AUTHOR=Xie Yu , Zhang Zhihui , Luo Mengmeng , Mo Yan , Wei Qiufen , Wang Laishuan , Zhang Rong , Zhong Hanlu , Li Yan 

TITLE=Construction and validation of a risk prediction model for extrauterine growth restriction in preterm infants born at gestational age less than 34 weeks

JOURNAL=Frontiers in Pediatrics

VOLUME=Volume 12 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2024.1381193

DOI=10.3389/fped.2024.1381193

ISSN=2296-2360

ABSTRACT=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.