# Approximation of Glomerular Filtration Rate after 1 Year Using Annual Medical Examination Data

## TL;DR

This study developed a formula to predict kidney function after one year using common health data from annual check-ups.

## Contribution

A novel formula using machine learning and regression analysis to approximate future glomerular filtration rate (GFR) from annual medical data.

## Key findings

- The formula includes age, hemoglobin, uric acid, and current eGFR to predict future GFR.
- The predicted GFR showed a strong correlation (r = 0.884) with actual eGFR after one year.

## Abstract

Background: This cohort study was conducted to devise an approximation formula for predicting the glomerular filtration rate (GFR) after 1 year using annual medical examination data from the general population. Methods: Consecutive annual medical examination data were obtained for 41,337 inhabitants. Machine learning with the random forest method was used to assess the importance of each clinical parameter in terms of its association with estimated GFR (eGFR) after 1 year. An approximation formula was developed by multiple linear regression analysis based on the four most important clinical parameters. The relationship between the GFR after 1 year approximated by our formula and the eGFR after 1 year was analyzed using Pearson’s correlation coefficient. Results: The following approximation formula was obtained by multiple linear regression analysis: approximate GFR after 1 year (mL/min/1.73 m2) = −0.054 × age + 0.162 × hemoglobin − 0.085 × uric acid + 0.849 × eGFR + 11.5. The approximate GFR after 1 year was significantly and strongly correlated with the eGFR at that time (r = 0.884; p < 0.001). Conclusions: An approximation formula including age, hemoglobin, uric acid, and eGFR may be useful for predicting GFR after 1 year among members of the general population.