A woman who suffered seven miscarriages has benefited from a new app developed by London hospitals.

The app can help predict if a woman is going to give birth prematurely, which could reduce unnecessary treatment and hospital admissions.

Around 60,000 babies are born prematurely in the UK each year, putting them at a higher risk of developing health problems.

Jo Deery, 36, was referred to St Thomas’s hospital in 2015 after seven miscarriages.

When the Margate mother-of-three fell pregnant again she had a transabdominal cerclage – a stitch that closes the cervix during pregnancy to help prevent miscarriage or premature birth.

Jo, a research nurse, said: “During an antenatal appointment it was thought that the stitch wasn’t working as best as it could.

“As soon as I heard that I felt instant panic and dread and was sick to my stomach that I was going to lose my baby again."

Researchers from Guy’s and St Thomas’ and King’s College London developed the QUiPP app to help healthcare professionals identify and treat those women who are more likely to give birth prematurely.

The app calculates the risk of premature birth using an algorithm that assesses several factors, including a history of previous premature births.

Jo said: “The doctors used the QUiPP app on me and it turned out that my chance of going into pre-term labour again was very very small.

“It gave me a brilliant peace of mind. I actually carried my son, Cooper, to full term.”

Guidelines advise treating all women who are 30-weeks pregnant and who present symptoms of potential pre-term labour, even though many will not go on to deliver early.

Two previous clinical trials have shown that the app is an accurate tool for predicting pre-term birth within seven days.

The new EQUIPTT study (Evaluation of QUiPP app for Triage and Transfer) has been launched at 13 obstetric centres across the UK.

The study aims to recruit 580 women over the next 12 months to look at whether the QUiPP app can reduce the number of women with symptoms of threatened pre-term labour.