Chen Lishan, Zhou Huichang, Tang Zhiming, Deng Haiyin, Li Zhihao
Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou City, China.
Department of Rehabilitation Medicine, The First People's Hospital of Foshan City, Foshan City, China.
Front Pediatr. 2025 May 14;13:1562778. doi: 10.3389/fped.2025.1562778. eCollection 2025.
To investigate the influencing factors associated with feeding disorders in preterm infants and to construct a prediction model.
314 cases of preterm infants admitted to our hospital from January 2019 to December 2022 were retrospectively analyzed and divided into feeding disorder group and non-feeding disorder group according to the presence of feeding disorder at 37 weeks of corrected gestational age. Statistical analysis of children's general information, hospitalization measures, laboratory tests, feeding time, etc. Multifactorial Logistic regression analysis of the occurrence of feeding disorders related to the influence of factors, and the use of subjects to make a work characteristic curve to analyze the predictive value of the relevant factors on feeding disorders.
Multifactorial logistic regression analysis suggested that lower birth gestational age, birth weight, white blood cell count, absolute value of monocytes, blood calcium value, Apgar score at 1 min after birth, and longer duration of noninvasive ventilation were risk factors for feeding disorders in preterm infants. ROC curve analysis suggested that the area under the curve of the feeding disorders was predicted by the combination of the above seven indexes to construct the feeding disorders prediction model The AUC was 0.866 ( < 0.001, 95% CI 0.801-0.932), and it had a maximum Yoden index of 0.699, an optimal cutoff value of 0.169, a sensitivity of 85.4%, a specificity of 84.5%, and a prediction accuracy of 91.4%.
Lower birth gestational age, birth weight, white blood cell count, absolute monocyte value, blood calcium value, low Apgar score at 1 min after birth, and prolonged noninvasive ventilation are risk factors for feeding disorders in preterm infants, and the present prediction model is a good predictor of the occurrence of feeding disorders in preterm infants.
探讨早产儿喂养障碍的相关影响因素并构建预测模型。
回顾性分析2019年1月至2022年12月我院收治的314例早产儿，根据矫正胎龄37周时是否存在喂养障碍分为喂养障碍组和无喂养障碍组。对患儿的一般资料、住院措施、实验室检查、喂养时间等进行统计分析。对与喂养障碍发生相关的因素进行多因素Logistic回归分析，并采用受试者工作特征曲线分析相关因素对喂养障碍的预测价值。
多因素Logistic回归分析提示，出生胎龄小、出生体重低、白细胞计数、单核细胞绝对值、血钙值、出生后1分钟Apgar评分低以及无创通气时间长是早产儿喂养障碍的危险因素。ROC曲线分析提示，由上述7项指标联合构建的喂养障碍预测模型预测喂养障碍的曲线下面积AUC为0.866（<0.001，95%CI 0.801-0.932），最大约登指数为0.699，最佳截断值为0.169，灵敏度为85.4%，特异度为84.5%，预测准确率为91.4%。
出生胎龄小、出生体重低、白细胞计数、单核细胞绝对值、血钙值、出生后1分钟Apgar评分低以及无创通气时间延长是早产儿喂养障碍的危险因素，本预测模型对早产儿喂养障碍的发生有较好的预测作用。
