Pan Yibo, Min Chen, Li Jing, Lin Wang, Li Baiyu, Wang Yuchao, Zhang Shenhao, Jing Wang, Dang Xiangguo
Department of Coronary Care Unit, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.
School of Nursing, Shandong First Medical University, Jinan, China.
Medicine (Baltimore). 2025 Jun 27;104(26):e42993. doi: 10.1097/MD.0000000000042993.
This study aims to develop and validate a risk prediction model for enteral nutrition feeding intolerance (ENFI) in patients with severe traumatic brain injury (STBI), providing a foundation for the prevention and management of ENFI in this population. STBI is a prevalent acute and severe condition encountered in neurosurgery. STBI patients are prone to diarrhea, reflux, and other manifestations of feeding intolerance during enteral nutrition, which not only affects the patient's systemic therapy and prolongs hospital stay, but also increases the risk of infection. We conducted a retrospective cohort study. Clinical assessment and data collection were obtained through an electronic medical record system. Data were collected from January 2019 to July 2023, we conducted a retrospective analysis of patients with STBI who met the inclusion criteria but did not meet the exclusion criteria, formed the development cohort and validation cohort. The dynamic nomogram was constructed and validated in R software. A total of 302 patients in the development cohort and 107 patients in the validation cohort were included, with incidences of ENFI at 50.7% and 56.1%, respectively. We developed a dynamic nomogram in patients with STBI and the mean arterial pressure, mechanical ventilation, intake and output, and combined antibiotics were independent predictors of ENFI. The C-index and the Hosmer-Lemeshow indicated good calibration; The calibration curve showed strong consistency between actual and predicted outcomes. The decision curve analysis confirmed the model's clinical utility. The prediction of enteral feeding intolerance can be conveniently facilitated by the ENFI which integrates general information, condition monitoring, and therapeutic factors in patients with STBI. Based on the dynamic nomogram, medical and nursing staff in the intensive care unit can assess patients at high risk for ENFI at an early stage. This has the potential to prevent the occurrence of ENFI and enhance various clinical outcomes for patients.
本研究旨在开发并验证一种针对重型颅脑损伤(STBI)患者肠内营养喂养不耐受(ENFI)的风险预测模型,为该人群ENFI的预防和管理提供依据。STBI是神经外科常见的急性重症疾病。STBI患者在肠内营养期间容易出现腹泻、反流及其他喂养不耐受表现,这不仅影响患者的全身治疗、延长住院时间,还增加感染风险。我们进行了一项回顾性队列研究。通过电子病历系统进行临床评估和数据收集。收集2019年1月至2023年7月的数据,对符合纳入标准但不符合排除标准的STBI患者进行回顾性分析,形成开发队列和验证队列。在R软件中构建并验证动态列线图。开发队列纳入302例患者,验证队列纳入107例患者,ENFI发生率分别为50.7%和56.1%。我们在STBI患者中开发了一种动态列线图,平均动脉压、机械通气、出入量及联合使用抗生素是ENFI的独立预测因素。C指数和Hosmer-Lemeshow检验显示校准良好;校准曲线显示实际结果与预测结果之间具有很强的一致性。决策曲线分析证实了该模型的临床实用性。整合STBI患者一般信息、病情监测和治疗因素的ENFI可方便地促进肠内喂养不耐受的预测。基于动态列线图,重症监护病房的医护人员可在早期评估ENFI高危患者。这有可能预防ENFI的发生并改善患者的各项临床结局。