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A new study published in The Lancet Digital Health has yielded a significant breakthrough in pre-eclampsia risk assessment. The global study, encompassing over 8,800 women across 11 countries, successfully categorized women into five distinct risk groups within two days of their initial evaluation.
Pre-eclampsia, impacting 2% to 4% of pregnancies worldwide, is a major contributor to maternal morbidity and mortality. Each year, it leads to roughly 46,000 maternal deaths along with half a million stillbirths and newborn deaths, primarily in low- and middle-income countries.
While most pre-eclampsia cases subside soon after childbirth, roughly 1 in 10 women in the UK encounter severe complications, including life-threatening situations like stroke.
The newly developed risk-prediction model, PIERS-ML (Pre-eclampsia Integrated Estimate of Risk—Machine Learning), leverages machine learning for international applicability. The model, a collaborative effort by researchers from the University of Strathclyde in Glasgow and Kings College London, aims to assist healthcare professionals in establishing personalized risk assessments for women diagnosed with pre-eclampsia. An additional 2,901 women from Southeast England were included to validate the model externally, confirming its effectiveness as observed in the primary study.
The researchers plan to convert this model into a user-friendly application designed for clinical use in the future. This study represents a substantial leap forward in pre-eclampsia risk assessment and management.
This article incorporates essential details from the provided study, highlighting the potential of AI in enhancing pre-eclampsia risk evaluation. It emphasizes the study’s global scope, the model’s accuracy, and its intended clinical application.
