App May Predict Preterm Birth Risk

A new app named QUiPP may help doctors better guess which women are at risk of giving birth prematurely.

Researchers at King's College London tested the app in two studies on high-risk women who were monitored at ante-natal clinics. In both studies, the app worked well as  a predictive tool and was far better than other components--including previous pregnancy, cervical length or fetal fibronectin. The app developed by the researchers uses an algorithm that combines the gestation of previous pregnancies and the length of the cervix with levels of fetal fibronectin to classify a woman's risk. 

"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. It can be difficult to accurately assess a woman's risk, given that many women who show symptoms of preterm labour do not go on to deliver early," said Professor Andrew Shennan, lead author who is Professor of Obstetrics at King's College London and consultant obstetrician at Guy's and St Thomas' NHS Foundation Trust, in a news release. "The more accurately we can predict her risk, the better we can manage a woman's pregnancy to ensure the safest possible birth for her and her baby, only intervening when necessary to admit these 'higher risk' women to hospital, prescribe steroids or offer other treatments to try to prevent an early birth."

In the first study, researchers collected data from 1,249 women at high risk for pre-term birth attending pre-term surveillance clinics. The model was developed on the first 624 consecutive women and validated on the subsequent 625. The estimated probability of delivery before 30, 34 or 37 weeks' gestation and within two or four weeks of testing for fetal 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 and the model was developed for the first 190 women and validated on the remaining 192. Probabilities of delivery early were indicated as previously mentioned. 

Future studies will be necessary in order to clinically evaluate the model in practice and determine if interventions improve pregnancy outcomes for women identified as high risk by the app.

Both studies are published in the journal Ultrasound in Obstetrics & Gynecology.

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