Prospective validation of a model‐informed precision dosing tool for vancomycin in intensive care patients.

INTENSIVE care patients; STANDARD deviations; GRAM-positive bacterial infections; GENERIC drugs; CRITICALLY ill; ACQUISITION of data

Aims: Vancomycin is an important antibiotic for critically ill patients with Gram‐positive bacterial infections. Critically ill patients typically have severely altered pathophysiology, which leads to inefficacy or toxicity. Model‐informed precision dosing may aid in optimizing the dose, but prospectively validated tools are not available for this drug in these patients. We aimed to prospectively validate a population pharmacokinetic model for purpose model‐informed precision dosing of vancomycin in critically ill patients. Methods: We first performed a systematic evaluation of various models on retrospectively collected pharmacokinetic data in critically ill patients and then selected the best performing model. This model was implemented in the Insight Rx clinical decision support tool and prospectively validated in a multicentre study in critically ill patients. The predictive performance was obtained as mean prediction error and relative root mean squared error. Results: We identified 5 suitable population pharmacokinetic models. The most suitable model was carried forward to a prospective validation. We found in a prospective multicentre study that the selected model could accurately and precisely predict the vancomycin pharmacokinetics based on a previous measurement, with a mean prediction error and relative root mean squared error of respectively 8.84% (95% confidence interval 5.72–11.96%) and 19.8% (95% confidence interval 17.47–22.13%). Conclusion: Using a systematic approach, with a retrospective evaluation and prospective verification we showed the suitability of a model to predict vancomycin pharmacokinetics for purposes of model‐informed precision dosing in clinical practice. The presented methodology may serve a generic approach for evaluation of pharmacometric models for the use of model‐informed precision dosing in the clinic. [ABSTRACT FROM AUTHOR]

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