Artificial neural network modeling to predict neonatal metabolic bone disease in the pre- and postnatal period

Jiang, H., Guo, J., Li, J., Li, C., Du, V. ORCID: 0000-0002-5115-7214, Canavese, F., Baker, C., Hua, J. and Ying, H., 2023. Artificial neural network modeling to predict neonatal metabolic bone disease in the pre- and postnatal period. JAMA Network Open, 6 (1): e2251849. ISSN 2574-3805

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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: 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.

Item Type: Journal article
Publication Title: JAMA Network Open
Creators: Jiang, H., Guo, J., Li, J., Li, C., Du, V., Canavese, F., Baker, C., Hua, J. and Ying, H.
Publisher: American Medical Association
Date: January 2023
Volume: 6
Number: 1
ISSN: 2574-3805
Rights: © 2023 Jiang H. et al. This is an open access article distributed under the terms of the CC-BY License.
Divisions: Schools > School of Social Sciences
Record created by: Laura Ward
Date Added: 09 Dec 2022 10:46
Last Modified: 31 Jan 2023 11:51

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