Construction and validation of a 28-day mortality prediction model for refeeding syndrome in critically ill patients

Background and aims: Refeeding syndrome (RFS) is a common and serious metabolic reaction during nutritional support in critically ill patients, associated with substantially increased mortality risk and poor prognosis. Existing studies mainly focus on predicting its occurrence, while predictive models for 28-day mortality risk remain lacking. This study aimed to develop and validate an interpretable machine learning model to support short-term prognostic assessment in clinical practice.