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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Metabolites |
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| Online Access: | https://www.mdpi.com/2218-1989/12/11/1040 |
| _version_ | 1797467279784411136 |
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| author | Ning Wang Haonan Guo Yingyu Jing Lin Song Huan Chen Mengjun Wang Lei Gao Lili Huang Yanan Song Bo Sun Wei Cui Jing Xu |
| author_facet | Ning Wang Haonan Guo Yingyu Jing Lin Song Huan Chen Mengjun Wang Lei Gao Lili Huang Yanan Song Bo Sun Wei Cui Jing Xu |
| author_sort | Ning Wang |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T18:51:22Z |
| format | Article |
| id | doaj.art-e80860602fe84bde9479e05c0095df37 |
| institution | Directory Open Access Journal |
| issn | 2218-1989 |
| language | English |
| last_indexed | 2024-03-09T18:51:22Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metabolites |
| spelling | doaj.art-e80860602fe84bde9479e05c0095df372023-11-24T05:50:03ZengMDPI AGMetabolites2218-19892022-10-011211104010.3390/metabo12111040Development and Validation of Risk Prediction Models for Gestational Diabetes Mellitus Using Four Different MethodsNing Wang0Haonan Guo1Yingyu Jing2Lin Song3Huan Chen4Mengjun Wang5Lei Gao6Lili Huang7Yanan Song8Bo Sun9Wei Cui10Jing Xu11Department of Endocrinology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710004, ChinaDepartment of Endocrinology and Second Department of Geriatrics, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, ChinaDepartment of Endocrinology and Second Department of Geriatrics, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, ChinaDepartment of Physiology and Pathophysiology, School of Basic Medical Sciences, Xi’an Jiaotong University Health Science Center, Xi’an 710061, ChinaDepartment of Endocrinology and Second Department of Geriatrics, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, ChinaDepartment of Physiology and Pathophysiology, School of Basic Medical Sciences, Xi’an Jiaotong University Health Science Center, Xi’an 710061, ChinaDepartment of Endocrinology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710004, ChinaDepartment of Medical Ultrasound, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710004, ChinaDepartment of Endocrinology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710004, ChinaDepartment of Physiology and Pathophysiology, School of Basic Medical Sciences, Xi’an Jiaotong University Health Science Center, Xi’an 710061, ChinaInternational Center for Obesity and Metabolic Disease Research of Xi’an Jiaotong University, Xi’an 710061, ChinaDepartment of Endocrinology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710004, ChinaGestational 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.https://www.mdpi.com/2218-1989/12/11/1040gestational diabetes mellitusprediction modelsrisk factorsearly pregnancy |
| spellingShingle | Ning Wang Haonan Guo Yingyu Jing Lin Song Huan Chen Mengjun Wang Lei Gao Lili Huang Yanan Song Bo Sun Wei Cui Jing Xu Development and Validation of Risk Prediction Models for Gestational Diabetes Mellitus Using Four Different Methods Metabolites gestational diabetes mellitus prediction models risk factors early pregnancy |
| 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 | gestational diabetes mellitus prediction models risk factors early pregnancy |
| url | https://www.mdpi.com/2218-1989/12/11/1040 |
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