Switching Vancomycin Monitoring From Trough Concentration to Area Under the Curve Estimation by Bayesian Forecasting: A Short Communication on a Cost-Benefit Study in Resource-Limited Settings.

BACKGROUND: This study was conducted to evaluate the cost-benefit indicators of a vancomycin monitoring protocol based on area under the curve estimation using commercial Bayesian software. METHODS: This quasi-experimental study included patients who were aged >18 years with a vancomycin prescription for >24 hours. Patients who were terminally ill or those with acute kidney injury (AKI) ≤24 hours were excluded. During the preintervention period, doses were adjusted based on the trough concentration target of 15-20 mg/L, whereas the postintervention period target was 400-500 mg × h/L for the area under the curve. The medical team was responsible for deciding to stop the antimicrobial prescription without influence from the therapeutic drug monitoring team. The main outcomes were the incidence of AKI and length of stay. Cost-benefit simulation was performed after statistical analysis. RESULTS: There were 96 patients in the preintervention group and 110 in the postintervention group. The AKI rate decreased from 20% (n = 19) to 6% (n = 6; P = 0.003), whereas the number of vancomycin serum samples decreased from 5 (interquartile range: 2-7) to 2 (interquartile range: 1-3) examinations per patient ( P < 0.001). The mean length of hospital stay for patients was 26.19 days after vancomycin prescription, compared with 17.13 days for those without AKI ( P = 0.003). At our institution, the decrease in AKI rate and reduced length of stay boosted yearly savings of up to US$ 369,000 for 300 patients receiving vancomycin therapy. CONCLUSIONS: Even in resource-limited settings, a commercial Bayesian forecasting-based protocol for vancomycin is important for determining cost-benefit outcomes.

Failure to predict amikacin elimination in critically ill patients with cancer based on the estimated glomerular filtration rate: applying PBPK approach in a therapeutic drug monitoring study.

PURPOSE: The aim of this work was to integrate the Therapeutic Drug Monitoring (TDM) with the model-informed precision dosing (MIPD) approach, using Physiologically-based Pharmacokinetic/Pharmacodynamic (PBPK/PD) modelling and simulation, to explore the relationship between amikacin exposure and estimated glomerular filtration rate (GFR) in critically ill patients with cancer. METHODS: In the TDM study, samples from 51 critically-ill patients with cancer treated with amikacin were analysed. Patients were stratified according to renal function based on GFR status. A full-body PBPK model with 12 organs model was developed using Simcyp V. 21, including steady-state volume of distribution of 0.21 L/kg and renal clearance of 6.9 L/h in healthy adults. PK parameters evaluated were within the 2-fold error range. RESULTS: During the validation step, predicted vs observed amikacin clearance values after single infusion dose in patients with normal renal function, mild and moderate renal impairment were 7.6 vs 8.1 L/h (7.5 mg/kg dose); 3.8 vs 4.5 L/h (1500 mg dose) and 2.2 vs 3.1 L/h (25 mg/kg dose), respectively. However, predicted vs observed amikacin clearance after a single dose infusion of 1400 mg in critically-ill patients with cancer were 1.46 vs 1.63 (P = 0.6406) L/h (severe), 2.83 vs 1.08 (P < 0.05) L/h (moderate), 4.23 vs 2.49 (P = 0.0625) L/h (mild) and 7.41 vs 3.36 (P < 0.05) L/h (normal renal function). CONCLUSION: This study demonstrated that estimated GFR did not predict amikacin elimination in critically-ill patients with cancer. Further studies are necessary to find amikacin PK covariates to optimize the pharmacotherapy in this population. Therefore, TDM of amikacin is imperative in cancer patients.