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Development and validation of risk prediction models for large for gestational age infants using logistic regression and two machine learning algorithms

BACKGROUND: Large for gestational age (LGA) is one of the adverse outcomes during pregnancy that endangers the life and health of mothers and offspring. We aimed to establish prediction models for LGA at late pregnancy. METHODS: Data were obtained from an established Chinese pregnant women cohort of...

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Autores principales: Wang, Ning, Guo, Haonan, Jing, Yingyu, Zhang, Yifan, Sun, Bo, Pan, Xingyan, Chen, Huan, Xu, Jing, Wang, Mengjun, Chen, Xi, Song, Lin, Cui, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wiley Publishing Asia Pty Ltd 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10101839/
https://www.ncbi.nlm.nih.gov/pubmed/36890429
http://dx.doi.org/10.1111/1753-0407.13375
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author Wang, Ning
Guo, Haonan
Jing, Yingyu
Zhang, Yifan
Sun, Bo
Pan, Xingyan
Chen, Huan
Xu, Jing
Wang, Mengjun
Chen, Xi
Song, Lin
Cui, Wei
author_facet Wang, Ning
Guo, Haonan
Jing, Yingyu
Zhang, Yifan
Sun, Bo
Pan, Xingyan
Chen, Huan
Xu, Jing
Wang, Mengjun
Chen, Xi
Song, Lin
Cui, Wei
author_sort Wang, Ning
collection PubMed
description BACKGROUND: Large for gestational age (LGA) is one of the adverse outcomes during pregnancy that endangers the life and health of mothers and offspring. We aimed to establish prediction models for LGA at late pregnancy. METHODS: Data were obtained from an established Chinese pregnant women cohort of 1285 pregnant women. LGA was diagnosed as >90th percentile of birth weight distribution of Chinese corresponding to gestational age of the same‐sex newborns. Women with gestational diabetes mellitus (GDM) were classified into three subtypes according to the indexes of insulin sensitivity and insulin secretion. Models were established by logistic regression and decision tree/random forest algorithms, and validated by the data. RESULTS: A total of 139 newborns were diagnosed as LGA after birth. The area under the curve (AUC) for the training set is 0.760 (95% confidence interval [CI] 0.706–0.815), and 0.748 (95% CI 0.659–0.837) for the internal validation set of the logistic regression model, which consisted of eight commonly used clinical indicators (including lipid profile) and GDM subtypes. For the prediction models established by the two machine learning algorithms, which included all the variables, the training set and the internal validation set had AUCs of 0.813 (95% CI 0.786–0.839) and 0.779 (95% CI 0.735–0.824) for the decision tree model, and 0.854 (95% CI 0.831–0.877) and 0.808 (95% CI 0.766–0.850) for the random forest model. CONCLUSION: We established and validated three LGA risk prediction models to screen out the pregnant women with high risk of LGA at the early stage of the third trimester, which showed good prediction power and could guide early prevention strategies.
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spelling pubmed-101018392023-04-15 Development and validation of risk prediction models for large for gestational age infants using logistic regression and two machine learning algorithms Wang, Ning Guo, Haonan Jing, Yingyu Zhang, Yifan Sun, Bo Pan, Xingyan Chen, Huan Xu, Jing Wang, Mengjun Chen, Xi Song, Lin Cui, Wei J Diabetes Original Articles BACKGROUND: Large for gestational age (LGA) is one of the adverse outcomes during pregnancy that endangers the life and health of mothers and offspring. We aimed to establish prediction models for LGA at late pregnancy. METHODS: Data were obtained from an established Chinese pregnant women cohort of 1285 pregnant women. LGA was diagnosed as >90th percentile of birth weight distribution of Chinese corresponding to gestational age of the same‐sex newborns. Women with gestational diabetes mellitus (GDM) were classified into three subtypes according to the indexes of insulin sensitivity and insulin secretion. Models were established by logistic regression and decision tree/random forest algorithms, and validated by the data. RESULTS: A total of 139 newborns were diagnosed as LGA after birth. The area under the curve (AUC) for the training set is 0.760 (95% confidence interval [CI] 0.706–0.815), and 0.748 (95% CI 0.659–0.837) for the internal validation set of the logistic regression model, which consisted of eight commonly used clinical indicators (including lipid profile) and GDM subtypes. For the prediction models established by the two machine learning algorithms, which included all the variables, the training set and the internal validation set had AUCs of 0.813 (95% CI 0.786–0.839) and 0.779 (95% CI 0.735–0.824) for the decision tree model, and 0.854 (95% CI 0.831–0.877) and 0.808 (95% CI 0.766–0.850) for the random forest model. CONCLUSION: We established and validated three LGA risk prediction models to screen out the pregnant women with high risk of LGA at the early stage of the third trimester, which showed good prediction power and could guide early prevention strategies. Wiley Publishing Asia Pty Ltd 2023-03-08 /pmc/articles/PMC10101839/ /pubmed/36890429 http://dx.doi.org/10.1111/1753-0407.13375 Text en © 2023 The Authors. Journal of Diabetes published by Ruijin Hospital, Shanghai JiaoTong University School of Medicine and John Wiley & Sons Australia, Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Articles
Wang, Ning
Guo, Haonan
Jing, Yingyu
Zhang, Yifan
Sun, Bo
Pan, Xingyan
Chen, Huan
Xu, Jing
Wang, Mengjun
Chen, Xi
Song, Lin
Cui, Wei
Development and validation of risk prediction models for large for gestational age infants using logistic regression and two machine learning algorithms
title Development and validation of risk prediction models for large for gestational age infants using logistic regression and two machine learning algorithms
title_full Development and validation of risk prediction models for large for gestational age infants using logistic regression and two machine learning algorithms
title_fullStr Development and validation of risk prediction models for large for gestational age infants using logistic regression and two machine learning algorithms
title_full_unstemmed Development and validation of risk prediction models for large for gestational age infants using logistic regression and two machine learning algorithms
title_short Development and validation of risk prediction models for large for gestational age infants using logistic regression and two machine learning algorithms
title_sort development and validation of risk prediction models for large for gestational age infants using logistic regression and two machine learning algorithms
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10101839/
https://www.ncbi.nlm.nih.gov/pubmed/36890429
http://dx.doi.org/10.1111/1753-0407.13375
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