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Development and Validation of Risk Prediction Models for Gestational Diabetes Mellitus Using Four Different Methods
Gestational diabetes mellitus (GDM), a common perinatal disease, is related to increased risks of maternal and neonatal adverse perinatal outcomes. We aimed to establish GDM risk prediction models that can be widely used in the first trimester using four different methods, including a score-scaled m...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9697464/ https://www.ncbi.nlm.nih.gov/pubmed/36355123 http://dx.doi.org/10.3390/metabo12111040 |
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author | Wang, Ning Guo, Haonan Jing, Yingyu Song, Lin Chen, Huan Wang, Mengjun Gao, Lei Huang, Lili Song, Yanan Sun, Bo Cui, Wei Xu, Jing |
author_facet | Wang, Ning Guo, Haonan Jing, Yingyu Song, Lin Chen, Huan Wang, Mengjun Gao, Lei Huang, Lili Song, Yanan Sun, Bo Cui, Wei Xu, Jing |
author_sort | Wang, Ning |
collection | PubMed |
description | Gestational diabetes mellitus (GDM), a common perinatal disease, is related to increased risks of maternal and neonatal adverse perinatal outcomes. We aimed to establish GDM risk prediction models that can be widely used in the first trimester using four different methods, including a score-scaled model derived from a meta-analysis using 42 studies, a logistic regression model, and two machine learning models (decision tree and random forest algorithms). The score-scaled model (seven variables) was established via a meta-analysis and a stratified cohort of 1075 Chinese pregnant women from the Northwest Women’s and Children’s Hospital (NWCH) and showed an area under the curve (AUC) of 0.772. The logistic regression model (seven variables) was established and validated using the above cohort and showed AUCs of 0.799 and 0.834 for the training and validation sets, respectively. Another two models were established using the decision tree (DT) and random forest (RF) algorithms and showed corresponding AUCs of 0.825 and 0.823 for the training set, and 0.816 and 0.827 for the validation set. The validation of the developed models suggested good performance in a cohort derived from another period. The score-scaled GDM prediction model, the logistic regression GDM prediction model, and the two machine learning GDM prediction models could be employed to identify pregnant women with a high risk of GDM using common clinical indicators, and interventions can be sought promptly. |
format | Online Article Text |
id | pubmed-9697464 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96974642022-11-26 Development and Validation of Risk Prediction Models for Gestational Diabetes Mellitus Using Four Different Methods Wang, Ning Guo, Haonan Jing, Yingyu Song, Lin Chen, Huan Wang, Mengjun Gao, Lei Huang, Lili Song, Yanan Sun, Bo Cui, Wei Xu, Jing Metabolites Article Gestational diabetes mellitus (GDM), a common perinatal disease, is related to increased risks of maternal and neonatal adverse perinatal outcomes. We aimed to establish GDM risk prediction models that can be widely used in the first trimester using four different methods, including a score-scaled model derived from a meta-analysis using 42 studies, a logistic regression model, and two machine learning models (decision tree and random forest algorithms). The score-scaled model (seven variables) was established via a meta-analysis and a stratified cohort of 1075 Chinese pregnant women from the Northwest Women’s and Children’s Hospital (NWCH) and showed an area under the curve (AUC) of 0.772. The logistic regression model (seven variables) was established and validated using the above cohort and showed AUCs of 0.799 and 0.834 for the training and validation sets, respectively. Another two models were established using the decision tree (DT) and random forest (RF) algorithms and showed corresponding AUCs of 0.825 and 0.823 for the training set, and 0.816 and 0.827 for the validation set. The validation of the developed models suggested good performance in a cohort derived from another period. The score-scaled GDM prediction model, the logistic regression GDM prediction model, and the two machine learning GDM prediction models could be employed to identify pregnant women with a high risk of GDM using common clinical indicators, and interventions can be sought promptly. MDPI 2022-10-29 /pmc/articles/PMC9697464/ /pubmed/36355123 http://dx.doi.org/10.3390/metabo12111040 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wang, Ning Guo, Haonan Jing, Yingyu Song, Lin Chen, Huan Wang, Mengjun Gao, Lei Huang, Lili Song, Yanan Sun, Bo Cui, Wei Xu, Jing Development and Validation of Risk Prediction Models for Gestational Diabetes Mellitus Using Four Different Methods |
title | Development and Validation of Risk Prediction Models for Gestational Diabetes Mellitus Using Four Different Methods |
title_full | Development and Validation of Risk Prediction Models for Gestational Diabetes Mellitus Using Four Different Methods |
title_fullStr | Development and Validation of Risk Prediction Models for Gestational Diabetes Mellitus Using Four Different Methods |
title_full_unstemmed | Development and Validation of Risk Prediction Models for Gestational Diabetes Mellitus Using Four Different Methods |
title_short | Development and Validation of Risk Prediction Models for Gestational Diabetes Mellitus Using Four Different Methods |
title_sort | development and validation of risk prediction models for gestational diabetes mellitus using four different methods |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9697464/ https://www.ncbi.nlm.nih.gov/pubmed/36355123 http://dx.doi.org/10.3390/metabo12111040 |
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