD; Sandall, J; et al. Townsend, R; Manji, A; Allotey, J; Heazell, A; Jorgensen, L; Magee, LA; Mol, BW; Snell, K; Riley, RD; Sandall, J; Smith, G; Patel, M; Thilaganathan, B; von Dadelszen, P; Thangaratinam, S; Khalil, A (2021) Can risk prediction models help us individualise stillbirth prevention? A systematic review and critical appraisal of published risk models. BJOG, 128 (2). pp. 214-224. ISSN 1471-0528 https://doi.org/10.1111/1471-0528.16487
SGUL Authors: Khalil, Asma

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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.

Item Type: Article
Additional Information: This is the peer reviewed version of the following article: Townsend, R, Manji, A, Allotey, J, Heazell, AEP, Jorgensen, L, Magee, LA, Mol, BW, Snell, KIE, Riley, RD, Sandall, J, Smith, GCS, Patel, M, Thilaganathan, B, von Dadelszen, P, Thangaratinam, S, Khalil, A. Can risk prediction models help us individualise stillbirth prevention? A systematic review and critical appraisal of published risk models. BJOG: Int J Obstet Gy. 2021; 128: 214– 224, which has been published in final form at https://doi.org/10.1111/1471-0528.16487. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.
Keywords: Fetal medicine, Systematic reviews, epidemiology, model, perinatal, prediction, serum screening, stillbirth, Obstetrics & Reproductive Medicine, 11 Medical and Health Sciences
SGUL Research Institute / Research Centre: Academic Structure > Molecular and Clinical Sciences Research Institute (MCS)
Journal or Publication Title: BJOG
ISSN: 1471-0528
Language: eng
Dates:
DateEvent
3 January 2021Published
13 October 2020Published Online
2 September 2020Accepted
Publisher License: Publisher's own licence
PubMed ID: 32894620
Go to PubMed abstract
URI: https://openaccess.sgul.ac.uk/id/eprint/112374
Publisher's version: https://doi.org/10.1111/1471-0528.16487

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