Predicting Perinatal Morbidity in Fetal Growth Restriction: Evidence, Challenges, and Opportunities

Area of Science:

• Perinatal medicine
• Maternal-fetal medicine
• Diagnostic technologies

Background:

• Fetal growth restriction (FGR) is a clinical finding, not a single diagnosis, associated with significant morbidity.
• Accurate risk stratification is crucial for timely intervention in FGR cases.
• Current methods for identifying at-risk fetuses are diverse and require further validation.

Purpose of the Study:

• To review current approaches for risk stratification in fetal growth restriction (FGR).
• To highlight the potential of integrating multiple data domains for improved FGR management.
• To identify areas requiring further investigation in FGR risk assessment.

Main Methods:

• Review of existing literature on FGR risk stratification methods.
• Analysis of techniques including Doppler ultrasound, maternal biomarkers, multivariable modeling, and artificial intelligence (AI).
• Discussion on the potential benefits of combining data from various domains.

Main Results:

• FGR risk stratification is challenging due to its complex etiology and association with morbidity.
• Multiple domains, including Doppler ultrasound, maternal biomarkers, multivariable modeling, and AI, are being explored to identify at-risk fetuses.
• No single method currently provides definitive risk stratification.

Conclusions:

• Integrating findings from Doppler ultrasound, maternal biomarkers, multivariable modeling, and AI holds promise for advancing FGR risk stratification.
• Further research is necessary to validate and optimize these integrated approaches for clinical application.
• Improved risk stratification will enhance the management and outcomes for fetuses with FGR.