Predictive performance of renal function estimate equations in renal allografts

Abstract

Aims. Various mathematical models have been developed to estimate glomerular filtration rate (GFR) incorporating variables such as age, gender, height, weight, serum creatinine, and body surface area (BSA). Because adjustments in drug dosing are often based on estimated values of renal function, it is important to define which, if any, of the available models, is appropriate for a specific patient population. A study was undertaken to determine the bias and precision of four mathematical models to estimate GFR in renal allograft recipients. Methods. A retrospective review of 142 stable renal allograft patients, using iohexol clearance as a determinant of GFR, was performed. Renal allograft recipients followed in an outpatient clinic setting underwent iohexol clearance studies as part of clinical monitoring in the post-transplant period. Measured GFR values were compared with four mathematical models used to estimate GFR: the Cockcroft-Gault equation, the Jelliffe equation, the Walser equation, and the Mawer equation. Bias and precision were determined for each model as the mean squared error and the mean squared error, respectively. Results. Patients had a mean age of 44 ± 13 years, 92 were male, and 50 were female. The serum creatinine concentration was 176.8 ± 88.4 μmol l-1 (mean ± s.d.). The mean time post-transplant was 5.1 ± 5.0 years and 38% of patients had insulin-requiring diabetes mellitus. The bias and precision results for the Jelliffe, Walser, Cockcroft-Gault, and Mawer models were: -3 and 414; -5 and 381; 16 and 688; and 23 and 1084, respectively. Conclusions. The Jelliffe and Walser equations gave the least biased and most precise estimations of GFR when compared with iohexol-derived measures in patients with renal allografts.