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Risk prediction model developed for preterm births

The health outcomes of babies born prematurely can be significantly improved by timely and appropriate interventions.
The health outcomes of babies born prematurely can be significantly improved by timely and appropriate interventions. The health outcomes of babies born prematurely can be significantly improved by timely and appropriate interventions.

A newly developed risk prediction tool may help to improve the prediction of preterm births, research suggests.

The health outcomes of babies born prematurely can be significantly improved by timely and appropriate interventions in women presenting with preterm labour.

But the non-specific nature of signs and symptoms makes it difficult to diagnose, and unnecessary overtreatment can be common and costly, experts say.

To improve the prediction of impending preterm birth, Sarah Stock at the University of Edinburgh and colleagues developed and validated a risk prediction model.

First they identified clinical risk factors for preterm labour by analysing individual data from five European prospective cohort studies, including 1,783 pregnant European women.

They used this information to develop a model to predict risk of spontaneous preterm birth.

Researchers validated the model in a study of 2,924 women with signs and symptoms of preterm labour from 26 consultant-led obstetric units in the UK, to demonstrate the difference between predicted and observed outcomes.

The study found that using a risk prediction model that included measuring a specific protein concentration analysis alongside clinical risk factors improved the prediction of impending spontaneous preterm birth and was cost-effective in comparison to measuring the protein alone.

However the researchers note several limitations to their study, including that there were few non-white participants, as well as missing data in the risk predictor development cohort.

Further studies are needed to determine whether the risk prediction model improves clinical outcomes in practice.

The authors said: “The risk prediction model showed promising performance in the prediction of spontaneous preterm birth within seven days of testing and can be used as part of a decision support tool to help guide management decisions for women at risk of preterm labour.

“It is readily implementable, with potential for immediate benefit to women and babies and health services, through avoidance of unnecessary admission and treatment.”

Dr Stock 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.”

Jane Brewin, chief executive of Tommy’s charity which funded the study, said: “Most mothers with signs of premature labour are still pregnant a week later, and this study shows the typical NHS tests aren’t accurate enough to tell who’s really at risk, which can put undue strain on expectant parents and on the NHS.

“Our latest research clearly demonstrates that fetal fibronectin testing can reduce the economic and emotional toll of this issue, being more cost-effective while reassuring families who may otherwise be very anxious about the risk of premature birth and helping to make pregnancy safer for those who do need special care.

“With 60,000 babies born prematurely each year in the UK, there’s an urgent need for better ways to predict and manage that risk.

“Fetal fibronectin tests are used in our specialist clinics already, but everyone should be able to benefit from this pioneering and potentially lifesaving tool; we urge the Government to include these tests as standard in national maternity care guidelines, so that precious NHS resources can be focused on those most in need.”

The study is published in the PLOS Medicine journal.