A Global Review of Organ Allocation Simulation Models.

Area of Science:

• Transplantation research
• Health policy analysis
• Biomedical informatics

Background:

• Simulated Allocation Models (SAMs) have been pivotal since the 1980s for forecasting organ allocation policy impacts.
• US models like KP-SAM, LSAM, and TSAM have influenced organ allocation policies.
• Global SAMs address region-specific challenges in organ transplantation.

Purpose of the Study:

• To categorize and compare global Simulated Allocation Models (SAMs) based on assumptions, data, and methodologies.
• To identify challenges in SAM validation, synthetic data usage, and model transparency.
• To inform future research and policy development in organ transplantation.

Main Methods:

• Review and categorization of existing Simulated Allocation Models (SAMs) worldwide.
• Comparative analysis of SAMs' core assumptions, data sources, and modeling approaches.
• Identification and discussion of challenges related to model validation and transparency.

Main Results:

• SAMs vary globally, with specific models developed for regions like Eurotransplant (e.g., ETKidney, ELAS).
• Key challenges include model validation, reliance on synthetic data, and lack of transparency.
• Simplifying assumptions in SAMs necessitate clear communication of their impact on predictions.

Conclusions:

• Effective use of SAMs requires clear communication of assumptions and their influence on outcomes.
• Robust model validation using retrospective and prospective data is crucial for assessing performance.
• Enhanced transparency, open-source models, and detailed reporting can improve collaboration and confidence in transplant research.