Presentation (CTI)

OBJECTIVES: Suboptimal dosing and antimicrobial resistance are major contributors to treatment failure in infectious diseases. These challenges are particularly critical in low- and middle-income countries, where the burden of infectious diseases is high and healthcare resources are limited. Optimizing dosing strategies is essential to improving clinical outcomes and reducing the economic burden. This paper aims to conceptualize an integrated framework that combines a pharmacometric multistate model with cost-effectiveness analysis to support evidence-based decision-making.
METHODS: Pharmacometric multistate modelling offers a robust framework for describing patient transitions between key health states such as infection, hospitalization, recovery, and death. This approach can account for competing risks, providing a more realistic representation of clinical pathways. Importantly, it allows for the evaluation of patient-specific factors (e.g., age, sex, comorbidities), drug dose, and time-varying drug exposure on transition rates, thereby enhancing the understanding of disease progression and treatment responses. The developed model can simulate clinically relevant outcomes across a range of dosing scenarios, including untested or novel strategies, and identify optimized regimens for patient subgroups at greater risk of treatment failure or death. Model-predicted outcomes generated by this type of model can serve as inputs for cost-effectiveness analysis.
RESULTS: Incorporating model-predicted outcomes from the pharmacometric multistate model into cost-effectiveness analysis by comparing standard treatment with optimized dosing strategies offers a comprehensive framework for assessing both clinical benefits and economic impact. This approach thus addresses the current disconnect between clinical and economic modelling.
CONCLUSIONS: The integration of a pharmacometric multistate model with cost-effectiveness analysis could support model-based informed decision-making in both clinical practice and health policy. Advancing this methodology may enhance the efficiency, equity, and long-term sustainability of treatment strategies, particularly in resource-limited settings, where optimizing both health outcomes and costs is essential.