Evaluation of Pre-eclampsia Prediction Models Using First Trimester Markers: comprehensive review analysis

Abstract

Background:
Preeclampsia (PE) is a leading cause of maternal and perinatal morbidity and mortality globally, affecting approximately 2–8% of pregnancies. Early identification of women at risk has become a clinical priority. First-trimester prediction models integrating biochemical, biophysical, and maternal characteristics have shown promise in forecasting PE risk before clinical manifestation.

Objective:
To systematically review and evaluate the predictive performance of first-trimester models for preeclampsia, with emphasis on applicability in Saudi Arabia.

Methods:
A comprehensive literature search was conducted across PubMed, Scopus, Web of Science, and Google Scholar for studies published between 2010 and 2024. Inclusion criteria were original research articles reporting first-trimester screening models for preeclampsia that included at least one biochemical or biophysical marker. Key data such as sensitivity, specificity, and area under the curve (AUC) were extracted and compared. Relevance to Saudi Arabia was highlighted based on regional studies and population risk profiles.

Results:
Several models, including the Fetal Medicine Foundation (FMF) algorithm, NICE guidelines, and machine learning-based tools, demonstrate strong predictive value, particularly when maternal history is combined with mean arterial pressure (MAP), uterine artery pulsatility index (UtA-PI), placental growth factor (PlGF), and pregnancy-associated plasma protein-A (PAPP-A). The FMF model showed AUC values of 0.85–0.95 for early-onset PE. Limited local data from Saudi Arabia indicate rising prevalence, especially among women with obesity, diabetes, and advanced maternal age.

Conclusion:
First-trimester models show high potential in early detection of preeclampsia, with the FMF model being the most validated globally. However, further regional validation in Saudi Arabia is needed to account for unique demographic and clinical factors. Early integration of such models into antenatal care may improve maternal and fetal outcomes.