Machine Learning-Driven Prediction of Early Enteral Nutrition Intolerance in ICU Patients Using Intra-Abdominal Pressure and Selected Gut Microbial Taxa.

To develop and validate a machine learning model integrating intra-abdominal pressure (IAP) and gut microbial characteristics for early identification of enteral nutrition intolerance (EENI) in critically ill ICU patients. This cohort study (January 2023-December 2025) included 300 ICU patients receiving early enteral nutrition. Baseline clinical characteristics, intra-abdominal pressure, laboratory indices, and quantitative gut microbial taxa were collected. Candidate predictors were selected using the least absolute shrinkage and selection operator regression, and independent predictors were incorporated into a multivariable logistic regression model, which was presented as a nomogram. Model performance was assessed using discrimination, calibration, and decision curve analyses. The incidence of EENI was 49.00% (147/300). LASSO selected 16 features: age, analgesic use, serum albumin, glucose, IAP, and the absolute counts of Enterococcus, Bacteroides, Escherichia-Shigella, Klebsiella, Bifidobacterium, and Parabacteroides. The absolute counts of Enterococcus, Bacteroides, Escherichia-Shigella, Klebsiella, Bifidobacterium, and Parabacteroides were significant independent predictors (P < 0.05). The model achieved an AUC of 0.900 (95% CI: 0.863-0.936) in the training set and 0.900 (95% CI: 0.859-0.941) in the validation set. Calibration was good (Hosmer-Lemeshow P = 0.425 and P = 0.423, respectively). DCA demonstrated clinical utility across a wide range of risk thresholds. The developed machine learning model, combining IAP and selected genus-level gut microbial markers, demonstrates strong predictive performance and clinical potential for forecasting and managing early enteral nutrition intolerance in ICU patients.