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Prediction of insulin treatment in women with gestational diabetes mellitus

INTRODUCTION: The identification of pregnant women with Gestational Diabetes Mellitus (GDM) who will require insulin therapy, may modify their management to closer monitoring and probable early interventions. The aim of the study was to develop a predictive model for the necessity of insulin treatme...

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Autores principales: Eleftheriades, Makarios, Chatzakis, Christos, Papachatzopoulou, Eftychia, Papadopoulos, Vassilis, Lambrinoudaki, Irene, Dinas, Konstantinos, Chrousos, George, Sotiriadis, Alexandros
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8487424/
https://www.ncbi.nlm.nih.gov/pubmed/34601490
http://dx.doi.org/10.1038/s41387-021-00173-0
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author Eleftheriades, Makarios
Chatzakis, Christos
Papachatzopoulou, Eftychia
Papadopoulos, Vassilis
Lambrinoudaki, Irene
Dinas, Konstantinos
Chrousos, George
Sotiriadis, Alexandros
author_facet Eleftheriades, Makarios
Chatzakis, Christos
Papachatzopoulou, Eftychia
Papadopoulos, Vassilis
Lambrinoudaki, Irene
Dinas, Konstantinos
Chrousos, George
Sotiriadis, Alexandros
author_sort Eleftheriades, Makarios
collection PubMed
description INTRODUCTION: The identification of pregnant women with Gestational Diabetes Mellitus (GDM) who will require insulin therapy, may modify their management to closer monitoring and probable early interventions. The aim of the study was to develop a predictive model for the necessity of insulin treatment in women with GDM. MATERIALS AND METHODS: This was a prospective cohort study. Data from 775 women diagnosed with GDM per the IADPSG criteria were analyzed using logistic regression and a machine learning algorithm, the Classification and Regression Trees (CART). Potential predictors routinely recorded at follow-up visits were tested and used for the development of the model. The resultant model was externally validated using the data from two different perinatology clinics. RESULTS: Preconceptional maternal BMI and morning fasting blood glucose levels at baseline and at 1 h during an Oral Glucose Tolerance Test (OGTT) were independent significant predictors for the treatment modality of GDM. Baseline blood glucose greater than 98 mg/dl and preconceptional maternal Body Mass Index (BMI) between 26 and 31 kg/height(2) increased substantially the probability of insulin therapy (odds ratio [OR] 4.04, 95% confidence interval [CI] CI 2.65–6.17 and 2.21, 95%CI 1.42–3.43, respectively). The area under the curve (AUC) for the internal and external validation of the predictive model was 0.74 and 0.77, respectively. CONCLUSIONS: A simple model based on maternal characteristics and the values of an OGTT can predict the need for insulin treatment with accuracy. Overweight women with an abnormal baseline blood glucose at OGTT are at high likelihood for insulin treatment. KEY MESSAGE: Fifteen to 30% of women with Gestational Diabetes Mellitus (GDM) require insulin therapy. Overweight women with baseline blood glucose greater than 98 mg/dl at OGTT are at increased risk for insulin treatment and close monitoring and increased physical exercise are required.
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spelling pubmed-84874242021-10-07 Prediction of insulin treatment in women with gestational diabetes mellitus Eleftheriades, Makarios Chatzakis, Christos Papachatzopoulou, Eftychia Papadopoulos, Vassilis Lambrinoudaki, Irene Dinas, Konstantinos Chrousos, George Sotiriadis, Alexandros Nutr Diabetes Article INTRODUCTION: The identification of pregnant women with Gestational Diabetes Mellitus (GDM) who will require insulin therapy, may modify their management to closer monitoring and probable early interventions. The aim of the study was to develop a predictive model for the necessity of insulin treatment in women with GDM. MATERIALS AND METHODS: This was a prospective cohort study. Data from 775 women diagnosed with GDM per the IADPSG criteria were analyzed using logistic regression and a machine learning algorithm, the Classification and Regression Trees (CART). Potential predictors routinely recorded at follow-up visits were tested and used for the development of the model. The resultant model was externally validated using the data from two different perinatology clinics. RESULTS: Preconceptional maternal BMI and morning fasting blood glucose levels at baseline and at 1 h during an Oral Glucose Tolerance Test (OGTT) were independent significant predictors for the treatment modality of GDM. Baseline blood glucose greater than 98 mg/dl and preconceptional maternal Body Mass Index (BMI) between 26 and 31 kg/height(2) increased substantially the probability of insulin therapy (odds ratio [OR] 4.04, 95% confidence interval [CI] CI 2.65–6.17 and 2.21, 95%CI 1.42–3.43, respectively). The area under the curve (AUC) for the internal and external validation of the predictive model was 0.74 and 0.77, respectively. CONCLUSIONS: A simple model based on maternal characteristics and the values of an OGTT can predict the need for insulin treatment with accuracy. Overweight women with an abnormal baseline blood glucose at OGTT are at high likelihood for insulin treatment. KEY MESSAGE: Fifteen to 30% of women with Gestational Diabetes Mellitus (GDM) require insulin therapy. Overweight women with baseline blood glucose greater than 98 mg/dl at OGTT are at increased risk for insulin treatment and close monitoring and increased physical exercise are required. Nature Publishing Group UK 2021-10-02 /pmc/articles/PMC8487424/ /pubmed/34601490 http://dx.doi.org/10.1038/s41387-021-00173-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Eleftheriades, Makarios
Chatzakis, Christos
Papachatzopoulou, Eftychia
Papadopoulos, Vassilis
Lambrinoudaki, Irene
Dinas, Konstantinos
Chrousos, George
Sotiriadis, Alexandros
Prediction of insulin treatment in women with gestational diabetes mellitus
title Prediction of insulin treatment in women with gestational diabetes mellitus
title_full Prediction of insulin treatment in women with gestational diabetes mellitus
title_fullStr Prediction of insulin treatment in women with gestational diabetes mellitus
title_full_unstemmed Prediction of insulin treatment in women with gestational diabetes mellitus
title_short Prediction of insulin treatment in women with gestational diabetes mellitus
title_sort prediction of insulin treatment in women with gestational diabetes mellitus
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8487424/
https://www.ncbi.nlm.nih.gov/pubmed/34601490
http://dx.doi.org/10.1038/s41387-021-00173-0
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