Analysis of Risk Factors for Brain Injury in Infants With Small Gestational Age and Construction and Evaluation of Prediction Model

IF 1.6 4区医学 Q2 PEDIATRICS

Journal of paediatrics and child health Pub Date : 2025-02-27 DOI:10.1111/jpc.70019

Qunfang Shi, Ye Yang, Ling Wang, Yu Wang

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引用次数: 0

Abstract

Aims

To evaluate brain injury risk factors in small for gestational age (SGA) infants, develop and validate a risk prediction model.

Methods

Medical records from 326 SGA infants in the Neonatal Intensive Care Unit at Hebei University's Affiliated Hospital from September 2019 to September 2024 were reviewed. The infants were categorised into brain injury (n = 135) and no brain injury (n = 191) groups based on cranial MRI outcomes. Risk factors for brain injuries in SGA infants were identified through both univariate and multivariate logistic regression analyses, followed by constructing a nomogram to visualise these risk determinants. The predictive model's calibration, discrimination, clinical net benefit, and clinical usefulness were verified using calibration and ROC curves, along with decision curve analysis (DCA) and clinical impact curves (CIC).

Results

Significant risk factors included advanced maternal age, gestational hypertension (GH), fetal distress, reduced gestational age, neonatal septicemia, abnormal platelet counts and elevated neutrophil-to-lymphocyte ratio (NLR). The calibration curve and Hosmer–Lemeshow test verified the model's high accuracy. The model demonstrated good discrimination with an ROC curve AUC of 87.4%. Both DCA and CIC evaluations indicated the model's high clinical utility.

Conclusion

The developed multivariate logistic regression model effectively predicts the risk of craniocerebral injuries in SGA infants, serving as a valuable tool for early identification of at-risk neonates.

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小胎龄儿脑损伤危险因素分析及预测模型的构建与评价。

目的：评价小胎龄儿脑损伤的危险因素，建立并验证风险预测模型。方法：回顾河北大学附属医院2019年9月至2024年9月新生儿重症监护病房326例SGA患儿的病历。根据颅脑MRI结果将婴儿分为脑损伤组（n = 135）和无脑损伤组（n = 191）。通过单变量和多变量逻辑回归分析确定SGA婴儿脑损伤的危险因素，然后构建一个nomogram来可视化这些风险决定因素。采用校正曲线、ROC曲线、决策曲线分析（DCA）和临床影响曲线（CIC）对预测模型的校正、鉴别、临床净效益和临床有用性进行验证。结果：产妇高龄、妊娠期高血压（GH）、胎儿窘迫、胎龄降低、新生儿败血症、血小板计数异常、中性粒细胞与淋巴细胞比值（NLR）升高是显著危险因素。标定曲线和Hosmer-Lemeshow试验验证了该模型具有较高的精度。该模型具有良好的判别性，ROC曲线AUC为87.4%。DCA和CIC评价均表明该模型具有较高的临床应用价值。结论：建立的多变量logistic回归模型可有效预测SGA患儿颅脑损伤风险，为早期识别高危新生儿提供了有价值的工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of paediatrics and child health 医学-小儿科

CiteScore

2.90

自引率

5.90%

发文量

487

审稿时长

3-6 weeks

期刊介绍： The Journal of Paediatrics and Child Health publishes original research articles of scientific excellence in paediatrics and child health. Research Articles, Case Reports and Letters to the Editor are published, together with invited Reviews, Annotations, Editorial Comments and manuscripts of educational interest.