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; Smith, G; 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.

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

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Publisher:: WILEY
Publication Type:: Journal Article
Citation:: BJOG : an international journal of obstetrics and gynaecology, 2021, 128, (2), pp. 214-224
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Townsend, R
dc.contributor.author	Manji, A
dc.contributor.author	Allotey, J
dc.contributor.author	Heazell, A
dc.contributor.author	Jorgensen, L
dc.contributor.author	Magee, LA
dc.contributor.author	Mol, BW
dc.contributor.author	Snell, K
dc.contributor.author	Riley, RD
dc.contributor.author	Sandall, J https://orcid.org/0000-0003-2000-743X
dc.contributor.author	Smith, G
dc.contributor.author	Patel, M
dc.contributor.author	Thilaganathan, B
dc.contributor.author	von Dadelszen, P
dc.contributor.author	Thangaratinam, S
dc.contributor.author	Khalil, A
dc.date.accessioned	2021-09-13T01:23:24Z
dc.date.available	2020-09-02
dc.date.available	2021-09-13T01:23:24Z
dc.date.issued	2021-01-01
dc.identifier.citation	BJOG : an international journal of obstetrics and gynaecology, 2021, 128, (2), pp. 214-224
dc.identifier.issn	1470-0328
dc.identifier.issn	1471-0528
dc.identifier.uri	http://hdl.handle.net/10453/150497
dc.description.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.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	WILEY
dc.relation.ispartof	BJOG : an international journal of obstetrics and gynaecology
dc.relation.isbasedon	10.1111/1471-0528.16487
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	11 Medical and Health Sciences
dc.subject.classification	Obstetrics & Reproductive Medicine
dc.subject.mesh	Female
dc.subject.mesh	Humans
dc.subject.mesh	Perinatal Death
dc.subject.mesh	Predictive Value of Tests
dc.subject.mesh	Pregnancy
dc.subject.mesh	Risk Assessment
dc.subject.mesh	Stillbirth
dc.subject.mesh	Humans
dc.subject.mesh	Risk Assessment
dc.subject.mesh	Predictive Value of Tests
dc.subject.mesh	Pregnancy
dc.subject.mesh	Female
dc.subject.mesh	Stillbirth
dc.subject.mesh	Perinatal Death
dc.subject.mesh	Female
dc.subject.mesh	Humans
dc.subject.mesh	Perinatal Death
dc.subject.mesh	Predictive Value of Tests
dc.subject.mesh	Pregnancy
dc.subject.mesh	Risk Assessment
dc.subject.mesh	Stillbirth
dc.title	Can risk prediction models help us individualise stillbirth prevention? A systematic review and critical appraisal of published risk models.
dc.type	Journal Article
utslib.citation.volume	128
utslib.location.activity	England
utslib.for	11 Medical and Health Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Health
pubs.organisational-group	/University of Technology Sydney/Faculty of Health/Midwifery
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-09-13T01:23:23Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	128
utslib.citation.issue	2

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.

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http://hdl.handle.net/10453/150497