ORIGINAL RESEARCH article
Front. Med.
Sec. Intensive Care Medicine and Anesthesiology
Development and Validation of a Prediction Model for Refeeding Syndrome in ICU mechanically ventilated patients Receiving Enteral Nutrition Support:a single-centreretrospective study from China
Provisionally accepted
- Shenzhen Nanshan District People's Hospital, shenzhen, China
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Objective To develop and validate a risk prediction model for refeeding syndrome (RFS) in intensive care unit (ICU) mechanically ventilated patients receiving initial enteral nutrition therapy. Participants Patients who were admitted to the ICU of a tertiary hospital in Shenzhen for the first time and received enteral nutrition support between January 2022 and December 2024 were selected. The cohort was divided into a modeling set (n=664) and a validation set (n=284). Methods Factors potentially associated with refeeding syndrome (RFS) were collected, including patients’ clinical indicators and refeeding-related conditions. Patients were divided into the RFS group and non-RFS group according to the presence or absence of RFS. Potential variables were screened using the least absolute shrinkage and selection operator (LASSO) regression, followed by multivariate logistic regression analysis; a nomogram model was then constructed and validated. Results Among the 664 patients in the modeling cohort, a total of 300 cases (45.18%) developed refeeding syndrome (RFS). Following LASSO regression, multivariate logistic regression analysis was performed, and the results revealed that age ≥ 60 years, Nutritional Risk Screening 2002 (NRS-2002) score ≥ 3 points, Sequential Organ Failure Assessment (SOFA) score ≥ 10 points, Acute Physiology and Chronic Health Evaluation Ⅱ (APACHE Ⅱ) score ≥ 20 points, and pre-feeding albumin (ALB) < 30 g/L were identified as independent risk factors for RFS in mechanically ventilated ICU patients receiving enteral nutrition support (P<0.05). Results of receiver operating characteristic (ROC) curve analysis demonstrated that the area under the curve (AUC) for predicting RFS risk in mechanically ventilated ICU patients was 0.859 (95% confidence interval [95% CI]: 0.815–0.903) in the modeling cohort and 0.832 (95% CI: 0.802–0.862) in the validation cohort. Calibration curve analysis showed that the predicted curves of both the modeling and validation cohorts were in good agreement with the ideal curve. Conclusion The prediction model demonstrates good discrimination and calibration, enabling intuitive and convenient identification of ICU patients receiving enteral nutrition who are at high risk of refeeding syndrome, thereby providing a reference for early screening and intervention.
Keywords: clinical outcomes, ICU, LASSO regression, nomogram, Nutritional Support, Predictionmodel, Refeeding Syndrome, Risk factors
Received: 25 Aug 2025; Accepted: 19 Jan 2026.
Copyright: © 2026 Feng, Piao, QI, Xiao, Wang, Qin and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yuju Qin
Haigang Zhang
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