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Can risk prediction models help us individualise stillbirth prevention? A systematic review and critical appraisal of published risk models
Author(s) -
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
Publication year - 2021
Publication title -
bjog: an international journal of obstetrics and gynaecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.157
H-Index - 164
eISSN - 1471-0528
pISSN - 1470-0328
DOI - 10.1111/1471-0528.16487
Subject(s) - medicine , data extraction , checklist , medline , pregnancy , systematic review , predictive modelling , critical appraisal , meta analysis , risk assessment , computer science , psychology , machine learning , alternative medicine , computer security , pathology , biology , political science , law , cognitive psychology , genetics
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.