Sonographic prediction of macrosomia in pregnancies complicated by maternal diabetes: finding the best formula
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To evaluate the best performing formula for macrosomia prediction in pregnancies complicated by diabetes.
A retrospective analysis was performed of 1060 sonographic fetal biometrical measurements performed within 7 days of delivery in term pregnancies (37–42 gestational weeks) complicated by diabetes. Sonographic prediction of macrosomia (≥ 4000, ≥ 4250, and ≥ 4500 g) was evaluated utilizing ten previously published formulas by: (1) calculating for each macrosomia threshold the sensitivity, specificity, positive and negative predictive value, and ± likelihood ratio for macrosomia prediction; (2) comparing the systematic and random error and the proportion of estimates < 10% of birth weights between macrosomic and non-macrosomic neonates. Best performing formula was determined based on Euclidean distance.
97 (9.2%) macrosomic neonates (> 4000 g) were included. Median birth weight was 3380 (1866–3998) g for non-macrosomic and 4198 (4000–5180) g for macrosomic neonates. Higher macrosomia cutoff was associated with higher specificity and lower sensitivity. We found a considerable variation between formulas in different accuracy parameters. Hadlock’s formula (1985), based on abdominal circumference, femur length, head circumference and biparietal diameter, had the shortest Euclidean distance, reflecting the highest accuracy.
Prediction of macrosomia among women with diabetes differs significantly between formulas. In our cohort, the best performing formula for macrosomia prediction was Hadlock’s formula (1985).
KeywordsMacrosomia Fetal weight estimation Diabetes in pregnancy
AS: data collection and management, manuscript writing and editing, data analysis. LS: protocol development, manuscript editing. EH: protocol development, data management. A. Aviram: data collection and management, data analysis. RB: project development, data collection, manuscript editing. EA: project development, data collection. RG-B: project development, data collection, data analysis, manuscript editing.
Compliance with ethical standards
Conflict of interest
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