Literature DB >> 30327863

Sonographic prediction of macrosomia in pregnancies complicated by maternal diabetes: finding the best formula.

Anat Shmueli1,2, Lina Salman3,4, Eran Hadar3,4, Amir Aviram4,5, Ron Bardin3,4, Eran Ashwal4,5, Rinat Gabbay-Benziv6,7.   

Abstract

PURPOSE: To evaluate the best performing formula for macrosomia prediction in pregnancies complicated by diabetes.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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).

Entities:  

Keywords:  Diabetes in pregnancy; Fetal weight estimation; Macrosomia

Mesh:

Year:  2018        PMID: 30327863     DOI: 10.1007/s00404-018-4934-y

Source DB:  PubMed          Journal:  Arch Gynecol Obstet        ISSN: 0932-0067            Impact factor:   2.344


  2 in total

1.  Analytical Comparison of Risk Prediction Models for the Onset of Macrosomia Based on Three Statistical Methods.

Authors:  Jinbo Zhang; Xiaozhi Wu; Qingqing Song
Journal:  Dis Markers       Date:  2022-09-10       Impact factor: 3.464

2.  Effective Macrosomia Prediction Using Random Forest Algorithm.

Authors:  Fangyi Wang; Yongchao Wang; Xiaokang Ji; Zhiping Wang
Journal:  Int J Environ Res Public Health       Date:  2022-03-10       Impact factor: 3.390

  2 in total

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