Background Clinical practice guidelines or recommendations often require timely and regular updating as new evidence emerges, because this can alter the risk-benefit trade-off. The scientific process of developing and updating guidelines accompanied by adequate implementation can improve outcomes. To promote better management of patients receiving vancomycin therapy, we updated the guideline for the therapeutic drug monitoring (TDM) of vancomycin published in 2015. Methods Our updated recommendations complied with standards for developing trustworthy guidelines, including timeliness and rigor of the updating process, as well as the use of the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. We also followed the methodology handbook published by the National Institute for Health and Clinical Excellence and the Spanish National Health System. Results We partially updated the 2015 guideline. Apart from adults, the updated guideline also focuses on pediatric patients and neonates requiring intravenous vancomycin therapy. The guideline recommendations involve a broadened range of patients requiring TDM, modified index of TDM (both 24-hour area under the curve and trough concentration), addition regarding the necessity and timing of repeated TDM, and initial dose for specific subpopulations. Overall, 1 recommendation was deleted and 3 recommendations were modified. Eleven new recommendations were added, and no recommendation was made for 2 clinical questions. Conclusions We updated an evidence-based guideline regarding the TDM of vancomycin using a rigorous and multidisciplinary approach. The updated guideline provides more comprehensive recommendations to inform rational and optimized vancomycin use and is thus of greater applicability.
AIMSeveral tacrolimus population pharmacokinetic models in adult renal transplant recipients have been established to facilitate dose individualization. However, their applicability when extrapolated to other clinical centres is not clear. This study aimed to (1) evaluate model external predictability and (2) analyze potential influencing factors. METHODSPublished models were screened from the literature and were evaluated using an external dataset with 52 patients (609 trough samples) collected by postoperative day 90 via methods that included (1) prediction-based prediction error (PE%), (2) simulation-based prediction-and variability-corrected visual predictive check (pvcVPC) and normalized prediction distribution error (NPDE) tests and (3) Bayesian forecasting to assess the influence of prior observations on model predictability. The factors influencing model predictability, particularly the impact of structural models, were evaluated. RESULTSSixteen published models were evaluated. In prediction-based diagnostics, the PE% within ±30% was less than 50% in all models, indicating unsatisfactory predictability. In simulation-based diagnostics, both the pvcVPC and the NPDE indicated model misspecification. Bayesian forecasting improved model predictability significantly with prior 2-3 observations. The various factors influencing model extrapolation included bioassays, the covariates involved (CYP3A5*3 polymorphism, postoperative time and haematocrit) and whether non-linear kinetics were used. CONCLUSIONSThe published models were unsatisfactory in prediction-and simulation-based diagnostics, thus inappropriate for direct extrapolation correspondingly. However Bayesian forecasting could improve the predictability considerably with priors. The incorporation of non-linear pharmacokinetics in modelling might be a promising approach to improving model predictability. WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT• Population pharmacokinetics of tacrolimus in adult renal transplant recipients are continuously published to characterize pharmacokinetics and to identify covariates for dose individualization.• Whether these models can be extrapolated to clinical centres other than those where they were established remains unclear. WHAT THIS STUDY ADDS• For the first time, the predictive performances of all relevant models were evaluated in an independent cohort. • Models provide unsatisfactory results in the prediction-and simulation-based evaluations. However, Bayesian forecasting could improve the predictability considerably.• The factors influencing model predictability included bioassays, involved covariates and whether non-linear kinetics were employed.
The data suggests that the CYP3A4*18B genotype affects CsA pharmacokinetics during the first month following surgery in Chinese renal transplant recipients. Patients with CYP3A4*18B alleles may require higher doses of CsA to reach the target levels. Large prospective studies may be needed to further explore the impact of MDR1 and CYP3A5*3 polymorphisms on CsA pharmacokinetics in renal transplant recipients.
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