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. 2023 Jul 17:14:1199628.
doi: 10.3389/fendo.2023.1199628. eCollection 2023.

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

Tian Wu  1   2 , Yi-Yan Huang  1   3 , Wei Song  2 , Sharon R Redding  4 , Wei-Peng Huang  5   6 , Yan-Qiong Ouyang  1
Affiliations

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

Tian Wu et al. Front Endocrinol (Lausanne). .

Abstract

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.

Keywords: neonatal hypoglycemia; nomogram; predictive model; retrospective study; risk factors.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flow chart of participant inclusion.
Figure 2
Figure 2
Forest plot of neonatal hypoglycemia.
Figure 3
Figure 3
Prediction nomogram for neonatal hypoglycemia.
Figure 4
Figure 4
Dynamic nomogram for neonatal hypoglycemia.
Figure 5
Figure 5
Calibration curve of nomogram.
Figure 6
Figure 6
Receiver operating characteristic.
See this image and copyright information in PMC

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