New equations to estimate 24-h urinary creatinine excretion

Area of Science:

• Nephrology
• Clinical Chemistry
• Biostatistics

Background:

• Accurate estimation of 24-hour creatinine excretion is crucial for assessing kidney function and nutritional status.
• Existing prediction equations may not be optimal for diverse populations, necessitating localized validation and development.
• Creatinine excretion is influenced by factors such as age, sex, body composition, and anthropometric measurements.

Purpose of the Study:

• To develop and validate novel prediction equations for estimating 24-hour creatinine excretion specifically in Brazilian adults.
• To compare the performance of different equation models incorporating various anthropometric and demographic variables.
• To provide accurate and reliable tools for clinical use in Brazil.

Main Methods:

• A cross-sectional study involving 330 Brazilian adults from Vitória, with measurements of anthropometry, blood pressure, and blood analytes.
• Validated 24-hour urine collections were used to measure actual creatinine excretion.
• Least Absolute Shrinkage and Selection Operator (LASSO) regression with cross-validation was employed to develop prediction equations, considering sex-specific models.

Main Results:

• The study successfully developed and validated prediction equations for 24-hour creatinine excretion in Brazilian adults.
• Sex-specific equations incorporating weight, age, and height demonstrated strong performance, with correlation coefficients (r) ranging from 0.77 to 0.80.
• The developed equations showed improved accuracy and outperformed previous models, with minimal bias in estimations.

Conclusions:

• The newly developed prediction equations are accurate and reliable for estimating 24-hour creatinine excretion in the Brazilian adult population.
• These equations, particularly those stratified by sex and including readily available variables like weight, age, and height, offer practical clinical utility.
• The findings support the use of these validated equations for improved assessment of kidney function and nutritional status in Brazil.