Current preeclampsia prediction model and biomarker - Kusuma, Anak Agung Ngurah Jaya (Majalah Obstetri & Ginekologi, 2024)
HIGHLIGHTS Most studies report that FMF predictive models involving a combination of maternal factor screening and biomarkers have significantly better detection capacity than risk factor screening alone. All predictive models generally use maternal factors as the basis for calculations and algorithms. Several biomarkers that have been reported in studies to act as elements of prediction models include MAP, UtA-PI, and the ratio of sFlt-1/PlGF level.   ABSTRACT Preeclampsia (PE) is a serious hypertensive disorder that occurs during pregnancy and is often accompanied by proteinuria (excessive protein in the urine), posing significant risks to both maternal and neonatal health worldwide. PE is a leading cause of maternal and neonatal morbidity and mortality and is notably challenging to predict due to its unpredictable nature and steadily rising incidence rates globally. As a result, substantial efforts have been directed toward developing predictive models and identifying biomarkers to assess the risk and progression of PE. However, existing models vary widely in their design, methodologies, and efficacy. Current prediction models recommended by notable organizations, including the National Institute for Health and Care Excellence (NICE), the American College of Obstetricians and Gynecologists (ACOG), the Fetal Medicine Foundation (FMF), and the World Health Organization (WHO), generally involve screening based on maternal characteristics and known risk factors. These include parameters such as maternal age, body mass index (BMI), number of pregnancies and births, blood pressure, and uterine arterial pulse index (UtA-PI). Additionally, biomarkers like mean arterial pressure (MAP), UtA-PI, and the ratio of soluble fms-like tyrosine kinase-1 to placental growth factor (sFlt-1/PlGF) are employed to improve predictive accuracy. Despite the diversity of predictive models and biomarkers, there is no consensus on the optimal model for PE prediction, largely due to the limitations in comparative studies and the challenges involved in cross-study comparisons. However, literature suggests that the FMF model demonstrates superior detection capacity compared to other predictive models.