ArticleNutrients2023
Using Machine-Learning to Assess the Prognostic Value of Early Enteral Feeding Intolerance in Critically Ill Patients: A Retrospective Study.
Article in Nutrients, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Risk prediction models for enteral feeding intolerance in critically ill patients: a systematic review.Frontiers in public health · 2026Pooled it
- Risk prediction models for feeding intolerance in patients with enteral nutrition: a systematic review and meta-analysis.Frontiers in nutrition · 2024Pooled it
- 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
- Article
- Nutritional Risk and 30-Day In-Hospital Mortality in Adults With Sepsis and Septic Shock: A Retrospective Cohort Study.Journal of nursing management · 2026Article
- An evaluation based on explainable machine learning: analysis of risk factors for enteral nutrition-related diarrhea in patients with severe stroke.Frontiers in nutrition · 2026Article
- 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
- Construction and evaluation of a machine learning-based predictive model for enteral nutrition feeding intolerance risk in ICU patients.Frontiers in nutrition · 2025Article
- Role of artificial intelligence in critical care nutrition support and research.Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition · 2024Review
- Development and validation of a nomogram for predicting 28-day mortality in critically ill patients with acute gastrointestinal injury: prospective observational study.Frontiers in nutrition · 2024Article
- 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
- Research and exploration of quality control indicators for nutritional therapy in critically ill patients-a multicenter before-and-after controlled study.Frontiers in nutrition · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundThe association between gastrointestinal intolerance during early enteral nutrition (EN) and adverse clinical outcomes in critically ill patients is controversial. We aimed to assess the prognostic value of enteral feeding intolerance (EFI) markers during early ICU stays and to predict early EN failure using a machine learning (ML) approach.
methodsWe performed a retrospective analysis of data from adult patients admitted to Beilinson Hospital ICU between January 2011 and December 2018 for more than 48 h and received EN. Clinical data, including demographics, severity scores, EFI markers, and medications, along with 72 h after admission, were analyzed by ML algorithms. Prediction performance was assessed by the area under the receiver operating characteristics (AUCROC) of a ten-fold cross-validation set.
resultsThe datasets comprised 1584 patients. The means of the cross-validation AUCROCs for 90-day mortality and early EN failure were 0.73 (95% CI 0.71-0.75) and 0.71 (95% CI 0.67-0.74), respectively. Gastric residual volume above 250 mL on the second day was an important component of both prediction models.
conclusionsML underlined the EFI markers that predict poor 90-day outcomes and early EN failure and supports early recognition of at-risk patients. Results have to be confirmed in further prospective and external validation studies.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.