Renal transplant recipients provide a unique model for protein-binding studies in that patients experience hypoalbuminemia and renal dysfunction, both of which alter protein binding. The purposes of this investigation were to model the relationship between serum creatinine, blood urea nitrogen (BUN), albumin, and the unbound fraction of phenytoin (FU, as a percentage) in patients who had undergone renal transplant, and to determine the value of these measurements in predicting FU. Blood from 29 patients was collected at various time points after establishment of graft function. Sera were spiked with phenytoin to a concentration of 15 mg/L, and total/unbound phenytoin concentrations were determined. Correlations between FU and the biochemical indices of serum creatinine, BUN, and albumin were determined using multiple regression. The algorithm with the highest correlation at all times after the transplant became the method to predict future FU. This algorithm was applied prospectively in 23 samples from 14 other patients with variable renal function after transplant. Samples were analyzed as above and the corresponding biochemical indices of serum creatinine, BUN, and albumin were used to calculate FU values. Accuracy of the predictions was evaluated using prediction-error analysis. The best relationship between FU and the measured biochemical indices incorporated serum creatinine and albumin [y = 24.3 + 0.6(serum creatinine) - 3.9(albumin)] and served as the method for FU prediction. Prediction-error analysis resulted in a bias of -5.1% and a precision of 5.7%. This method failed to estimate FU with sufficient accuracy to permit clinical utility. The predicted value underestimated the measured value, and some other variable(s) must be affecting the binding even though serum creatinine and albumin are within or approaching the reference range. Consequently, estimating FU in patients with a history of uremia and hypoalbuminemia, based on measures of serum creatinine and albumin alone, should not be used.
All Science Journal Classification (ASJC) codes
- Health, Toxicology and Mutagenesis
- Biochemistry, Genetics and Molecular Biology(all)
- Pharmacology (medical)
- Public Health, Environmental and Occupational Health