Improving accuracy of estimating glomerular filtration rate using artificial neural network: model development and validation

Ningshan Li,Peijia Liu,Hui Lu,Hui Huang,Xun Liu,Han-Zhu Qian

doi:10.1186/s12967-020-02287-y

Abstract

BackgroundThe performance of previously published glomerular filtration rate (GFR) estimation equations degrades when directly used in Chinese population. We incorporated more independent variables and using complicated non-linear modeling technology (artificial neural network, ANN) to develop a more accurate GFR estimation model for Chinese population.MethodsThe enrolled participants came from the Third Affiliated Hospital of Sun Yat-sen University, China from Jan 2012 to Jun 2016. Participants with age < 18, unstable kidney function, taking trimethoprim or cimetidine, or receiving dialysis were excluded. Among the finally enrolled 1952 participants, 1075 participants (55.07%) from Jan 2012 to Dec 2014 were assigned as the development data whereas 877 participants (44.93%) from Jan 2015 to Jun 2016 as the internal validation data. We in total developed 3 GFR estimation models: a 4-variable revised CKD-EPI (chronic kidney disease epidemiology collaboration) equation (standardized serum creatinine and cystatin C, age and gender), a 9-variable revised CKD-EPI equation (additional auxiliary variables: body mass index, blood urea nitrogen, albumin, uric acid and hemoglobin), and a 9-variable ANN model.ResultsCompared with the 4-variable equation, the 9-variable equation could not achieve superior performance in the internal validation data (mean of difference: 5.00 [3.82, 6.54] vs 4.67 [3.55, 5.90], P = 0.5; interquartile range (IQR) of difference: 18.91 [17.43, 20.48] vs 20.11 [18.46, 21.80], P = 0.05; P30: 76.6% [73.7%, 79.5%] vs 75.8% [72.9%, 78.6%], P = 0.4), but the 9-variable ANN model significantly improve bias and P30 accuracy (mean of difference: 2.77 [1.82, 4.10], P = 0.007; IQR: 19.33 [17.77, 21.17], P = 0.3; P30: 80.0% [77.4%, 82.7%], P < 0.001).ConclusionsIt is suggested that using complicated non-linear models like ANN could fully utilize the predictive ability of the independent variables, and then finally achieve a superior GFR estimation model.

Highlights

The performance of previously published glomerular filtration rate (GFR) estimation equations degrades when directly used in Chinese population
We first developed an equation for GFR estimation using a combination of conventional 4 variables including age, sex, serum creatinine (Scr) and serum cystatin C (Scys), we further developed a 9-variable equation by incorporating 5 more auxiliary variables including body mass index (BMI), blood urea nitrogen (BUN), albumin (ALB), uric acid (UA) and hemoglobin (HGB)
We developed the equations with 4- and 9-variable which fit the piecewise linear splines with a knot of both Scr and Scys by using splines Package in R software

Summary

Introduction

The performance of previously published glomerular filtration rate (GFR) estimation equations degrades when directly used in Chinese population. The most accepted eGFR equations are modification of diet in renal disease (MDRD) [5] and chronic kidney disease epidemiology collaboration (CKD-EPI) equations [7], which can provide acceptable GFR estimates for the North American population. These eGFR estimations may not perform well among Chinese population, as these equations were not developed based on Chinese population [10]. Studies have been conducted to develop accurate equations for Chinese or Asian population [11]. Most of these studies focus on either establishing an ethnic factor [10] or developing a new equation just using traditional regression method

Methods

Results

Discussion

Conclusion