ArticleSaudi journal of gastroenterology : official journal of the Saudi Gastroenterology Association
Development and validation of a predictive model for feeding intolerance in intensive care unit patients with sepsis.
Article in Saudi journal of gastroenterology : official journal of the Saudi Gastroenterology Association. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 4 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
- 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 malnutrition in cancer patients: a systematic review and meta-analysis.Frontiers in 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
- 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
- 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
- Risk prediction models for enteral nutrition feeding intolerance in critically ill patients: an overview of systematic reviews.Frontiers in nutrition · 2025Review
- Trajectory of breastfeeding among Chinese women and risk prediction models based on machine learning: a cohort study.BMC pregnancy and childbirth · 2024Article
- 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
- Clinical practice guidelines for nutritional assessment and monitoring of adult ICU patients in China.Journal of intensive medicine · 2024Article
- Enhancing sepsis therapy: the evolving role of enteral nutrition.Frontiers in nutrition · 2024Review
- Different definitions of feeding intolerance and their associations with outcomes of critically ill adults receiving enteral nutrition: a systematic review and meta-analysis.Journal of intensive care · 2023Review
- Using Machine-Learning to Assess the Prognostic Value of Early Enteral Feeding Intolerance in Critically Ill Patients: A Retrospective Study.Nutrients · 2023Article
- Gut Microbiota and Enteral Nutrition Tolerance in Non-Abdominal Infection Septic ICU Patients: An Observational Study.Nutrients · 2022Observational
- Building a predictive model to identify clinical indicators for COVID-19 using machine learning method.Medical & biological engineering & computing · 2022Article
- Predicting enteral feeding intolerance in patients with sepsis: Why and how?Saudi journal of gastroenterology : official journal of the Saudi Gastroenterology AssociationArticle
Corrections and comments
- Commented on by
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFeeding intolerance in patients with sepsis is associated with a lower enteral nutrition (EN) intake and worse clinical outcomes. The aim of this study was to develop and validate a predictive model for enteral feeding intolerance in the intensive care unit patients with sepsis.
methodsIn this dual-center, retrospective, case-control study, a total of 195 intensive care unit patients with sepsis were enrolled from June 2018 to June 2020. Data of 124 patients for 27 clinical indicators from one hospital were used to train the model, and data from 71 patients from another hospital were used to assess the external predictive performance. The predictive models included logistic regression, naive Bayesian, random forest, gradient boosting tree, and deep learning (multilayer artificial neural network) models.
resultsEighty-six (44.1%) patients were diagnosed with enteral feeding intolerance. The deep learning model achieved the best performance, with areas under the receiver operating characteristic curve of 0.82 (95% confidence interval = 0.74-0.90) and 0.79 (95% confidence interval = 0.68-0.89) in the training and external sets, respectively. The deep learning model showed good calibration; based on the decision curve analysis, the model's clinical benefit was considered useful. Lower respiratory tract infection was the most important contributing factor, followed by peptide EN and shock.
conclusionsThe new prediction model based on deep learning can effectively predict enteral feeding intolerance in intensive care unit patients with sepsis. Simple clinical information such as infection site, nutrient type, and septic shock can be useful in stratifying a septic patient's risk of EN intolerance.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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 OpenQuestion graph.