Abstract:

Objective To analyze the independent risk factors for enteral nutrition feeding intolerance （ENFI） in neurocritical patients， construct a risk prediction model， and validate its performance so as to provide a reference for the early clinical identification of high-risk patients. Methods A prospective cohort study was carried out on 248 neurocritical patients who were receiving enteral nutrition therapy at a tertiary hospital in Guangzhou from July 2023 to July 2025. Univariate and multivariate logistic regression analyses were conducted to identify independent risk factors. A prediction model was developed based on these factors， and its performance was assessed using the receiver operating characteristic （ROC） curve and decision curve analysis （DCA）. Results The incidence of FI was 67.3%. Multivariate analysis identified the following independent risk factors： the length of ICU stay （OR = 1.136， 95%CI： 1.043 - 1.238）， mechanical ventilation （OR = 2.831， 95%CI： 1.168 - 6.858）， targeted temperature management （OR = 3.595， 95%CI： 1.709 - 7.565）， the concomitant use of multiple sedatives （≥ 2 types） （OR = 2.786， 95%CI： 1.102 - 7.045）， and the use of dehydrating agents （OR = 3.692， 95%CI： 1.535 - 8.876）. The area under the ROC curve was 0.851 （95%CI： 0.802 - 0.900， P < 0.001）， with a sensitivity of 90.4% and a specificity of 57.6%， respectively. DCA confirmed the clinical utility of the model. Conclusions By incorporating variables specific to neurocritical care， such as targeted temperature management and combined sedative use， the constructed risk prediction model exhibits excellent performance. This model can function as an effective instrument for predicting the risk of enteral nutrition FI in neurocritical patients.

Key words: neurosurgical critical care, enteral nutrition, feeding intolerance, risk factors, prediction model