Evidence for better decisions.

Since implementation of the Composite Allocation Score (CAS) in March 2023, exception requests have increased, suggesting potential misalignment between policy intent and the actual prioritization achieved. Using the Scientific Registry of Transplant Recipients, we computed CAS component scores and quantified each component's contribution to total CAS variability. Medical urgency constitutes 25 of the 100 possible CAS points but was responsible for 40.6% of variation in total scores. Biological disadvantage is assigned 15 points but accounted for 33.3% of CAS variation; and geographic efficiency is assigned 10 points but drove 16.6% of variation. Posttransplant survival (25 points) accounted for the most points received but explained only 6.4% of variation. These results indicate that lung transplant candidate prioritization in the United States is dominated by medical urgency, biological disadvantage, and geographic efficiency, revealing a divergence between intended and actual contributions of CAS components to total scores.

Importance: Inequitable access to transplant in the US is well recognized, yet the nature and extent of upstream disparities in care prior to transplant are unknown.

Objective: To understand patterns of referral for lung transplant by race, ethnicity, and neighborhood-level socioeconomic status.

Design, setting, and participants: This retrospective cohort study included adults aged 18 to 80 years with obstructive and restrictive lung disease from a single large-volume transplant center in Cleveland, Ohio, who were diagnosed between January 1, 2006, and May 11, 2023.

Exposures: Neighborhood resources.

Main outcomes and measures: The main outcome was the transition to the next stage of the transplant care continuum, death, or a lapse in care. Cause-specific Cox proportional hazards regression models were used to account for death as a competing risk, adjusting for age at index encounter (respective to each cohort), diagnosis, and sex as covariates.

Results: This study included 30 050 patients with obstructive and restrictive lung disease with primary care encounters (mean [SD] age, 65 [13] years; 56.1% female), 73 817 with a pulmonary medicine encounter, 4198 undergoing lung transplant evaluation, and 1378 on the lung transplant waiting list. In a multivariable model including age, diagnosis, sex, area deprivation index, and race and ethnicity (including 3.3% Hispanic, 15.2% non-Hispanic Black, and 81.5% non-Hispanic White individuals), patients residing in the least-resourced neighborhoods were 97% more likely to die without transitioning to pulmonary medicine (hazard ratio [HR], 1.97 [95% CI, 1.78-2.17]), 90% more likely to die prior to lung transplant evaluation (HR, 1.90 [95% CI, 1.77-2.04]), 40% more likely to die prior to placement on the waiting list (HR, 1.40 [95% CI, 1.11-1.76]), and 97% more likely to die prior to transplant (HR, 1.97 [95% CI, 1.18-3.29]) compared with patients residing in the most-resourced neighborhoods. These patients were also 13% less likely to transition to pulmonary medicine (HR, 0.87 [95% CI, 0.82-0.92]) and 45% less likely to be placed on the waiting list (HR, 0.55 [95% CI, 0.44-0.68]) despite a 69% increased likelihood of transplant evaluation (HR, 1.69 [95% CI, 1.36-2.09]). While non-Hispanic Black patients had lower risks of death across all stages of care, they experienced a 39% lower likelihood of proceeding to lung transplant evaluation (HR, 0.61 [95% CI, 0.51-0.74]). Racial differences in the cumulative incidence of waiting list placement were found, but differences were not consistent across levels of neighborhood resources.

Conclusions and relevance: In this retrospective cohort study of patients diagnosed with restrictive and obstructive pulmonary disease, increased mortality risks and decreased likelihood of care escalations for patients who were socioeconomically disadvantaged and for racial and ethnic minority patients were found. These results suggest potential interventions for advancing equitable access to lung transplant.

The lung continuous distribution system was modified on September 27, 2023, with the goal of increasing transplant access for blood type O candidates after an error was discovered in the simulation used to support the development of the initial allocation policy. This retrospective observational study compares national waitlist outcomes (transplant rate, waitlist mortality) under continuous distribution before (March 10, 2023, through September 26, 2023; premodification) and after (September 27, 2023, through April 14, 2024; postmodification) the blood type score modification. We fit adjusted Poisson regression models of the transplant rate and mortality rate. The transplant rate was lowest for type O candidates in both eras, but significantly increased after the score modification, from a premodification adjusted rate ratio (95% CI) of 0.40 (0.36, 0.45) to postmodification 0.52 (0.45, 0.59), relative to premodification type A candidates. The adjusted mortality incidence (95% CI) decreased in type O candidates from 3.6% (3.0%, 4.3%) premodification to 3.2% (2.6%, 3.8%) postmodification. In an exploratory analysis, we estimated there would have been the same number of waitlist deaths (approximately 105) if the modified score had been adopted at the start of continuous distribution; however, transplants would have shifted toward type O candidates (57.8 [95% CI: 35.1, 80.9] additional transplants) and deaths would have shifted away from type O candidates (4.6 [95% CI: 2.7, 6.8] fewer deaths).

Background: The lung Composite Allocation Score (CAS) accounts separately for biological disadvantages stemming from candidate blood type and height using consensus-derived heuristics, which do not reflect the true supply of compatible organs available to candidates with specific combinations of blood type and height. Here, we develop an alternative CAS biological disadvantages subscore using a novel measure of donor supply.

Methods: Using Scientific Registry of Transplant Recipients data from February 19, 2015 to September 1, 2021, we modeled daily distance-adjusted supply of compatible donors, as a function of candidate blood type, height, and diagnosis group, using Poisson rate regression and applied the model to create a 10-point supply-based subscore. Substituting this subscore in place of the 10 total points allocated to blood type and height in CAS created a "Supply-Adjusted CAS". We simulated population outcomes under Supply-Adjusted CAS, original CAS (March 2023) and "ABO Modified" CAS (September 2023).

Results: The supply-based subscore was more responsive to variations in candidate blood type, height, and diagnosis group than corresponding CAS or ABO-Modified CAS subscores. In simulation, waitlist mortality improved from 13.95 per 100 waitlist years under CAS and 14.12 under ABO-Modified CAS to 13.09 under Supply-Adjusted CAS. Transplant rates improved from 121.6 and 126.2 under CAS and ABO-Modified CAS, respectively, to 128.8 under Supply-Adjusted CAS. Height disparities improved substantially, while blood type disparities grew slightly relative to ABO-Modified CAS.

Conclusions: Supply-Adjusted CAS may improve lung transplant population outcomes overall while providing a more empirically based method to address equity.

Background: Predicting post-transplant (PT) survival in lung allocation remains an elusive goal. We analyzed the impact of donor factors on PT survival and how these relationships vary among transplant recipients.

Methods: We studied primary bilateral lung transplant recipients (n = 7,609) from the US Scientific Registry of Transplant Recipients (19 February 2015-1 February 2020). Main and interaction effects were evaluated and adjusted across candidate age, sex, and diagnosis. Models predicting PT survival were compared to the PT Composite Allocation Score model (PT-CAS): (1) Cox regression donor multivariable model (COX), (2) COX + PT-CAS, (3) random forest model (RF), and (4) RF + PT-CAS. Model discrimination and calibration measures were compared.

Results: Interactions between donor and recipient factors emerged by age: lower survival for donation after circulatory death organs for recipients aged 55 to 69 years, donor smoking for recipients aged 30 to 54 and 70+, Hispanic donor for recipients <30, non-Hispanic Black donor for recipients aged 30+; sex: cytomegalovirus mismatch for males; diagnosis: higher donor recipient weight ratio for diagnosis group C (e.g., cystic fibrosis), donor diabetes for diagnosis group D (e.g., idiopathic pulmonary fibrosis). COX and RF models performed similarly to PT-CAS; however, the combined COX + PT-CAS model had improved discrimination (1-year area under the receiver operator characteristic curve [AUC] PT-CAS 0.609 vs 1-year AUC COX + PT-CAS 0.626) and improved calibration across a broader range of predicted risk.

Conclusions: The influence of donor factors on recipient PT survival differed by age, sex, and diagnosis. The addition of donor factors to existing models predicting PT survival led to only modest improvement in prediction accuracy. Future efforts may focus on optimizing matching strategies to improve donor utilization.

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.

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.

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.

Rationale: U.S. lung transplant mortality risk models do not account for patients' disease progression as time accrues between mandated clinical parameter updates. Objectives: To investigate the effects of accrued waitlist (WL) time on mortality in lung transplant candidates and recipients beyond those expressed by worsening clinical status and to present a new framework for conceptualizing mortality risk in end-stage lung disease. Methods: Using Scientific Registry of Transplant Recipients data (2015-2020, N = 12,616), we modeled transitions among multiple clinical states over time: WL, posttransplant, and death. Using cause-specific and ordinary Cox regression to estimate trajectories of composite 1-year mortality risk as a function of time from waitlisting to transplantation, we quantified the predictive accuracy of these estimates. We compared multistate model-derived candidate rankings against composite allocation score (CAS) rankings. Measurements and Main Results: There were 11.5% of candidates whose predicted 1-year mortality risk increased by >10% by day 30 on the WL. The multistate model ascribed lower numerical rankings (i.e., higher priority) than CAS for those who died while on the WL (multistate mean; median [interquartile range] ranking at death, 227; 154 [57-334]; CAS median [interquartile range] ranking at death, 329; 162 [11-668]). Patients with interstitial lung disease were more likely to have increasing risk trajectories as a function of time accrued on the WL compared with other lung diagnoses. Conclusions: Incorporating the effects of time accrued on the WL for lung transplant candidates and recipients in donor lung allocation systems may improve the survival of patients with end-stage lung diseases on the individual and population levels.

Background: Predicting risk of waitlist mortality and subsequent classification of lung transplant candidates has been difficult due to inter-relatedness of risk factors, differential risk across populations, and changes in relationships over time. We developed a clinically intuitive indexing system to simplify mortality risk assessment.

Methods: Scientific Registry of Transplant Recipients data from February 19, 2015, to May 26, 2020 (n = 13,726) were used to estimate 3 constructs. Airway and oxygen function indices were estimated using confirmatory factor analysis and hierarchical clustering was used to derive respiratory support clusters. Cox proportional hazards regression was used to characterize event-free waitlist survival by constructs (3), age, sex, and diagnosis group. Model performance was compared to the Lung Allocation Score/Composite Allocation Score (LAS/CAS).

Results: Airway and oxygen function indices were created with substantive factor loadings forced expiratory volume (0.86), forced vital capacity (0.64), partial pressure of carbon dioxide (0.56) and PO2/fraction of inspired oxygen (0.83), partial pressure of oxygen (0.59), and mean pulmonary artery pressure (0.30), respectively. Four respiratory support clusters (C1: as needed O2, C2: continuous O2, C3: continuous O2/positive pressure ventilation (PPV), C4: PPV + extracorporeal membrane oxygenation) were identified. Constructs were used to identify patient profiles. Model area under the receiver operating characteristic curve was 0.85 [0.84, 0.87] compared to the LAS 0.92 [0.91, 0.94] at 4 weeks. Risk predictions were relatively insensitive to airway and oxygen function indices in C1 and C4 but varied across C2 and C3.

Conclusions: Reducing the dimensionality of waitlist mortality risk offers an opportunity to identify clinical phenotypes that are more nuanced and thus more interpretable than current risk assessment provided by the LAS/CAS models.

Background: The use of extracorporeal membrane oxygenation (ECMO) is not currently incorporated into US allocation models due to the historical lack of complete data in the national US registry which changed in 2016 to include ECMO at the time of waitlist removal and more granular timing and configuration data.

Methods: We studied adult lung transplant candidates from May 1, 2016 to June 1, 2020 with data abstracted from multiple sources in the US Scientific Registry of Transplant Recipients. Waitlist analyses included cumulative incidence functions and Cox proportional hazards models considering ECMO as a time-dependent variable. Post-transplant analyses included Kaplan Meier, Cox proportional hazards models, and observed to expected survival ratios.

Results: A total of 867 candidates were on ECMO prior to transplant; 247 were identified using new sources of data. Candidates on ECMO had a 23.9 increased adjusted likelihood of waitlist removal for being too sick or death, but only a 4.08 increased adjusted likelihood of transplant. Candidates bridged with ECMO who underwent lung transplant (N = 587) experienced an increased overall hazard of post-transplant mortality with veno-arterial and veno-venous configurations conferring hazard ratio (HR) = 1.67 (95% CI, 1.16, 2.40), HR = 1.45 (95% CI, 1.15, 1.82), respectively.

Conclusions: We identified an additional 28.5% of candidates bridged with ECMO prior to transplant using new data. This study of the newly identified full cohort of ECMO candidates demonstrates higher utilization of ECMO as well as an underestimation of waitlist mortality risk factors that should inform strategies to provide timely access to transplants for this population.

Importance: A recent National Academies of Sciences, Engineering, and Medicine study found that transplant outcomes varied greatly based on multiple factors, including race, ethnicity, and geographic location. They proposed a number of recommendations including studying opportunities to improve equity in organ allocation.

Objective: To evaluate the role of donor and recipient socioeconomic position and region as a mediator of observed racial and ethnic differences in posttransplant survival.

Design, setting, and participants: This cohort study included lung transplant donors and recipients with race and ethnicity information and a zip code tabulation area-defined area deprivation index (ADI) from September 1, 2011, to September 1, 2021, whose data were in the US transplant registry. Data were analyzed from June to December 2022.

Exposures: Race, neighborhood disadvantage, and region of donors and recipients.

Main outcomes and measures: Univariable and multivariable Cox proportional hazards regression were used to study the association of donor and recipient race with ADI on posttransplant survival. Kaplan-Meier method estimation was performed by donor and recipient ADI. Generalized linear models by race were fit, and mediation analysis was performed. Bayesian conditional autoregressive Poisson rate models (1, state-level spatial random effects; 2, model 1 with fixed effects for race and ethnicity, 3; model 2 excluding region; and 4: model 1 with fixed effects for US region) were used to characterize variation in posttransplant mortality and compared using ratios of mortality rates to the national average.

Results: Overall, 19 504 lung transplant donors (median [IQR] age, 33 [23-46] years; 3117 [16.0%] Hispanic individuals, 3667 [18.8%] non-Hispanic Black individuals, and 11 935 [61.2%] non-Hispanic White individuals) and recipients (median [IQR] age, 60 [51-66] years; 1716 [8.8%] Hispanic individuals, 1861 [9.5%] non-Hispanic Black individuals, and 15 375 [78.8%] non-Hispanic White individuals) were included. ADI did not mediate the difference in posttransplant survival between non-Hispanic Black and non-Hispanic White recipients; it mediated only 4.1% of the survival difference between non-Hispanic Black and Hispanic recipients. Spatial analysis revealed the increased risk of posttransplant death among non-Hispanic Black recipients may be associated with region of residence.

Conclusions and relevance: In this cohort study of lung transplant donors and recipients, socioeconomic position and region of residence did not explain most of the difference in posttransplant outcomes among racial and ethnic groups, which may be due to the highly selected nature of the pretransplant population. Further research should evaluate other potentially mediating effects contributing to inequity in posttransplant survival.

The importance of waitlist (WL) mortality risk estimates will increase with the adoption of the US Composite Allocation Score (CAS) system. Calibration is rarely assessed in clinical prediction models, yet it is a key factor in determining access to lung transplant. We assessed the calibration of the WL-lung allocation score (LAS)/CAS models and developed alternative models to minimize miscalibration. Scientific Registry of Transplant Recipients data from 2015 to 2020 were used to assess the calibration of the WL model and for subgroups (age, sex, diagnosis, and race/ethnicity). Three recalibrated models were developed and compared: (1) simple recalibration model (SRM), (2) weighted recalibration model 1 (WRM1), and (3) weighted recalibration model 2 (WRM2). The current WL-LAS/CAS model underestimated risk for 78% of individuals (predicted mortality risk, <42%) and overpredicted risk for 22% of individuals (predicted mortality risk, ≥42%), with divergent results among subgroups. Error measures improved in SRM, WRM1, and WRM2. SRM generally preserved candidate rankings, whereas WRM1 and WRM2 led to changes in ranking by age and diagnosis. Differential miscalibration occurred in the WL-LAS/CAS model, which improved with recalibration measures. Further inquiry is needed to develop mortality models in which risk predictions approximate observed data to ensure accurate ranking and timely access to transplant. IMPACT: With changes to the lung transplant allocation system planned in 2023, evaluation of the accuracy and precision of survival models used to rank candidates for lung transplant is important. The waitlist model underpredicts risk for 78% of US transplant candidates with an unequal distribution of miscalibration across subgroups leading to inaccurate ranking of transplant candidates. This work will serve to inform future efforts to improve modeling efforts in the US lung transplant allocation system.

Background: As broader geographic sharing is implemented in lung transplant allocation through the Composite Allocation Score (CAS) system, models predicting waitlist and posttransplant (PT) survival will become more important in determining access to organs.

Research question: How well do CAS survival models perform, and can discrimination performance be improved with alternative statistical models or machine learning approaches?

Study design and methods: Scientific Registry for Transplant Recipients (SRTR) data from 2015-2020 were used to build seven waitlist (WL) and data from 2010-2020 to build similar PT models. These included the (I) current lung allocation score (LAS)/CAS model; (II) re-estimated WL-LAS/CAS model; (III) model II incorporating nonlinear relationships; (IV) random survival forests model; (V) logistic model; (VI) linear discriminant analysis; and (VII) gradient-boosted tree model. Discrimination performance was evaluated at 1, 3, and 6 months on the waiting list and 1, 3, and 5 years PT. Area under the curve (AUC) values were estimated across subgroups.

Results: WL model performance was similar across models with the greatest discrimination in the baseline cohort (AUC 0.93) and declined to 0.87-0.89 for 3-month and 0.84-0.85 for 6-month predictions and further diminished for residual cohorts. Discrimination performance for PT models ranged from AUC 0.58-0.61 and remained stable with increasing forecasting times but was slightly worse for residual cohorts. WL and PT variability in AUC was greatest for individuals with Medicaid insurance.

Interpretation: Use of alternative modeling strategies and contemporary cohorts did not improve performance of models determining access to lung transplant.