RAFAELA ALKMIN DA COSTA
Projetos de Pesquisa
Unidades Organizacionais
Instituto Central, Hospital das Clínicas, Faculdade de Medicina
LIM/57 - Laboratório de Fisiologia Obstétrica, Hospital das Clínicas, Faculdade de Medicina
LIM/57 - Laboratório de Fisiologia Obstétrica, Hospital das Clínicas, Faculdade de Medicina
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bookPart Emergências nos Distúrbios Endócrinos na Gravidez: Diabetes Melito e Doenças da Tireoide(2019) FRANCISCO, Rossana Pulcineli Vieira; CODARIN, Rodrigo Rocha; COSTA, Rafaela Alkmin da; MIKAMI, Fernanda Cristina Ferreira- Can we stratify the risk for insulin need in women diagnosed early with gestational diabetes by fasting blood glucose?(2019) SOUZA, Ana C. R. L. A.; COSTA, Rafaela A.; PAGANOTI, Cristiane F.; RODRIGUES, Agatha S.; ZUGAIB, Marcelo; HADAR, Eran; HOD, Moshe; FRANCISCO, Rossana P. V.Objective: The objective of this study is to evaluate risk factors and propose a model for the prediction of insulin requirement during the treatment of early-diagnosed gestational diabetes mellitus (GDM). Research design and methods: Retrospective cohort analysis of all pregnant women who were diagnosed with GDM by abnormal fasting blood glucose at the first prenatal visit. According to the requirement for insulin therapy to achieve good glycemic control (insulin or diet group), women were compared regarding clinical and laboratory variables. The performance of these variables in predicting insulin need for GDM treatment was identified by a logistic regression model, and a nomogram was created based on the model to facilitate clinical interpretation. Results: In total, 408 women were included for analysis. Among them, 135 (33%) needed insulin therapy. In the logistic regression model, maternal age, prepregnancy body mass index, fasting blood glucose (FBG) value, prior GDM, and family history of diabetes were significant independent variables for the prediction of insulin need. Conclusions: The need for insulin therapy in women with early diagnosis of GDM can be predicted by a logistic regression model, which can be converted to a clinically usable nomogram that could help to properly address follow-up strategies for GDM treatment in regions where health resources are limited.