Calculate premature birth risk with new application software
A new app called QUiPP could help doctors better identify preterm birth risk. The application software developed at King’s College London was tested by the researchers in two studies with women who monitored doctors in maternity hospitals.
New software estimates premature birth risk
Worldwide, 15 million babies are born prematurely every year, i.e. before the 37th week of pregnancy. Unfortunately, over a million of these die from premature complications. Medicine uses a number of factors to determine whether a woman is at risk of giving birth prematurely. This also includes a history of premature birth or late miscarriage. Two other factors that doctors can consider are the length of the cervix and a biomarker in the vaginal fluid. The latter is known as fetal fibronectin. Doctors typically test the two factors from the 23rd week of pregnancy. The researchers have further developed the fetal fibronectin test, which can be used precisely from the first half of pregnancy.
The app developed by King’s therefore uses a mathematical algorithm. This combines pregnancy and the length of the cervix with the content of fetal fibronectin. This enables the software to better classify a woman's premature birth risk. The first study focused primarily on women at high risk of preterm birth. Usually this happens due to a previous pregnancy, although they showed no symptoms. The second study predicted the likelihood of early delivery in a group of women. These showed symptoms of early labor, which often do not lead to an actual birth.
In the first study, the researchers collected data from 1,249 women at high risk of giving birth in premature surveillance clinics. They developed the model on the first 624 consecutive women and validated it on the following 625. The estimated probability of delivery before the 30th, 34th or 37th week of pregnancy and within two or four weeks after testing for fetal fibronectin was calculated for each patient. In addition, the scientists analyzed risk as a predictive test for the actual occurrence of each event.
In the second study, they collected data from 382 women at high risk. The researchers developed the model on the first 190 women and the remaining 192 validated. They also estimated the likelihood of premature delivery as above.
In both studies, the scientists found that the app performs well as a forecast and delivers information far better than any other component. Accordingly, the authors conclude that physicians can use this software to improve the likelihood of premature delivery and potentially make better clinical decisions.
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