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.

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.

Posttransplant outcome assessments at listing: Long-term outcomes are more important than short-term outcomes.

Posttransplant outcome assessments are publicly reported for patient and regulatory use. However, the currently reported 1-year posttransplant graft survival assessments are commonly criticized for not identifying clinically meaningful differences between programs, and not providing information about longer-term posttransplant outcomes. We investigated the association of different posttransplant outcome assessments available to patients at the time of listing with subsequent posttransplant graft survival. The posttransplant assessments were from period prevalent, rather than incident, cohorts with more timely 1-, 3-, and 5-year follow-up and 6-, 12-, 18-, 24-, and 30-month cohort windows. The association of these assessments at listing with subsequent posttransplant graft survival included candidates listed between July 12, 2011, and December 15, 2015, who subsequently underwent transplant before December 31, 2018. The assessments with 1-year follow-up had uniformly weaker associations than the assessments with 3- and 5-year follow-up. The assessments with 5-year follow-up had the strongest association in kidney and liver transplantation. For kidney, liver, and lung transplantation, assessment windows of at least 18 months typically had the strongest associations with subsequent graft survival. Posttransplant assessments with 5-year follow-up and 18-30-month cohort windows are better than the current posttransplant assessment with 1-year follow-up, particularly at the time of listing.

Effect of Including Important Clinical Variables on Accuracy of the Lung Allocation Score for Cystic Fibrosis and Chronic Obstructive Pulmonary Disease.

Rationale: Clinical variables associated with shortened survival in patients with advanced-stage cystic fibrosis (CF) are not included in the lung allocation score (LAS).Objectives: To identify variables associated with wait-list and post-transplant mortality for CF lung transplant candidates using a novel database and to analyze the impact of including new CF-specific variables in the LAS system.Methods: A deterministic matching algorithm identified patients from the Scientific Registry of Transplant Recipients and the Cystic Fibrosis Foundation Patient Registry. LAS wait-list and post-transplant survival models were recalculated using CF-specific variables. This multicenter, retrospective, population-based study of all lung transplant wait-list candidates aged 12 years or older from January 1, 2011, to December 31, 2014, included 9,043 patients on the lung transplant waiting list and 6,110 lung transplant recipients between 2011 and 2014, comprising 1,020 and 677 with CF, respectively.Measurements and Main Results: Measured outcomes were changes in LAS and lung allocation rank. For CF candidates, any Burkholderia sp. (hazard ratio [HR], 2.8; 95% confidence interval [CI], 1.2-6.6), 29-42 days hospitalized (HR 2.8; CI 1.3-5.9), massive hemoptysis (HR 2.1; CI 1.1-3.9), and relative drop in FEV1 ≥30% over 12 months (HR 1.7; CI 1.0-2.8) increased wait-list mortality risk; pulmonary exacerbation time 15-28 days (1.8; 1.1-2.9) increased post-transplant mortality risk. A relative drop in FEV1 ≥10% in chronic obstructive pulmonary disease (COPD) candidates was associated with increased wait-list mortality risk (HR 2.6; CI 1.2-5.4). Variability in LAS score and rank increased in patients with CF. Priority for transplant increased for COPD candidates. Access did not change for other diagnosis groups.Conclusions: Adding CF-specific variables improved discrimination among wait-listed CF candidates and benefited COPD candidates.

The relationship between the C-statistic and the accuracy of program-specific evaluations.

The C-statistic of the risk-adjustment model is often used to judge the accuracy of program evaluations. However, the C-statistic depends on the variability in risk for individual transplants and may be inappropriate for determining the accuracy of program evaluations. A simulation study investigated the association of the C-statistic with several metrics of program evaluation accuracy, including categorizing programs into the 5-tier system and identifying programs for regulatory review. The simulation study used data from deceased donor kidney-alone transplants for adult recipients in the program-specific reports released January 2018. A range of C-statistics was generated by changing the variability in risk for individual transplants. The C-statistic had no association with any metric of program evaluation accuracy. Instead, the number of expected events at a program was the most important factor. For example, Spearman's rho, which is the correlation of ranks, was -0.27 and -0.72 between the true program-specific hazard ratios and assigned tiers for programs with, respectively, <3 and >10 expected events. Presence of unadjusted risk factors did not modify the associations, although the accuracy of program evaluations was systematically lower. Therefore, the C-statistic provides no information on the accuracy of program evaluations.

Comparing Scientific Registry of Transplant Recipients posttransplant program-specific outcome ratings at listing with subsequent recipient outcomes after transplant.

To improve accessibility of program-specific reports to patients, the Scientific Registry of Transplant Recipients released a 5-tier system for categorizing 1-year posttransplant program evaluations. Whether this system predicts subsequent posttransplant outcomes at the time patients are waitlisted has been questioned. We investigated the association of tier at listing and the corresponding continuous score used for tier assignment, which ranges from 0 (poor outcomes) to 1 (good outcomes), with eventual 1-year posttransplant graft survival for candidates listed between July 12, 2011, and June 16, 2014, who underwent transplant before December 31, 2016. One additional tier at listing was associated with better 1-year posttransplant outcomes in liver (hazard ratio [HR], 0.93; 95% confidence interval [CI], 0.89-0.97) and lung transplant (HR, 0.90; 95% CI, 0.84-0.97) but not kidney (HR, 0.96; 95% CI, 0.92-1.01) or heart transplant (HR, 1.02; 95% CI, 0.93-1.10). In liver and lung transplant, longer time between listing and transplant was associated with stronger protective effects for high-tier programs. In kidney, liver, and lung transplant, posttransplant evaluations at listing had nonlinear associations with eventual posttransplant outcomes: relatively flat for 5-tier scores <0.5 and decreasing for scores >0.5. After adjustment for measured recipient and donor risk factors, posttransplant evaluations at listing predicted differences in eventual outcomes in liver and lung transplant, providing useful information to patients.

Association of pretransplant and posttransplant program ratings with candidate mortality after listing.

The Scientific Registry of Transplant Recipients (SRTR) is responsible for understandable reporting of program metrics, including transplant rate, waitlist mortality, and posttransplant outcomes. SRTR developed five-tier systems for each metric to improve accessibility for the public. We investigated the associations of the five-tier assignments at listing with all-cause candidate mortality after listing, for candidates listed July 12, 2011-June 16, 2014. Transplant rate evaluations with one additional tier were associated with lower mortality after listing in kidney (hazard ratio [HR], 0.93 0.950.97 ), liver (HR, 0.87 0.900.92 ), and heart (HR, 0.92 0.961.00 ) transplantation. For lung transplant patients, mortality after listing was highest at programs with above- and below-average transplant rates and lowest at programs with average transplant rates, suggesting that aggressive acceptance behavior may not always provide a survival benefit. Waitlist mortality evaluations with one additional tier were associated with lower mortality after listing in kidney (HR, 0.94 0.960.99 ) transplantation, and posttransplant graft survival evaluations with one additional tier were associated with lower mortality after listing in lung (HR, 0.90 0.940.98 ) transplantation. Transplant rate typically had the strongest association with mortality after listing, but the strength of associations differed by organ.

Heart and lung organ offer acceptance practices of transplant programs are associated with waitlist mortality and organ yield.

Variation in heart and lung offer acceptance practices may affect numbers of transplanted organs and create variability in waitlist mortality. To investigate these issues, offer acceptance ratios, or adjusted odds ratios, for heart and lung transplant programs individually and for all programs within donation service areas (DSAs) were estimated using offers from donors recovered July 1, 2016, and June 30, 2017. Logistic regressions estimated the association of DSA-level offer acceptance ratios with donor yield and local placement of organs recovered in the DSA. Competing risk methodology estimated the association of program-level offer acceptance ratios with incidence and rate of waitlist removals due to death or becoming too sick to undergo transplant. Higher DSA-level offer acceptance was associated with higher yield (odds ratios [ORs]: lung, 1.04 1.111.19 ; heart, 1.09 1.211.35 ) and more local placement of transplanted organs (ORs: lung, 1.01 1.121.24 ; heart, 1.47 1.691.93 ). Higher program-level offer acceptance was associated with lower incidence of waitlist removal due to death or becoming too sick to undergo transplant (hazard ratios [HRs]: heart, 0.80 0.860.93 ; lung, 0.67 0.750.83 ), but not with rate of waitlist removal (HRs: heart, 0.91 0.981.06 ; lung, 0.89 0.991.10 ). Heart and lung offer acceptance practices affected numbers of transplanted organs and contributed to program-level variability in the probability of waitlist mortality.

Clinical diagnosis of metabolic syndrome: predicting new-onset diabetes, coronary heart disease, and allograft failure late after kidney transplant.

Metabolic syndrome is associated with coronary heart disease (CHD) and new-onset diabetes after kidney transplant (NODAT). Using data collected from transplant centers worldwide for the Patient Outcomes in Renal Transplantation study, we examined associations of metabolic syndrome (n = 2253 excluding recipients with diabetes pretransplant), CHD (n = 2253), and NODAT (n = 1840 further excluding recipients with diabetes in the first year post-transplant), with the primary outcome of allograft failure. We assessed risk factors associated with secondary outcomes of metabolic syndrome, NODAT, and CHD after adjusting for type of baseline immunosuppression and transplant center effects. Metabolic syndrome prevalence was 39.8% at 12-24 months post-transplant and 35.4% at 36-48 months. Metabolic syndrome was independently associated with NODAT (hazard ratio 3.46, 95% confidence interval 2.40-4.98, P < 0.0001), CHD (2.03, 1.16-3.52, P = 0.013), and allograft failure (1.36, 1.03-1.79, P = 0.028). Allograft failure occurred in 218 patients (14.6%). After adjustment for metabolic syndrome, NODAT (1.63, 1.18-2.24, P = 0.003) and CHD (5.48, 3.27-9.20, P < 0.0001) remained strongly associated with increased risk of allograft failure. Metabolic syndrome, NODAT, and CHD are risk factors for allograft failure. NODAT and CHD are risk factors for allograft failure, independent of metabolic syndrome.

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.

The relationship between kidney function and long-term graft survival after kidney transplant.

BACKGROUND: Whether chronic kidney disease (CKD) staging provides a useful framework for predicting outcomes after kidney transplant is unclear. STUDY DESIGN: Retrospective cohort study. SETTING & PARTICIPANTS: We used data from the Patient Outcomes in Renal Transplantation (PORT) Study, including 13,671 transplants from 12 centers during 10 years of follow-up. PREDICTOR: Estimated glomerular filtration rate (eGFR; in milliliters per minute per 1.73 m(2)) at 12 months posttransplant. OUTCOMES: All-cause graft failure (a composite end point consisting of return to dialysis therapy, pre-emptive retransplant, or death with function), death-censored graft failure, and death with a functioning graft. MEASUREMENTS: The relationship between 12-month eGFR and subsequent graft outcomes through 10 years posttransplant was assessed using Cox proportional hazards analyses. RESULTS: Stage 3 included 63% of patients and was subdivided into stages 3a (eGFR, 45-59 mL/min/1.73 m(2); 34%) and 3b (eGFR, 30-44 mL/min/1.73 m(2); 29%). Compared with stage 2 (eGFR, 60-89 mL/min/1.73 m(2); 24%), adjusted Cox proportional HRs for graft failure were 1.12 (95% CI, 1.01-1.24; P = 0.04) for stage 3a, 1.50 (95% CI, 1.35-1.66; P < 0.001) for stage 3b, 2.86 (95% CI, 2.53-3.22; P < 0.001) for stage 4 (eGFR, 15-29 mL/min/1.73 m(2); 9%), and 13.2 (95% CI, 10.7-16.4; P < 0.001) for stage 5 (eGFR <15 mL/min/1.73 m(2); 1%). For stage 1 (eGFR ≥ 90 mL/min/1.73 m(2); 3%), risk of graft failure was increased (1.41 [95% CI, 1.13-1.75]; P < 0.001), likely due to serum creatinine associations independent of kidney function. Similar associations were seen between CKD stages and mortality. LIMITATIONS: Retrospective study; lack of gold-standard measurements of true GFR; lack of measures of comorbidity, inflammation, muscle mass, proteinuria, and other noncreatinine markers of eGFR. CONCLUSIONS: CKD stages validated in the general population provide a useful framework for predicting outcomes after kidney transplant.

Blood transfusions in kidney transplant candidates are common and associated with adverse outcomes.

Surprisingly, there are no data regarding transfusion frequency, factors associated with transfusion administration in patients on the kidney transplant waiting list, or transfusion impact on graft and recipient outcomes. We used United States Renal Data System data to identify 43,025 patients added to the waiting list in 1999-2004 and followed through 2006 to assess the relative risk of post-listing transfusions. In 69,991 patients who underwent transplants during the same time period, we assessed the association between pre-transplant transfusions and level of panel-reactive antibody (PRA) at the time of transplant, and associations between PRA and patient outcomes. The three-yr cumulative incidence of transfusions was 26% for patients added to the waiting list in 1999, rising to 30% in 2004. Post-listing transfusions were associated with a 28% decreased likelihood of undergoing transplant, and a more than fourfold increased risk of death. There was a graded association between percent PRA at the time of transplant and adjusted risk of death-censored graft failure, death with function, and the combined event of graft failure and death. These data demonstrate that transfusions remain common and confirm the adverse association between transfusions and PRA, and high PRA and inferior graft and patient outcomes.

Cost burden of post-transplant lymphoproliferative disease following kidney transplants in Medicare-eligible patients by survival status.

AIMS AND OBJECTIVES: Patients diagnosed with post-transplant lymphoproliferative disease (PTLD) experience high mortality within the first 2 years of diagnosis; however, few data exist on the economic burden of PTLD in these patients. We determined the healthcare resource utilization (HRU) and cost burden of post-kidney transplant PTLD and evaluated how these differ by survival status. MATERIALS AND METHODS: Utilizing data from the United States Renal Data System and the Scientific Registry of Transplant Recipients, we identified 83,818 Medicare-covered kidney transplant recipients between 2007 and 2016, of which 347 had at least one Medicare claim during the first year after diagnosis of PTLD. We tabulated Medicare Part A and Part B and calculated per patient-year (PPY) costs. RESULTS: Patients diagnosed with PTLD in the first year post-transplant had Part A + B costs of $222,336 PPY, in contrast with $83,546 PPY in all kidney transplants. Post-transplant costs in the first year of PTLD diagnosis were similar regardless of the year of diagnosis. Cost burden for PTLD patients who died within 2 years of diagnosis was >3.3 times higher than PTLD patients still alive after 2 years. Of those who died within 2 years, the majority died within 6 months and costs were highest for these patients, with almost 7 times higher costs than PTLD patients who were still alive after 2 years. LIMITATIONS: Medicare costs were the only costs examined in this study and may not be representative of other costs incurred, nor be generalizable to other insured populations. Patients were only Medicare eligible for 3 years after transplant unless aged ≥62 years, therefore any costs after this cut-off were not included. CONCLUSIONS: PTLD represents a considerable HRU and cost burden following kidney transplant, and the burden is most pronounced in patients who die within 6 months.

A simple tool to predict outcomes after kidney transplant.

BACKGROUND: Surprisingly few tools have been developed to predict outcomes after kidney transplant. STUDY DESIGN: Retrospective observational cohort study. SETTING & PARTICIPANTS: Adult patients from US Renal Data System (USRDS) data who underwent deceased donor kidney transplant in 2000-2006. PREDICTOR: Full and abbreviated prediction tools for graft loss using candidate predictor variables available in the USRDS registry, including data from the Organ Procurement and Transplantation Network and the Centers for Medicare & Medicaid Services End-Stage Renal Disease Program. OUTCOMES: Graft loss within 5 years, defined as return to maintenance dialysis therapy, preemptive retransplant, or death with a functioning graft. MEASUREMENTS: We used Cox proportional hazards analyses to develop separate tools for assessment (1) pretransplant, (2) at 7 days posttransplant, and (3) at 1 year posttransplant to predict subsequent risk of graft loss within 5 years of transplant. We used measures of discrimination and explained variation to determine the number of variables needed to predict outcomes at each assessment time in the full and abbreviated equations, creating simple user-friendly prediction tools. RESULTS: Although we could identify 32, 29, and 18 variables that predicted graft loss assessed pretransplant and at 7 days and 1 year posttransplant ("full" models), 98% of the discriminatory ability and >80% of the variability explained by the full models could be achieved using only 11, 8, and 6 variables, respectively. LIMITATIONS: Comorbidity data were from the Centers for Medicare & Medicaid Medical Evidence Report, which may significantly underreport comorbid conditions; C statistic values may indicate only modest ability to discriminate risk for an individual patient. CONCLUSIONS: This method produced risk-prediction tools that can be used easily by patients and clinicians to aid in understanding the absolute and relative risk of graft loss within 5 years of transplant.

Validating thoracic simulated allocation model predictions for impact of broader geographic sharing of donor lungs on transplant waitlist outcomes.

BACKGROUND: The thoracic simulated allocation model (TSAM) is used by the Scientific Registry of Transplant Recipients to predict the relative effect of organ allocation policy changes. A new lung allocation policy changing the first unit of allocation from donation service area to 250 nautical miles took effect on November 24, 2017. We studied TSAM's ability to correctly predict trends caused by changes in allocation policy. METHODS: We compared the population characteristics from the TSAM cohort, 6,386 lung transplant candidates from 2009 to 2011, with the observed cohort of 7,601 candidates from the year before the policy change on November 24, 2017, and the year after. Simulations were run 10 times. Waitlist mortality and transplant rates were calculated and compared with observed mortality and transplant rates in the years before and after the policy change. RESULTS: TSAM correctly predicted no change in overall waitlist mortality or transplant rates with the policy change. Observed waitlist mortality values were higher, as were transplant rates, because of increased organ donation and population change. TSAM predicted increased transplant rates for diagnosis group D (idiopathic pulmonary fibrosis), decreased rates for group A (chronic obstructive pulmonary disease), and increased rates for candidates with lung allocation score ≥50, but these changes did not occur in the waitlist and transplant populations after the policy change. CONCLUSIONS: TSAM correctly predicted the relative trends caused by a change in allocation policy but smaller sub-group predictions were not seen.

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.

Eosinophil and T cell markers predict functional decline in COPD patients.

BACKGROUND: The major marker utilized to monitor COPD patients is forced expiratory volume in one second (FEV1). However, a single measurement of FEV1 cannot reliably predict subsequent decline. Recent studies indicate that T lymphocytes and eosinophils are important determinants of disease stability in COPD. We therefore measured cytokine levels in the lung lavage fluid and plasma of COPD patients in order to determine if the levels of T cell or eosinophil related cytokines were predictive of the future course of the disease. METHODS: Baseline lung lavage and plasma samples were collected from COPD subjects with moderately severe airway obstruction and emphysematous changes on chest CT. The study participants were former smokers who had not had a disease exacerbation within the past six months or used steroids within the past two months. Those subjects who demonstrated stable disease over the following six months (DeltaFEV1 % predicted = 4.7 +/- 7.2; N = 34) were retrospectively compared with study participants who experienced a rapid decline in lung function (DeltaFEV1 % predicted = -16.0 +/- 6.0; N = 16) during the same time period and with normal controls (N = 11). Plasma and lung lavage cytokines were measured from clinical samples using the Luminex multiplex kit which enabled the simultaneous measurement of several T cell and eosinophil related cytokines. RESULTS AND DISCUSSION: Stable COPD participants had significantly higher plasma IL-2 levels compared to participants with rapidly progressive COPD (p = 0.04). In contrast, plasma eotaxin-1 levels were significantly lower in stable COPD subjects compared to normal controls (p < 0.03). In addition, lung lavage eotaxin-1 levels were significantly higher in rapidly progressive COPD participants compared to both normal controls (p < 0.02) and stable COPD participants (p < 0.05). CONCLUSION: These findings indicate that IL-2 and eotaxin-1 levels may be important markers of disease stability in advanced emphysema patients. Prospective studies will need to confirm whether measuring IL-2 or eotaxin-1 can identify patients at risk for rapid disease progression.