OPTN/SRTR 2022 Annual Data Report: Lung.

For the first time since the COVID-19 pandemic, the annual number of lung transplants performed in the United States increased. The year 2022, encompassed in this report, marks the last full calendar year where the Lung Allocation Score was used for ranking transplant candidates based on their estimated transplant benefit and donor lung allocation in the United States. In March 2023, a major change in transplant allocation policy occurred with the implementation of the Composite Allocation Score. Transplant rates have increased over the past decade, although there is variability among age, diagnosis, racial and ethnic, and blood groups. Over half of candidates received a lung transplant within 3 months of placement on the waiting list, with nearly 75% of candidates accessing transplant by 1 year. Pretransplant mortality rates remained stable, with approximately 13% of lung transplant candidates dying or being removed from the waiting list within a year of listing. Posttransplant survival remained stable; however, variability exists by age, diagnosis, and racial and ethnic groups.

Costs of End-of-Life Hospitalizations in the United States for People With Pulmonary Diseases.

BACKGROUND: Lung transplantation is a lifesaving intervention for people with advanced lung disease, but it is costly and resource-intensive. To investigate the cost-effectiveness of lung transplantation as a treatment option in pulmonary disease, we must understand costs attributable to end-of-life hospitalizations for end-stage lung disease. RESEARCH QUESTION: What are the costs associated with end-of-life hospitalizations for people with pulmonary disease, and how have these trends changed over time? STUDY DESIGN AND METHODS: Adults aged 18 to 74 years with hospitalization data in the Cost and Utilization Project National Inpatient Sample data from 2009 to 2019 with a pulmonary disease admission were included in this analysis. Those with a history of lung transplantation were excluded. International Classification of Diseases codes were used to identify pulmonary disease admissions, complications, and procedures and interventions. Total charges were calculated for hospitalizations and stratified by patient status at time of discharge. Trends in charges over time were assessed by demographic and hospital factors. RESULTS: One hundred nine thousand nine hundred twenty-four (4.1%) hospital admissions for pulmonary disease resulted in in-hospital mortality. Those with obstructive lung disease accounted for 94.1% of hospitalizations and 88.1% cases of in-hospital mortality. Estimated costs for end-of-life hospitalizations were $29,981 on average with wide variation in cost by diagnosis and procedure utilization. Inpatient costs were highest for younger people who received more procedures. Among the most expensive admissions, mechanical ventilation accounted for the greatest proportion of interventions. Significant increases in the use of mechanical ventilation, extracorporeal membrane oxygenation, and dialysis occurred over the time period. The rate of hospital transfers increased with a proportionately greater increase across admissions resulting in in-hospital mortality. INTERPRETATION: Costs accrued during end-of-life hospitalizations vary across people but represent a significant health care cost that can be averted for selected people who undergo lung transplantation. These costs should be considered in studies of cost-effectiveness in lung transplantation.

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.

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.