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Development of a prediction model for neonatal hypoglycemia risk factors: a retrospective study

BACKGROUND: It’s challenging for healthcare workers to detect neonatal hypoglycemia due to its rapid progression and lack of aura symptoms. This may lead to brain function impairment for the newborn, placing a significant care burden on the family and creating an economic burden for society. Tools f...

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Autores principales: Wu, Tian, Huang, Yi-Yan, Song, Wei, Redding, Sharon R., Huang, Wei-Peng, Ouyang, Yan-Qiong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10389046/
https://www.ncbi.nlm.nih.gov/pubmed/37529595
http://dx.doi.org/10.3389/fendo.2023.1199628
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author Wu, Tian
Huang, Yi-Yan
Song, Wei
Redding, Sharon R.
Huang, Wei-Peng
Ouyang, Yan-Qiong
author_facet Wu, Tian
Huang, Yi-Yan
Song, Wei
Redding, Sharon R.
Huang, Wei-Peng
Ouyang, Yan-Qiong
author_sort Wu, Tian
collection PubMed
description BACKGROUND: It’s challenging for healthcare workers to detect neonatal hypoglycemia due to its rapid progression and lack of aura symptoms. This may lead to brain function impairment for the newborn, placing a significant care burden on the family and creating an economic burden for society. Tools for early diagnosis of neonatal hypoglycemia are lacking. This study aimed to identify newborns at high risk of developing neonatal hypoglycemia early by developing a risk prediction model. METHODS: Using a retrospective design, pairs (470) of women and their newborns in a tertiary hospital from December 2021 to September 2022 were included in this study. Socio-demographic data and clinical data of mothers and newborns were collected. Univariate and multivariate logistic regression were used to screen optimized factors. A neonatal hypoglycemia risk nomogram was constructed using R software, and the calibration curve and receiver operator characteristic curve (ROC) was utilized to evaluate model performance. RESULTS: Factors integrated into the prediction risk nomogram were maternal age (odds ratio [OR] =1.10, 95% CI: 1.04, 1.17), fasting period (OR=1.07, 95% CI: 1.03, 1.12), ritodrine use (OR=2.00, 95% CI: 1.05, 3.88), gestational diabetes mellitus (OR=2.13, 95% CI: 1.30, 3.50), gestational week (OR=0.80, 95% CI: 0.66, 0.96), fetal distress (OR=1.76, 95% CI: 1.11, 2.79) and neonatal body mass index (OR=1.50, 95% CI: 1.24, 1.84). The area under the curve (AUC) was 0.79 (95% confidence interval [CI]: 0.75, 0.82), specificity was 0.82, and sensitivity was 0.62. CONCLUSION: The prediction model of this study demonstrated good predictive performance. The development of the model identifies advancing maternal age, an extended fasting period before delivery, ritodrine use, gestational diabetes mellitus diagnosis, fetal distress diagnosis and an increase in neonatal body mass index increase the probability of developing neonatal hypoglycemia, while an extended gestational week reduces the probability of developing neonatal hypoglycemia.
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spelling pubmed-103890462023-08-01 Development of a prediction model for neonatal hypoglycemia risk factors: a retrospective study Wu, Tian Huang, Yi-Yan Song, Wei Redding, Sharon R. Huang, Wei-Peng Ouyang, Yan-Qiong Front Endocrinol (Lausanne) Endocrinology BACKGROUND: It’s challenging for healthcare workers to detect neonatal hypoglycemia due to its rapid progression and lack of aura symptoms. This may lead to brain function impairment for the newborn, placing a significant care burden on the family and creating an economic burden for society. Tools for early diagnosis of neonatal hypoglycemia are lacking. This study aimed to identify newborns at high risk of developing neonatal hypoglycemia early by developing a risk prediction model. METHODS: Using a retrospective design, pairs (470) of women and their newborns in a tertiary hospital from December 2021 to September 2022 were included in this study. Socio-demographic data and clinical data of mothers and newborns were collected. Univariate and multivariate logistic regression were used to screen optimized factors. A neonatal hypoglycemia risk nomogram was constructed using R software, and the calibration curve and receiver operator characteristic curve (ROC) was utilized to evaluate model performance. RESULTS: Factors integrated into the prediction risk nomogram were maternal age (odds ratio [OR] =1.10, 95% CI: 1.04, 1.17), fasting period (OR=1.07, 95% CI: 1.03, 1.12), ritodrine use (OR=2.00, 95% CI: 1.05, 3.88), gestational diabetes mellitus (OR=2.13, 95% CI: 1.30, 3.50), gestational week (OR=0.80, 95% CI: 0.66, 0.96), fetal distress (OR=1.76, 95% CI: 1.11, 2.79) and neonatal body mass index (OR=1.50, 95% CI: 1.24, 1.84). The area under the curve (AUC) was 0.79 (95% confidence interval [CI]: 0.75, 0.82), specificity was 0.82, and sensitivity was 0.62. CONCLUSION: The prediction model of this study demonstrated good predictive performance. The development of the model identifies advancing maternal age, an extended fasting period before delivery, ritodrine use, gestational diabetes mellitus diagnosis, fetal distress diagnosis and an increase in neonatal body mass index increase the probability of developing neonatal hypoglycemia, while an extended gestational week reduces the probability of developing neonatal hypoglycemia. Frontiers Media S.A. 2023-07-17 /pmc/articles/PMC10389046/ /pubmed/37529595 http://dx.doi.org/10.3389/fendo.2023.1199628 Text en Copyright © 2023 Wu, Huang, Song, Redding, Huang and Ouyang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Endocrinology
Wu, Tian
Huang, Yi-Yan
Song, Wei
Redding, Sharon R.
Huang, Wei-Peng
Ouyang, Yan-Qiong
Development of a prediction model for neonatal hypoglycemia risk factors: a retrospective study
title Development of a prediction model for neonatal hypoglycemia risk factors: a retrospective study
title_full Development of a prediction model for neonatal hypoglycemia risk factors: a retrospective study
title_fullStr Development of a prediction model for neonatal hypoglycemia risk factors: a retrospective study
title_full_unstemmed Development of a prediction model for neonatal hypoglycemia risk factors: a retrospective study
title_short Development of a prediction model for neonatal hypoglycemia risk factors: a retrospective study
title_sort development of a prediction model for neonatal hypoglycemia risk factors: a retrospective study
topic Endocrinology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10389046/
https://www.ncbi.nlm.nih.gov/pubmed/37529595
http://dx.doi.org/10.3389/fendo.2023.1199628
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