Many hospitals face barriers in the implementation of TDM services, this study aimed to evaluate a pharmacist-led TDM service to optimize patients’ outcomes. Adult patients who were administered vancomycin, gentamicin, or amikacin were included. The pre-phase included a retrospective assessment of patients and the intervention phase consisted of an educational program. The post-phase assessed patients based on TDM services provided by inpatient pharmacists on a 24-h, 7-day basis for 3 months. The primary outcome was to assess the mean difference in proportion of correct initial doses of prescribing orders. Secondary outcomes included assessing the mean differences in proportions of correct dose adjustments and correct drug sampling time. Seventy-five patients in each phase were eligible. Patients who received optimal initial dosing in the post-phase showed a higher statistical significance, mean difference of 0.31, [95% CI (0.181–0.4438), p < 0.0001]. Patients in the post-phase received more optimal dose adjustments, mean difference of 0.1, [95% CI (−0.560–0.260), p = 0.2113]. Drug levels were ordered more correctly in the post-phase, mean difference of 0.03, [95% CI (−0.129–0.189), p = 0.7110]. This study demonstrated the important role of TDM services led by pharmacists in optimizing the initial dosing for these antibiotics.

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The AUC0–24 is the most accurate way to track the vancomycin level while the Cmin 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 AUC0–24 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 AUC0–24 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 AUC0–24 and trough level. Among 342 patients included in the present study, the mean of the estimated vancomycin AUC0–24 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 AUC0–24 (400 to 600 mg.hr/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 AUC0–24 > 600 mg.hr/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 AUC0–24 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.