机构：[1]Department of Neonatology, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, China.首都医科大学附属北京儿童医院[2]Department of Neonatology, The Affiliated Hospital of Southwest Medical University, Sichuan, China.[3]Department of Neonatology, The First Affiliated Hospital of Nanchang University, Jiangxi, China.[4]Department of Neonatology, The Affiliated Hospital Inner Mongolia Medical University, Inner Mongolia, China.[5]Department of Neonatology, Wuhan Woman and Children Medical Care Center, Hubei, China.[6]Department of Neonatology, Guilin Maternal and Child Health Hospital, Guangxi, China.[7]Department of Neonatology, The First Affiliated Hospital of Xinjiang Medical University, Xinjiang, China.[8]Department of Neonatology, Xinjiang Uygur Autonomous Region People’s Hospital, Xinjiang, China.[9]Department of Neonatology, Hainan Women and Children’s Medical Center, Hainan, China.[10]Department of Neonatology, Qilu Hospital of Shandong University, Shandong, China.[11]Department of Neonatology, Hunan Maternal and Child Health Care Hospital, Hunan, China.[12]Department of Neonatology, Chengdu Woman’s and Children’s Center Hospital, Sichuan, China.[13]Department of Neonatology, Kunming Children’s Hospital, Yunnan, China.[14]Department of Neonatology, Fourth Hospital of Hebei Medical University, Hebei, China.河北医科大学第四医院
BackgroundExtremely preterm infants (EPIs) are at high-risk of white matter injury (WMI), leading to long-term neurodevelopmental impairments. We aimed to develop nomograms for WMI.MethodsThe study included patients from 31 provinces, spanning ten years. 6074 patients before 2018 were randomly divided into a training and internal validation group (7:3). The external validation group comprised 1492 patients from 2019. Predictors were identified using the least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression and nomograms were constructed. Models' performance was evaluated using receiver operating characteristic (ROC), decision curve analysis (DCA) and calibration curves.ResultsThe prenatal nomogram included multiple gestation, premature rupture of membranes (PROM), chorioamnionitis, prenatal glucocorticoids, hypertensive disorder complicating pregnancy (HDCP) and Apgar 1 min, with area under the curve (AUC) of 0.805, 0.816 and 0.799 in the training, internal validation and external validation group, respectively. Days of mechanical ventilation (MV), shock, patent ductus arteriosus (PDA) ligation, intraventricular hemorrhage (IVH) grade III-IV, septicemia, hypothermia and necrotizing enterocolitis (NEC) stage II-III were identified as postpartum predictors. The AUCs were 0.791, 0.813 and 0.823 in the three groups, respectively. DCA and calibration curves showed good clinical utility and consistency.ConclusionThe two nomograms provide clinicians with precise and efficient tools for prediction of WMI.ImpactThis study is a large-sample multicenter study, spanning 10 years. The two nomograms are convenient for identifying high-risk infants early, allowing for reducing poor prognosis.
基金：
National Natural Science Foundation of China [82001596]
第一作者机构：[1]Department of Neonatology, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, China.
通讯作者：
推荐引用方式(GB/T 7714)：
Song Shuting,Zhu Zhicheng,Zhang Ke,et al.Two risk assessment models for predicting white matter injury in extremely preterm infants[J].PEDIATRIC RESEARCH.2025,97(1):246-252.doi:10.1038/s41390-024-03402-1.
APA：
Song, Shuting,Zhu, Zhicheng,Zhang, Ke,Xiao, Mili,Gao, Ruiwei...&Zhu, Li.(2025).Two risk assessment models for predicting white matter injury in extremely preterm infants.PEDIATRIC RESEARCH,97,(1)
MLA：
Song, Shuting,et al."Two risk assessment models for predicting white matter injury in extremely preterm infants".PEDIATRIC RESEARCH 97..1(2025):246-252
