Towards precision dosing of vancomycin in critically ill patients: an evaluation of the predictive performance of pharmacometric models in ICU patients

Abstract

Objectives: Vancomycin dose recommendations depend on population pharmacokinetic models. These models have not been adequately assessed in critically ill patients, who exhibit large pharmacokinetic variability. This study evaluated model predictive performance in intensive care unit (ICU) patients and identified factors influencing model performance. Methods: Retrospective data from ICU adult patients administered vancomycin were used to evaluate model performance to predict serum concentrations a priori (no observed concentrations included) or with Bayesian forecasting (using concentration data). Predictive performance was determined using relative bias (rBias, bias) and relative root mean squared error (rRMSE, precision). Models were considered clinically acceptable if rBias was between ±20% and 95% confidence intervals included zero. Models were compared with rRMSE; no threshold was used. The influence of clinical factors on model performance was assessed with multiple linear regression. Results: Data from 82 patients were used to evaluate 12 vancomycin models. The Goti model was the only clinically acceptable model with both a priori (rBias 3.4%) and Bayesian forecasting (rBias 1.5%) approaches. Bayesian forecasting was superior to a priori prediction, improving with the use of more recent concentrations. Four models were clinically acceptable with Bayesian forecasting. Renal replacement therapy status (p < 0.001) and sex (p ¼ 0.007) significantly influenced the performance of the Goti model. Conclusions: The Goti, Llopis and Roberts models are clinically appropriate to inform vancomycin dosing in critically ill patients. Implementing the Goti model in dose prediction software could streamline dosing across both ICU and non-ICU patients, considering it is also the most accurate model in non-ICU patients.

A Model Averaging/Selection Approach Improves the Predictive Performance of Model‐Informed Precision Dosing: Vancomycin as a Case Study

“…The performance of the models was considered clinically acceptable if the rBias was between −20% and 20%, with the 95% confidence intervals (CI) including zero 25 . Additionally, the precision metric (rRMSE) should be as small as possible.…”

Do Vancomycin Pharmacokinetics Differ Between Obese and Non-obese Patients? Comparison of a General-Purpose and Four Obesity-Specific Pharmacokinetic Models

“…General-purpose PK models might be more useful in this context with expectations of being more generalizable than other models and, as shown in this study, could replace subgroup-specific models without compromising performance. Notably, Cunio et al 11 recently showed that the authors' general-purpose vancomycin model outperformed several ICU-specific PK models for vancomycin. Most notably, in line with the results of this study, Cunio et al 11 found that the a posteriori predictions of the authors' model showed a clinically acceptable performance (ie, relative bias between −20% and 20% and 95% CI including zero) in ICU patients.…”

From Therapeutic Drug Monitoring to Model‐Informed Precision Dosing for Antibiotics

“…The algorithm selects the best-fitting model (model selection) or a combination of models (model averaging) for an individual patient when TDM data become available in the course of therapy. When using this approach for heterogenous data sets for vancomycin in general ward and ICU populations, 47,51 the predictive performance was better after model averaging than for the best single model in previous external evaluations. 47,50,51 Another challenge for the use of population models in MIPD is that the PK in patients might not be stable over the entire course of therapy, and the most recent measured drug concentration usually carries the most information for Bayesian forecasting.…”

“…The algorithm selects the best‐fitting model (model selection) or a combination of models (model averaging) for an individual patient when TDM data become available in the course of therapy. When using this approach for heterogenous data sets for vancomycin in general ward and ICU populations, 47,51 the predictive performance was better after model averaging than for the best single model in previous external evaluations 47,50,51 …”