ArticleEuropean journal of clinical nutrition2026
Early prediction of enteral nutrition feeding intolerance risk in neurocritical patients and development of a simplified risk scoring tables.
Article in European journal of clinical nutrition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Observational
- 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
- Post-operative early enteral nutrition intolerance in elderly patients undergoing laparoscopic gastric cancer surgery: current status and nursing strategies.Frontiers in nutrition · 2026Article
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6 authors.
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Abstract
backgroundThis study collected and analyzed clinical data on enteral nutrition therapy in neurocritical patients, developed and validated a risk prediction model for feeding intolerance (FI), and transformed the model into a visual risk scoring tool,provide a reference for clinical staff to screen for people at high risk of enteral nutrition feeding intolerance in neurocritically ill patients.
methodsUsing prospective study,440 eligible inpatients from a Chinese tertiary hospital (April-December 2022) were divided into derivation (70%) and validation (30%) cohorts.Univariate and binary logistic regression analyses were conducted to construct the FI prediction model, and a simplified risk assessment scale for FI in the neurological intensive care unit (NCU) was developed.
resultsFI incidence was 71.0% (213/300) in the derivation cohort. Independent risk factors included age, Glasgow Coma Scale (GCS) score, APACHE II score, mechanical ventilation, nasogastric tube feeding, hyperglycemia, and hypoalbuminemia (P < 0.05). The model showed excellent discrimination (AUC = 0.941, 95% CI:0.912-0.970) and calibration (Hosmer-Lemeshow P = 0.293), with 85.9% sensitivity and 90.8% specificity. In the validation cohort (140 patients, FI incidence was 72.1%), predictive accuracy was 82.9% (AUC = 0.924, 95% CI:0.878-0.970; sensitivity=96.0%, specificity=74.4%). The visual scoring tool achieved 84.3% accuracy (Kappa=0.700, P < 0.001), aligning with the original model.
conclusionThe early enteral nutrition FI risk prediction model and corresponding scoring table developed in this study showed good predictive performance and could serve as a useful reference for the clinical assessment of FI risk in neurocritical patients.
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