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

ABSTRACT:

Background

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.

Methods

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

SUBMITTER: Li N 

PROVIDER: S-EPMC7063770 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature