Association of Vancomycin AUC/MIC and Trough Concentration With Early Clinical Response in Enterococcus or Coagulase-Negative Staphylococcus Infection: A Prospective Study.

This study was condcuted to examine the association of area under the curve (AUC)/minimum inhibitory concentration (MIC) and trough concentration (Ctrough) of vancomycin with treatment outcome and nephrotoxicity in infections caused by Enterococcus spp. and coagulase-negative Staphylococci (CoNS). Peak and trough concentrations were used to calculate AUC in 89 patients receiving vancomycin for infections with Enterococcus spp. (n = 65) or CoNS (n = 24). Correlations between Ctrough, AUC/MIC, early clinical response (ECR), and nephrotoxicity were assessed and cutoff values were determined. Sixty-three (70.8%) patients showed improvement in ECR and 10 (11.2%) experienced nephrotoxicity. Enterococcus spp. infections displayed correlations between AUC/MIC and ECR for AUC0-24 h/MIC (r2 = 0.27, P ≤ .05) and AUC24-48 h/MIC (r2 = 0.28, P ≤ .05), but not for Ctrough (r2 = 0.21, P > .05). There were no correlations between Ctrough (r2 = 0.26, P > .05), AUC0-24 h/MIC (r2 = -0.12, P > .05), AUC24-48 h/MIC (r2 = 0.01, P > .05) and ECR for CoNS. In the CoNS group, a moderate correlation was found between ECR and Ctrough at a cutoff value of 6.9 µg/mL. In addition, nephrotoxicity is also moderately associated with AUC0-24 h and AUC24-48 h at 505.7 and 667.1 µg•h/mL, respectively. A strong correlation between nephrotoxicity and Ctrough was observed when the cutoff value was 18.9 µg/mL. AUC/MIC during the first 48 h was a determinant of vancomycin efficacy in Enterococcus infections but not for CoNS. Ctrough was not correlated with clinical outcome. Nephrotoxicity could be predicted using Ctrough and AUC for infections with both pathogens.

Effect of drug use calendar on adherence to iron chelation therapy in young thalassemia patients.

Background: Regular blood transfusions in thalassemia patients can lead to severe complications and iron chelation therapy is required as a treatment. Thalassemia is common in Thailand and the drugs used in iron chelation therapy are deferoxamine and deferiprone. Adherence to the therapy is a key factor for treatment success. Objective: To assess the impact of a drug use calendar on deferiprone and deferoxamine adherence in young thalassemia patients. Methods: A total of 86 young thalassemia outpatients at a Thai tertiary care hospital were recruited into the study. Patients were stratified into two groups based on self-assessment of adherence using a visual analogue scale. One group (n=41) was given a calendar with the schedule of drug use in addition to counselling as standard pharmaceutical care. The second group (n=45) only received the counselling. Adherence to iron chelation therapy was assessed by deferiprone pill or deferoxamine vial counts on six visits (V1 to V6) and results were compared between visits and groups using a multilevel linear regression model. Change in serum ferritin levels after 6 visits (n = 81) were compared using a linear regression model. Results: Adherence significantly increased in both the calendar and non-calendar groups for deferiprone mono- and combination-therapy and for deferoxamine monotherapy. In the calendar groups, average adherence increased by between 2.05 and 5.66% per visit compared to increases of 0.31 to3.92% per visit in the non-calendar groups. A significant difference in the increase in adherence per visit between the calendar and non-calendar groups was only observed for deferiprone monotherapy (3.03% SEM = 0.49vs 1.42% SEM =0.49, respectively, P-value = 0.0078). The serum ferritin level decreased in the calendar group by 20.25ng/mL (SEM = 23.80) and increased in the non-calendar group by 59.63 ng/mL (SEM = 23.01, P-value = 0.0147). Conclusion: Provision of a drug use calendar improved adherence to deferoxamine and deferiprone and decreased serum ferritin levels in young Thai thalassemia patients over the improvements obtained from standard counselling.

Population Pharmacokinetic Modeling of Vancomycin in Thai Patients With Heterogeneous and Unstable Renal Function.

BACKGROUND: Vancomycin is widely used to treat gram-positive bacterial infections. However, given significant interpatient variability in its pharmacokinetics, maintaining plasma concentrations is difficult within its characteristically narrow therapeutic window. This is especially challenging in patients with unstable renal function. Thus, the aim of this study was to develop a population pharmacokinetic model for vancomycin that is suitable for Thai patients with variable renal functions, including those with unstable renal function. METHODS: Data from 213 patients, including 564 blood samples, were retrospectively collected; approximately 70% patients exhibited unstable renal function during vancomycin treatment. The model building group was randomly assigned 108 patients and the remaining 33 patients comprised the validation group. A population pharmacokinetic model was developed that incorporated drug clearance (CL) as a function of time-varying creatine clearance (CrCL). The predictive ability of the resulting population model was evaluated using the validation data set, including its ability to forecast serum concentrations within a Bayesian feedback algorithm. RESULTS: A 2-compartment model with drug CL values that changed with time-varying CrCL adequately described vancomycin pharmacokinetics in the evaluated heterogeneous patient population with unstable renal function. Vancomycin CL was related to time-varying CrCL as follows: CL (t) = 0.11 + 0.021 × CrCL (t) (CrCL <120 mL/min. Using the population model, Bayesian estimation with at least one measured serum concentration resulted in a forecasting error of small bias (-2.4%) and adequate precision (31.5%). CONCLUSIONS: In hospitals with a high incidence of unstable renal function, incorporating time-varying CrCL with Bayesian estimation and at least one measured drug concentration, along with frequent CrCL monitoring, improves the predictive performance of therapeutic drug monitoring of vancomycin.

Prediction Of Serum Digoxin Concentration Using Estimated Glomerular Filtration Rate In Thai Population.

PURPOSE: Serum digoxin concentration (SDC) monitoring may be unavailable in some healthcare settings. Predicted SDC comes into play in the efficacy and toxicity monitoring of digoxin. Renal function is the important parameter for predicting SDC. This study was conducted to compare measured and predicted SDC when using creatinine clearance (CrCl) from Cockcroft-Gault (CG) equation and estimated glomerular filtration rate (eGFR) calculated from CKD-Epidemiology Collaboration (CKD-EPI), re-expressed Modification of Diet in Renal Disease (Re-MDRD4), Thai-MDRD4, and Thai-eGFR equations in Sheiner's and Konishi's pharmacokinetic models. PATIENTS AND METHODS: In this retrospective study, patients with cardiovascular disease with a steady-state of SDC within 0.5-2.0 mcg/L were enrolled. CrCl and studied eGFR adjusted for body surface area (BSA) were used in the models to determine the predicted SDC. The discrepancies of the measured and the predicted SDC were analyzed and compared. RESULTS: One hundred and twenty-four patients ranging in age from 22 to 88 years (median 60 years, IQR 50.2, 69.2) were studied. Their serum creatinine ranged from 0.40 to 1.80 mg/dL (median 0.90 mg/dL, IQR 0.79, 1.10). The mean±SD of measured SDC was 1.12±0.34 mcg/L. In the Sheiner's model, the mean predicted SDC was calculated by using the CG and the BSA adjusted CKD-EPI equations and was not different when compared with the measured levels (1.10±0.36 mcg/L (p=0.669) and 1.08±0.42 mcg/L (p=0.374), respectively). The CG, CKD-EPI, and Re-MDRD4 equations were a better fit for patients with creatinine ≥0.9 mg/dL for prediction with minimal errors. In the Konishi's model, the predicted SDC using the CG and the studied eGFR equation was lower than the measured SDC (p<0.05). CONCLUSION: In Sheiner's model, the CG and the BSA adjusted CKD-EPI equations should be used for predicting SDC, especially in patients with serum creatinine ≥0.9 mg/dL. The other studied eGFRs underestimated SDC in both Sheiner's and Konishi's model.