First trimester prediction of preeclampsia

Area of Science:

• Obstetrics and Gynecology
• Maternal-Fetal Medicine
• Biomarker Discovery

Background:

• Preeclampsia (PE) poses significant risks to maternal and fetal health.
• Accurate prediction of PE in early pregnancy is a growing clinical need.
• Existing prediction methods require further refinement for widespread implementation.

Purpose of the Study:

• To review and evaluate biomarker algorithms for predicting preeclampsia in the first and early second trimesters.
• To assess the efficacy of novel biomarkers and established placental markers in PE prediction.
• To analyze the impact of prophylactic treatment on PE outcomes.

Main Methods:

• Systematic review of literature published between 2011 and 2015.
• Evaluation of six biomarker algorithms for PE prediction.
• Inclusion of one randomized clinical trial on prophylactic treatment.
• Analysis of algorithms based on placental biomarkers (PAPP-A, PLGF, s-FLT-1) and novel markers (free HbF, A1M).

Main Results:

• Placental biomarker algorithms demonstrated high prediction rates for early-onset PE (44-92% at 5% FPR).
• A novel model using free HbF and A1M achieved a 69% prediction rate at 5% FPR, without requiring UtAD-PI.
• The randomized clinical trial showed that prophylactic low-dose aspirin reduced early-onset and preterm PE in high-risk pregnancies.

Conclusions:

• Preeclampsia prediction is feasible using various established and novel biomarker algorithms.
• Prophylactic treatment with low-dose aspirin is beneficial for high-risk pregnancies identified through screening.
• The development of screening methods not reliant on techniques like UtAD-PI could improve accessibility, particularly in resource-limited settings.