Development and validation of a risk prediction model for refeeding syndrome in adults with critical illness: A prospective observational study
Description
Title: Development and validation of a risk prediction model for refeeding syndrome in adults with critical illness: A prospective cohort study Description: This dataset contains de-identified individual participant data from a prospective cohort study aimed at developing and validating a multivariable risk prediction model for refeeding syndrome (RFS) in critically ill adults. The study was conducted to facilitate early identification and preventive interventions for RFS, a potentially life-threatening condition arising from the initiation of nutritional therapy in malnourished or metabolically compromised patients. Data Content and File Description: The dataset includes the following components: patient_data: De-identified patient-level data for 400 critically ill adults. Variables include: Baseline Characteristics: Age, sex, BMI, APACHE II score, NRS2002 score. Comorbidities and History: Diabetes, alcohol use, surgery, radiotherapy, chemotherapy. Clinical Symptoms: Fever, dysphagia, diarrhea, vomiting, loss of appetite. Nutritional Support: Feeding route (enteral, parenteral, or combined), calorie intake level. Laboratory Values: Albumin, prealbumin, lactate. Outcome: Presence of refeeding syndrome symptoms (binary outcome). data_dictionary: A codebook detailing variable names, descriptions, coding schemes, and value labels. Methodological Overview: Study Design: Prospective observational cohort. Participants: 400 adult patients admitted to the ICU who were at nutritional risk and initiated nutritional support. Ethics: The study was approved by the institutional ethics committee. Informed consent was obtained from all participants or their legal representatives. RFS Diagnosis: Defined based on the presence of specific clinical and laboratory criteria as detailed in the original publication. Potential Reuse Value: This dataset is a valuable resource for: External validation of the RFS prediction model. Research on nutritional support and metabolic complications in critical care. Educational use in clinical prediction modeling and critical care nutrition.