Fetal overgrowth in pregnancies complicated by diabetes: development of a clinical prediction index View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-07

AUTHORS

Tracy M. Tomlinson, Dorothea J. Mostello, Kee-Hak Lim, Jennifer S. Pritchard, Gil Gross

ABSTRACT

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. More... »

PAGES

67-74

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00404-018-4758-9

DOI

http://dx.doi.org/10.1007/s00404-018-4758-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1103662459

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29700600


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