A new app, Quipp, which uses an algorithm, has been developed by researchers at King’s College London, in order to help doctors to better identify women at risk of giving birth prematurely. This application was tested in two studies of high-risk women being monitored at ante-natal clinics.
The app combines the gestation of previous pregnancies and the length of the cervix with levels of foetal fibronectin to classify a woman’s risk.The first study focused on women deemed to be at high risk of preterm birth, usually because of a previous early pregnancy, despite not showing any symptoms and the second study predicted the likelihood of early delivery in a group of women showing symptoms of early labour which often does not progress to real labour.
In the first study, researchers collected data from 1,249 women at high risk for pre-term birth attending pre-term surveillance clinics. The estimated probability of delivery before 30, 34 or 37 weeks’ gestation and within two or four weeks of testing for foetal fibronectin was calculated for each patient and analyzed as a predictive test for the actual occurrence of each event.
In the second study, data from 382 high-risk women was collected. The model was developed on the first 190 women and validated on the remaining 192.In both studies, the app performed well as a predictive tool.
Researchers said that around 15 million babies are born preterm each year and over a million of these die of prematurity-related complications. Various factors are used to determine if a woman is at risk of giving birth prematurely, such as, history of preterm births or late miscarriages, the length of cervix and levels of a biomarker found in vaginal fluid known as foetal fibronectin.
Andrew Shennan from King’s College said, “Despite advances in prenatal care, the rate of preterm birth has never been higher in recent years, including in the US and UK, so doctors need reliable ways of predicting whether a woman is at risk of giving birth early.”