Can risk prediction models help us individualise stillbirth prevention? A systematic review and critical appraisal of published risk models.
Epidemiology
fetal medicine
model
perinatal
prediction
serum screening
stillbirth
systematic reviews
Journal
BJOG : an international journal of obstetrics and gynaecology
ISSN: 1471-0528
Titre abrégé: BJOG
Pays: England
ID NLM: 100935741
Informations de publication
Date de publication:
01 2021
01 2021
Historique:
accepted:
02
09
2020
pubmed:
8
9
2020
medline:
5
3
2021
entrez:
7
9
2020
Statut:
ppublish
Résumé
Stillbirth prevention is an international priority - risk prediction models could individualise care and reduce unnecessary intervention, but their use requires evaluation. To identify risk prediction models for stillbirth, and assess their potential accuracy and clinical benefit in practice. 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. Studies developing and/or validating prediction models for risk of stillbirth developed for application during pregnancy. Study screening and data extraction were conducted in duplicate, using the CHARMS checklist. Risk of bias was appraised using the PROBAST tool. 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. 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. Prediction models using maternal factors, blood tests and ultrasound could individualise stillbirth prevention, but existing models are at high risk of bias.
Sections du résumé
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.
Identifiants
pubmed: 32894620
doi: 10.1111/1471-0528.16487
doi:
Types de publication
Journal Article
Systematic Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
214-224Subventions
Organisme : Medical Research Council
ID : MR/P027938/1
Pays : United Kingdom
Organisme : Stillbirth and Neonatal Death Society
Commentaires et corrections
Type : CommentIn
Informations de copyright
© 2020 John Wiley & Sons Ltd.
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