Impact of the Composite Allocation Score on Lung Transplant Waitlist and Post- Transplant Outcomes

Impact of the Composite Allocation Score on Lung Transplant Waitlist and Post- Transplant Outcomes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impact of the Composite Allocation Score on Lung Transplant Waitlist and Post- Transplant Outcomes Ye In Christopher Kwon, Holly Caboti-Jones, Michael Keller, Andrew Min-Gi Park, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6448565/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose: On March 9, 2023, the Composite Allocation Score (CAS) was introduced for all lung transplantation (LT) candidates. We analyzed waitlist and post-transplant outcomes following CAS implementation. Methods: Using the UNOS registry (2022–2024), adult patients listed for isolated LT were divided into Era 1 (pre-CAS: 3/1/2022–3/8/2023) and Era 2 (post-CAS: 3/9/2023–9/30/2024). Competing risk regression analyzed waitlist events. Recipient/donor characteristics and mortality risk factors were assessed with Cox models. Survival was evaluated with Kaplan-Meier analysis. Results: Among 6,398 LTs, 2,598 (40.6%) occurred in Era 2. More Black patients (16.9% vs. 15%, p=0.04) and those with a high school education (35.4% vs. 33.4%, p=0.0003) were transplanted. ABO type O patients were less likely to undergo LT (42.5% vs. 46.6%, p=0.04). Era 2 had longer transport distances (231 vs. 202 miles, p<0.0001), ischemic times (5.1 vs. 4.9 hours, p<0.0001), and increased use of flights (79.1% vs. 72.8%, p<0.0001). DCD (9.4% vs. 6.2%, p<0.0001) and NRP (2.2% vs. 1.2%, p=0.02) usage rose. Waitlist times decreased (29 vs. 31 days, p=0.009), with improved outcomes (SHR 0.73, p<0.0001). Era 2 showed superior 6-month and 1-year survival (p<0.0001) and reduced rejection treatment (2.6% vs. 14.5%, p<0.0001). Conclusions: CAS implementation reduced waitlist mortality, improved access for marginalized groups, and enhanced survival. Lungs were procured from greater distances with increased use of DCD with NRP or ex vivo perfusion. Disparities remain for ABO type O patients, warranting closer follow-up. lung transplantation composite allocation score donor distance lung allocation Figures Figure 1 Figure 2 Figure 3 Introduction Lung transplantation (LT) is the gold standard therapy for end-stage pulmonary failure. However, as demand for donor lungs outpaces supply [1, 2], challenges persist with effective and equitable allocation to the most optimal candidates. The antecedent Lung Allocation Score (LAS) model prioritized geographic donor-recipient proximity as the principal determinant for lung allocation [3, 4]. However, such systems have resulted in arbitrary cut-offs, leading to lower-priority candidates receiving LT before higher-priority candidates outside designated boundaries [5]. Additionally, concerns emerged regarding disparities affecting male [6], Black [7], ABO blood type O [8], and allosensitized [7]. In response, the United Network for Organ Sharing (UNOS) implemented the Composite Allocation Score (CAS) in March 2023 - a weighted system accounting for medical urgency, post-transplant survival, biological factors, and patient access [9] while still considering geographical proximity factors [5, 10]. Early data following CAS implementation indicated similar short-term survival despite the increased transplant distances and longer ischemic times [10]. The CAS system aimed to reduce waitlist mortality and expand donor lung access, especially for ABO type O groups [11]. Thus, we provide a timely assessment of changes in patient characteristics, waitlist outcomes, and 1-year post-transplant outcomes in adult LT under the lung CAS policy change. We also analyze usage trends in the growing use of donation after circulatory death (DCD) LT, ex-vivo lung perfusion (EVLP), and normothermic regional perfusion (NRP) in the post-CAS era. Materials and Methods Study design. We retrospectively reviewed the UNOS Standard Analysis and Research (STAR) database to identify all adult patients (≥18 years) listed for first-time LT from 3/1/2022 to 9/30/2024 with at least 6 months of follow-up information. Patients who underwent multi-organ transplants or lost to follow-up were excluded. Patients were stratified into Era 1 (pre-CAS; 3/1/2022 – 3/8/2023) and Era 2 (post-CAS; 3/9/2023 – 9/30/2024) ( Figure 1 ). The “flight for transport” variable was a binary data element defined as donor lungs traveling> 100 nautical miles to the recipient’s transplant center[10]. As all data was de-identified, this study was exempt from the Virginia Commonwealth University Institutional Review Board. Statistical analysis. For waitlist outcomes, LT candidates who were waitlisted before the CAS implementation were censored by 3/8/2023 to limit the confounding occurring in patients listed before the CAS implementation and to remain on the waitlist after the allocation system change ( Figure S1 ). We used competing risk regression models [12] to evaluate the association between CAS implementation and the following outcomes: waitlist death or deterioration with subsequent removal from the waitlist, transplant, or recovery with removal from the waitlist. We reported adjusted sub-hazard ratios (SHRs) with 95% confidence intervals (CIs). Competing risk regression and cumulative incidence curves were analyzed using the Fine and Gray method [13, 14]. Recipient and matched donor characteristics were collected, with categorical variables reported as percentages and continuous variables as means with standard deviations (SD) or medians with interquartile ranges (IQR). Pearson’s Chi-square or Fisher’s exact test was used to compare binary and categorical variables, while Wilcoxon rank-sum tests were used to compare medians of continuous variables. Outcomes included recipient survival at 30-, 90-days, 6-months, and 1-year, rates of post-transplant complication, lung function tests, and the need for circulatory support. The Kaplan-Meier method with log-rank tests were used to plot and assess survival. We used the methodology from Wall et al. to distinguish between NRP and direct procurement and preservation (DPP) [15]. A multivariate Cox proportional hazards regression model was constructed for covariates with biological plausibility and adjusted for recipient age, body mass index (BMI), diabetes, smoking, race, and transplant center volume. The Schoenfeld residual test assessed the hypothesis of proportional hazards to determine the proportionality assumption. All statistical analyses were conducted using SAS (version 9.4; SAS Inc., Cary, NC, USA). All p -values were based on two-sided statistical tests, with significance at p <0.05. Results Baseline characteristics. A total of 6,398 patients underwent LT during the study period- 3,800 (59.4%) in Era 1 and 2,598 (40.6%) in Era 2. Compared to Era 1, recipients in Era 2 were younger (age 60 vs. 61 years, p =0.004), more likely to be Black (16.9% vs. 15.0%, p =0.049), and experienced shorter waitlist times (29 vs. 31 days, p =0.010). Additionally, fewer recipients in Era 2 underwent transplantation for severe SARS-CoV-2 (COVID-19) infection (5.1% vs. 6.0%, p =0.027), while recipients with bronchiectasis as an indication increased (1.4% vs. 0.8%, p =0.014). Pre-transplant inotropes were more common in Era 2 (14.3% vs. 11.6%, p =0.001). Recipients with blood type O decreased in Era 2 (42.5% vs. 46.6%, p =0.039), while recipients with blood types A (39.3% vs. 37.0%), B (14.2% vs. 12.6%), and AB (3.9% vs. 3.5%) increased, contributing to a significant drop in donor-recipient ABO matching rates (84.1% vs. 92.2%, p <.0001). Donors in Era 2 were more often male (65.4% vs. 62.2%, p =0.011) and had a PaO2/FiO2 (P/F) ratio less than 300 (17.3% vs 14.1%), p=0.002), while less likely to have a history of cocaine use (17.1% vs. 24.9%, p <.0001). Other extended donor criteria, including age, body mass index (BMI), smoking history, diabetes, alcohol use, lung bronchoscopy, and chest radiographs, remained similar between eras. There were no significant differences in gender mismatch ( p =0.051) or human leukocyte antigen (HLA) mismatch ( p =0.176). Use of DCD (9.4% vs. 6.2%, p <.0001), NRP (1.8% vs. 1.2%, p <.0001), and EVLP (5.1% vs. 3.8%, p =0.012) increased significantly in Era 2. However, donor lungs were procured from greater distances (231 vs. 202 nautical miles, p <.0001), required more flights (79.1% vs. 72.8%, p <.0001), and experienced longer ischemic times (5.1 vs. 4.9 hours, p <.0001). Regional differences in LT between eras remained insignificant ( p =0.750). Waitlist outcomes. A comparison of competing waitlist outcomes found significant changes in Era 2 ( Table S1 ). Risk of death or clinical deterioration was reduced (SHR 0.73 [95% CI 0.65 – 0.82], p<. 0001) ( Figure 2A ), while the likelihood of LT was significantly increased in Era 2 (SHR 1.05 [95% CI 1.01 – 1.08], p =0.005) ( Figure 2B ). Odds of removal from the waitlist due to clinical recovery remained low in both eras and were significantly reduced in Era 2 (SHR 0.46 [95% CI 0.36 – 0.59], p<. 0001) ( Figure 2C ). Overall, waitlist mortality rates in Era 2 were significantly lowered (9.8 vs. 13.9%, p< .0001) ( Table S2 ), with significantly lower mortality at 30- ( p =0.004), 90-days ( p =0.001), 6-months ( p <.0001), and 1-year ( p <.0001). Post-transplant outcomes. Short-term survival at 30- ( p =0.437) ( Figure 3A ) and 90-days ( p =0.163) ( Figure 3B ) post-transplant remained similar between eras. However, Era 2 recipients had superior survival at 6-months ( p< .0001) ( Figure 3C ) and 1-year ( p <.0001) ( Figure 3D ), with fewer overall mortalities (6.2 vs. 10.5%, p <.0001) ( Table 2 ). Extracorporeal membrane oxygenation (ECMO) or intra-aortic balloon pump (IABP) use remained low during the study period; more recipients in Era 2 were supported by ECMO, IABP, or any life support measures at 24- and 72-hours post-transplant (all p <.0001). Rates of ventilator use between eras were comparable ( p =0.116). Era 2 recipients had shorter hospital stays post-transplant ( p =0.040) and significantly fewer rejection events within 1-year (2.6 vs.14.5%, p <.0001). Post-operative complications, including stroke, pacemaker implantation, dialysis, airway dehiscence, acute rejection, and re-transplant, remained comparable between eras. Risk of mortality. LT after the CAS change was not associated with post-transplant mortality (HR 1.29 [95% CI 0.97 – 1.54], p =0.054), whereas increased age (HR 1.02 [95% CI 1.01 – 1.02], p =0.001) was associated with increased risk of mortality ( Table 3 ). However, use of DCD (HR 0.83 [95% CI 0.62 – 0.93], p =0.017) and NRP (HR 0.91 [95% CI 0.79 – 0.95], p =0.002) were associated with decreased risk of mortality in Era 2. Use of flight for transport (HR 1.53 [95% CI 1.02 – 1.99], p =0.022) was linked with increased risk in Era 2. Use of EVLP (HR 1.24 [95% CI 0.91 – 1.59], p =0.421), increased ischemic times (HR 1.06 [95% CI 0.99 – 1.09], p =0.312), increased donor-recipient distance (HR 1.00 [95% CI 0.99 – 1.00], p =0.349), and increased time on the waitlist (HR 1.00 [95% CI 0.99 – 1.00], p =0.308) were not associated with mortality risk. Discussion The CAS system for LT represents a fundamental shift in organ allocation from a hierarchical to a continuous distribution framework [11]. Notably, the CAS system employs a modular construct, which allows timely adjustments to each component’s relative weights, reflecting the most contemporary needs of LT candidates or surgical teams [16]. A prior study [10] examined the early impact of the CAS change but lacked waitlist outcomes and adequate follow-up compared to the current study. This national analysis demonstrates that the CAS system has successfully reduced waitlist mortality while increasing access to LT. In the CAS era, recipient survival up to 1-year has been significantly improved compared to the LAS era, despite increased lung travel distances and ischemic times. While waitlist mortality and odds of transplant have improved under the CAS policy change, there is room for further optimization. Both the LAS and CAS ignore the fact that some candidates’ mortality risk will progress more rapidly than others. While the CAS system attempts to address these issues by heavily weighing the severity and urgency of an individual’s lung disease and probability of long-term survival, it still ignores the disproportionate impact of waitlist duration on candidates. Most patients indeed receive LT within 6 months of being listed [17] , and we report the average time on the waitlist to be under 1 month with the CAS system. Thus, one may argue that accrued time on the waitlist may not significantly impact survival. However, Dalton et al. demonstrate that the extent to which mortality risk changes over time varies considerably among patients in a predictable manner [18]. Ultimately, it will become necessary for the CAS system to account for days spent on the waitlist as a component in determining lung allocation. As intended, the geographic limits appear to be largely negated, consistent with earlier simulations that predicted the CAS system’s ability to reduce geographic variability [11]. However, the greater organ transport distances may also indicate decreased efficiency [11]. Such unintended consequences of increased geographic distances for procurement have been previously demonstrated under the 2017 LAS policy change from donor service areas to nautical miles [19–21]. Travel efficiency was considered in the CAS modeling to account for financial costs associated with LT [22]. However, the UNOS/OPTN LT Committee decided to use a general placement efficiency scale as a surrogate non-cost-related efficiency score [4, 22]. The CAS algorithm does not include factors such as odds of organ acceptance, candidate and hospital density, ease of organ recovery, or “aura” placement in which various solid organ offers are grouped and allocated to a transplant center for candidates within the CAS range [11, 22]. It is possible that decreased length of post-transplant hospital stay observed in LT recipients in the post-CAS era may help to offset the expected increased donor lung transport costs. However, these issues remain undetermined until the UNOS database can adequately report the actual total costs of LT. Regardless, transplant centers should be wary of these economic factors in the post-CAS era to ensure the ability to deliver sustainable, efficient, and effective LTs to a broader patient population. In the post-CAS era, use of flights for transport has been associated with increased mortality risk whereas neither increased ischemic times nor donor-recipient distance had any impact on 1-year mortality. Prior studies have demonstrated that transplanting distant donors compared to local donors had no impact on short-intermediate term outcomes [23]. Thus, it is unlikely that the > 100 miles cut off required for flights have any impact on mortality risk. However, it is notable that while the use of EVLPs have increased, it remains largely underutilized (5.1%) in the post-CAS era. Indeed, recent evidence has demonstrated that the use of EVLP [24] or controlled hypothermic storage [25] may help to counterbalance the adverse impacts of longer out-of-body times [26]. Furthermore, contemporary studies continue to demonstrate that lung ischemic times even greater than 8 hours may result in acceptable perioperative outcomes and post-transplant survival [27]. Similarly, despite the increased risk of ischemic-reperfusion injuries [28, 29], lungs exposed to increased ischemic times were not associated with primary graft failure or up to 5-year recipient survival [30]. These impacts are particularly pronounced among low transplant volume centers that often consider 6-hours as the upper limit of acceptable ischemic times [31]. While we’ve adjusted our models for center volumes, the effects of air travel for donor lungs, in the context of increased ischemic times and donor-recipient distances, remain unclear. Future studies should closely monitor the association between flights for lung transport and access to novel perfusion and preservation distances. We also report that the use of extended criteria donors with P/F ratio <300, DCD, and NRP has been steadily increasing in the post-CAS era. Although these changes are unlikely to be related to changes in the CAS system, they merit discussion as these trends signal more confidence and experience with these techniques in recent years by many transplant centers. Utilization of lungs procured using DPP in DCD has not been shown to have inferior outcomes compared to lungs procured from DBD donors [32, 33]. In the post-CAS era, we found that NRP and DCD were both associated with improved survival for LT recipients. These findings are corroborated by early reports that have demonstrated favorable early postoperative pulmonary graft function using NRP in DCD lung procurement [34–36]. Ultimately, these results are promising in the post-CAS era in our ongoing efforts to augment the lung donor pools. Further efforts to utilize high risk, extended criteria donors, including those with positive hepatitis C status [37], cocaine use, smoking, older age, and diabetes [38] should be evaluated in the post-CAS era. Another major criticism of the previous LAS system was that patients with certain biologic factors, including ABO blood type O, were disproportionately less likely to be transplanted [8]. However, these issues have persisted in the first two years of experience with the CAS system. ABO blood type O recipients continue to be at a disadvantage for receiving LTs, and the number of identical ABO matches has also decreased. These effects have continued after UNOS/OPTN implemented a policy change to award additional allocation scores to ABO type O candidates in September 2023 [39]. Thus, this population should be closely monitored in the following years to determine whether the updated CAS can adequately address these disparities. In other aspects, the CAS system appears to have ensured comparable access to allosensitized and Black patients, trending towards amending several issues previously found under the LAS system [7]. Limitations. First, the retrospective nature of our study may introduce selection bias. Second, the lack of granularity and data censoring regarding variations in individual transplant center practices and policies cannot be adequately addressed. As time passes under the CAS system, individual transplant centers will also likely adjust practices, leading to expected changes in outcome trends observed in the current study. However, we have used transplant center volume to adjust our multivariate modeling to account for these confounders. We are also unable to report definitive cost comparisons due to their absence in the UNOS database. Third, most data elements are recorded during waitlist registration and transplant. Thus, variables such as duration of mechanical circulatory support or real-time hemodynamic and lung function data are unavailable. Finally, these complications could not be analyzed between eras due to the inconsistent reporting of chronic lung allograft dysfunction in the UNOS database. Conclusion In this national analysis, the implementation of the CAS system for LT has achieved its intended goals of reducing waitlist mortality and improving transplant access to allosensitized and Black patients. Despite increased donor lung travel distances and ischemic times, 1-year post-transplant survival improved in the CAS era. Growing utilization of extended criteria donors further supports the potential to expand the donor pool without compromising outcomes. However, persistent disparities for ABO blood type O candidates, and the association between air transport and mortality warrant continued monitoring and refinement of the system. Future efforts should focus on integrating waitlist duration and real-time clinical deterioration into allocation models while ensuring equitable and efficient organ utilization. Declarations Acknowledgements: We thank the Virginia Commonwealth University Department of Biostatistics for excellent statistical and data analysis support. We want to thank the Pauley Heart Center and the Hume-Lee Transplant Center for their support of this research. This paper will be presented at the 51 st Western Thoracic Surgical Association Annual Meeting, June 25 – 28, Dana Point, CA, USA. Funding: The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interests: The authors have no relevant financial or non-financial interests to disclose. Author Contributions : Ye In Christopher Kwon and Holly Caboti-Jones contributed equally to the production and editing of this manuscript. Ye In Christopher Kwon, Holly Caboti-Jones, and Michael Keller contributed to the data and statistical analyses. Ye In Christopher Kwon, Holly Caboti-Jones, Michael Keller, Andrew Min-Gi Park, Alan Lai, and Zubair A. Hashmi conceived and designed the study. Rachit D. Shah, Zachary Fitch, Vigneshwar Kasirajan, Vipul Patel, and Zubair A. Hashmi reviewed and edited this manuscript. Ethics approval: Because all data were de-identified, this study was deemed exempt from the Virginia Commonwealth University Institutional Review Board. It also complies with the International Society for Heart and Lung Transplantation (ISHLT) ethics policies. References Holm AM, Fedson S, Courtwright A, Olland A, Bryce K, Kanwar M, Sweet S, Egan T, Lavee J (2022) International society for heart and lung transplantation statement on transplant ethics. 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The Journal of Heart and Lung Transplantation 42:693–696. https://doi.org/10.1016/j.healun.2023.03.001 Cain MT, Park SY, Schäfer M, Hay-Arthur E, Justison GA, Zhan QP, Campbell D, Mitchell JD, Randhawa SK, Meguid RA, David EA, Reece TB, Cleveland JC, Hoffman JRH (2023) Lung recovery utilizing thoracoabdominal normothermic regional perfusion during donation after circulatory death: The Colorado experience. JTCVS Techniques 22:350–358. https://doi.org/10.1016/j.xjtc.2023.09.027 Bashian EJ, Bashian EE, Kwon YIC, Ambrosio M, Fitch Z, Taylor LJ, Patel V, Julliard W, Kasirajan V, Hashmi ZA (2025) Promising Long-Term Outcomes of Lung Transplantation With Hepatitis C Positive Donors: Insights From the UNOS Registry. Transplantation Proceedings S0041134525001538. https://doi.org/10.1016/j.transproceed.2025.02.044 Christie IG, Chan EG, Ryan JP, Harano T, Morrell M, Luketich JD, Sanchez PG (2021) National Trends in Extended Criteria Donor Utilization and Outcomes for Lung Transplantation. The Annals of Thoracic Surgery 111:421–426. https://doi.org/10.1016/j.athoracsur.2020.05.087 UNOS/OPTN Modify Lung Allocation by Blood Type Tables Table 1. Comparison of baseline recipient and donor characteristics of lung transplantation candidates before and after implementing the Composite Allocation Score system. Characteristics Total (N=6,398) Era 1 (N=3,800) Era 2 (N=2,598) P-Value Recipient Characteristics Age, years, median [IQR] 60 [51-66] 61 [52-67] 60 [52-66] 0.004 Sex, N (%) 0.209 Male 4053 2431 (64%) 1622 (62.4%) Female 2345 1369 (36%) 976 (37.6%) BMI, kg/m 2 , median [IQR] 26.8 [23.3-30.3] 26.8 [23.4-30.2] 26.9 [23.5-30.2] 0.565 Race, N (%) 0.049 White 4191 2518 (66.3%) 1673 (64.4%) Black 1011 571 (15%) 440 (16.9%) Hispanic/Latino 871 516 (13.6%) 355 (13.7%) Asian 251 159 (4.2%) 92 (3.5%) Others 74 36 (1%) 38 (1.5%) Education, N (%) 0.003 None 14 9 (0.2%) 5 (0.2%) Grade School (0-8) 240 145 (3.8%) 95 (3.7%) High School (9-12) or GED 2190 1270 (33.4%) 920 (35.4%) Attended College/ Technical School 1609 959 (25.2%) 650 (25%) Associate/ Bachelor’s Degree 1460 911 (24%) 549 (21.1%) Post-College Graduate Degree 658 402 (10.6%) 256 (9.9%) Payment Types, N (%) 0.071 Private Insurance 2678 1542 (40.6%) 1136 (43.7%) Public Insurance 3678 2234 (58.8%) 1444 (55.6%) Self 19 12 (0.3%) 7 (0.3%) UNOS Regions, N (%) 0.750 1 211 115 (3%) 96 (3.7%) 2 654 390 (10.3%) 264 (10.2%) 3 719 423 (11.1%) 296 (11.4%) 4 683 395 (10.4%) 288 (11.1%) 5 1143 688 (18.1%) 455 (17.5%) 6 146 86 (2.3%) 60 (2.3%) 7 654 395 (10.4%) 259 (10%) 8 355 214 (5.6%) 141 (5.4%) 9 516 320 (8.4%) 196 (7.5%) 10 630 381 (10%) 249 (9.6%) 11 687 393 (10.3%) 294 (11.3%) Karnofsky Functional Status, N (%) 0.392 10% 116 68 (1.8%) 48 (1.9%) 20% 1142 658 (17.3%) 484 (18.6%) 30% 506 318 (8.4%) 188 (7.2%) 40% 1019 613 (16.1%) 406 (15.6%) 50% 636 367 (9.7%) 269 (10.4%) 60% 1352 809 (21.3%) 543 (20.9%) 70% 986 579 (15.2%) 407 (15.7%) 80% 375 232 (6.1%) 143 (5.5%) 90% 94 63 (1.7%) 31 (1.2%) 100% 17 9 (0.2%) 8 (0.3%) Waitlist Time, days, median [IQR] 27 [9-85] 31 [11-106] 29 [10-96] 0.010 Waitlist Recipient Diagnosis, N (%) Pulmonary Hypertension 195 110 (2.9%) 85 (3.3%) 0.389 Secondary Pulmonary Hypertension 30 19 (0.5%) 11 (0.4%) 0.660 Cystic Fibrosis 72 38 (1%) 34 (1.3%) 0.250 Idiopathic Pulmonary Fibrosis 1475 908 (23.9%) 567 (21.8%) 0.054 Sarcoidosis 81 50 (1.3%) 31 (1.2%) 0.667 A1AT Deficiency 57 33 (0.9%) 24 (0.9%) 0.817 COPD/Emphysema 649 380 (10%) 269 (10.4%) 0.645 Bronchiectasis 67 30 (0.8%) 37 (1.4%) 0.014 Rheumatoid Disease 54 38 (1%) 16 (0.6%) 0.099 Other Causes of Pulmonary Fibrosis 561 332 (8.7%) 229 (8.8%) 0.914 Severe COVID-19 423 228 (6%) 195 (5.1%) 0.027 Preop Labs, mg/dL, median, [IQR] Creatinine 0.9 [0.7-1.1] 0.9 [0.7-1.1] 0.9 [0.7-1.1] 0.966 Total Bilirubin 0.5 [0.3-0.8] 0.5 [0.3-0.8] 0.5 [0.3-0.8] 0.514 Preop Lung Function Tests FEV1, L, median [IQR] 4.6 [2.9-6.2] 4.6 [2.9-6.2] 4.5 [3.0-6.1] 0.565 FVC, L, median [IQR] 5.0 [3.9-65] 5.0 [3.9-6.4] 5.0 [3.8-6.4] 0.508 PCO2, mmHg, median [IQR] 44.2 [39-52] 45 [39-52.1] 45 [39.9-53] 0.117 Blood Types, N (%) 0.039 A 2428 1407 (37%) 1021 (39.3%) B 845 477 (12.6%) 368 (14.2%) AB 234 133 (3.5%) 101 (3.9%) O 2874 1770 (46.6%) 1104 (42.5%) Circulatory Support at Transplant, N (%) ECMO 249 158 (4.2%) 91 (3.5%) 0.183 IABP 353 226 (6%) 127 (4.9%) 0.069 Ventilator 240 152 (4%) 88 (3.4%) 0.205 Inotropes 812 440 (11.6%) 372 (14.3%) 0.001 Comorbidities, N (%) Diabetes 1431 822 (21.6%) 609 (23.4%) 0.088 Steroid 1502 917 (24.1%) 585 (22.5%) 0.135 Smoking 3159 1858 (48.9%) 1301 (50.1%) 0.353 Malignancy 734 432 (11.4%) 302 (11.6%) 0.752 CVA 197 114 (3%) 83 (3.2%) 0.658 AICD 1368 810 (21.3%) 558 (21.5%) 0.877 Prior Cardiac Surgery 896 535 (14.1%) 361 (13.9%) 0.835 Prior Lung Surgery 119 71 (1.9%) 48 (1.9%) 0.952 Donor Characteristics Age, years, median [IQR] 34 [26-43] 33 [25-43] 33 [25-43] 0.222 Sex, N (%) 0.011 Male 4063 2365 (62.2%) 1698 (65.4%) Female 2335 1435 (37.8%) 900 (34.6%) BMI, kg/m 2 , median [IQR] 26.1 [23.1-30.1] 25.9 [22.9-30] 25.9 [22.8-29.9] 0.151 Race, N (%) 0.919 White 3773 2223 (58.5%) 1550 (59.7%) Black 1125 679 (17.9%) 446 (17.2%) Hispanic/Latino 1232 738 (19.4%) 494 (19%) Asian 182 109 (2.9%) 73 (2.8%) Other 86 51 (1.3%) 35 (1.4%) LVEF, %, N (%) 60 [55-65] 60 [55-65] 60 [55-65] 0.610 Abnormal Chest X-ray, N (%) 4339 2561 (67.4%) 1778 (68.4%) 0.381 Abnormal Purulence on Bronchoscopy, N (%) Left 1137 640 (16.8%) 497 (19.1%) 0.051 Right 1294 750 (19.7%) 544 (20.9%) 0.240 Comorbidities, N (%) Diabetes 446 276 (7.3%) 170 (6.5%) 0.267 Smoking 433 242 (6.4%) 191 (7.4%) 0.124 Cocaine 1389 946 (24.9%) 443 (17.1%) <.0001 Alcohol abuse 1320 764 (20.1%) 556 (21.4%) 0.208 Malignancy 109 65 (1.7%) 44 (1.7%) 0.959 Hypertension 1315 802 (21.1%) 513 (19.8%) 0.186 IV drug use 809 497 (13.1%) 312 (12%) 0.206 MI 109 72 (1.9%) 37 (1.4%) 0.153 Blood Types, N (%) 0.616 A 1009 599 (15.8%) 410 (15.8%) B 661 388 (10.2%) 273 (10.5%) AB 42 31 (0.8%) 11 (0.4%) O 3517 2074 (54.6%) 1443 (55.5%) Causes of Death, N (%) 0.015 Anoxia 2685 1580 (41.6%) 1105 (42.5%) CNS Tumor 28 11 (0.3%) 17 (0.7%) Cerebrovascular/Stroke 1260 781 (20.6%) 479 (18.4%) Head Trauma 2281 1354 (35.6%) 927 (35.7%) Others 144 74 (2%) 70 (2.7%) P/F Ratio <300, N (%) 986 536 (14.1%) 450 (17.3%) 0.002 Gender Mismatch, N (%) 974 608 (16%) 366 (14.1%) 0.051 Identical ABO Match, N (%) 6319 3504 (92.2%) 2815 (84.1%) <.0001 HLA Mismatch, # of alleles, mean (SD) 4.6 4.9 (1.1) 4.4 (0.9) 0.176 Ex-vivo Perfusion, N (%) 278 145 (3.8%) 133 (5.1%) 0.012 Donor Type, N (%) <.0001 DBD 5918 3564 (93.8%) 2354 (90.6%) DCD 480 236 (6.2%) 244 (9.4%) Procurement, N (%) <.0001 DPP 388 190 (5%) 198 (7.6%) NRP 92 46 (1.2%) 46 (1.8%) Donor-Recipient Distance, nautical miles, median [IQR] 318 [131-576] 202 [86-394] 231 [100-457] <.0001 Flight for Transport, N (%) 4820 2765 (72.8%) 2055 (79.1%) <.0001 Total Ischemic Time, hours, median [IQR] 5.4 [3.8-7.3] 4.9 [3.7-6.5] 5.1 [3.7-6.9] <.0001 Table 2. Comparison of short- and long-term outcomes of lung transplantation before and after the implementation of the Composite Allocation System. Outcomes Total (N=6,398) Era 1 (N=3,800) Era 2 (N=2,598) P-Value Mortality, N (%) Overall 558 397 (10.5%) 161 (6.2%) <.0001 30 Days 17 8 (0.2%) 9 (0.4%) 0.299 90 Days 94 55 (1.5%) 39 (1.5%) 0.861 6 Months 260 171 (4.5%) 96 (3.7%) 0.011 1 Year 414 258 (6.8%) 156 (6%) 0.021 Ventilator, N (%) 4202 2525 (66.5%) 1677 (64.6%) 0.116 Acute Rejection, N (%) 402 244 (6.4%) 158 (6.1%) 0.583 FVC, L, median [IQR] 2.78 [2.18 - 3.45] 2.83 [2.24 - 3.53] 2.70 [2.09 - 3.35] 0.001 Length of Hospital Stay, N (%) 20 [14-33] 20 [14-33] 1369 [14-32] 0.040 Stroke, N (%) 191 114 (3%) 77 (3%) 0.933 Pacemaker, N (%) 54 37 (1%) 17 (0.7%) 0.170 Dialysis, N (%) 736 439 (11.6%) 297 (11.4%) 0.882 Treated for Rejection, N (%) 619 551 (14.5%) 68 (2.6%) <.0001 Hospitalization for Infection, N (%) 46 22 (0.6%) 24 (0.9%) 0.109 Hospitalization for Rejection, N (%) 402 244 (6.4%) 158 (6.1%) 0.583 Re-Transplant, N (%) 8 6 (0.2%) 2 (0.1%) 0.368 Airway Dehiscence, N (%) 73 47 (1.2%) 26 (1%) 0.383 Table 3. Multivariate cox proportional hazards regression model for mortality after lung transplantation following CAS change. Covariates Hazard Ratio (95% CI) P -Value Transplant after CAS change 1.25 (0.97 – 1.54) 0.054 Age (increasing, per 1 year) 1.02 (1.01 – 1.02) 0.001 Female Sex (male as reference) 1.19 (0.99 – 1.43) 0.054 BMI (increasing, per 1 unit) 1.00 (0.99 – 1.43) 0.054 Comorbidities Diabetes 1.27 (1.04 – 1.54) 0.019 Steroid 1.29 (1.06 – 1.56) 0.010 Smoking 1.01 (0.85 – 1.20) 0.931 Malignancy 1.25 (0.98 – 1.60) 0.070 Prior Cardiac Surgery 1.43 (1.11 – 1.83) 0.005 Prior Lung Surgery 0.92 (0.49 – 1.73) 0.800 Pre-Transplant Circulatory Support ECMO 1.25 (0.77 – 2.05) 0.367 IABP 1.24 (0.83 – 1.86) 0.300 Ventilator 1.33 (0.84 – 2.11) 0.227 Inotropes 1.05 (0.76 – 1.45) 0.765 Ex-vivo Perfusion 1.24 (0.91 – 1.59) 0.421 DCD (DBD as reference) 0.83 (0.62 – 0.93) 0.017 NRP (DPP as reference) 0.91 (0.79 – 0.95) 0.002 Donor-Recipient Distance (increasing, per 1 nautical miles) 1.00 (0.99 – 1.00) 0.349 Flight for Transport 1.53 (1.02 – 1.99) 0.022 Time on Waitlist (increasing, per 1 day) 1.00 (0.99 – 1.00) 0.308 Ischemic Time (increasing, per 1 hour) 1.06 (0.99 – 1.09) 0.312 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6448565","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":443310840,"identity":"e05fdc15-dad5-4e93-a642-685ea2e78311","order_by":0,"name":"Ye In Christopher Kwon","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApklEQVRIiWNgGAWjYBACCQhlA6F4SNCSRrqWwyRokZyRfOwxT835xP4ZCYwP3rYRoUVaIi3dmOfY7cQZNxKYDecSo0VOOsdMmrfhtrGBRAKbNC8JWs6BtLD/JkqLNETLATmQLcxEaZGc/yxNcs6xZDmJMw+bJeecI0KLxJnDxyTe1Njx8LcnH/zwpowILSDABIkOxgYi1YPU/iBe7SgYBaNgFIxEAADkXS5w+/kFVAAAAABJRU5ErkJggg==","orcid":"","institution":"Virginia Commonwealth University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Ye","middleName":"In Christopher","lastName":"Kwon","suffix":""},{"id":443310841,"identity":"5aa1c46a-3a24-45ed-b0f5-872ec2f51345","order_by":1,"name":"Holly Caboti-Jones","email":"","orcid":"","institution":"Virginia Commonwealth University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Holly","middleName":"","lastName":"Caboti-Jones","suffix":""},{"id":443310842,"identity":"6b4781ea-1e04-4073-b3fd-6861b17ec1ad","order_by":2,"name":"Michael Keller","email":"","orcid":"","institution":"Virginia Commonwealth University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Keller","suffix":""},{"id":443310843,"identity":"9a0649b9-3a91-4479-8cac-8d5a55d641ea","order_by":3,"name":"Andrew Min-Gi Park","email":"","orcid":"","institution":"Virginia Commonwealth University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"Min-Gi","lastName":"Park","suffix":""},{"id":443310844,"identity":"73d4b963-85a7-4925-af43-ad801d133180","order_by":4,"name":"Alan Lai","email":"","orcid":"","institution":"Virginia Commonwealth University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Alan","middleName":"","lastName":"Lai","suffix":""},{"id":443310845,"identity":"9d1a8368-8fbb-4413-a8a6-93d3092d9fae","order_by":5,"name":"Rachit Dipesh Shah","email":"","orcid":"","institution":"Virginia Commonwealth University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Rachit","middleName":"Dipesh","lastName":"Shah","suffix":""},{"id":443310846,"identity":"e7be133c-9b69-4dee-a8ff-1aacd21c5037","order_by":6,"name":"Zachary Fitch","email":"","orcid":"","institution":"Virginia Commonwealth University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zachary","middleName":"","lastName":"Fitch","suffix":""},{"id":443310847,"identity":"318693c7-b5cc-4b7f-897f-0d92a7203ad4","order_by":7,"name":"Vigneshwar Kasirajan","email":"","orcid":"","institution":"Virginia Commonwealth University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Vigneshwar","middleName":"","lastName":"Kasirajan","suffix":""},{"id":443310848,"identity":"788b832c-5f27-4ef6-ac38-492243f3ca4d","order_by":8,"name":"Vipul Patel","email":"","orcid":"","institution":"Virginia Commonwealth University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Vipul","middleName":"","lastName":"Patel","suffix":""},{"id":443310849,"identity":"479107a8-2d4b-41f0-a2f2-324154368199","order_by":9,"name":"Zubair A. Hashmi","email":"","orcid":"","institution":"Virginia Commonwealth University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zubair","middleName":"A.","lastName":"Hashmi","suffix":""}],"badges":[],"createdAt":"2025-04-14 18:38:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6448565/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6448565/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81197663,"identity":"e11b557c-0ed8-44cf-b4bd-47a8faa944a7","added_by":"auto","created_at":"2025-04-23 10:40:28","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1991831,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart for patient inclusion and exclusion using the United Network for Organ Donation registry.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6448565/v1/934344323923b1cebbf637ee.jpg"},{"id":81198514,"identity":"f78d791c-23c2-4a35-a6f9-d35a0dc17564","added_by":"auto","created_at":"2025-04-23 10:48:28","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":530025,"visible":true,"origin":"","legend":"\u003cp\u003eCompeting risk regression analysis for recipients’ waitlist death/deterioration (\u003cstrong\u003eA\u003c/strong\u003e), transplant (\u003cstrong\u003eB\u003c/strong\u003e), or recovery (\u003cstrong\u003eC\u003c/strong\u003e) before and after the implementation of the Composite Allocation Score.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6448565/v1/41bee64dc38ef8f03269a909.jpg"},{"id":81197664,"identity":"e166fe46-5333-42ff-884e-b8cca6816d75","added_by":"auto","created_at":"2025-04-23 10:40:28","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1182280,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival analysis for recipients of lung transplantation before and after the Composite Allocation Score system change at 30-days (\u003cstrong\u003eA\u003c/strong\u003e), 90-days (\u003cstrong\u003eB\u003c/strong\u003e), 6-months (\u003cstrong\u003eC\u003c/strong\u003e), and 1-year (\u003cstrong\u003eD\u003c/strong\u003e) post-transplant. Shaded regions represent 95% confidence intervals.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6448565/v1/0ae60422914f7c1dcd44bccc.jpg"},{"id":81199259,"identity":"b31d8e59-ef40-423c-866d-7ef1ec59cd68","added_by":"auto","created_at":"2025-04-23 10:56:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5450978,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6448565/v1/f3842de4-7f6e-4beb-9f75-8f6d8c1d7464.pdf"},{"id":81197660,"identity":"494bf3d7-e37c-4c5c-9fdc-2453b4fed6fa","added_by":"auto","created_at":"2025-04-23 10:40:28","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":16251,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-6448565/v1/08c3b733714f58bd1c060e19.docx"},{"id":81197661,"identity":"8f6aaaab-ad5f-42f2-b6d8-cfefedd5998a","added_by":"auto","created_at":"2025-04-23 10:40:28","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":632197,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6448565/v1/26d8c40a1bbd5b415b64fcee.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of the Composite Allocation Score on Lung Transplant Waitlist and Post- Transplant Outcomes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung transplantation (LT) is the gold standard therapy for end-stage pulmonary failure. However, as\u0026nbsp;demand for donor lungs\u0026nbsp;outpaces\u0026nbsp;supply [1, 2], challenges persist with\u0026nbsp;effective\u0026nbsp;and equitable allocation to the most optimal candidates. The antecedent Lung Allocation Score (LAS) model\u0026nbsp;prioritized geographic donor-recipient proximity as the principal determinant for lung allocation [3, 4]. However, such systems have resulted in arbitrary cut-offs, leading to lower-priority candidates receiving LT before higher-priority candidates outside\u0026nbsp;designated\u0026nbsp;boundaries\u0026nbsp;[5]. Additionally, concerns emerged regarding\u0026nbsp;disparities affecting\u0026nbsp;male\u0026nbsp;[6],\u0026nbsp;Black\u0026nbsp;[7], ABO blood type O\u0026nbsp;[8], and\u0026nbsp;allosensitized [7]. In response, the United Network for Organ Sharing (UNOS) implemented the\u0026nbsp;Composite Allocation Score (CAS) in March 2023 -\u0026nbsp;a weighted system accounting\u0026nbsp;for medical urgency, post-transplant survival, biological factors, and patient access\u0026nbsp;[9]\u0026nbsp;while still considering geographical proximity factors\u0026nbsp;[5, 10].\u003c/p\u003e\n\u003cp\u003eEarly data following CAS implementation indicated similar short-term survival despite the increased transplant distances and longer ischemic times [10]. The CAS system aimed to reduce waitlist mortality and expand donor lung access, especially for ABO type O groups [11]. Thus, we provide a timely assessment of changes in patient characteristics, waitlist outcomes, and 1-year post-transplant outcomes in adult LT under the lung CAS policy change. We also analyze usage trends in the growing use of donation after circulatory death (DCD) LT, \u003cem\u003eex-vivo\u0026nbsp;\u003c/em\u003elung perfusion (EVLP), and normothermic regional perfusion (NRP) in the post-CAS era.\u0026nbsp;\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cem\u003eStudy design.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe retrospectively reviewed the UNOS Standard Analysis and Research (STAR) database to identify all adult patients\u0026nbsp;(\u0026ge;18 years) listed for first-time LT from 3/1/2022 to 9/30/2024 with at least 6 months of follow-up information. Patients who underwent multi-organ transplants or lost to follow-up were excluded. Patients were stratified into Era 1 (pre-CAS; 3/1/2022 \u0026ndash; 3/8/2023) and Era 2 (post-CAS; 3/9/2023 \u0026ndash; 9/30/2024) (\u003cstrong\u003eFigure 1\u003c/strong\u003e). The \u0026ldquo;flight for transport\u0026rdquo; variable was a binary data element defined as donor lungs traveling\u0026gt; 100 nautical miles to the recipient\u0026rsquo;s transplant center[10]. As all data was de-identified, this study was exempt from the Virginia Commonwealth University Institutional Review Board.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical analysis.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor waitlist outcomes, LT candidates who were waitlisted before the CAS implementation were censored by 3/8/2023 to limit the confounding occurring in patients listed before the CAS implementation and to remain on the waitlist after the allocation system change (\u003cstrong\u003eFigure S1\u003c/strong\u003e). We used competing risk regression models [12] to evaluate the association between CAS implementation and the following outcomes: waitlist death or deterioration with subsequent removal from the waitlist, transplant, or recovery with removal from the waitlist. We reported adjusted sub-hazard ratios (SHRs) with 95% confidence intervals (CIs). Competing risk regression and cumulative incidence curves were analyzed using the Fine and Gray method [13, 14].\u003c/p\u003e\n\u003cp\u003eRecipient and matched donor characteristics were collected, with categorical variables reported as percentages and continuous variables as means with standard deviations (SD) or medians with interquartile ranges (IQR). Pearson\u0026rsquo;s Chi-square or Fisher\u0026rsquo;s exact test was used to compare binary and categorical variables, while Wilcoxon rank-sum tests were used to compare medians of continuous variables. Outcomes included recipient survival at 30-, 90-days, 6-months, and 1-year, rates of post-transplant complication, lung function tests, and the need for circulatory support. The Kaplan-Meier method with log-rank tests were used to plot and assess survival. We used the methodology from Wall et al. to distinguish between NRP and direct procurement and preservation (DPP) [15]. A multivariate Cox proportional hazards regression model was constructed for covariates with biological plausibility and adjusted for recipient age, body mass index (BMI), diabetes, smoking, race, and transplant center volume. The Schoenfeld residual test assessed the hypothesis of proportional hazards to determine the proportionality assumption. All statistical analyses were conducted using SAS (version 9.4; SAS Inc., Cary, NC, USA). All \u003cem\u003ep\u003c/em\u003e-values were based on two-sided statistical tests, with significance at \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eBaseline characteristics.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA total of 6,398 patients underwent LT during the study period-\u0026nbsp;3,800 (59.4%) in Era 1 and 2,598 (40.6%) in Era 2. Compared to Era 1, recipients in Era 2 were younger (age 60 vs. 61 years, \u003cem\u003ep\u003c/em\u003e=0.004),\u0026nbsp;more likely to be Black (16.9% vs.\u0026nbsp;15.0%, \u003cem\u003ep\u003c/em\u003e=0.049), and experienced shorter waitlist times (29 vs. 31 days, \u003cem\u003ep\u003c/em\u003e=0.010). Additionally, fewer recipients in Era 2 underwent transplantation for severe SARS-CoV-2 (COVID-19) infection (5.1% vs. 6.0%, \u003cem\u003ep\u003c/em\u003e=0.027), while recipients with bronchiectasis as an indication increased (1.4% vs. 0.8%, \u003cem\u003ep\u003c/em\u003e=0.014).\u0026nbsp;Pre-transplant inotropes\u0026nbsp;were more common in Era 2 (14.3% vs. 11.6%, \u003cem\u003ep\u003c/em\u003e=0.001).\u0026nbsp;Recipients\u0026nbsp;with blood type O decreased in Era 2 (42.5% vs. 46.6%, \u003cem\u003ep\u003c/em\u003e=0.039), while recipients with blood types A (39.3% vs. 37.0%), B (14.2% vs. 12.6%), and AB (3.9% vs. 3.5%) increased, contributing to a significant drop in donor-recipient ABO matching rates\u0026nbsp;(84.1% vs. 92.2%, \u003cem\u003ep\u003c/em\u003e\u0026lt;.0001).\u003c/p\u003e\n\u003cp\u003eDonors in Era 2 were more often male (65.4% vs. 62.2%, \u003cem\u003ep\u003c/em\u003e=0.011) and had a PaO2/FiO2 (P/F) ratio less than 300 (17.3% vs 14.1%), p=0.002), while less likely to have a history of cocaine use (17.1% vs.\u0026nbsp;24.9%, \u003cem\u003ep\u003c/em\u003e\u0026lt;.0001).\u0026nbsp;Other\u0026nbsp;extended donor criteria, including age, body mass index (BMI), smoking history, diabetes, alcohol use, lung bronchoscopy, and chest radiographs, remained similar between eras. There were no significant differences in gender mismatch (\u003cem\u003ep\u003c/em\u003e=0.051) or human leukocyte antigen (HLA) mismatch (\u003cem\u003ep\u003c/em\u003e=0.176).\u0026nbsp;Use of\u0026nbsp;DCD (9.4% vs. 6.2%, \u003cem\u003ep\u003c/em\u003e\u0026lt;.0001), NRP (1.8% vs. 1.2%, \u003cem\u003ep\u003c/em\u003e\u0026lt;.0001),\u0026nbsp;and EVLP (5.1% vs. 3.8%, \u003cem\u003ep\u003c/em\u003e=0.012) increased significantly in Era 2.\u0026nbsp;However, donor lungs were procured from greater distances (231 vs. 202 nautical miles, \u003cem\u003ep\u003c/em\u003e\u0026lt;.0001), required more flights (79.1% vs. 72.8%, \u003cem\u003ep\u003c/em\u003e\u0026lt;.0001), and experienced longer ischemic times (5.1 vs. 4.9 hours, \u003cem\u003ep\u003c/em\u003e\u0026lt;.0001).\u0026nbsp;Regional\u0026nbsp;differences in LT between eras\u0026nbsp;remained insignificant (\u003cem\u003ep\u003c/em\u003e=0.750).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eWaitlist outcomes.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA comparison of competing waitlist outcomes found significant changes in Era 2 (\u003cstrong\u003eTable S1\u003c/strong\u003e).\u0026nbsp;Risk\u0026nbsp;of death or clinical deterioration was reduced (SHR 0.73 [95% CI 0.65 – 0.82],\u0026nbsp;\u003cem\u003ep\u0026lt;.\u003c/em\u003e0001) (\u003cstrong\u003eFigure 2A\u003c/strong\u003e), while the likelihood of LT was significantly increased in Era 2 (SHR 1.05 [95% CI 1.01 – 1.08],\u0026nbsp;\u003cem\u003ep\u003c/em\u003e=0.005) (\u003cstrong\u003eFigure 2B\u003c/strong\u003e).\u0026nbsp; Odds\u0026nbsp;of removal from the waitlist due to clinical recovery remained low in both eras and were significantly reduced in Era 2 (SHR 0.46 [95% CI 0.36 – 0.59],\u0026nbsp;\u003cem\u003ep\u0026lt;.\u003c/em\u003e0001) (\u003cstrong\u003eFigure 2C\u003c/strong\u003e). Overall,\u0026nbsp;waitlist mortality rates in Era 2 were significantly lowered (9.8 vs. 13.9%,\u0026nbsp;\u003cem\u003ep\u0026lt;\u003c/em\u003e.0001) (\u003cstrong\u003eTable S2\u003c/strong\u003e), with significantly lower mortality at 30- (\u003cem\u003ep\u003c/em\u003e=0.004), 90-days (\u003cem\u003ep\u003c/em\u003e=0.001), 6-months (\u003cem\u003ep\u003c/em\u003e\u0026lt;.0001), and 1-year (\u003cem\u003ep\u003c/em\u003e\u0026lt;.0001). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePost-transplant outcomes.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eShort-term survival at 30- (\u003cem\u003ep\u003c/em\u003e=0.437) (\u003cstrong\u003eFigure 3A\u003c/strong\u003e) and 90-days (\u003cem\u003ep\u003c/em\u003e=0.163) (\u003cstrong\u003eFigure 3B\u003c/strong\u003e) post-transplant remained similar between eras. However, Era 2 recipients had superior survival at 6-months (\u003cem\u003ep\u0026lt;\u003c/em\u003e.0001) (\u003cstrong\u003eFigure 3C\u003c/strong\u003e) and 1-year (\u003cem\u003ep\u003c/em\u003e\u0026lt;.0001) (\u003cstrong\u003eFigure 3D\u003c/strong\u003e), with fewer overall mortalities\u0026nbsp;(6.2 vs. 10.5%, \u003cem\u003ep\u003c/em\u003e\u0026lt;.0001) (\u003cstrong\u003eTable 2\u003c/strong\u003e).\u0026nbsp; Extracorporeal\u0026nbsp;membrane oxygenation (ECMO) or intra-aortic balloon pump (IABP)\u0026nbsp;use remained low during the study period;\u0026nbsp;more recipients in Era 2 were supported by ECMO, IABP, or any life support measures at 24- and 72-hours post-transplant (all\u0026nbsp;\u003cem\u003ep\u003c/em\u003e\u0026lt;.0001). Rates of ventilator use between eras were comparable (\u003cem\u003ep\u003c/em\u003e=0.116). Era 2 recipients\u0026nbsp;had shorter hospital stays\u0026nbsp;post-transplant (\u003cem\u003ep\u003c/em\u003e=0.040) and\u0026nbsp;significantly fewer\u0026nbsp;rejection events within 1-year (2.6 vs.14.5%,\u0026nbsp;\u003cem\u003ep\u003c/em\u003e\u0026lt;.0001). Post-operative complications, including stroke, pacemaker implantation, dialysis, airway dehiscence, acute rejection, and re-transplant, remained comparable between eras.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRisk of mortality.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLT after the CAS change was not associated with\u0026nbsp;post-transplant mortality (HR 1.29 [95% CI 0.97 – 1.54],\u0026nbsp;\u003cem\u003ep\u003c/em\u003e=0.054), whereas increased\u0026nbsp;age (HR 1.02 [95% CI 1.01 – 1.02],\u0026nbsp;\u003cem\u003ep\u003c/em\u003e=0.001) was associated with increased risk of mortality (\u003cstrong\u003eTable 3\u003c/strong\u003e).\u0026nbsp;However,\u0026nbsp;use of DCD (HR 0.83 [95% CI 0.62 – 0.93],\u0026nbsp;\u003cem\u003ep\u003c/em\u003e=0.017) and NRP (HR 0.91 [95% CI 0.79 – 0.95],\u0026nbsp;\u003cem\u003ep\u003c/em\u003e=0.002) were associated with decreased risk of mortality in Era 2. Use of flight for transport (HR 1.53 [95% CI 1.02 – 1.99],\u0026nbsp;\u003cem\u003ep\u003c/em\u003e=0.022) was\u0026nbsp;linked with increased risk in Era 2. Use of EVLP (HR 1.24 [95% CI 0.91 – 1.59],\u0026nbsp;\u003cem\u003ep\u003c/em\u003e=0.421), increased ischemic times (HR 1.06 [95% CI 0.99 – 1.09],\u0026nbsp;\u003cem\u003ep\u003c/em\u003e=0.312), increased donor-recipient distance (HR 1.00 [95% CI 0.99 – 1.00],\u0026nbsp;\u003cem\u003ep\u003c/em\u003e=0.349), and increased time on the waitlist (HR 1.00 [95% CI 0.99 – 1.00],\u0026nbsp;\u003cem\u003ep\u003c/em\u003e=0.308) were not associated with mortality risk. \u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe CAS system for LT represents a fundamental shift in organ allocation from a hierarchical to a continuous distribution framework\u0026nbsp;[11]. Notably, the CAS system employs a modular construct, which allows timely adjustments to each component\u0026rsquo;s relative weights, reflecting the most contemporary needs of LT candidates or surgical teams\u0026nbsp;[16]. A prior study\u0026nbsp;[10]\u0026nbsp;examined the early impact of the CAS change but lacked\u0026nbsp;waitlist\u0026nbsp;outcomes and adequate follow-up compared to the current study. This national analysis demonstrates that the CAS system has successfully reduced waitlist mortality while increasing access to LT. In the CAS era, recipient survival up to 1-year has been significantly improved compared to the LAS era, despite increased lung travel distances and ischemic times.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile waitlist mortality and odds of transplant have improved under the CAS policy change, there\u0026nbsp;is room for further optimization. Both the LAS and CAS ignore the fact that some candidates\u0026rsquo; mortality risk will progress more rapidly than others. While the CAS system attempts to address these issues by heavily weighing the severity and urgency of an individual\u0026rsquo;s lung disease and probability of long-term survival, it still ignores the disproportionate impact of waitlist duration on candidates. Most patients indeed receive LT within 6 months of being listed\u0026nbsp;[17]\u0026nbsp;, and we report the\u0026nbsp;average time on the waitlist to be under 1 month\u0026nbsp;with the CAS system. Thus, one may argue\u0026nbsp;that accrued\u0026nbsp;time on the waitlist may not significantly impact survival. However, Dalton et al. demonstrate that the extent to which mortality risk changes over time varies considerably among patients in a predictable manner\u0026nbsp;[18]. Ultimately, it will become necessary for the CAS system to account for days spent on the waitlist as a component in determining lung allocation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs intended, the geographic limits appear to be largely negated, consistent with earlier simulations that predicted the CAS system\u0026rsquo;s ability to reduce geographic variability\u0026nbsp;[11]. However, the greater organ transport distances may also indicate decreased efficiency\u0026nbsp;[11]. Such unintended consequences of increased geographic distances for procurement have been previously demonstrated under the 2017 LAS policy change from donor service areas to nautical miles\u0026nbsp;[19\u0026ndash;21]. Travel efficiency was considered in the CAS modeling to account for financial costs associated with LT\u0026nbsp;[22]. However, the UNOS/OPTN LT Committee decided to use a general placement efficiency scale as a surrogate non-cost-related efficiency score\u0026nbsp;[4, 22]. The CAS algorithm does not include factors such as odds of organ acceptance, candidate and hospital density, ease of organ recovery, or \u0026ldquo;aura\u0026rdquo; placement in which various solid organ offers are grouped and allocated to a transplant center for candidates within the CAS range\u0026nbsp;[11, 22]. It is possible that decreased length of post-transplant hospital stay observed in LT recipients in the post-CAS era may help to offset the expected increased donor lung transport costs. However, these issues remain undetermined until the UNOS database can adequately report the actual total costs of LT. Regardless, transplant centers should be wary of these economic factors in the post-CAS era to ensure the\u0026nbsp;ability to deliver sustainable, efficient, and effective LTs to a broader patient population.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the post-CAS era, use of flights for transport has been associated with increased mortality risk whereas neither increased ischemic times nor donor-recipient distance had any impact on 1-year mortality. Prior studies have demonstrated that transplanting distant donors compared to local donors had no impact on short-intermediate term outcomes\u0026nbsp;[23]. Thus, it is unlikely that the \u0026gt; 100 miles cut off required for flights have any impact on mortality risk. However, it is notable that while the use of EVLPs have increased, it remains largely underutilized (5.1%) in the post-CAS era. Indeed, recent evidence has demonstrated that the use of EVLP\u0026nbsp;[24]\u0026nbsp;or controlled hypothermic storage\u0026nbsp;[25]\u0026nbsp;may help to counterbalance the adverse impacts of longer out-of-body times\u0026nbsp;[26]. Furthermore, contemporary studies continue to demonstrate that lung ischemic times even greater than 8 hours may result in acceptable perioperative outcomes and post-transplant survival\u0026nbsp;[27]. Similarly, despite the increased risk of ischemic-reperfusion injuries\u0026nbsp;[28, 29], lungs exposed to increased ischemic times were not associated with primary graft failure or up to 5-year recipient survival\u0026nbsp;[30]. These impacts are particularly pronounced among low transplant volume centers that often consider 6-hours as the upper limit of acceptable ischemic times\u0026nbsp;[31]. While we\u0026rsquo;ve adjusted our models for center volumes, the effects of air travel for donor lungs, in the context of increased ischemic times and donor-recipient distances, remain unclear. Future studies should closely monitor the association between flights for lung transport and access to novel perfusion and preservation distances.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe also report that the use of extended criteria donors with P/F ratio \u0026lt;300, DCD, and NRP has been steadily increasing in the post-CAS era. Although these changes are unlikely to be related to changes in the CAS system, they merit discussion as these trends signal more confidence and experience with these techniques in recent years by many transplant centers. Utilization of lungs procured using DPP in DCD has not been shown to have inferior outcomes compared to lungs procured from DBD donors\u0026nbsp;[32, 33]. In the post-CAS era, we found that NRP and DCD were both associated with improved survival for LT recipients. These findings are corroborated by early reports that have demonstrated favorable early postoperative pulmonary graft function using NRP in DCD lung procurement\u0026nbsp;[34\u0026ndash;36]. Ultimately, these results are promising in the post-CAS era in our ongoing efforts to augment the lung donor pools. Further efforts to utilize high risk, extended criteria donors, including those with positive hepatitis C status\u0026nbsp;[37], cocaine use, smoking, older age, and diabetes\u0026nbsp;[38]\u0026nbsp;should be evaluated in the post-CAS era.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnother major criticism of the previous LAS system was that patients with certain biologic factors, including ABO blood type O, were disproportionately less likely to be transplanted\u0026nbsp;[8]. However, these issues have persisted in the first two years of experience with the CAS system. ABO blood type O recipients continue to be at a disadvantage for receiving LTs, and the number of identical ABO matches has also decreased. These effects have continued after UNOS/OPTN implemented a policy change to award additional allocation scores to ABO type O candidates in September 2023\u0026nbsp;[39]. Thus, this population should be closely monitored in the following years to determine whether the updated CAS can adequately address these disparities. In other aspects, the CAS system appears to have ensured comparable access to allosensitized and Black patients, trending towards amending several issues previously found under the LAS system\u0026nbsp;[7].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLimitations.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFirst, the retrospective nature of our study may introduce selection bias. Second, the lack of granularity and data censoring regarding variations in individual transplant center practices and policies cannot be adequately addressed. As time passes under the CAS system, individual transplant centers will also likely adjust practices, leading to expected changes in outcome trends observed in the current study. However, we have used transplant center volume to adjust our multivariate modeling to account for these confounders. We are also unable to report definitive cost comparisons due to their absence in the UNOS database. Third, most data elements are recorded during waitlist registration and transplant. Thus, variables such as duration of mechanical circulatory support or real-time hemodynamic and lung function data are unavailable. Finally, these complications could not be analyzed between eras due to the inconsistent reporting of chronic lung allograft dysfunction in the UNOS database.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this national analysis, the implementation of the CAS system for LT has achieved its intended goals of reducing waitlist mortality and improving transplant access to allosensitized and Black patients. Despite increased donor lung travel distances and ischemic times, 1-year post-transplant survival improved in the CAS era. Growing utilization of extended criteria donors further supports the potential to expand the donor pool without compromising outcomes. However, persistent disparities for ABO blood type O candidates, and the association between air transport and mortality warrant continued monitoring and refinement of the system. Future efforts should focus on integrating waitlist duration and real-time clinical deterioration into allocation models while ensuring equitable and efficient organ utilization.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the Virginia Commonwealth University Department of Biostatistics for excellent statistical and data analysis support. We want to thank the Pauley Heart Center and the Hume-Lee Transplant Center for their support of this research. This paper will be presented at the 51\u003csup\u003est\u003c/sup\u003e Western Thoracic Surgical Association Annual Meeting, June 25 – 28, Dana Point, CA, USA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u0026nbsp;\u003c/strong\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e: Ye In Christopher Kwon and Holly Caboti-Jones contributed equally to the production and editing of this manuscript. Ye In Christopher Kwon, Holly Caboti-Jones, and Michael Keller contributed to the data and statistical analyses. Ye In Christopher Kwon, Holly Caboti-Jones, Michael Keller, Andrew Min-Gi Park, Alan Lai, and Zubair A. Hashmi conceived and designed the study. Rachit D. Shah, Zachary Fitch, Vigneshwar Kasirajan, Vipul Patel, and Zubair A. Hashmi reviewed and edited this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eBecause all data were de-identified, this study was deemed exempt from the Virginia Commonwealth University Institutional Review Board. It also complies with the International Society for Heart and Lung Transplantation (ISHLT) ethics policies.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHolm AM, Fedson S, Courtwright A, Olland A, Bryce K, Kanwar M, Sweet S, Egan T, Lavee J (2022) International society for heart and lung transplantation statement on transplant ethics. The Journal of Heart and Lung Transplantation 41:1307\u0026ndash;1308. https://doi.org/10.1016/j.healun.2022.05.012\u003c/li\u003e\n\u003cli\u003eChaney J, Suzuki Y, Cantu E, van Berkel V (2014) Lung donor selection criteria. J Thorac Dis 6:1032\u0026ndash;1038. https://doi.org/10.3978/j.issn.2072-1439.2014.03.24\u003c/li\u003e\n\u003cli\u003eArnaoutakis GJ, Allen JG, Merlo CA, Sullivan BE, Baumgartner WA, Conte JV, Shah AS (2011) Impact of the lung allocation score on resource utilization after lung transplantation in the United States. The Journal of Heart and Lung Transplantation 30:14\u0026ndash;21. https://doi.org/10.1016/j.healun.2010.06.018\u003c/li\u003e\n\u003cli\u003eSnyder JJ, Salkowski N, Wey A, Pyke J, Israni AK, Kasiske BL (2018) Organ distribution without geographic boundaries: A possible framework for organ allocation. Am J Transplant 18:2635\u0026ndash;2640. https://doi.org/10.1111/ajt.15115\u003c/li\u003e\n\u003cli\u003eCalhoun K, Smith J, Gray AL (2023) Social and biologic determinants in lung transplant allocation. Curr Opin Organ Transplant 28:163\u0026ndash;167. https://doi.org/10.1097/MOT.0000000000001069\u003c/li\u003e\n\u003cli\u003eWille KM, Harrington KF, deAndrade JA, Vishin S, Oster RA, Kaslow RA (2013) Disparities in lung transplantation before and after introduction of the lung allocation score. J Heart Lung Transplant 32:684\u0026ndash;692. https://doi.org/10.1016/j.healun.2013.03.005\u003c/li\u003e\n\u003cli\u003eRiley LE, Lascano J (2021) Gender and racial disparities in lung transplantation in the United States. The Journal of Heart and Lung Transplantation 40:963\u0026ndash;969. https://doi.org/10.1016/j.healun.2021.06.004\u003c/li\u003e\n\u003cli\u003eBarac YD, Mulvihill MS, Cox ML, Bishawi M, Klapper J, Haney J, Daneshmand M, Hartwig MG (2019) Implications of blood group on lung transplantation rates: A propensity-matched registry analysis. J Heart Lung Transplant 38:73\u0026ndash;82. https://doi.org/10.1016/j.healun.2018.09.013\u003c/li\u003e\n\u003cli\u003eUNOS/OPTN (2023) New lung allocation policy in effect\u003c/li\u003e\n\u003cli\u003eLi SS, Pugacheva A, Singh R, Rabi SA, Michel E, Kreso A, Langer NB, Osho AA (2024) Going the distance: Geographic effects of the lung transplant composite allocation score. JHLT Open 6:100128. https://doi.org/10.1016/j.jhlto.2024.100128\u003c/li\u003e\n\u003cli\u003eValapour M, Lehr CJ, Wey A, Skeans MA, Miller J, Lease ED (2022) Expected effect of the lung Composite Allocation Score system on US lung transplantation. American Journal of Transplantation 22:2971\u0026ndash;2980. https://doi.org/10.1111/ajt.17160\u003c/li\u003e\n\u003cli\u003eTsuang WM, Arrigain S, Lopez R, Budev M, Schold JD (2021) Lung transplant waitlist outcomes in the United States and patient travel distance. American Journal of Transplantation 21:272\u0026ndash;280. https://doi.org/10.1111/ajt.16193\u003c/li\u003e\n\u003cli\u003eSapir‐Pichhadze R, Pintilie M, Tinckam KJ, Laupacis A, Logan AG, Beyene J, Kim SJ (2016) Survival Analysis in the Presence of Competing Risks: The Example of Waitlisted Kidney Transplant Candidates. American Journal of Transplantation 16:1958\u0026ndash;1966. https://doi.org/10.1111/ajt.13717\u003c/li\u003e\n\u003cli\u003eFine JP, Gray RJ (1999) A Proportional Hazards Model for the Subdistribution of a Competing Risk. Journal of the American Statistical Association 94:496\u0026ndash;509. https://doi.org/10.1080/01621459.1999.10474144\u003c/li\u003e\n\u003cli\u003eWall A, Rosenzweig M, McKenna GJ, Ma T-W, Asrani SK, Testa G (2023) Six-month abdominal transplant recipient outcomes from donation after circulatory death heart donors: A retrospective analysis by procurement technique. Am J Transplant 23:987\u0026ndash;995. https://doi.org/10.1016/j.ajt.2023.04.021\u003c/li\u003e\n\u003cli\u003eStewart DE, Wood DW, Alcorn JB, Lease ED, Hayes M, Hauber B, Goff RE (2021) A revealed preference analysis to develop composite scores approximating lung allocation policy in the U.S. BMC Med Inform Decis Mak 21:8. https://doi.org/10.1186/s12911-020-01377-7\u003c/li\u003e\n\u003cli\u003eValapour M, Lehr CJ, Schladt DP, Smith JM, Goff R, Mupfudze TG, Swanner K, Gauntt K, Snyder JJ (2023) OPTN/SRTR 2021 Annual Data Report: Lung. American Journal of Transplantation 23:S379\u0026ndash;S442. https://doi.org/10.1016/j.ajt.2023.02.009\u003c/li\u003e\n\u003cli\u003eDalton JE, Gunsalus PR, Lehr CJ, Rose J, Udeh BL, Valapour M (2023) Incorporating Effects of Time Accrued on the Waiting List into Lung Transplantation Survival Models. Am J Respir Crit Care Med 208:983\u0026ndash;989. https://doi.org/10.1164/rccm.202306-0968OC\u003c/li\u003e\n\u003cli\u003ePuri V, Hachem RR, Frye CC, Harrison MS, Semenkovich TR, Lynch JP, Ridolfi G, Rowe C, Meyers BF, Patterson GA, Kozower BD, Pasque MK, Nava RG, Marklin GF, Brockmeier D, Sweet SC, Chapman WC, Kreisel D (2019) Unintended consequences of changes to lung allocation policy. American Journal of Transplantation 19:2164\u0026ndash;2167. https://doi.org/10.1111/ajt.15307\u003c/li\u003e\n\u003cli\u003eHaywood N, Mehaffey JH, Kilbourne S, Mannem H, Weder M, Lau C, Krupnick AS, Agarwal A (2022) Influence of broader geographic allograft sharing on outcomes and cost in smaller lung transplant centers. The Journal of Thoracic and Cardiovascular Surgery 163:339\u0026ndash;345. https://doi.org/10.1016/j.jtcvs.2020.09.008\u003c/li\u003e\n\u003cli\u003eLehman R, Carrico B (2019) Monitoring of the Lung Allocation Change, 1 Year Report Removal of DSA as a Unit of Allocation\u003c/li\u003e\n\u003cli\u003eUNOS/OPTN Update on the Continuous Distribution of Organs Project\u003c/li\u003e\n\u003cli\u003eGerull WD, Yang Z, Kreisel D, Nava R, Meyers BF, Patterson GA, Kozower BD, Hachem RR, Witt C, Byers D, Kulkarni H, Guillamet RV, Marklin G, Puri V (2021) Local versus distant lung donor procurement does not influence short-term clinical outcomes. The Journal of Thoracic and Cardiovascular Surgery 162:1284-1293.e4. https://doi.org/10.1016/j.jtcvs.2020.07.115\u003c/li\u003e\n\u003cli\u003eDivithotawela C, Cypel M, Martinu T, Singer LG, Binnie M, Chow C-W, Chaparro C, Waddell TK, De Perrot M, Pierre A, Yasufuku K, Yeung JC, Donahoe L, Keshavjee S, Tikkanen JM (2019) Long-term Outcomes of Lung Transplant With Ex Vivo Lung Perfusion. JAMA Surg 154:1143. https://doi.org/10.1001/jamasurg.2019.4079\u003c/li\u003e\n\u003cli\u003eNovysedlak R, Provoost A-L, Langer NB, Van Slambrouck J, Barbarossa A, Cenik I, Van Raemdonck D, Vos R, Vanaudenaerde BM, Rabi SA, Keller BC, Svorcova M, Ozaniak Strizova Z, Vachtenheim J, Lischke R, Ceulemans LJ (2024) Extended ischemic time (\u0026gt;15 hours) using controlled hypothermic storage in lung transplantation: A multicenter experience. The Journal of Heart and Lung Transplantation 43:999\u0026ndash;1004. https://doi.org/10.1016/j.healun.2024.02.006\u003c/li\u003e\n\u003cli\u003eBisbee CR, Sherard C, H. Kwon J, Hashmi ZA, Gibney BC, Konrad Rajab T (2022) Devices for donor lung preservation. Expert Review of Medical Devices 19:959\u0026ndash;964. https://doi.org/10.1080/17434440.2022.2151359\u003c/li\u003e\n\u003cli\u003eHalpern SE, Au S, Kesseli SJ, Krischak MK, Olaso DG, Bottiger BA, Haney JC, Klapper JA, Hartwig MG (2021) Lung transplantation using allografts with more than 8 hours of ischemic time: A single-institution experience. The Journal of Heart and Lung Transplantation 40:1463\u0026ndash;1471. https://doi.org/10.1016/j.healun.2021.05.008\u003c/li\u003e\n\u003cli\u003eChen-Yoshikawa TF (2021) Ischemia-Reperfusion Injury in Lung Transplantation. Cells 10:1333. https://doi.org/10.3390/cells10061333\u003c/li\u003e\n\u003cli\u003eTalaie T, DiChiacchio L, Prasad NK, Pasrija C, Julliard W, Kaczorowski DJ, Zhao Y, Lau CL (2021) Ischemia-reperfusion Injury in the Transplanted Lung: A Literature Review. Transplantation Direct 7:e652. https://doi.org/10.1097/TXD.0000000000001104\u003c/li\u003e\n\u003cli\u003eGrimm JC, Valero V, Kilic A, Magruder JT, Merlo CA, Shah PD, Shah AS (2015) Association Between Prolonged Graft Ischemia and Primary Graft Failure or Survival Following Lung Transplantation. JAMA Surg 150:547. https://doi.org/10.1001/jamasurg.2015.12\u003c/li\u003e\n\u003cli\u003eHayes D, Hartwig MG, Tobias JD, Tumin D (2017) Lung Transplant Center Volume Ameliorates Adverse Influence of Prolonged Ischemic Time on Mortality. American Journal of Transplantation 17:218\u0026ndash;226. https://doi.org/10.1111/ajt.13916\u003c/li\u003e\n\u003cli\u003eLevvey B, Keshavjee S, Cypel M, Robinson A, Erasmus M, Glanville A, Hopkins P, Musk M, Hertz M, McCurry K, Van Raemdonck D, Snell G (2019) Influence of lung donor agonal and warm ischemic times on early mortality: Analyses from the ISHLT DCD Lung Transplant Registry. The Journal of Heart and Lung Transplantation 38:26\u0026ndash;34. https://doi.org/10.1016/j.healun.2018.08.006\u003c/li\u003e\n\u003cli\u003eCypel M, Levvey B, Van Raemdonck D, Erasmus M, Dark J, Love R, Mason D, Glanville AR, Chambers D, Edwards LB, Stehlik J, Hertz M, Whitson BA, Yusen RD, Puri V, Hopkins P, Snell G, Keshavjee S (2015) International Society for Heart and Lung Transplantation Donation After Circulatory Death Registry Report. The Journal of Heart and Lung Transplantation 34:1278\u0026ndash;1282. https://doi.org/10.1016/j.healun.2015.08.015\u003c/li\u003e\n\u003cli\u003eUrban M, Castleberry AW, Markin NW, Chacon MM, Strah HM, Um JY, Berkheim D, Merani S, Siddique A (2022) Successful lung transplantation with graft recovered after thoracoabdominal normothermic perfusion from donor after circulatory death. American Journal of Transplantation 22:294\u0026ndash;298. https://doi.org/10.1111/ajt.16806\u003c/li\u003e\n\u003cli\u003eZhou AL, Ruck JM, Casillan AJ, Larson EL, Shou BL, Karius AK, Ha JS, Shah PD, Merlo CA, Bush EL (2023) Early United States experience with lung donation after circulatory death using thoracoabdominal normothermic regional perfusion. The Journal of Heart and Lung Transplantation 42:693\u0026ndash;696. https://doi.org/10.1016/j.healun.2023.03.001\u003c/li\u003e\n\u003cli\u003eCain MT, Park SY, Sch\u0026auml;fer M, Hay-Arthur E, Justison GA, Zhan QP, Campbell D, Mitchell JD, Randhawa SK, Meguid RA, David EA, Reece TB, Cleveland JC, Hoffman JRH (2023) Lung recovery utilizing thoracoabdominal normothermic regional perfusion during donation after circulatory death: The Colorado experience. JTCVS Techniques 22:350\u0026ndash;358. https://doi.org/10.1016/j.xjtc.2023.09.027\u003c/li\u003e\n\u003cli\u003eBashian EJ, Bashian EE, Kwon YIC, Ambrosio M, Fitch Z, Taylor LJ, Patel V, Julliard W, Kasirajan V, Hashmi ZA (2025) Promising Long-Term Outcomes of Lung Transplantation With Hepatitis C Positive Donors: Insights From the UNOS Registry. Transplantation Proceedings S0041134525001538. https://doi.org/10.1016/j.transproceed.2025.02.044\u003c/li\u003e\n\u003cli\u003eChristie IG, Chan EG, Ryan JP, Harano T, Morrell M, Luketich JD, Sanchez PG (2021) National Trends in Extended Criteria Donor Utilization and Outcomes for Lung Transplantation. The Annals of Thoracic Surgery 111:421\u0026ndash;426. https://doi.org/10.1016/j.athoracsur.2020.05.087\u003c/li\u003e\n\u003cli\u003eUNOS/OPTN Modify Lung Allocation by Blood Type\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eComparison of baseline recipient and donor characteristics of lung transplantation candidates before and after implementing the Composite Allocation Score system.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=6,398)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEra 1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;(N=3,800)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEra 2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=2,598)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP-Value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecipient Characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, years, median [IQR]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e60 [51-66]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e61 [52-67]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e60 [52-66]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.004\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.209\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e2431 (64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1622 (62.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1369 (36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e976 (37.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e, median [IQR]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e26.8 [23.3-30.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e26.8 [23.4-30.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e26.9 [23.5-30.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.565\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.049\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e2518 (66.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1673 (64.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e571 (15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e440 (16.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eHispanic/Latino\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e516 (13.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e355 (13.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e159 (4.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e92 (3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e36 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e38 (1.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.003\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e9 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e5 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eGrade School (0-8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e145 (3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e95 (3.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eHigh School (9-12) or GED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1270 (33.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e920 (35.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eAttended College/ Technical School\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e959 (25.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e650 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eAssociate/ Bachelor\u0026rsquo;s Degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e911 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e549 (21.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003ePost-College Graduate Degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e402 (10.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e256 (9.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePayment Types, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.071\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003ePrivate Insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1542 (40.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1136 (43.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003ePublic Insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e3678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e2234 (58.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1444 (55.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eSelf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e12 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e7 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUNOS Regions, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.750\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e115 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e96 (3.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e390 (10.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e264 (10.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e423 (11.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e296 (11.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e395 (10.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e288 (11.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e688 (18.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e455 (17.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e86 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e60 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e395 (10.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e259 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e214 (5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e141 (5.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e320 (8.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e196 (7.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e381 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e249 (9.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e393 (10.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e294 (11.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKarnofsky Functional Status, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.392\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e10%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e68 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e48 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e20%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e658 (17.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e484 (18.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e30%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e318 (8.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e188 (7.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e40%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e613 (16.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e406 (15.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e50%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e367 (9.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e269 (10.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e60%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e809 (21.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e543 (20.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e70%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e579 (15.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e407 (15.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e80%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e232 (6.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e143 (5.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e90%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e63 (1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e31 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e100%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e9 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e8 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWaitlist Time, days, median [IQR]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e27 [9-85]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e31 [11-106]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e29 [10-96]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.010\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWaitlist Recipient Diagnosis, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003ePulmonary Hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e110 (2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e85 (3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.389\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eSecondary Pulmonary Hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e19 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e11 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.660\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eCystic Fibrosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e38 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e34 (1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.250\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eIdiopathic Pulmonary Fibrosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e908 (23.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e567 (21.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.054\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eSarcoidosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e50 (1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e31 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.667\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eA1AT Deficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e33 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e24 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.817\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eCOPD/Emphysema\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e380 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e269 (10.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.645\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eBronchiectasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e30 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e37 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.014\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eRheumatoid Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e38 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e16 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.099\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eOther Causes of Pulmonary Fibrosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e332 (8.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e229 (8.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.914\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eSevere COVID-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e228 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e195 (5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.027\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreop Labs, mg/dL, median, [IQR]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eCreatinine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.9 [0.7-1.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.9 [0.7-1.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.9 [0.7-1.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.966\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eTotal Bilirubin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.5 [0.3-0.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.5 [0.3-0.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.5 [0.3-0.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.514\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreop Lung Function Tests\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eFEV1, L, median [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4.6 [2.9-6.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e4.6 [2.9-6.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e4.5 [3.0-6.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.565\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eFVC, L, median [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e5.0 [3.9-65]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e5.0 [3.9-6.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e5.0 [3.8-6.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.508\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003ePCO2, mmHg, median [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e44.2 [39-52]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e45 [39-52.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e45 [39.9-53]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.117\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlood Types, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.039\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1407 (37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1021 (39.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e477 (12.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e368 (14.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eAB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e133 (3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e101 (3.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1770 (46.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1104 (42.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCirculatory Support at Transplant, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eECMO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e158 (4.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e91 (3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.183\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eIABP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e226 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e127 (4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.069\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eVentilator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e152 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e88 (3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.205\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eInotropes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e440 (11.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e372 (14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidities, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e822 (21.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e609 (23.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.088\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eSteroid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e917 (24.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e585 (22.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.135\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e3159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1858 (48.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1301 (50.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.353\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eMalignancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e432 (11.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e302 (11.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.752\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eCVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e114 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e83 (3.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.658\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eAICD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e810 (21.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e558 (21.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.877\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003ePrior Cardiac Surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e535 (14.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e361 (13.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.835\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003ePrior Lung Surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e71 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e48 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.952\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 623px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDonor Characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, years, median [IQR]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e34 [26-43]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e33 [25-43]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e33 [25-43]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.222\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.011\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e2365 (62.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1698 (65.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1435 (37.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e900 (34.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e, median [IQR]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e26.1 [23.1-30.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e25.9 [22.9-30]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e25.9 [22.8-29.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.151\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.919\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e3773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e2223 (58.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1550 (59.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e679 (17.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e446 (17.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eHispanic/Latino\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e738 (19.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e494 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e109 (2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e73 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e51 (1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e35 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLVEF, %, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e60 [55-65]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e60 [55-65]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e60 [55-65]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.610\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbnormal Chest X-ray, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e2561 (67.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1778 (68.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.381\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbnormal Purulence on Bronchoscopy, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eLeft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e640 (16.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e497 (19.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.051\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eRight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e750 (19.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e544 (20.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.240\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidities, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e276 (7.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e170 (6.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.267\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e242 (6.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e191 (7.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.124\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eCocaine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e946 (24.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e443 (17.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;.0001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eAlcohol abuse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e764 (20.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e556 (21.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.208\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eMalignancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e65 (1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e44 (1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.959\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e802 (21.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e513 (19.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.186\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eIV drug use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e497 (13.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e312 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.206\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e72 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e37 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.153\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlood Types, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.616\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e599 (15.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e410 (15.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e388 (10.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e273 (10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eAB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e31 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e11 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e3517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e2074 (54.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1443 (55.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCauses of Death, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.015\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eAnoxia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1580 (41.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1105 (42.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eCNS Tumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e11 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e17 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eCerebrovascular/Stroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e781 (20.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e479 (18.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eHead Trauma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1354 (35.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e927 (35.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e74 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e70 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP/F Ratio \u0026lt;300, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e536 (14.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e450 (17.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.002\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender Mismatch, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e608 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e366 (14.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.051\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIdentical ABO Match, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e6319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e3504 (92.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2815 (84.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;.0001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHLA Mismatch, # of alleles, mean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e4.9 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e4.4 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.176\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eEx-vivo\u0026nbsp;\u003c/em\u003ePerfusion, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e145 (3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e133 (5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.012\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDonor Type, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;.0001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eDBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e5918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e3564 (93.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2354 (90.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eDCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e236 (6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e244 (9.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProcurement, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;.0001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eDPP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e190 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e198 (7.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eNRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e46 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e46 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDonor-Recipient Distance, nautical miles, median [IQR]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e318 [131-576]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e202 [86-394]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e231 [100-457]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;.0001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFlight for Transport, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e4820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e2765 (72.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e2055 (79.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;.0001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Ischemic Time, hours, median [IQR]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e5.4 [3.8-7.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e4.9 [3.7-6.5]\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e5.1 [3.7-6.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;.0001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eComparison of short- and long-term outcomes of lung transplantation before and after the implementation of the Composite Allocation System.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eOutcomes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=6,398)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEra 1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;(N=3,800)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEra 2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=2,598)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP-Value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMortality, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Overall\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e397 (10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e161 (6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;.0001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;30 Days\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e8 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e9 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.299\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;90 Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e55 (1.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e39 (1.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.861\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;6 Months\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e171 (4.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e96 (3.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.011\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;1 Year\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e258 (6.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e156 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.021\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVentilator, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e4202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e2525 (66.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e1677 (64.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.116\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcute Rejection, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e244 (6.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e158 (6.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.583\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFVC, L, median [IQR]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e2.78 [2.18 - 3.45]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e2.83 [2.24 - 3.53]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e2.70 [2.09 - 3.35]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLength of Hospital Stay, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e20 [14-33]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e20 [14-33]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1369 [14-32]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.040\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStroke, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e114 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e77 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.933\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePacemaker, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e37 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e17 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.170\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDialysis, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e439 (11.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e297 (11.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.882\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreated for Rejection, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e551 (14.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e68 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;.0001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHospitalization for Infection, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e22 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e24 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.109\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHospitalization for Rejection, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e244 (6.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e158 (6.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.583\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRe-Transplant, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e6 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.368\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAirway Dehiscence, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e47 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e26 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.383\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eMultivariate cox proportional hazards regression model for mortality after lung transplantation following CAS change.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCovariates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHazard Ratio (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTransplant after CAS change\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e1.25 (0.97 \u0026ndash; 1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.054\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (increasing, per 1 year)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e1.02 (1.01 \u0026ndash; 1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale Sex (male as reference)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e1.19 (0.99 \u0026ndash; 1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.054\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (increasing, per 1 unit)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e1.00 (0.99 \u0026ndash; 1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.054\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e1.27 (1.04 \u0026ndash; 1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.019\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Steroid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e1.29 (1.06 \u0026ndash; 1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.010\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Smoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e1.01 (0.85 \u0026ndash; 1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.931\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Malignancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e1.25 (0.98 \u0026ndash; 1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.070\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Prior Cardiac Surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e1.43 (1.11 \u0026ndash; 1.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.005\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Prior Lung Surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e0.92 (0.49 \u0026ndash; 1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.800\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-Transplant Circulatory Support\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003eECMO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e1.25 (0.77 \u0026ndash; 2.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.367\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003eIABP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e1.24 (0.83 \u0026ndash; 1.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.300\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003eVentilator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e1.33 (0.84 \u0026ndash; 2.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.227\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003eInotropes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e1.05 (0.76 \u0026ndash; 1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.765\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eEx-vivo\u0026nbsp;\u003c/em\u003ePerfusion\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e1.24 (0.91 \u0026ndash; 1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.421\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDCD (DBD as reference)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e0.83 (0.62 \u0026ndash; 0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.017\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNRP (DPP as reference)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e0.91 (0.79 \u0026ndash; 0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.002\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDonor-Recipient Distance (increasing, per 1 nautical miles)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e1.00 (0.99 \u0026ndash; 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.349\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFlight for Transport\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e1.53 (1.02 \u0026ndash; 1.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.022\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime on Waitlist (increasing, per 1 day)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e1.00 (0.99 \u0026ndash; 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.308\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIschemic Time (increasing, per 1 hour)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e1.06 (0.99 \u0026ndash; 1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.312\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"lung transplantation, composite allocation score, donor distance, lung allocation","lastPublishedDoi":"10.21203/rs.3.rs-6448565/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6448565/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003ePurpose:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOn March 9, 2023, the Composite Allocation Score (CAS) was introduced for all lung transplantation (LT) candidates. We analyzed waitlist and post-transplant outcomes following CAS implementation.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMethods:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eUsing the UNOS registry (2022–2024), adult patients listed for isolated LT were divided into Era 1 (pre-CAS: 3/1/2022–3/8/2023) and Era 2 (post-CAS: 3/9/2023–9/30/2024). Competing risk regression analyzed waitlist events. Recipient/donor characteristics and mortality risk factors were assessed with Cox models. Survival was evaluated with Kaplan-Meier analysis.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eResults:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAmong 6,398 LTs, 2,598 (40.6%) occurred in Era 2. More Black patients (16.9% vs. 15%, p=0.04) and those with a high school education (35.4% vs. 33.4%, p=0.0003) were transplanted. ABO type O patients were less likely to undergo LT (42.5% vs. 46.6%, p=0.04). Era 2 had longer transport distances (231 vs. 202 miles, p\u0026lt;0.0001), ischemic times (5.1 vs. 4.9 hours, p\u0026lt;0.0001), and increased use of flights (79.1% vs. 72.8%, p\u0026lt;0.0001). DCD (9.4% vs. 6.2%, p\u0026lt;0.0001) and NRP (2.2% vs. 1.2%, p=0.02) usage rose. Waitlist times decreased (29 vs. 31 days, p=0.009), with improved outcomes (SHR 0.73, p\u0026lt;0.0001). Era 2 showed superior 6-month and 1-year survival (p\u0026lt;0.0001) and reduced rejection treatment (2.6% vs. 14.5%, p\u0026lt;0.0001).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConclusions:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCAS implementation reduced waitlist mortality, improved access for marginalized groups, and enhanced survival. Lungs were procured from greater distances with increased use of DCD with NRP or ex vivo perfusion. Disparities remain for ABO type O patients, warranting closer follow-up.\u003c/p\u003e","manuscriptTitle":"Impact of the Composite Allocation Score on Lung Transplant Waitlist and Post- Transplant Outcomes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-23 10:40:23","doi":"10.21203/rs.3.rs-6448565/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eb6f6697-7c86-46ee-9e53-8c010458d0bf","owner":[],"postedDate":"April 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-23T10:40:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-23 10:40:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6448565","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6448565","identity":"rs-6448565","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}