Estimation of glomerular filtration rate by a radial basis function neural network in patients with type-2 diabetes mellitus

Accuracy of the methods used to estimate glomerular filtration rate compared to 24-hour urinary creatinine clearance in patients with chronic spinal cord injury

OBJECTIVE

To compare the accuracy of glomerular filtration rate (GFR) estimation by 24-hour urinary creatinine clearance with GFR estimation by the Modification of Diet in Renal Disease (MDRD) equation, the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation, the Mayo Clinic Quadratic equation (MCQE), and the modified Cockcroft-Gault formula in patients with spinal cord injury (SCI).

DESIGN

Cross-sectional study.

PARTICIPANTS

Fifty-nine consecutive subjects, who were admitted to our hospital SCI rehabilitation and no additional acute medical disorders, were enrolled in this study. A 24-hour urine sample was collected for the determination of 24-hour urinary creatinine clearance, which was assumed as the standard technique for estimation of the GFR. The accuracy of several estimation formulas includes the 4-variable MDRD equation, the 6-variable MDRD equation, the Cockcroft and Gault equation, the CKD-EPI equation, and the MCQE.

RESULTS

GFRs calculated by the Cockcroft-Gault equation and 4-variable MDRD were significantly different from the 24-hour urinary creatinine clearance, whereas there were no significant differences in GFRs calculated by CKD-EPI (P = 1.000), Mayo Clinic Quadratic formula (P = 0.794), and 6-variable MDRD equations (P = 0.435) and 24-hour urinary creatinine clearance. Both the 6-variable MDRD equation and CKD-EPI were accurate within ±20 of the reference methods in 52.54% of the subjects.

CONCLUSIONS

Among the methods used for estimation of the GFR including the 4- and 6-variable MDRD, the CKD-EPI, the modified Cockcroft-Gault equation, and the MCQE, the 6-variable MDRD equation and the CKD-EPI demonstrated best performance to estimate the GFR. However, none of the formulas were sufficient to estimate the GFR in SCI patients accurately.

Improving glomerular filtration rate estimation by semi-supervised learning: a development and external validation study

BACKGROUND

Accurate estimating glomerular filtration rate (GFR) is crucial both in clinical practice and epidemiological survey. We incorporated semi-supervised learning technology to improve GFR estimation performance.

METHODS

AASK [African American Study of Kidney Disease and Hypertension], CRIC [Chronic Renal Insufficiency Cohort] and DCCT [Diabetes Control and Complications Trial] studies were pooled together for model development, whereas MDRD [Modification of Diet in Renal Disease] and CRISP [Consortium for Radiological Imaging Studies of Polycystic Kidney Disease] studies for model external validation. A total of seven variables (Serum creatinine, Age, Sex, Black race, Diabetes status, Hypertension and Body Mass Index) were included as independent variables, while the outcome variable GFR was measured as the urinary clearance of ¹²⁵I-iothalamate. The revised CKD-EPI [Chronic Kidney Disease Epidemiology Collaboration] creatinine equations was selected as benchmark for performance comparisons. Head-to-head performance comparisons from four-variable to seven-variable combination were conducted between revised CKD-EPI equations and semi-supervised models.

RESULTS

In each independent variables combination, the semi-supervised models consistently achieved superior results in all three performance indicators compared with corresponding revised CKD-EPI equations in the external validation data set. Furthermore, compared with revised four-variable CKD-EPI equation, the seven-variable semi-supervised model performed less biased (mean of difference: 0.03 [- 0.28, 0.34] vs 1.53 [1.28, 1.85], P < 0.001), more precise (interquartile range of difference: 7.94 [7.37, 8.50] vs 8.28 [7.76, 8.83], P = 0.1) and accurate (P30: 88.9% [87.4%, 90.2%] vs 86.0% [84.4%, 87.4%], P < 0.001.

CONCLUSIONS

The superior performance of the semi-supervised models during head-to-head comparisons supported the hypothesis that semi-supervised learning technology could improve GFR estimation performance.

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

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%) 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.

RESULTS

Compared 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).

CONCLUSIONS

It 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.