These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: Evaluation of limited sampling strategies for tacrolimus.
    Author: Op den Buijsch RA, van de Plas A, Stolk LM, Christiaans MH, van Hooff JP, Undre NA, van Dieijen-Visser MP, Bekers O.
    Journal: Eur J Clin Pharmacol; 2007 Nov; 63(11):1039-44. PubMed ID: 17712551.
    Abstract:
    OBJECTIVE: In literature, a great diversity of limited sampling strategies (LSS) have been recommended for tacrolimus monitoring, however proper validation of these strategies to accurately predict the area under the time concentration curve (AUC0-12) is limited. The aim of this study was to determine whether these LSS might be useful for AUC prediction of other patient populations. METHODS: The LSS from literature studied were based on regression equations or on Bayesian fitting using MWPHARM 3.50 (Mediware, Groningen, the Netherlands). The performance was evaluated on 24 of these LSS in our population of 37 renal transplant patients with known AUCs. The results were also compared with the predictability of the regression equation based on the trough concentrations C0 and C12 of these 37 patients. Criterion was an absolute prediction error (APE) that differed less than 15% from the complete AUC0-12 calculated by the trapezoidal rule. RESULTS: Thirteen of the 18 (72%) LSS based on regression analysis were capable of predicting at least 90% of the 37 individual AUC0-12 within an APE of 15%. Additionally, all but three LSS examined gave a better prediction of the complete AUC0-12 in comparison with the trough concentrations C0 or C12 (mean 62%). All six LSS based on Bayesian fitting predicted <90% of the 37 complete AUC0-12 correctly (mean 67%). CONCLUSIONS: The present study indicated that implementation of LSS based on regression analysis could produce satisfactory predictions although careful evaluation is necessary.
    [Abstract] [Full Text] [Related] [New Search]