Prospective Evaluation of Screening Performance of First-Trimester Prediction Models for Preterm Preeclampsia

A BSTRACT Preterm preeclampsia (PPE) is a serious pregnancy complication with significant risks for maternal and fetal health. This review aims to evaluate the prospective performance of first-trimester prediction models for PPE, highlighting their effectiveness, limitations, and clinical applicability. A comprehensive narrative review of studies published from 2020 to 2024 was conducted. Studies focusing on first-trimester prediction models for PPE were included, with an emphasis on their sensitivity, specificity, predictive values, and performance across different populations. The review revealed that multifactorial models combining biomarkers (e.g., pregnancy-associated plasma protein-A (PAPP-A), placental growth factor (PlGF), soluble fms-like tyrosine kinase-1 (sFlt-1)), clinical risk factors, and ultrasound markers (e.g., uterine artery Doppler, mean arterial pressure (MAP)) show promising results with high sensitivity and specificity in predicting PPE. It is concluded that first-trimester prediction models for PPE are effective tools for early risk assessment but require further refinement and validation in diverse populations. Continued research and technological advancements, including machine learning and artificial intelligence (AI), are necessary to enhance the models’ accuracy and generalizability for widespread clinical use.