Shui Zhongping, Liu Ying, Duan Haimei, Sun Qiuyi, He Huan, He Huayun, Wang Jianhui, Yin Huaying
Department of Neonatology, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China.
Department of Child Health Care, Children's Hospital of Chongqing Medical University, Chongqing, China.
Sci Rep. 2025 Aug 3;15(1):28325. doi: 10.1038/s41598-025-14085-x.
To construct a nomogram for feeding intolerance (FI) during therapeutic hypothermia (TH) in neonates with hypoxic-ischemic encephalopathy (HIE). 179 neonates with HIE were recruited between March 2017 and July 2023 and clinical data subjected to least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression analysis. A predictive model was constructed and verified by receiver operating characteristic (ROC) curve analysis, calibration plots and decision curve analysis (DCA). Neonatal infection, 5-min Apgar score, hypoglycemia, time of enteral nutrition initiation, initial enteral feeding volume (15-30 mL/kg/day) and rate of feeding advancement (1-5 mL/kg/day) were found to be independent predictors for FI. Earlier initiation, larger initial volume and rapid feeding progression increased FI risk and slow advancement was protective. ROC analysis gave an area under the curve (AUC) of 0.83 (95% CI: 0.77-0.89) and internal verification concordance index (C-index) was 0.829. DCA showed a favorable net clinical benefit for the FI predictive model. The predictive model may identify the causes of FI at an early stage and inform clinical decisions.
构建用于预测缺氧缺血性脑病（HIE）新生儿治疗性低温（TH）期间喂养不耐受（FI）的列线图。2017年3月至2023年7月招募了179例HIE新生儿，对其临床数据进行最小绝对收缩和选择算子（LASSO）回归及多变量逻辑回归分析。通过受试者操作特征（ROC）曲线分析、校准图和决策曲线分析（DCA）构建并验证预测模型。发现新生儿感染、5分钟阿氏评分、低血糖、肠内营养开始时间、初始肠内喂养量（15 - 30 mL/kg/天）和喂养推进速度（1 - 5 mL/kg/天）是FI的独立预测因素。更早开始、更大初始量和快速喂养推进增加FI风险，缓慢推进具有保护作用。ROC分析得出曲线下面积（AUC）为0.83（95%CI：0.77 - 0.89），内部验证一致性指数（C指数）为0.829。DCA显示FI预测模型具有良好的净临床效益。该预测模型可早期识别FI的原因并为临床决策提供依据。
