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

Abstract

Vancomycin is an important antibiotic for critically ill patients with Grampositive 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.

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

“…A number of barriers toward the implementation of MIPD have been identified, including the lack of clinically oriented training in MIPD, the lack of acceptance of more complex dosing strategies (by prescribers), 7 unclear reimbursement, or the lack of pharmaceutical industry support 8 . Another challenge associated with MIPD is the selection of an appropriate pharmacometric model and the related fit‐for‐purpose validation required 9 …”

Evaluation of the MeroRisk Calculator, A User-Friendly Tool to Predict the Risk of Meropenem Target Non-Attainment in Critically Ill Patients

“…The observed precision (50% prediction error interval ranging from −5.0 mg/L (−59%) to 1.2 mg/L (32%)) was judged to be acceptable for a critically ill patient population based on retrospective data from clinical routines. Compared to a prospective dataset collected in a controlled clinical trial, retrospective data collected during clinical routine is associated with a higher degree of uncertainty [ 32 ], which can inflate the observed imprecision in the model evaluation. The comparison of our results to previously published evaluations of meropenem models in critically ill patients further confirms the suitability of the investigated PK model: D’Haese et al evaluated eight meropenem population PK models in critically ill patients receiving meropenem as continuous infusion [ 31 ].…”

Continuous Learning in Model‐Informed Precision Dosing: A Case Study in Pediatric Dosing of Vancomycin

“…Jasmine H. Hughes 1, *, Dominic M. H. Tong 1 , Sarah Scarpace Lucas 2 , Jonathan D. Faldasz 1 , Srijib Goswami 1 and Ron J. Keizer 2 Model-informed precision dosing (MIPD) leverages pharmacokinetic (PK) models to tailor dosing to an individual patient's needs, improving attainment of therapeutic drug exposure targets and thus potentially improving drug efficacy or reducing adverse events.…”

“…Naive application of a previously published model could introduce bias or imprecision in dose selection. Developing a new model or validating existing models for each new patient population requires a sufficiently large prior data set collected from a sufficiently diverse group of patients and ideally from multiple institutions, and is time‐consuming 1,18 . These efforts could delay implementation of MIPD in a particular clinical site, which comes with its own risks of drug overexposure or underexposure.…”

“…Pharmacokinetic (PK) models have been brought to the point of care, aided by development and deployment of software tools that allow clinicians to estimate their patients’ PK parameters and simulate dosing regimens 1–3 . Preliminary studies suggest that this model‐informed precision dosing (MIPD) facilitates attainment of therapeutic targets, reduces drug‐induced adverse events, and improves clinical outcomes 4–6 .…”