ArticleWorld journal of gastrointestinal surgery2022
Development of a prediction model for enteral feeding intolerance in intensive care unit patients: A prospective cohort study.
Article in World journal of gastrointestinal surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 3 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 3 syntheses or guidelines pooled it, 18 citations in OpenAlex.
- Risk prediction models for enteral feeding intolerance in critically ill patients: a systematic review.Frontiers in public health · 2026Pooled it
- Systematic evaluation of risk prediction models for feeding intolerance in ICU patients during enteral nutrition.Asia Pacific journal of clinical nutrition · 2025Pooled it
- Risk prediction models for feeding intolerance in patients with enteral nutrition: a systematic review and meta-analysis.Frontiers in nutrition · 2024Pooled it
- Efficacy and Tolerability of Non-Routine Gastric Residual Volume Monitoring in Mechanically Ventilated Adults Receiving Early Enteral Nutrition: A Randomised Controlled Non-Inferiority Trial.Nursing in critical care · 2026Trial
- Machine Learning-Driven Prediction of Early Enteral Nutrition Intolerance in ICU Patients Using Intra-Abdominal Pressure and Selected Gut Microbial Taxa.Polish journal of microbiology · 2026Article
- External validation of the feeding intolerance prediction model (NOFI) in critically ill patients: A post hoc analysis of a large-scale randomized controlled trial.Journal of intensive medicine · 2026Article
- Review
- Early prediction of enteral nutrition feeding intolerance risk in neurocritical patients and development of a simplified risk scoring tables.European journal of clinical nutrition · 2026Article
- Exploring the incidence and influencing factors of acute gastrointestinal injury in ICU patients undergoing mechanical ventilation: a retrospective cohort study.BMC research notes · 2026Article
- Construction and evaluation of a machine learning-based predictive model for enteral nutrition feeding intolerance risk in ICU patients.Frontiers in nutrition · 2025Article
- Risk prediction models for enteral nutrition feeding intolerance in critically ill patients: an overview of systematic reviews.Frontiers in nutrition · 2025Review
- Use of Machine Learning Models to Predict Microaspiration Measured by Tracheal Pepsin A.American journal of critical care : an official publication, American Association of Critical-Care Nurses · 2025Article
- Feeding Intolerance in Critically Ill Patients with Enteral Nutrition: A Meta-Analysis and Systematic Review.Journal of critical care medicine (Universitatea de Medicina si Farmacie din Targu-Mures) · 2024Review
- Nomogram to predict feeding intolerance in critically ill children.European journal of pediatrics · 2023Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEnteral nutrition (EN) is essential for critically ill patients. However, some patients will have enteral feeding intolerance (EFI) in the process of EN.
aimTo develop a clinical prediction model to predict the risk of EFI in patients receiving EN in the intensive care unit.
methodsA prospective cohort study was performed. The enrolled patients' basic information, medical status, nutritional support, and gastrointestinal (GI) symptoms were recorded. The baseline data and influencing factors were compared. Logistic regression analysis was used to establish the model, and the bootstrap resampling method was used to conduct internal validation.
resultsThe sample cohort included 203 patients, and 37.93% of the patients were diagnosed with EFI. After the final regression analysis, age, GI disease, early feeding, mechanical ventilation before EN started, and abnormal serum sodium were identified. In the internal validation, 500 bootstrap resample samples were performed, and the area under the curve was 0.70 (95%CI: 0.63-0.77).
conclusionThis clinical prediction model can be applied to predict the risk of EFI.
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Registered trials
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