Would they accept it? An interview study to identify barriers and facilitators to user acceptance of a prescribing advice service

Model-informed precision dosing of vancomycin in clinical practice: an intervention development study

BACKGROUND

Current guidelines recommend dosing vancomycin based on the area under the concentration time curve (AUC) to maximise efficacy and minimise the risk of nephrotoxicity. The preferred approach to AUC-guided therapy is to apply model-informed precision dosing (MIPD). However, the adoption in clinical practice has been slow.

AIM

We aimed to develop an intervention, including a standardised MIPD workflow and an implementation plan for vancomycin AUC-guided dosing, in a Swedish tertiary hospital.

METHOD

The intervention was developed in a framework-guided process. The design phase included stakeholder feedback (nurses, pharmacists, physicians), local data collection and feasibility testing of intervention components with parallel consideration of implementation aspects. The hypothesised relationships between the different components, implementation strategies and the mechanism of action resulting in expected outcomes were represented by a logic model.

RESULTS

The final intervention consisted of a workflow for MIPD, with defined roles and responsibilities, as well as processes for data and information transfer. Details were provided in supportive documents; an instruction on therapeutic drug monitoring (TDM) sampling and documentation for nurses, and a detailed dosing software instruction for MIPD consultants and clinical pharmacists. Activities to facilitate implementation included the development of a local clinical routine for vancomycin dosing, staff training and recurring MIPD rounds.

CONCLUSION

An intervention for MIPD, with an implementation plan for AUC-guided dosing of vancomycin, was developed for a tertiary hospital setting. The process can be used as guidance for other institutions with similar context wishing to initiate MIPD.

Implementation Strategies Addressing Stakeholder-Perceived Barriers and Enablers to the Establishment of a Beta-Lactam Antibiotic Therapeutic Drug Monitoring Program: A Qualitative Analysis

BACKGROUND

Therapeutic drug monitoring (TDM) of beta-lactam antibiotics (beta-lactams) is increasingly recommended for optimizing antibiotic exposure in intensive care patients with sepsis. However, limited data are available on the implementation of beta-lactam TDM in complex health care settings. Theory-based approaches were used to systematically explore barriers and enablers perceived by key stakeholders in the implementation of beta-lactam TDM in the intensive care unit.

METHODS

In this qualitative descriptive study, the authors interviewed key stakeholders (n = 40): infectious disease physicians, intensive care unit physicians, pharmacists, clinical leaders, scientists, and nurses. The data were thematically analyzed and coded using the theoretical domains framework, and the codes and themes were mapped to the relevant domains of the capability, opportunity, and motivation behavior-change wheel model.

RESULTS

Barriers included a lack of knowledge, experience, evidence, and confidence, which led to concerns about capability, lack of resources, and harm in straying from standard practice. Access to education and guidelines, on-site assays with short turnaround times, communication among teams, and workflow integration were identified as enablers. A focus on patient care, trust in colleagues, and endorsement by hospital leaders were strong motivators. Pharmacist and nursing stakeholder groups emerged as key targets in the implementation of strategies.

CONCLUSIONS

Using theory-based approaches, the authors identified the key barriers and enablers to establishing beta-lactam TDM. These data were used to identify strategies, policies, and key target groups for the implementation of interventions.

Implementing Vancomycin Population Pharmacokinetic Models: An App for Individualized Antibiotic Therapy in Critically Ill Patients

In individualized therapy, the Bayesian approach integrated with population pharmacokinetic models (PopPK) for predictions together with therapeutic drug monitoring (TDM) to maintain adequate objectives is useful to maximize the efficacy and minimize the probability of toxicity of vancomycin in critically ill patients. Although there are limitations to implementation, model-informed precision dosing (MIPD) is an approach to integrate these elements, which has the potential to optimize the TDM process and maximize the success of antibacterial therapy. The objective of this work was to present an app for individualized therapy and perform a validation of the implemented vancomycin PopPK models. A pragmatic approach was used for selecting the models of Llopis, Goti and Revilla for developing a Shiny app with R. Through ordinary differential equation (ODE)-based mixed effects models from the mlxR package, the app simulates the concentrations' behavior, estimates whether the model was simulated without variability and predicts whether the model was simulated with variability. Moreover, we evaluated the predictive performance with retrospective trough concentration data from patients admitted to the adult critical care unit. Although there were no significant differences in the performance of the estimates, the Llopis model showed better accuracy (mean 80.88%; SD 46.5%); however, it had greater bias (mean -34.47%, SD 63.38%) compared to the Revilla et al. (mean 10.61%, SD 66.37%) and Goti et al. (mean of 13.54%, SD 64.93%) models. With respect to the RMSE (root mean square error), the Llopis (mean of 10.69 mg/L, SD 12.23 mg/L) and Revilla models (mean of 10.65 mg/L, SD 12.81 mg/L) were comparable, and the lowest RMSE was found in the Goti model (mean 9.06 mg/L, SD 9 mg/L). Regarding the predictions, this behavior did not change, and the results varied relatively little. Although our results are satisfactory, the predictive performance in recent studies with vancomycin is heterogeneous, and although these three models have proven to be useful for clinical application, further research and adaptation of PopPK models is required, as well as implementation in the clinical practice of MIPD and TDM in real time.