Towards personalized medicine in pregnancy: Early prediction of pregnancy complications to improve maternal and fetal outcomes

Purpose.

The existing antenatal care program is similar for most women with increasing frequency of visits as the pregnancy progresses, aiming to find established pregnancy complications. Among these, preeclampsia and fetal growth restriction are of utmost importance.

Accurate early pregnancy prediction models for risk assessment in early pregnancy would enable detection before onset of disease, personalizing preventative treatment and follow up.Aims To create first trimester risk prediction models for preeclampsia and fetal growth restriction.Design We will use our large Swedish nationwide database and biobank IMPACT.

This comprise 13,000 pregnancies with blood samples and data obtained through interviews and examinations at 11-14 weeks of gestation, as well as register-based data. Data will be used to create prediction models by machine learning methods. Thereafter we will use biobank blood samples to analyze promising related biomarkers discovered by our research group.

We will incorporate these to the prediction models for improved accuracy.

Finally, we will use cutting edge proteomics techniques to discover novel biomarkers that may further improve prediction models.Relevance Identifying high risk women in early pregnancy would enable prevention strategies that could mitigate the risk of complications, and to offer increased individualized surveillance for early detection of complications, which has been shown beneficial for the mother and child.