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

Journal of Translational Medicine
Ningshan LiXun Liu

Abstract

The 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. The 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. Compared with the 4-variable equation, the 9-...Continue Reading

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Citations

Apr 17, 2020·Journal of Clinical Medicine·Charat ThongprayoonWisit Cheungpasitporn

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Software Mentioned

splines
MathWorks
Python
MATLAB
R
mathord
R Development Core Team
ANN
Keras

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