Optimizing Lung Transplant Benefit Using Agent-Based Simulation Modeling Approaches

PROJECT SUMMARY/ABSTRACT US lung allocation policy has made steady advances in its goal of improving survival of patients with end-stage lung diseases, but progress is stymied by historically used methodologies that cannot be adapted to capture the complexity of contemporary lung transplantation. Limitations in the lung allocation policies – including poorly performing risk models and adverse impacts of strict geographic boundaries – have impeded the ability to achieve equity in allocation and to maximize utility by optimizing transplant outcomes to promote the aggregate societal good. This project applies innovative and advanced statistical and mathematical modeling methodologies toward the goal of achieving equity and maximizing transplant outcomes. Building on the prior award, we will apply the novel framework for capturing the dynamic state of end-stage lung disease and advanced simulation techniques, evaluating the effects of alternative allocation strategies on the donor supply, candidate and recipient outcomes, and system-wide efficiencies. We will seek to understand the impact of the transition of the US lung allocation system to the Composite Allocation Score (CAS) system, improve the equity and utility of CAS-based lung allocation, develop a simulation-based lung allocation model and evaluate the potential impact of using this model to inform allocation decisions. We will introduce and study the potential improvements in individual- and population-level outcomes that may be achievable by moving from the current score-based framework focused on individual urgency and benefit to a simulation-based lung allocation framework focused on the system as a whole.