Review Article

Current preeclampsia prediction model and biomarker

Anak Agung Ngurah Jaya Kusuma
Department of Obstetrics and Gynecology, Prof. Dr. I.G.N.G. Ngoerah Hospital/Medical Faculty of Udayana University, Bali, Indonesia

HIGHLIGHTS

1. 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.
2. All predictive models generally use maternal factors as the basis for calculations and algorithms.
3. 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 hypertension in pregnancy with positive protein urine, leading to morbidity and mortality in the mother or the neonate worldwide. Furthermore, cases of PE are known to be unpredictable, with increasing incidence rates each year. Several predictive models and biomarkers have been developed to investigate the risk and progression of PE. However, these predictive models have different characteristics and capacities. Some recent studies have also shown different findings regarding the ability and capacity of existing models. Currently, all prediction models, such as NICE, ACOG, FMF, and WHO recommendations, generally involve screening for maternal characteristics (number of pregnancies and births, BMI, blood pressure, maternal age, uterine arterial pulse index) and risk factors. Several biomarkers are also used, including mean arterial pressure (MAP), UtA-PI, and sFlt-1/PlGF ratio. However, there is yet to be a conclusion regarding the best predictive model due to the limitations of available comparative studies and some barriers to comparing studies. Based on the current literature, FMF recommendation has the best detection capacity compared to other predictive models.

Keywords: Biomarker, preeclampsia, prediction model, maternal health