The Bayesian-Based Area under the Curve of Vancomycin by Using a Single Trough Level: An Evaluation of Accuracy and Discordance at Tertiary Care Hospital in KSA

The AUC(0-24) is the most accurate way to track the vancomycin level while the C-min is not an accurate surrogate. Most hospitals in Saudi Arabia are under-practicing the AUC-guided vancomycin dosing and monitoring. No previous work has been conducted to evaluate such practice in the whole kingdom. The current study objective is to calculate the AUC(0-24) using the Bayesian dosing software (PrecisePK), identify the probability of patients who receive the optimum dose of vancomycin, and evaluate the accuracy and precision of the Bayesian platform. This retrospective study was conducted at King Abdulaziz medical city, Jeddah. All adult patients treated with vancomycin were included. Pediatric patients, critically ill patients requiring ICU admission, patients with acute renal failure or undergoing dialysis, and febrile neutropenic patients were excluded. The AUC(0-24) was predicted using the PrecisePK platform based on the Bayesian principle. The two-compartmental model by Rodvold et al. in this platform and patients' dose data were utilized to calculate the AUC(0-24) and trough level. Among 342 patients included in the present study, the mean of the estimated vancomycin AUC(0-24) by the posterior model of PrecisePK was 573 +/- 199.6 mg, and the model had a bias of 16.8%, whereas the precision was 2.85 mg/L. The target AUC(0-24) (400 to 600 mg center dot h/L) and measured trough (10 to 20 mg/L) were documented in 127 (37.1%) and 185 (54%), respectively. Furthermore, the result demonstrated an increase in odds of AUC(0-24) > 600 mg center dot h/L among trough level 15-20 mg/L group (OR = 13.2, p < 0.05) as compared with trough level 10-14.9 mg/L group. In conclusion, the discordance in the AUC(0-24) ratio and measured trough concentration may jeopardize patient safety, and implantation of the Bayesian approach as a workable alternative to the traditional trough method should be considered.