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Prospective Evaluation of Screening Performance of First-Trimester Prediction Models for Preterm Preeclampsia
1Department of Obstetric and Gynecology, College of Medicine, Majmaah University, Majmaah, Saudi Arabia.
Insights
First-trimester prediction models for preterm preeclampsia (PPE) show promise. Combining biomarkers, clinical factors, and ultrasound markers improves early risk assessment for this serious pregnancy complication.
Area of Science:
- Obstetrics and Gynecology
- Maternal-Fetal Medicine
- Reproductive Health
Background:
- Preterm preeclampsia (PPE) poses significant maternal and fetal health risks.
- Early identification of PPE is crucial for timely intervention and improved outcomes.
Purpose of the Study:
- To evaluate the prospective performance of first-trimester prediction models for PPE.
- To highlight the effectiveness, limitations, and clinical applicability of these models.
Main Methods:
- A comprehensive narrative review of studies published between 2020 and 2024.
- Inclusion of studies focusing on first-trimester PPE prediction models, analyzing sensitivity, specificity, and predictive values.
Main Results:
- Multifactorial models integrating biomarkers (PAPP-A, PlGF, sFlt-1), clinical risk factors, and ultrasound markers (uterine artery Doppler, MAP) demonstrate high sensitivity and specificity.
- These combined models show promising results for predicting PPE.
Conclusions:
- First-trimester PPE prediction models are effective for early risk assessment.
- Further validation in diverse populations and advancements in AI/machine learning are needed to enhance accuracy and generalizability.
Abstract:
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.
