Can risk prediction models help us individualise stillbirth prevention? A systematic review and critical appraisal of published risk models
Research output: Contribution to journal › Review article › peer-review
- St George's University
- St George's University Hospitals NHS Foundation Trust, London, UK.
- Institute of Metabolism and Systems Research (IMSR)
- Pragmatic Clinical Trials Unit, Centre for Primary Care and Public Health, Queen Marys University, London, UK.
- Barts and the London NHS Trust; London UK
- Plymouth University Peninsula School of Medicine and Dentistry, Plymouth, UK.
- Queen Mary, University of London
- Manchester University NHS Foundation Trust
- Katie's Team
- King's Health Partners Cancer Biobank, King's College London, London, UK.
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, Australia.
- Primary Care Centre Versus Arthritis, School of Primary, Community and Social Care, Keele University, Staffordshire
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge Department of Oncology, University of Cambridge, Addenbrooke's Hospital, Cambridge Cambridge Experimental Cancer Medicine Centre and NIHR Cambridge Biomedical Research Centre, Cambridge, UK.
- Sands (Stillbirth and Neonatal Death Society)
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 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 (BMI), uterine artery Doppler, pregnancy-associated plasma protein (PAPP-A) and placental growth factor (PlGF). Almost all models had significant concern 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.
|Journal||BJOG: An International Journal of Obstetrics & Gynaecology|
|Publication status||E-pub ahead of print - 7 Sep 2020|