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Artificial Neural Network Modeling to Predict Neonatal Metabolic Bone Disease in the Prenatal and Postnatal Periods
Honglin Jiang1,2, Jialin Guo1, Jing Li1
1Department of Mother and Children's Health Care, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, China.
Insights
Early recognition of infant metabolic bone disease (MBD) is crucial. Artificial neural networks effectively predict MBD risk using prenatal and postnatal factors, with extremely low birth weight being a key indicator.
Area of Science:
- Neonatal Health
- Pediatric Endocrinology
- Artificial Intelligence in Medicine
Background:
- Metabolic bone disease (MBD) in infants presents diagnostic challenges, necessitating effective screening tools.
- Early identification of infants at risk for MBD is critical for timely intervention and improved outcomes.
Purpose of the Study:
- To develop and validate predictive models for identifying neonates at risk of MBD.
- To pinpoint significant prenatal and postnatal factors contributing to MBD development.
Main Methods:
- A diagnostic study involving 10,801 pregnant women and their infants in Shanghai, China (2012-2021).
- Utilized an artificial neural network (ANN) framework to construct five predictive models.
- Evaluated model performance using receiver operating characteristic (ROC) curves and feature importance analysis.
Main Results:
- The combined prenatal and postnatal factors model (Model 1) achieved the highest Area Under the Curve (AUC) of 0.981.
- Extremely low birth weight was the most significant predictor (importance 50.5%), followed by maternal age and neonatal disorders.
- Magnesium sulfate use during pregnancy was the most influential prenatal factor (importance 21.2%).
Conclusions:
- Artificial neural networks (ANNs) provide an efficient tool for screening neonates at risk of MBD.
- Models integrating both prenatal and postnatal factors, or postnatal factors alone, offer the most accurate predictions.
- Extremely low birth weight and maternal magnesium sulfate use are key factors for MBD risk assessment.
Importance:
Early recognition of metabolic bone disease (MBD) in infants is necessary but difficult; an appropriate tool to screen infants at risk of developing MBD is needed.
Objectives:
To develop a predictive model for neonates at risk for MBD in the prenatal and postnatal periods and detect the pivotal exposed factors in each period.
Design, Setting, And Participants:
A diagnostic study was conducted from January 1, 2012, to December 31, 2021, in Shanghai, China. A total of 10 801 pregnant women (singleton pregnancy, followed up until 1 month after parturition) and their infants (n = 10 801) were included. An artificial neural network (ANN) framework was used to build 5 predictive models with different exposures from prenatal to postnatal periods. The receiver operating characteristic curve was used to evaluate the model performance. The importance of each feature was examined and ranked.
Results:
Of the 10 801 Chinese women who participated in the study (mean [SD] age, 29.7 [3.9] years), 7104 (65.8%) were local residents, 1001 (9.3%) had uterine scarring, and 138 (1.3%) gave birth to an infant with MBD. Among the 5 ANN models, model 1 (significant prenatal and postnatal factors) showed the highest AUC of 0.981 (95% CI, 0.970-0.992), followed by model 5 (postnatal factors; AUC, 0.977; 95% CI, 0.966-0.988), model 4 (all prenatal factors; AUC, 0.850; 95% CI, 0.785-0.915), model 3 (gestational complications or comorbidities and medication use; AUC, 0.808; 95% CI, 0.726-0.891), and model 2 (maternal nutritional conditions; AUC, 0.647; 95% CI, 0.571-0.723). Birth weight, maternal age at pregnancy, and neonatal disorders (anemia, respiratory distress syndrome, and septicemia) were the most important model 1 characteristics for predicting infants at risk of MBD; among these characteristics, extremely low birth weight (importance, 50.5%) was the most powerful factor. The use of magnesium sulfate during pregnancy (model 4: importance, 21.2%) was the most significant predictor of MBD risk in the prenatal period.
Conclusions And Relevance:
In this diagnostic study, ANN appeared to be a simple and efficient tool for identifying neonates at risk for MBD. Combining prenatal and postnatal factors or using postnatal exposures alone provided the most precise prediction. Extremely low birth weight was the most significant predictive factor, whereas magnesium sulfate use during pregnancy could be an important bellwether for MBD before delivery.
