Published: 08 July 2021
The health outcomes of preterm babies can be significantly improved by timely and appropriate interventions in women presenting with preterm labor, finds a new NIHR-funded study.
Led by researchers at University of Edinburgh, the QUIDS (Quantitative Fibronectin to help Decision-making in women with Symptoms of Preterm Labour) study suggests that a newly developed risk prediction model may improve the prediction of impending preterm births. However, the non-specific nature of presenting signs and symptoms of preterm labor make it challenging to diagnose, and unnecessary overtreatment is both common and costly.
To improve the prediction of impending preterm birth, the researchers developed and validated a risk prediction model. researchers first identified clinical risk factors for preterm labor by analyzing individual participant data from five European prospective cohort studies, including 1,783 pregnant European women, and used these to develop a model to predict risk of spontaneous preterm birth.
The authors found that using a risk prediction model, that included vaginal fluid fetal fibronectin concentration analysis, alongside clinical risk factors improved the prediction of impending spontaneous preterm birth and was cost-effective in comparison to fetal fibronectin alone.
The study, published in PLOS Medicine, noted several limitations, including few non-White participants, as well as missing data in the risk predictor development cohort. Further studies are required to determine whether the risk prediction model improves clinical outcomes in practice.
Dr Sarah Stock of University of Edinburgh, Chief Investigator of the study, said: “The vast majority of women with signs and symptoms of preterm labour don’t actually give birth early, but many receive unnecessary hospital admission just in case of preterm birth.
“The risk predictor developed by our research team will help women to understand their chance of giving birth early, so they can decide whether or not to have admission and treatment. We are now working towards linking the predictor to maternity records, so it can easily be used as part of women’s care and be continually improved as more women use it.”
The project was funded by the NIHR Health Technology and Assessment Programme. More information about this study is available on our Funding and Awards website.