Development and Validation of a Risk-Prediction Nomogram for Nutritional Risk in Non-Dialysis Chronic Renal Failure...

Area of Science:

• Nephrology
• Clinical Nutrition
• Medical Informatics

Background:

• Malnutrition is a significant concern in non-dialysis chronic renal failure (ND-CRF) patients.
• Early identification and intervention are crucial for managing ND-CRF patient health.
• Existing prediction tools may not adequately address the specific needs of ND-CRF populations.

Purpose of the Study:

• To develop and validate a risk prediction model for malnutrition in ND-CRF patients.
• To identify key clinical and psychological factors influencing malnutrition risk.
• To provide a tool for early detection and personalized intervention strategies.

Main Methods:

• A cohort of 421 ND-CRF patients was analyzed for malnutrition risk factors.
• Logistic regression identified predictors, forming a nomogram-based risk model.
• Internal and external validation was performed using Bootstrap resampling and independent cohorts.

Main Results:

• The incidence of malnutrition was 27.70% in the study population.
• The final model included gender, serum albumin, GFR, triglycerides, anxiety, and depression (AUC=0.795).
• Both internal (AUC=0.776) and external (AUC=0.825) validation confirmed good predictive performance.

Conclusions:

• A validated nomogram-based risk prediction model for malnutrition in ND-CRF patients has been developed.
• The model demonstrates good predictive accuracy and clinical utility.
• This tool can assist clinicians in early risk identification and guide personalized interventions.