Establishment of fetal biometric charts using quantile regression analysis

Area of Science:

• Obstetrics and Gynecology
• Prenatal Diagnosis
• Biostatistics

Background:

• Fetal growth evaluation is crucial for pregnancy surveillance.
• Current fetal growth charts rely on limited data and traditional statistical methods.
• There is a need for more accurate and comprehensive fetal growth charts.

Purpose of the Study:

• To develop improved fetal biometric charts using a large cohort and quantile regression analysis.
• To compare the newly developed charts with conventional methods.
• To enhance the accuracy of fetal growth assessment in clinical practice.

Main Methods:

• Utilized data from 17,708 sonographic examinations of pregnant women (12-42 weeks gestation).
• Collected fetal biometric measurements including head circumference, abdominal circumference, and femur length.
• Applied quantile regression analysis to construct new growth charts.

Main Results:

• Established new fetal biometric growth charts based on the large dataset.
• The new charts provide specific regression parameters for each percentile.
• Demonstrated a better definition of the normal fetal growth range compared to conventional charts.

Conclusions:

• Developed novel biometric growth charts for fetal assessment.
• These charts offer improved precision in defining normal fetal growth percentiles.
• Recommended for routine use in daily obstetric practice to enhance pregnancy surveillance.