Individualized Implementation of Enteral Nutrition Adjusted by Laboratory Indicators and Clinical Parameter Scoring

Abstract This study aimed to develop and validate an early predictive model using clinical and laboratory indicators to identify high-risk critically burned patients for Enteral Feeding Intolerance (ENFI) within 24 hours post-burn. A retrospective analysis was conducted on data from 290 adult patients meeting inclusion criteria, selected from 803 admitted to a Burn ICU between March 2014 and December 2023. Univariate and multivariate logistic regression identified key predictors significantly associated with ENFI: Total Body Surface Area burned (TBSA), shock status upon admission, inhalation injury, and Total Bilirubin (TB). Additional clinically relevant variables - Prealbumin (PA), Hemoglobin (HB), age, and Lactate (LAC) - were incorporated into the model. The model's performance was robust, demonstrating strong discrimination with Area Under the Curve (AUC) values of 0.821 in the training cohort and 0.785 in the validation cohort. This indicates excellent predictive ability and clinical utility. The developed model effectively evaluates ENFI risk early after severe burns, offering high accuracy and practical applicability in clinical settings. It enables the early identification of high-risk patients, allowing for optimized enteral nutrition strategies to improve care.