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.

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.

Incorporating Effects of Time Accrued on the Waiting List into Lung Transplantation Survival Models.

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.

A new method for classifying prognostic risk factors in lung transplant candidates.

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.

New OPTN/UNOS data demonstrates higher than previously reported waitlist mortality for lung transplant candidates supported with ECMO.

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.

OPTN/SRTR 2021 Annual Data Report: Lung.

The number of lung transplants has continued to decline since 2020, a period that coincides with the onset of the COVID-19 pandemic. Lung allocation policy continues to undergo considerable change in preparation for adoption of the Composite Allocation Score system in 2023, beginning with multiple adaptations to the calculation of the Lung Allocation Score that occurred in 2021. The number of candidates added to the waiting list increased after a decline in 2020, while waitlist mortality has increased slightly with a decreased number of transplants. Time to transplant continues to improve, with 38.0% of candidates waiting fewer than 90 days for a transplant. Posttransplant survival remains stable, with 85.3% of transplant recipients surviving to 1 year; 67%, to 3 years; and 54.3%, to 5 years.

Association of Socioeconomic Position With Racial and Ethnic Disparities in Survival After Lung Transplant.

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.

Miscalibration of lung allocation models leads to inaccurate waitlist mortality predictions.

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.

Refining the Lung Allocation Score Models Fails to Improve Discrimination Performance.

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.

Expected effect of the lung Composite Allocation Score system on US lung transplantation.

Efforts are underway to transition the current lung allocation system to a continuous distribution framework whereby multiple factors are simultaneously combined into a Composite Allocation Score (CAS) to prioritize candidates for lung transplant. The purpose of this study was to compare discrete CAS scenarios with the current concentric circle-based allocation system to assess their potential effects on the US lung transplantation system using the Scientific Registry of Transplant Recipients' thoracic simulated allocation model. Six alternative CAS scenarios were compared over 10 simulation runs using data from individuals on the lung transplant waiting list from January 1, 2018, through December 31, 2019. Outcome measures were transplant rate, count, waitlist deaths, posttransplant deaths within 2 years, donor-to-recipient distance, and percentage of organs predicted to have flown. Across scenarios, waitlist deaths decreased by 36% to 47%, with larger decreases in deaths at lower placement efficiency weight and higher weighting of the waitlist outcomes. When waitlist outcomes were equally weighted to posttransplant outcomes, more transplants occurred in individuals with the highest expected posttransplant survival. All CAS scenarios led to improved overall measures of equity compared with the current Lung Allocation Score system, including reduced waitlist deaths, and resulted in similar posttransplant survival.

Impact of incorporating long-term survival for calculating transplant benefit in the US lung transplant allocation system.

BACKGROUND: The lung allocation score prioritizes candidates for a lung transplant in the United States. As the country adopts the continuous distribution framework for organ allocation, we must reevaluate lung allocation score assumptions to maximize transplant benefit. METHODS: We used Scientific Registry of Transplant Recipients data to study the impact of these changes: (1) updating cohorts; (2) transitioning from 1- to 5-year posttransplant survival; (3) using time-varying effects for non-proportional hazards; and (4) weighting waitlist and posttransplant area under the curve differently. Models were compared using Spearman correlations and C-statistics. The thoracic simulation allocation model characterized transplant rates and proportions of recipient subgroups under the current and new systems. RESULTS: Posttransplant areas under the curve models were estimated with recipients aged ≥12 from January 1, 2014, to December 31, 2018. All models had similar C-statistics and Spearman correlations, indicating similar predictive performance and posttransplant area under the curve rankings. Five-year posttransplant area under the curve across age and diagnosis groups varied more than 1-year groups. Using the thoracic simulation allocation model, 1- and 5-year posttransplant model under the curve models showed similar transplant rates and recipient characteristics under the current system, but under continuous distribution, 5-year posttransplant area under the curve resulted in increased transplant rates with more recipients younger and in diagnosis groups B and C. CONCLUSION: Incorporating equally weighted waitlist and posttransplant models using 5-year posttransplant survival detected the largest variability in survival under the continuous distribution system, which could improve long-term survival in the United States.

Cystic fibrosis: candidate selection and impact of the cystic fibrosis transmembrane conductance regulator therapy.

PURPOSE OF REVIEW: Over the past decade, the development of highly effective cystic fibrosis (CF) transmembrane conductance regulator (CFTR) modulators has dramatically ameliorated the manifestations of CF for most patients. Perhaps most importantly, CFTR modulators impact the development and progression of advanced lung disease (ALD) and are changing the CF population accessing lung transplant. RECENT FINDINGS: A recent phase 3 trial of elexacaftor/tezacaftor/ivacaftor (ETI) demonstrated efficacy for individuals with at least one copy of the most common CF mutation, F508del. Studies of CFTR modulator therapy in patients with ALD have demonstrated similar improvements in lung function, nutrition, and pulmonary exacerbation frequency as seen in individuals with higher lung function. Due to improvements with ETI, rates of lung transplant for CF have declined and individuals are achieving stability in lung function. Nevertheless, the Cystic Fibrosis Foundation guidelines for lung transplant referral should be used to guide referral decisions for all individuals with CF, including those on CFTR modulator therapy, to allow remediation of modifiable barriers to transplant. ETI may be used in the posttransplant setting but for selected individuals and with close monitoring. SUMMARY: Increasing access to highly effective CFTR modulators has changed the trajectory of lung disease in CF for many, but not all, individuals and there remain individuals who cannot access therapy or whose mutations do not respond to modulators. Lung transplant remains an important treatment option for individuals with advanced CF lung disease. Increasing attention will be required to optimize decisions of when to list for transplant.

Effects of broader geographic distribution of donor lungs on travel mode and estimated costs of organ procurement.

On November 24, 2017, US lung transplant policy replaced donor service area with 250-nautical-mile radius as the first unit of allocation. Understanding this policy's economic impact is important, because the United States is poised to adopt the broadest feasible geographic organ distribution. All lung transplant recipients from January 1, 2015, to December 31, 2018, in the Scientific Registry of Transplant Recipients, were included. Recipients before and after November 24, 2017 were in the donor service area-first and 250-nautical-mile donor service area-free periods, respectively. Travel time was estimated using a Google application; mode was assigned as flying when driving time was longer than 60 min. Travel costs were estimated by mode and distance. Travel distance and time for organ procurement increased under the policy change. The estimated proportion of organs traveling by air increased from 61% to 76%. Estimated average costs increased by $14 051 if travel mode changed to flying, resulting in an average increase of $1264 for all transplants. Travel costs were highest for candidates <18 years and adults with high lung allocation scores. Broader geographic distribution increased estimated organ procurement costs for a small percentage of lung transplants. Further analysis should elucidate the broad economic impact of such policies.

Impact of Socioeconomic Position on Access to the U.S. Lung Transplant Waiting List in a Matched Cystic Fibrosis Cohort.

Rationale: Referrals for lung transplant and transplant rates in the United States are lower than in Canada for patients with advanced cystic fibrosis (CF) lung disease. Further study of factors limiting access are needed to optimize referral and transplant for this population.Objectives: To determine the effect of socioeconomic position, while accounting for disease severity, on the likelihood of wait-listing for lung transplant in the United States.Methods: A case-control study of 3,110 patients (1,555 wait-listed, 1,555 never wait-listed) in the linked CF Foundation Patient Registry/Scientific Registry of Transplant Recipients was performed with 1:1 matching for age, forced expiratory volume in 1 second, and year. Logistic regression was performed with univariate and multivariate analyses accounting for eight clinical factors (sex, oxygen use, body mass index, hemoptysis, forced vital capacity, methicillin-resistant Staphylococcus aureus, multidrug-resistant Pseudomonas aeruginosa, and i.v. antibiotic days) and six socioeconomic factors (race, marital status, education, health insurance, median zip code income, and distance to transplant program). The CF Health Score and Socioeconomic Barrier Score were created based on summation of variables. Interactions between scores were calculated.Results: We found an inverse relationship between the probability of wait-listing and CF Health Score and Socioeconomic Barrier Score. As the CF Health Score decreased (less healthy), the probability of wait-listing increased by 69.3% from a score of 7 to 2. As the Socioeconomic Barrier Score decreased (fewer barriers), the probability of wait-listing increased by 31.7% from a score of ≥5 to 1). Regardless of illness severity, socioeconomic barriers presented an impediment to wait-listing. Individuals with higher Socioeconomic Barrier Scores accessed transplant about half as often as those with lower scores at the same level of medical severity. Analysis of interactions demonstrated a higher probability of wait-listing for individuals with moderate health severity and fewer social barriers compared with sicker individuals with more socioeconomic barriers.Conclusions: Accrual of socioeconomic barriers limits access to lung transplant irrespective of disease severity, a finding of substantial concern for patients with CF and for transplant providers. Future interventions can focus on this at-risk population early in the disease course.

The impact of change in definition of increased-risk donors on survival after lung transplant.

OBJECTIVES: To study the impact of using the US Public Health Service broadened definition of "increased-risk" donors (2013) in comparison with "high-risk" (1994) and standard infectious risk donors on lung transplant recipient outcomes. METHODS: Patients who underwent lung transplant between January 1, 2006, and May 31, 2017, in the Scientific Registry of Transplant Recipients were divided into 2 cohorts, recipients of: (1) high-risk donors: January 1, 2006, to October 1, 2013, and (2) increased-risk donors: January 1, 2014, to May 31, 2017, and compared with matched recipients who received standard-risk donors. Risks for acute rejection, patient, and graft survival using propensity score matched cohorts, multivariable logistic, and Cox models were examined. RESULTS: In total, 18,490 lung transplant recipients were analyzed with 36% transplanted during the increased-risk donor definition period. The proportion of donors classified as nonstandard infectious risk increased with the definition change (8% high-risk donors vs 22% increased-risk donors; P < .001). In both cohorts, male patients with a lower forced expiratory volume in 1 second and greater creatinine were more likely to receive an organ from increased risk donors. Neither graft nor patient survival differed by donor type in either period. Acute treated rejection within 1 year did not differ by period for recipients of increased risk donors (odds ratio, 0.87; P = .23) or recipients of high-risk donors (odds ratio, 1.2; P = .27). CONCLUSIONS: The 2013 broadened definition of donor risk increased the proportion of nonstandard infectious risk donors. Recipients of increased/high-risk donors had similar graft and patient survival compared with standard-risk donors.