ArticleBMC nephrology2013
Estimation of glomerular filtration rate by a radial basis function neural network in patients with type-2 diabetes mellitus.
Article in BMC nephrology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Accuracy of the methods used to estimate glomerular filtration rate compared to 24-hour urinary creatinine clearance in patients with chronic spinal cord injury.The journal of spinal cord medicine · 2023Article
- Improving glomerular filtration rate estimation by semi-supervised learning: a development and external validation study.International urology and nephrology · 2021Article
- Improving accuracy of estimating glomerular filtration rate using artificial neural network: model development and validation.Journal of translational medicine · 2020Article
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8 authors.
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Abstract
backgroundAccurate and precise estimates of glomerular filtration rate (GFR) are essential for clinical assessments, and many methods of estimation are available. We developed a radial basis function (RBF) network and assessed the performance of this method in the estimation of the GFRs of 207 patients with type-2 diabetes and CKD.
methodsStandard GFR (sGFR) was determined by (99m)Tc-DTPA renal dynamic imaging and GFR was also estimated by the 6-variable MDRD equation and the 4-variable MDRD equation.
resultsBland-Altman analysis indicated that estimates from the RBF network were more precise than those from the other two methods for some groups of patients. However, the median difference of RBF network estimates from sGFR was greater than those from the other two estimates, indicating greater bias. For patients with stage I/II CKD, the median absolute difference of the RBF network estimate from sGFR was significantly lower, and the P50 of the RBF network estimate (n = 56, 87.5%) was significantly higher than that of the MDRD-4 estimate (n = 49, 76.6%) (p < 0.0167), indicating that the RBF network estimate provided greater accuracy for these patients.
conclusionsIn patients with type-2 diabetes mellitus, estimation of GFR by our RBF network provided better precision and accuracy for some groups of patients than the estimation by the traditional MDRD equations. However, the RBF network estimates of GFR tended to have greater bias and higher than those indicated by sGFR determined by (99m)Tc-DTPA renal dynamic imaging.
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