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ORIGINAL RESEARCH article

Front. Pediatr.
Sec. Neonatology
Volume 12 - 2024 | doi: 10.3389/fped.2024.1381193

Construction and Validation of a Risk Prediction Model for Extrauterine Growth Restriction in Preterm Infants Born at Gestational Age Less Than 34 Weeks

Provisionally accepted
Yu Xie Yu Xie 1Zhihui Zhang Zhihui Zhang 2,3Mengmeng Luo Mengmeng Luo 4Yan Mo Yan Mo 5Qiufen Wei Qiufen Wei 5Laishuan Wang Laishuan Wang 6Rong Zhang Rong Zhang 6Hanlu Zhong Hanlu Zhong 1Yan Li Yan Li 5*
  • 1 Guangxi University of Chinese Medicine, Nanning, China
  • 2 JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, Hong Kong, SAR China
  • 3 Department of Applied Mathematics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, SAR China
  • 4 University of Liverpool, Liverpool, North West England, United Kingdom
  • 5 Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi Zhuang Region, China
  • 6 Children's Hospital, Fudan University, Shanghai, Shanghai Municipality, China

The final, formatted version of the article will be published soon.

    Objective: This study aimed to develop and validate a model for predicting extrauterine growth restriction (EUGR) in preterm infants born ≤34 weeks gestation.Methods: Preterm infants from Guangxi Maternal and Child Health Hospital (2019-2021) were randomly divided into training (80%) and testing (20%) sets. Collinear clinical variables were excluded using Pearson coefficients. Predictive factors were identified using Lasso and logistic regression (LR). Random forest (RF), support vector machine (SVM), and multivariate logistic regression (MLR) models were built and evaluated using confusion matrix, area under the curve (AUC), and F1 score.The study included 387 infants, with no significant baseline differences between training (n=310) and testing (n=77) sets. LR identified gestational age, birth weight, premature rupture of membranes, patent ductus arteriosus, cholestasis, and neonatal sepsis as key EUGR predictors. The RF model (19 variables) showed 99.99% accuracy in training, and superior AUC (0.62), F1 score (0.80), and accuracy (0.72) in testing compared to other models.Gestational age, birth weight, premature rupture of membranes, patent ductus arteriosus, cholestasis, and neonatal sepsis are significant EUGR predictors in preterm infants ≤34 weeks. The model shows promise for early EUGR prediction in clinical practice, potentially enhancing screening efficiency and accuracy, thus saving medical resources.

    Keywords: preterm infants, EUGR, Prediction model, random forest, LASSO regression

    Received: 03 Feb 2024; Accepted: 05 Aug 2024.

    Copyright: © 2024 Xie, Zhang, Luo, Mo, Wei, Wang, Zhang, Zhong and Li. 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) or licensor 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.

    * Correspondence: Yan Li, Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi Zhuang Region, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.