Modern Aspects of Screening for Early-Onset Preeclampsia

Abstract

Preeclampsia (PE) remains a significant medical and social issue worldwide, affecting 2–8% of pregnant women. It has long been, and continues to be, a potentially fatal pregnancy complication. PE or eclampsia accounts for 10–15% of all maternal deaths, resulting in at least 70,000 deaths globally each year. Early-onset PE (developing before 34 weeks of gestation) is a primary factor contributing to both maternal and perinatal mortality. To this day, PE is considered an urgent, potentially critical condition in obstetrics. The long-term consequences of PE are unpredictable, as it is currently impossible to objectively assess the extent of functional and organic impairment (damage) to the organs of the mother, fetus, or newborn. Unfortunately, the etiology and pathogenesis of PE are not yet fully understood, which complicates efforts to predict the condition and implement effective preventive measures. The purpose of this scientific review is to analyze existing literature on a screening method for the detection and prevention of preeclampsia (PE). This approach—which integrates maternal characteristics and medical history, the uterine artery pulsatility index (PI), mean arterial pressure (MAP), and maternal serum levels of pregnancy-associated plasma protein-A (PAPP-A) and placental growth factor (PlGF) at 11–13 weeks of gestation—can identify a high proportion of pregnancies at risk for early-onset PE. Consequently, the most promising prognostic models incorporate a combination of tests that assess various aspects of maternal susceptibility and placentation, such as maternal risk factors, mean arterial pressure, uterine artery Doppler ultrasound, and biomarkers. There is sufficient evidence regarding certain combinations of markers to allow for a reasonable assessment of PE risk. Prospective studies are required to evaluate risk prediction across diverse populations. Furthermore, it is necessary to assess the potential of novel biomarkers—identified through innovative research strategies—to determine whether they can enhance the predictive performance of existing models.