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Updated: Feb 11, 2026

High Frequency Ultrasound for the Analysis of Fetal and Placental Development In Vivo
Published on: November 8, 2018
Fetal overgrowth in pregnancies complicated by diabetes: development of a clinical prediction index
Tracy M Tomlinson1, Dorothea J Mostello2, Kee-Hak Lim3
1Division of Maternal-Fetal Medicine, Department of Obstetrics, Gynecology and Women's Health, Saint Louis University School of Medicine, 6420 Clayton Road, Suite 2800, Saint Louis, MO, 63117, USA. tracy.tomlinson@health.slu.edu.
Purpose:
To develop an index to predict fetal overgrowth in pregnancies complicated by diabetes.
Methods:
Data were derived from a cohort of 275 women with singleton gestations in a collaborative diabetes in pregnancy program. Regression analysis incorporated clinical factors available in the first 20-30 weeks of pregnancy that were assigned beta-coefficient-based weights, the sum of which yielded a fetal overgrowth index (composite score).
Results:
Fifty-one (18.5%) pregnancies were complicated by fetal overgrowth. The derived index included five clinical factors: age ≤ 30, history of macrosomia, excessive gestational weight gain, enlarged fetal abdominal circumference, and fasting hyperglycemia. Area under the curve (AUC) for the index is 0.88 [95% confidence interval (CI) 0.82-0.92]. Cut-points were selected to identify "high-risk" and "low-risk" ranges (≥ 8 and ≤ 3) that have positive and negative predictive values of 84% (95% CI 70-98%) and 95% (95% CI 92-98%), respectively. The majority of women in our cohort (n = 182, 66%) had a "low-risk" index while 9% (n = 25) had a "high-risk" index. Sub-analyses of nulliparous women and women with gestational and pre-gestational diabetes revealed that the overgrowth index was equally or more predictive when applied separately to each of these groups.
Conclusion:
This fetal overgrowth index that incorporates five clinical factors provides a means of predicting fetal overgrowth and thereby serves as a tool for targeting the allocation of healthcare resources and treatment individualization.
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