Zhang Baoquan, Xiu Wenlong, Wei Enhuan, Zhong Ronghua, Wei Chunhui, Wang Qifan, Zheng Jianmin, Yan Zheng, Wu Xiaoying, Yang Changyi
Department of Neonatology, Fujian Maternity and Child Health Hospital College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou 350000, China.
Department of Neonatology, Affiliated Sanming First Hospital, Fujian Medical University, Sanming 365000, China.
Dig Liver Dis. 2025 Jan;57(1):231-240. doi: 10.1016/j.dld.2024.08.038. Epub 2024 Sep 2.
To construct a nomogram for predicting necrotizing enterocolitis (NEC) in preterm infants.
A total of 4,724 preterm infants who were admitted into 8 hospitals between April 2019 and September 2020 were initially enrolled this retrospective multicenter cohort study. Finally, 1,092 eligible cases were divided into training set and test set based on a 7:3 ratio. A univariate logistic regression analysis was performed to compare the variables between the two groups. Stepwise backward regression, LASSO regression, and Boruta feature selection were utilized in the multivariate analysis to identify independent risk factors. Then a nomogram model was constructed based on the identified risk factors.
Risk factors for NEC included gestational diabetes mellitus, gestational age, small for gestational age, patent ductus arteriosus, septicemia, red blood cell transfusion, intravenous immunoglobulin, severe feeding intolerance, and absence of breastfeeding. The nomogram model developed based on these factors showed well discriminative ability. Calibration and decision curve analysis curves confirmed the good consistency and clinical utility of the model.
We developed a nomogram model with strong discriminative ability, consistency, and clinical utility for predicting NEC. This model could be valuable for the early prediction of preterm infants at risk of developing NEC.
构建用于预测早产儿坏死性小肠结肠炎(NEC)的列线图。
本回顾性多中心队列研究初步纳入了2019年4月至2020年9月期间在8家医院收治的4724例早产儿。最终,1092例符合条件的病例按7:3的比例分为训练集和测试集。进行单因素逻辑回归分析以比较两组之间的变量。多因素分析采用逐步向后回归、LASSO回归和Boruta特征选择来识别独立危险因素。然后根据识别出的危险因素构建列线图模型。
NEC的危险因素包括妊娠期糖尿病、胎龄、小于胎龄儿、动脉导管未闭、败血症、红细胞输血、静脉注射免疫球蛋白、严重喂养不耐受以及未进行母乳喂养。基于这些因素建立的列线图模型具有良好的辨别能力。校准和决策曲线分析曲线证实了该模型具有良好的一致性和临床实用性。
我们开发了一种具有强大辨别能力、一致性和临床实用性的列线图模型用于预测NEC。该模型对于早期预测有发生NEC风险的早产儿可能具有重要价值。