Innovative non-invasive model for screening reduced estimated glomerular filtration rate in a working population

Area of Science:

• Nephrology
• Public Health
• Epidemiology

Background:

• Existing risk scores for reduced estimated glomerular filtration rate (eGFR) often rely on inconvenient laboratory factors.
• There is a need for cost-effective, non-invasive methods for large-scale population screening of reduced eGFR.

Purpose of the Study:

• To develop and validate a risk score using non-invasive variables to identify individuals with reduced eGFR.
• To assess the utility of this score in a Chinese working population for screening and resource allocation.

Main Methods:

• Utilized two independent study populations recruited in 2012 and 2015 for training, internal validation, and external validation.
• Employed stepwise logistic regression analysis incorporating age, gender, hypertension, and body mass index (BMI).
• Developed a nomogram based on the validated logistic regression model.

Main Results:

• Older females with higher BMI were more likely to have reduced eGFR.
• The developed model demonstrated excellent discrimination (AUC 0.887 internal, 0.880 external) and calibration.
• The nomogram showed a gradual increase in reduced eGFR probability with increasing total risk score.

Conclusions:

• Non-invasive variables can effectively identify individuals at high risk of reduced eGFR.
• This approach aids in decision-making for targeted kidney function testing and intervention in population screening.
• The risk score facilitates efficient resource allocation in public health initiatives.