Measuring Residual Renal Function in Hemodialysis Patients without Urine Collection

This is the peer reviewed version of the following article: Wong, J., Kaja Kamal, R. M., Vilar, E. and Farrington, K. (2017), 'Measuring Residual Renal Function in Hemodialysis Patients without Urine Collection', Seminars in Dialysis, Vol. 30 (1): 39–49, which has been published in final form at doi: 10.1111/sdi.12557. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. © 2016 Wiley Periodicals, Inc.Many patients on hemodialysis retain significant residual renal function (RRF) but currently measurement of RRF in routine clinical practice can only be achieved using inter-dialytic urine collections to measure urea and creatinine clearances. Urine collections are difficult and inconvenient for patients and staff, and therefore RRF is not universally measured. Methods to assess RRF without reliance on urine collections are needed since RRF provides useful clinical and prognostic information and also permits the application of incremental hemodialysis techniques. Significant efforts have been made to explore the use of serum based biomarkers such as cystatin C, β-trace protein and β2 -microglobulin to estimate RRF. This article reviews blood-based biomarkers and novel methods using exogenous filtration markers which show potential in estimating RRF in hemodialysis patients without the need for urine collection.Peer reviewedFinal Accepted Versio

Tikhonov adaptively regularized gamma variate fitting to assess plasma clearance of inert renal markers

The Tk-GV model fits Gamma Variates (GV) to data by Tikhonov regularization (Tk) with shrinkage constant, λ, chosen to minimize the relative error in plasma clearance, CL (ml/min). Using 169Yb-DTPA and 99mTc-DTPA (n = 46, 8–9 samples, 5–240 min) bolus-dilution curves, results were obtained for fit methods: (1) Ordinary Least Squares (OLS) one and two exponential term (E1 and E2), (2) OLS-GV and (3) Tk-GV. Four tests examined the fit results for: (1) physicality of ranges of model parameters, (2) effects on parameter values when different data subsets are fit, (3) characterization of residuals, and (4) extrapolative error and agreement with published correction factors. Test 1 showed physical Tk-GV results, where OLS-GV fits sometimes-produced nonphysical CL. Test 2 showed the Tk-GV model produced good results with 4 or more samples drawn between 10 and 240 min. Test 3 showed that E1 and E2 failed goodness-of-fit testing whereas GV fits for t > 20 min were acceptably good. Test 4 showed CLTk-GV clearance values agreed with published CL corrections with the general result that CLE1 > CLE2 > CLTk-GV and finally that CLTk-GV were considerably more robust, precise and accurate than CLE2, and should replace the use of CLE2 for these renal markers