Background Current guidelines for intravenous vancomycin identify drug exposure (as indicated by the AUC) as the best pharmacokinetic (PK) indicator of therapeutic outcome. Objectives To assess the accuracy of two Bayesian forecasting programs in estimating vancomycin AUC0–∞ in adults with limited blood concentration sampling. Methods The application of seven vancomycin population PK models in two Bayesian forecasting programs was examined in non-obese adults (n = 22) with stable renal function. Patients were intensively sampled following a single (1000 mg or 15 mg/kg) dose. For each patient, AUC was calculated by fitting all vancomycin concentrations to a two-compartment model (defined as AUCTRUE). AUCTRUE was then compared with the Bayesian-estimated AUC0–∞ values using a single vancomycin concentration sampled at various times post-infusion. Results Optimal sampling times varied across different models. AUCTRUE was generally overestimated at earlier sampling times and underestimated at sampling times after 4 h post-infusion. The models by Goti et al. (Ther Drug Monit 2018; 40: 212–21) and Thomson et al. (J Antimicrob Chemother 2009; 63: 1050–7) had precise and unbiased sampling times (defined as mean imprecision <25% and <38 mg·h/L, with 95% CI for mean bias containing zero) between 1.5 and 6 h and between 0.75 and 2 h post-infusion, respectively. Precise but biased sampling times for Thomson et al. were between 4 and 6 h post-infusion. Conclusions When using a single vancomycin concentration for Bayesian estimation of vancomycin drug exposure (AUC), the predictive performance was generally most accurate with sample collection between 1.5 and 6 h after infusion, though optimal sampling times varied across different population PK models.

BackgroundWe aimed to evaluate the effectiveness of contact investigation in comparison to passive case-detection alone and estimated the yield of co-prevalent and incident tuberculosis (TB), and latent tuberculosis infection (LTBI) among contacts of patients with TB.MethodsA systematic search was undertaken of studies published between January 1, 2011 and October 1, 2019 in the English language. The proportion of contacts diagnosed with co-prevalent TB, incident TB and/or LTBI was estimated. Evaluation of the effectiveness of contact investigation included randomised trials, while the yield of contact investigation (co-prevalent and incident TB and LTBI) was assessed in non-randomised studies.ResultsData were extracted from 244 studies, of which 187 studies measured the proportion of contacts diagnosed with TB disease and 135 studies measured LTBI prevalence. Individual randomised trials demonstrated that contact investigation increased TB case notification (RR 2.5 [95% CI: 2.0–3.2]), TB case detection (OR 1.34 [95% CI: 0.43–4.24]) and decreased mortality (RR 0.6 [95% CI: 0.4–0.8]) and population TB prevalence (risk ratio 0.82 [95% CI: 0.64–1.04]).The overall pooled prevalence of TB was 3.6% (95% CI: 3.3–4.0%; I2=98.9%, 181 studies). The pooled prevalence of microbiologically-confirmed TB was 3.2% (95% CI: 2.6–3.7%; I2=99.5%, 106 studies). The pooled incidence of TB was highest in the first year after exposure to index patients (2.0%, 95% CI: 1.1–3.3%; I2=96.2%, 14 studies) and substantially lower five years after exposure to index patient (0.5%, 95% CI: 0.3–0.9%; 1 study). The pooled prevalence of LTBI among contacts was 42.4% (95% CI: 38.5–46.4%; I2=99.8%, 135 studies).Conclusions and RelevanceThis systematic review and meta-analysis found that contact investigation was effective in high-burden settings. The higher pooled prevalence estimates of microbiologically-confirmed TB compared to previous reviews suggests newer rapid molecular diagnostics contribute to increased case detection.

Background: Vancomycin pharmacokinetics are best described using a 2-compartment model. However, 1-compartment population models are commonly used as the basis for dose prediction software. Therefore, the validity of using a 1-compartment model to guide vancomycin drug dosing was examined. Methods: Published plasma concentration–time data from adult subjects (n = 30) with stable renal function administered a single intravenous infusion of vancomycin were extracted from previous studies. The vancomycin area under the curve (AUC0–∞) was calculated for each subject using noncompartmental methods (AUCNCA) and by fitting 1- (AUC1CMT), 2- (AUC2CMT), and 3- (AUC3CMT) compartment infusion models. The optimal model fit was determined using the Akaike information criterion and visual inspection of the residual plots. The individual compartmental AUC0–∞ values from the 1- and 2-compartment models were compared with AUCNCA values using one-way repeated measures analysis of variance. Results: The mean (±SD) AUC estimates were similar for the different methods: AUCNCA 180 ± 86 mg·h/L, AUC1CMT 167 ± 79 mg·h/L, and AUC2CMT 183 ± 88 mg·h/L. Despite the overlapping AUC values, AUC2CMT and AUCNCA were significantly greater than AUC1CMT (P < 0.05). The 3-compartment model was excluded from the analysis because of the failure to converge in some instances. Conclusions: Dose prediction software using a 1-compartment model as the basis for Bayesian forecasting underestimates drug exposure (estimated as the AUC) by less than 10%. This is unlikely to be clinically significant with respect to dose adjustment. Therefore, a 1-compartment model may be sufficient to guide vancomycin dosing in adult patients with stable renal function.