Development and validation of a risk prediction model for feeding intolerance in neurocritical patients with enteral nutrition - Yuan, Rong; Liu, Lei; Mi, Jiao; Li, Xue; Yang, Fang; Mao, Shifang (Frontiers in Nutrition, 2024)
Background: This study collects and analyzes the clinical data of enteral nutrition therapy in neurocritical patients, constructs and validates the feeding intolerance (FI) risk prediction model, and provides a theoretical basis for screening patients with high risk of FI and personalized care. Methods: A convenience sampling method was adopted to select 300 patients who were admitted to a tertiary hospital in China for early enteral nutrition therapy in the neurointensive care unit from April 2022 to December 2022, and independent risk factors for FI were screened by using univariate and logistic regression analyses to establish a prediction model, and to evaluate the goodness of fit of the model and discriminant validity. Results: The incidence of FI in neurocritical patients receiving enteral nutrition was 71%. Logistic regression analysis showed that age, Glasgow Coma Scale (GCS) scores, Acute Physiology and Chronic Health Evaluation II (APACHE II) scores, mechanical ventilation, feeding via nasogastric tube route, hyperglycemia, and low serum albumin were independent risk factors for the development of FI (P < 0.05). A predictive formula for the risk of FI was established as Logit P = -14.737 + 1.184 × mechanical ventilation + 2.309 × feeding route + 1.650 × age + 1.336 × GCS tertile (6-8 points) + 1.696 × GCS tertile (3-5 points) + 1.753 × APACHE II score + 1.683 × blood glucose value + 1.954 × serum albumin concentration. The Hosmer-Lemeshow test showed the χ2 value=9.622，p = 0.293, and the area under the ROC curve was 0.941 (95% confidence interval: 0.912–0.970, P＜0.001). The optimal critical value was 0.767, the sensitivity was 85.9%, the specificity was 90.8%, and the Youden index was 0.715. Conclusion: The early enteral nutrition FI risk prediction model constructed in this study demonstrated good predictive ability. This could serve as a reference basis for the effective clinical assessment of the risk of FI occurrence in neurocritical patients.