Related Experiment Video
Updated: Jan 16, 2026
Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors
Published on: February 16, 2024
Development and evaluation of a risk prediction model for enteral nutrition feeding intolerance in intensive care
Xiaohua Cao1, Hua Wang2, Yinling Song1
1Department of Intensive Care Medicine, Jining No. 1 People's Hospital, Jining, China.
Background:
Patients in intensive care units (ICUs) who receive enteral nutrition (EN) treatment frequently experience feeding intolerance (FI), which, if not promptly managed, can adversely affect treatment outcomes and overall prognosis. This study aims to identify the risk factors associated with enteral nutrition feeding intolerance (ENFI) in critically ill ICU patients and to develop a predictive model to assess the risk of ENFI.
Methods:
This study enrolled 144 patients, who were categorized into an ENFI group and a non-ENFI group. Variable selection for model development was conducted through univariate analysis, multicollinearity testing, and binary logistic regression. Based on the logistic regression results, a visual predictive model for ENFI risk was constructed using a nomogram. The model's discriminative performance was evaluated using the area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Internal validation was performed using the bootstrap method with 1,000 resamples of the original dataset. A calibration curve was generated, and the Hosmer-Lemeshow goodness-of-fit test was applied to assess the model's calibration accuracy.
Results:
Based on the results of the binary logistic regression analysis, a nomogram model was developed to predict enteral nutrition feeding intolerance (ENFI) in critically ill ICU patients. The model incorporated five variables: Acute Physiology and Chronic Health Evaluation II (APACHE II) score, mechanical ventilation (MV), albumin (ALB), intra-abdominal pressure (IAP), and EN start time. AUC was 0.800 (95% confidence interval, 0.725-0.875), with a cutoff value of 0.306. The model demonstrated a sensitivity of 82.5%, specificity of 72.4%, positive predictive value (PPV) of 67.2%, and negative predictive value (NPV) of 86.3%. Following internal validation using the bootstrap method, the Hosmer-Lemeshow goodness-of-fit test produced a χ2 value of 2.9954 (p = 0.9346). The lack of statistically significant deviation between the predicted and observed risk values indicates that the model demonstrates good calibration and accurately reflects the actual risk of ENFI.
Conclusion:
The model demonstrated good predictive performance and can effectively assess the risk of ENFI in critically ill ICU patients.
More Related Videos
Related Concept Videos
01:15Enteral Nutrition II: Nasointestinal and Gastrostomy Feeding
Nasointestinal Feeding
Nasointestinal feeding involves placing a tube...
01:26Enteral Nutrition I: Orogastric and Nasogastric Feeding
Orogastric (OG) and nasogastric (NG) feeding are two standard methods used for enteral nutrition. Enteral nutrition is often preferred over...
01:30Inflammatory Bowel Disease III: Diagnostic Studies and Management I-Nutritional Therapy
Diagnostic studies
A colonoscopy is the definitive screening test, distinguishing ulcerative colitis from other colon diseases with similar symptoms. During a colonoscopy test, inflamed mucosa with exudate ulcerations can be observed, and biopsies are taken to determine the histologic characteristics of the...
01:27Parentral Nutrition: Centeral and Peripheral Parental Nutrition
PN can be administered through two primary routes:
1. Central Parenteral Nutrition (CPN):
CPN involves delivering a high concentration of nutrients through a large vein. This is typically achieved using a Peripherally Inserted Central Catheter (PICC) or,...
00:56One-Compartment Open Model for IV Bolus Administration: Estimation of Clearance
In the one-compartment open model for intravenous (IV) bolus administration, clearance is estimated by dividing the elimination rate by the plasma drug concentration. This equation leverages the elimination rate constant and the apparent...
01:19One-Compartment Open Model for IV Bolus Administration: General Considerations
The drug's presence in the body is defined by an equation representing the difference between the rates of drug entry and exit. Key parameters—elimination rate constant,...