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Smartphone App Computes Preterm Birth Risk

Pregnant woman

(Greyerbaby, Pixabay)

18 January 2016. A smartphone app based on recently published research aims to calculate the risk of women giving birth prematurely. The app, called Quipp, was designed at King’s College London in the U.K., and is available free of charge for Apple iPhones.

The Women’s Health Clinical Academic Group at Kings College developed Quipp — short for Quantitative instrument for the prediction of preterm — to provide a more reliable way of determining earlier in the pregnancy women at risk for spontaneous preterm birth, particularly with  high rates of premature births now in the U.K. and U.S. The Kings College team led by obstetrics professor Andrew Shennan, cites data showing 15 million births occur before 37 weeks worldwide, leading to 1.1 million deaths from complications.

The app is based on an algorithm developed by Shennan and colleagues that takes into account the woman’s history with preterm births and late miscarriages. The algorithm also considers length of the cervix and levels of fetal fibronectin, a protein that holds the fetus in the womb. After 35 weeks, fetal fibronectin begins to break down naturally, but if it breaks down earlier, it could be a sign of a premature birth.

To write the algorithm, researchers collected data from 624 women at prenatal clinics in the U.K. considered at high risk of preterm birth, but not showing symptoms. The team then validated the algorithm with another 625 women, also at high risk and not showing symptoms, The algorithm calculated the probability of delivery at 30, 34, and 37 weeks gestation, as well as within 2 or 4 weeks of testing for fetal fibronectin for each participant.

The results, published in the journal Ultrasound in Obstetrics & Gynecology on 5 January, show the algorithm closely predicts preterm birth among the study participants, with statistical prediction measures considered good to excellent. A second study, to be published in the same journal, replicated the original research with 382 participants — 190 for data collection and algorithm training and 192 for validation — and returns similar results. The predication rates for the algorithm, say the authors, are greater than using each of the components of the model alone: previous pregnancy, cervical length, or state of fetal fibronectin.

The software, written by app developer Appatta Ltd., is available for download from the iTunes App Store. Shennan and colleagues now want to evaluate the  app in practice and assess interventions aimed at improving outcomes by women rated at high risk by the app.

“The more accurately we can predict her risk,” says Shennan in a university statement, “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.”

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