Influencing factors and construction of a predictive model for neurodevelopmental outcomes in preterm low birth weight infants

影响早产低出生体重儿神经发育结果的因素及预测模型的构建

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Abstract

OBJECTIVE: To identify risk factors for adverse neurodevelopmental outcomes (ANO) in preterm low birth weight (PLBW) infants and to develop a predictive model. METHODS: A retrospective analysis was conducted on 146 PLBW infants who underwent neurodevelopmental assessment at the Women and Children's Hospital of Ningbo University between August 2022 and August 2024. Neurodevelopmental status was evaluated using the Bayley Scales of Infant Development at a corrected age of two years. Infants were classified into either a normal neurodevelopmental outcome (NNO, n=85) group or an ANO group (n=61), according to their Bayley Mental Index scores. Data collected included demographic information, maternal health data, neonatal cranial ultrasound findings, and neonatal complications. Multivariate logistic regression was used to identify significant predictors, and receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive performance of the model. RESULTS: There were no significant differences in baseline demographic factors such as gestational age or birth weight between the two groups (all P>0.05). However, the ANO group exhibited a significantly higher incidence of hemorrhagic and ischemic cerebral lesions, bronchopulmonary dysplasia, and required longer durations of mechanical ventilation and oxygen therapy (all P<0.05). Multivariate logistic regression confirmed these factors as independent predictors of ANO. The composite predictive model, incorporating these variables, achieved an area under the AUC of 0.833, indicating good predictive accuracy. Validation with an external sample of 71 infants yielded an AUC of 0.854, demonstrating good agreement between predicted and observed outcomes. CONCLUSION: Cerebral lesions and respiratory complications were significant predictors of adverse neurodevelopmental outcomes in PLBW infants. The developed predictive model may support early identification of high-risk infants and facilitate timely interventions to improve long-term neurodevelopmental outcomes.

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