The app developed at King’s uses an algorithm which combines the gestation of previous pregnancies and the length of the cervix with levels of fetal fibronectin to classify a woman’s risk. The first study focused on women deemed to be a high risk of preterm birth, usually because of a previous early pregnancy, despite not showing any symptoms. The second study predicted the likelihood of early delivery in a group of women showing symptoms of early labour which often doesn’t progress to real labour.
In the first study, published in the journal Ultrasound in Obstetrics & Gynecology, 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, also published in the journal Ultrasound in Obstetrics & Gynecology, data from 382 high-risk women was collected. The model was developed on the first 190 women and validated on the remaining 192. Probabilities of delivering early were estimated as above.
In both studies, the app was found to perform well as a predictive tool, and far better than each component (previous pregnancy, cervical length or fetal fibronectin) taken alone.
The authors conclude that the app can be used by clinicians to improve the estimation of the probability of premature delivery (before 34 weeks’ gestation or within two weeks of the fetal fibronectin test) and to potentially tailor clinical management decisions.
However, further work is needed to clinically evaluate the model in practice, and to ascertain whether interventions improve the pregnancy outcome for women identified as high risk by the app.
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, 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. 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.
“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.”
QUiPP is available to download for free from the Apple store.
Notes to editors
For more information please contact Jenny Gimpel in the King’s College London press office on +44 (0)207 848 4334, firstname.lastname@example.org
‘Development and validation of a predictive tool for spontaneous preterm birth incorporating cervical length and quantitative fetal fibronectin in asymptomatic high-risk women’ by Kuhrt et al is published in the journal Ultrasound in Obstetrics & Gynecology in January 2016 (DOI: 10.1002/uog.14865) and is accessible here.
‘Development and validation of a predictive tool for spontaneous preterm birth, incorporating quantitative fetal fibronectin, in symptomatic women’ by Kuhrt et al is published in the journal Ultrasound in Obstetrics & Gynecology in February 2016 (DOI: 10.1002/uog.14894) and is accessible here.
The studies were supported by Tommy’s Charity.
About King’s College London
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