Development and validation of a risk prediction model for refeeding syndrome in adults with critical illness: A prospective observational study

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.