Multicenter Study of the Real-World Use of Ceftaroline versus Vancomycin for Acute Bacterial Skin and Skin Structure Infections.

The objective of this study was to determine if real-world ceftaroline treatment in adults hospitalized for acute bacterial skin and skin structure infections (ABSSSI) is associated with decreased infection-related length of stay (LOSinf) compared to that with vancomycin. This was a retrospective, multicenter, cohort study from 2012 to 2017. Cox proportional hazard regression, propensity score matching, and inverse probability of treatment weighting (IPTW) were used to determine the independent effect of treatment group on LOSinf The patients were adults hospitalized with ABSSSI and treated with ceftaroline or vancomycin for ≥72 h within 120 h of diagnosis at four academic medical centers and two community hospitals in Arizona, Florida, Michigan, and West Virginia. A total of 724 patients were included (325 ceftaroline treated and 399 vancomycin treated). In general, ceftaroline-treated patients had characteristics consistent with a higher risk of poor outcomes. The unadjusted median LOSinf values were 5 (interquartile range [IQR], 3 to 7) days and 6 (IQR, 4 to 8) days in the vancomycin and ceftaroline groups, respectively (hazard ratio [HR], 0.866; 95% confidence interval [CI], 0.747 to 1.002). The Cox proportional hazard model (adjusted HR [aHR], 0.891; 95% CI, 0.748 to 1.060), propensity score-matched (aHR, 0.955; 95% CI, 0.786 to 1.159), and IPTW (aHR, 0.918; 95% CI, 0.793 to 1.063) analyses demonstrated no significant difference in LOSinf between groups. Patients treated with ceftaroline were significantly more likely to meet criteria for discharge readiness at day 3 in unadjusted and adjusted analyses. Although discharge readiness at day 3 was higher in ceftaroline-treated patients, LOSinf values were similar between treatment groups. Clinical and nonclinical factors were associated with LOSinf.

Towards precision dosing of vancomycin: a systematic evaluation of pharmacometric models for Bayesian forecasting.

OBJECTIVES: Vancomycin is a vital treatment option for patients suffering from critical infections, and therapeutic drug monitoring is recommended. Bayesian forecasting is reported to improve trough concentration monitoring for dose adjustment. However, the predictive performance of pharmacokinetic models that are utilized for Bayesian forecasting has not been systematically evaluated. METHOD: Thirty-one published population pharmacokinetic models for vancomycin were encoded in NONMEM®7.4. Data from 292 hospitalized patients were used to evaluate the predictive performance (forecasting bias and precision, visual predictive checks) of the models to forecast vancomycin concentrations and area under the curve (AUC) by (a) a priori prediction, i.e., solely by patient characteristics, and (b) also including measured vancomycin concentrations from previous dosing occasions using Bayesian forecasting. RESULTS: A priori prediction varied substantially-relative bias (rBias): -122.7-67.96%, relative root mean squared error (rRMSE) 44.3-136.8%, respectively-and was best for models which included body weight and creatinine clearance as covariates. The model by Goti et al. displayed the best predictive performance with an rBias of -4.41% and an rRMSE of 44.3%, as well as the most accurate visual predictive checks and AUC predictions. Models with less accurate predictive performance provided distorted AUC predictions which may lead to inappropriate dosing decisions. CONCLUSION: There is a diverse landscape of population pharmacokinetic models for vancomycin with varied predictive performance in Bayesian forecasting. Our study revealed the Goti model as suitable for improving precision dosing in hospitalized patients. Therefore, it should be used to drive vancomycin dosing decisions, and studies to link this finding to clinical outcomes are warranted.