Broadened Allocation of Pancreas Transplants Across Compatible ABO Blood Types

A modular simulation framework for organ allocation

BACKGROUND

We describe and validate a new simulation framework addressing important limitations of the Simulated Allocation Models (SAMs) long used to project population effects of transplant policy changes.

METHODS

We developed the Computational Open-source Model for Evaluating Transplantation (COMET), an agent-based model simulating interactions of individual donors and candidates over time to project population outcomes. COMET functionality is organized into interacting modules. Donors and candidates are synthetically generated using data-driven probability models which are adaptable to account for ongoing or hypothetical donor/candidate population trends and evolving disease management. To validate the first implementation of COMET, COMET-Lung, we attempted to reproduce lung transplant outcomes for U.S. adults from 2018-2019 and in the 6 months following adoption of the Composite Allocation Score (CAS) for lung transplant.

RESULTS

Simulated (median [Interquartile Range, IQR]) vs observed outcomes for 2018-2019 were: 0.162 [0.157, 0.167] vs 0.170 waitlist deaths per waitlist year; 1.25 [1.23, 1.28] vs 1.26 transplants per waitlist year; 0.115 [0.112, 0.118] vs 0.113 post-transplant deaths per patient year; 202 [102, 377] vs 165 nautical miles travel distance. The model accurately predicted the observed precipitous decrease in transplants received by type O lung candidates in the six months following CAS implementation.

CONCLUSIONS

COMET-Lung closely reproduced most observed outcomes. The use of synthetic populations in the COMET framework paves the way for examining possible transplant policy and clinical practice changes in populations reflecting realistic future states. Its flexible, modular nature can accelerate development of features to address specific research or policy questions across multiple organs.

Creating synthetic populations in transplantation: A Bayesian approach enabling simulation without registry re-sampling

Computer simulation has played a pivotal role in analyzing alternative organ allocation strategies in transplantation. The current approach to producing cohorts of organ donors and candidates for individual-level simulation requires directly re-sampling retrospective data from a transplant registry. This historical data may reflect outmoded policies and practices as well as systemic inequities in candidate listing, limiting contemporary applicability of simulation results. We describe the development of an alternative approach for generating synthetic donors and candidates using hierarchical Bayesian network probability models. We developed two Bayesian networks to model dependencies among 10 donor and 36 candidate characteristics relevant to waitlist survival, donor-candidate matching, and post-transplant survival. We estimated parameters for each model using Scientific Registry of Transplant Recipients (SRTR) data. For 100 donor and 100 candidate synthetic populations generated, proportions for each categorical donor or candidate attribute, respectively, fell within one percentage point of observed values; the interquartile ranges (IQRs) of each continuous variable contained the corresponding SRTR observed median. Comparisons of synthetic to observed stratified distributions demonstrated the ability of the method to capture complex joint variability among multiple characteristics. We also demonstrated how changing two upstream population parameters can exert cascading effects on multiple relevant clinical variables in a synthetic population. Generating synthetic donor and candidate populations in transplant simulation may help overcome critical limitations related to the re-sampling of historical data, allowing developers and decision makers to customize the parameters of these populations to reflect realistic or hypothetical future states.