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Original Clinical Science—General

An Assessment of Ineligible Donor Use in Solid Organ Transplant

DeRoos, Luke J. MS1; Tapper, Elliot B. MD2; Lavieri, Mariel S. PhD1; Hutton, David W. PhD1,3; Parikh, Neehar D. MD, MS2

Author Information
doi: 10.1097/TP.0000000000004084

Abstract

INTRODUCTION

The need for organ transplantation far outpaces the rate of organ donation in the United States, resulting in >7000 waitlisted candidates dying annually.1 Even patients who ultimately undergo transplantation experience prolonged morbidity because of long wait times. Wait times vary drastically across organ procurement organizations (OPOs)—for example, the proportion of patients receiving kidney transplants within the first 5 y of listing varies from 10% to nearly 80% across donation service areas.2 Variation in organ availability across OPOs is in part because of difference in organ acceptance patterns, including variation in the use of donors who do not meet Organ Procurement and Transplantation Network eligibility criteria.3-5 Eligible donors include people living within service area of each OPO who died 75 y or younger with a body mass index (BMI) <50 kg/m2, in addition to other organ-specific criteria.6 In general, donor eligibility criteria are designed to outline desirable health characteristics for organ donors. A formal eligibility definition is also helpful in understanding OPO and transplant center performance. For example, a key performance metric is the proportion of potential donors recovered and used for transplants within an OPO service area. However, the health characteristics of potential donor populations can vary among OPOs, and certain areas might have a particularly high prevalence of conditions that are prohibitive to the donation of certain organs. By measuring recovery metrics in terms of the proportion of eligible donors, we avoid penalizing centers for population characteristics out of their control. However, a drawback of the eligibility definition is that, in many cases, it only outlines desirable characteristics, not required characteristics. For example, there is a general age cutoff of 75 y for a patient to be considered eligible, yet there are many examples of patients older than this cutoff who have successfully donated. Across OPOs, the use of ineligible donors varies significantly, with anywhere from 5% to 39% of deceased organ donations coming from ineligible donors.5 Previous work has also found that donor eligibility plays a major role in our understanding of OPO performance and the state of the US transplant system more generally.5 In 2020, the Advancing American Kidney Health Executive Order called for improved organ donation metrics, and the Centers for Medicare and Medicaid Services issued a final rule standardizing the eligibility definition based on potential donors’ cause of death (COD).7,8 A national policy to standardize the use of ineligible donors could spur efforts within low-performing OPOs to improve organ procurement and increase overall organ availability.

A barrier to increasing the use of ineligible donors are potential concerns about worsening transplant recipient outcomes (eg, survival) when using “lower quality” organs.9 Historically, survival outcomes after receiving an ineligible organ donation vary by organ, center, and patient subgroup.9-16 This fact, combined with the significant differences in ineligible donor use by OPO, obscures the potential outcomes of a national policy to standardize ineligible donor use.

A comprehensive analysis on the association of donor eligibility with graft and patient survival can offer insight into the viability and impact of increasing ineligible donor use. We therefore aimed to combine national donation and survival data on solid organ transplants to compare the outcomes of eligible and ineligible donations and to isolate this association. We also aimed to model these results as potential life-year increases associated with best practice sharing across OPOs.

MATERIALS AND METHODS

Data Sources

Our analysis included data from the Standard Transplant and Research (STAR) files collected by the United Network of Organ Sharing (UNOS) from January 2008 through November 2020. UNOS is the private, nonprofit organization that manages organ transplant in the United States under contract with the federal government. We included adult recipients of deceased donor solid organ transplants: heart, kidney, liver, lung, and pancreas. Our data also included UNOS-reported information categorizing eligible versus noneligible deaths during the study period. Eligible deaths were patients declared brain dead according to state and local law with no exclusionary criteria as defined by Organ Procurement and Transplantation Network policy.17 Example criteria include that prospective donors must be 75 y or younger with a BMI of <50 kg/m2, in addition to other organ-specific criteria.6 A complete list of the eligibility criteria is included in the Supplemental Material (SDC, https://links.lww.com/TP/C369). For OPO level analyses, each organization was given a unique identifier distinct from the STAR data set. The University of Michigan institutional review board provided an exemption for the secondary use of deidentified data in this study.

Donor Eligibility

The broad spectrum of eligibility characteristics for individual donors is not stored in the UNOS data set in their entirety. Instead, the overall eligibility of an individual donor is reported to UNOS. As a result, we defined an eligible donor as any donor listed in both the STAR file deceased donor data set and the UNOS-reported eligible death data set. Likewise, an ineligible donor was any donor listed in the deceased donor data set but not in the eligible death data set. We compared the mean age and BMI of eligible and ineligible donors using t tests. We calculated the percent of donors meeting eligibility requirements by organ, sex, ethnicity, and year of donation. We tested for significant differences across eligible and ineligible groups using chi-square tests. Data preparation and statistical tests comparing populations were performed using Python version 3.7.9. Statistical significance was defined as a P value of <0.05.

Association of Donor Eligibility and Survival

We analyzed graft and patient survival by organ for both eligible and ineligible donors using Kaplan-Meier curves. We compared survival rates using log-rank tests. To better understand the influence of brain death versus circulatory death on eligibility-related survival, we compared Kaplan-Meier curves for (1) the entire study population and (2) exclusively deceased brain death (DBD) donations as donation after circulatory death (DCD) donor status is a common reason for ineligibility.

We calculated hazard ratios (HRs) for age, ethnicity, sex, BMI, blood type compatibility, donor COD, and OPO of donation using a Cox proportional hazards model. We included interaction effects between donor eligibility and all main effects for kidney, liver, lung, and pancreas transplants. We did not include these interaction terms for heart transplants, as the relatively small number of ineligible heart donations prevented convergence for several regression parameters, making the model uninterpretable. Our regression tests were performed using R version 4.0.5.

Estimated Impact of Increasing Ineligible Donor Use

We estimated the effect that increasing the use of ineligible donors would have had on the number of transplants and the number of life-years gained during the study period. For each organ (heart, kidney, liver, lung, pancreas), we began by calculating the ineligible donor use rate by OPO. In those OPOs that had an ineligible donor unitization rate below prespecified percentiles (50th, 75th, and 100th percentiles), we modeled scenarios where the OPOs would increase utilization to the prespecified percentile. In the simulations, we did not change the ineligible donor use rate of OPOs with rates at or above the specified percentile. After simulating the adjusted ineligible donor use rates, we calculated the number of additional transplants corresponding to this increase in donations.

After calculating the increase in the number of transplants, we converted this increase into a life-years gained metric using data from Rana et al.18 Rana et al used propensity score matching to estimate the increase in life-years from receiving a transplant versus remaining on the waitlist. We leveraged this propensity score matching to improve our estimates of life-years gained. We adjusted estimated life-years gained per transplant of the study by Rana et al by the survival difference between eligible and ineligible donations found in our own model. We mapped the estimated increases in life-years gained using Tableau version 2020.4.

RESULTS

Overall Cohort Characteristics

From January 2008 through November 2020, there were 296 095 adult solid organ transplants (61% male, 54% White individuals). Table 1 provides a description of the study population. Over the study period, 86% of donors met eligibility requirements. Eligibility rates varied by organ, with as many as 20% of kidney donations and as few as 2% of heart donations coming from ineligible donors. Figure S1 (SDC, https://links.lww.com/TP/C369) shows the distribution of ineligible donor use rates across OPOs.

TABLE 1. - Study population description
All donations Eligible donations Ineligible donations P
N 297 223 255 039 42 184
Organ, n (%) <0.001
 Heart 30 103 (10) 29 540 (12) 563 (1)
 Kidney 152 216 (51) 121 240 (48) 30 976 (73)
 Liver 79 180 (27) 70 102 (27) 9078 (22)
 Lung 24 802 (8) 23 598 (9) 1204 (3)
 Pancreas 10 922 (4) 10 559 (4) 363 (1)
Donor ethnicity, n (%) <0.001
 White 197 068 (66) 163 965 (64) 32 676 (78)
 Black 45 979 (15) 42 049 (16) 3860 (9)
 Hispanic 41 948 (14) 38 086 (15) 3804 (9)
 Other 12 228 (4) 10 939 (4) 1289 (3)
Recipient ethnicity, n (%) <0.001
 White 164 338 (56) 142 613 (57) 21 725 (52)
 Black 66 992 (23) 56 408 (22) 10 584 (25)
 Hispanic 42 439 (14) 36 195 (14) 6244 (15)
 Other 19 729 (7) 16 240 (6) 3489 (8)
Donor sex, n (%) <0.001
 Male 183 766 (62) 156 759 (61) 27 007 (64)
 Female 113 457 (38) 98 280 (39) 15 177 (36)
Recipient sex, n (%) 0.0395
 Male 187 645 (64) 160 954 (64) 15 351 (37)
 Female 105 853 (36) 90 502 (36) 26 691 (63)
Mean donor age (SD), y 39.38 (14.15) 38.82 (13.92) 42.75 (14.99) <0.001
Mean recipient age (SD), y 53.56 (12.53) 53.31 (12.58) 55.06 (12.09) <0.001
Donor BMI (kg/m2) 27.84 (6.43) 27.68 (6.26) 28.76 (7.30) <0.001
Recipient BMI (kg/m2) 27.96 (5.47) 27.86 (5.46) 28.56 (5.46) <0.001
P values are the result of χ2 test (categorical) and t test (continuous) variable comparisons between eligible and ineligible subgroups.
BMI, body mass index.

The distribution of eligible and ineligible donor groups was statistically different in terms of both donor and recipient sex and ethnicity (P < 0.01 for all). White individuals were more likely to be an ineligible donor and less likely to receive a transplant from an ineligible donor, when compared with non-White individuals. Similarly, men were more likely than women to be an ineligible donor but less likely to receive a transplant from an ineligible donor.

Donor Eligibility and Survival

Figure 1 shows Kaplan-Meier curves of graft survival for recipients of both eligible and ineligible donors by organ. There were statistically significant differences in graft survival between kidney and liver donations using eligible and ineligible donations (P < 0.01 for both). Recipients of ineligible kidney donations saw a 0.74%, 1.12%, 1.28%, and 2.20% relative decrease in 1-, 3-, 5-, and 10-y graft survival probability, respectively. Recipients of ineligible liver donations saw a 3.62%, 4.99%, 5.88%, and 9.38% relative decrease in 1-, 3-, 5-, and 10-y graft survival probability, respectively.

F1
FIGURE 1.:
A, Kaplan-Meier curves for heart transplant graft survival from January 2008 to November 2020, stratified by donor eligibility. A log-rank test found no significant difference in graft survival between recipients of eligible and ineligible donations (P = 0.35). B, Kaplan-Meier curves for kidney transplant graft survival from January 2008 to November 2020, stratified by donor eligibility. A log-rank test found a significant difference in graft survival between recipients of eligible and ineligible donations (P < 0.01). C, Kaplan-Meier curves for liver transplant graft survival from January 2008 to November 2020, stratified by donor eligibility. A log-rank test found a significant difference in graft survival between recipients of eligible and ineligible donations (P < 0.01). D, Kaplan-Meier curves for lung transplant graft survival from January 2008 to November 2020, stratified by donor eligibility. A log-rank test found no significant difference in graft survival between recipients of eligible and ineligible donations (P = 0.98). E, Kaplan-Meier curves for pancreas transplant graft survival from January 2008 to November 2020, stratified by donor eligibility. A log-rank test found no significant difference in graft survival between recipients of eligible and ineligible donations (P = 0.35).

Figure S2 (SDC, https://links.lww.com/TP/C369) shows Kaplan-Meier curves for patient survival. Log-rank tests also found statistically significant differences in patient survival for kidney and liver donors (P = 0.01 and P < 0.01, respectively). Recipients of ineligible kidney donations saw a 0.21%, 0.43%, 0.45%, and 1.48% relative decrease in 1-, 3-, 5-, and 10-y patient survival probability, respectively. Recipients in ineligible liver donations saw a 1.35%, 2.37%, 3.15%, and 7.22% decrease in 1-, 3-, 5-, and 10-y patient survival probability, respectively. There were no statistically significant differences in patient survival for heart, lung, and pancreas transplants using ineligible donors, when compared with transplants using eligible donors.

Circulatory Versus Brain Death Donor Eligibility and Survival

Figures S3–S5 (SDC, https://links.lww.com/TP/C369) show Kaplan-Meier curves by DBD and DCD status. Log-rank tests found that, across the entire population, DCD donations provided lower graft survival for kidney and liver transplants (P < 0.01 for both) and no statistically significant difference for other organs. However, when exclusively examining ineligible donations, ineligible DBD donations provided significantly lower graft survival for kidney and pancreas donations compared with eligible DBD donations (P < 0.01 and P = 0.01, respectively) with no statistically significant differences for other organs. Our analysis also found that survival loss was primarily associated with ineligible DBD donors, and not DCD donors. When looking exclusively at DBD donations across all organs, the 10-y graft survival probability loss associated with ineligible donors increased by 6.90% when compared with all donors.

Multivariate Survival Modeling

A Cox proportional hazard model for each organ type provided estimates of the association of graft survival with donor eligibility and OPO, as well as recipient age, sex, ethnicity, and BMI. Additionally, the models provided estimates for the interaction effects of each variable with donor eligibility. Figure 2 shows HR estimates for the main effect of each variable by organ, excluding the OPO variable. HRs for the OPO of donation are shown by organ in Figure S6 (SDC, https://links.lww.com/TP/C369). After accounting for demographic factors, likelihood ratio tests found that donor eligibility (including its interaction effects with other variables) was significantly associated with kidney, liver, and lung graft survival (kidney, P = 0.01; liver, P < 0.01; lung, P = 0.02). Figure S7 (SDC, https://links.lww.com/TP/C369) shows HR estimates for the interaction of eligibility and all other variables.

F2
FIGURE 2.:
Selected hazard ratios calculated via Cox regression from January 2008 to November 2020 for: A, heart transplant graft survival. Recipient age, eth., and BMI were all significantly associated with graft survival (P ≤ 0.05). B, kidney transplant graft survival. Donor eligibility as well as recipient age, eth., sex, and BMI were all significantly associated with graft survival (P ≤ 0.05). C, liver transplant graft survival. Recipient age, eth., sex, and BMI were all significantly associated with graft survival (P ≤ 0.05). D, lung transplant graft survival. Recipient age, eth., sex, and BMI were all significantly associated with graft survival (P ≤ 0.05). E, pancreas transplant graft survival. Recipient age, eth., sex, and BMI were all significantly associated with graft survival (P ≤ 0.05). Recipient age is scaled to be in decades. BMI, body mass index; eth., ethnicity. 1Baseline category: eligibile; 2baseline category: White; 3baseline category: male.

Eligibility Interactions

Recipient age and ethnicity were significantly associated with graft survival for all organs (age—heart, P < 0.01; kidney, P < 0.01; liver, P < 0.01; lung, P < 0.01; pancreas, P = 0.04; and ethnicity—heart, P = 0.04; kidney, P < 0.01; liver, P < 0.01; lung, P = 0.03; pancreas, P < 0.01). Interaction terms suggested that the survival loss associated with ineligibility was found to be significantly increased for older recipients of kidney and lung transplants (P < 0.01 for both). Two significant interactions between eligibility and ethnicity were found: 1 in Black recipients of kidney transplants, with a reduced HR effect of 0.93 (95% confidence interval, 0.87-0.9873; P = 0.02) and another in lung transplant recipients of other ethnicities, with an increased HR effect of 2.4520 (95% confidence interval, 1.30-4.62; P < 0.01).

Sex was significantly associated with graft survival for kidney, liver, lung, and pancreas transplants (kidney, P < 0.01; liver, P < 0.01; lung, P < 0.01; pancreas, P = 0.02); however, there was no significant interaction found between donor eligibility and recipient sex. Similarly, although BMI was associated with graft survival for all organs (heart, P < 0.01; kidney, P < 0.01; liver, P = 0.03; lung, P = 0.03; pancreas, P = 0.02 for all), there was no statistically significant interaction found between donor eligibility and recipient BMI.

The OPO of donation was associated with graft survival for all organs (P < 0.01 for all). There were statistically significant interactions with OPO and eligibility that are not listed here for brevity.

Estimated Impact of Increasing Ineligible Donor Use

We simulated the impact of individual OPOs increasing ineligible donor use rates to match the 50th, 75th, and 100th percentiles of use across the study period. For example, across all OPOs, the 75th percentile of ineligible liver donor use was 13.7%. Any OPO that had an ineligible donor use rate of <13.7% had its rate increased to this value in the 75th percentile match scenario. We did not change the ineligible donor use rate of any OPO having a rate of ≥13.7%. We then calculated the corresponding increase in donations and transplants with this increased donation rate. We performed this same technique for all organs and the other percentiles. Table 2 shows the estimated mean increase in transplants and life-years gained for each percentile. Across all organs and OPOs, the estimated increase in the number of transplants ranged from 6061 to 33 470 throughout the study period, depending on the percentile. This translated to an estimated increase of 38 409 to 206 741 life-years gained during the same time frame.

TABLE 2. - Estimated annual increases in transplants and life-years gained associated with increasing ineligible donor use under a range of scenarios
Estimated life-years gained per transplant Annual increase in number of transplants Annual increase in life-years gained
50th percentile match 75th percentile match 100th percentile match 50th percentile match 75th percentile match 100th percentile match
Heart 6.80 7.18 21.37 88.64 48.94 145.31 602.78
Kidney 6.62 376.74 752.22 1623.25 2457.33 4906.58 10 588.36
Liver 6.47 67.35 176.29 585.58 404.35 1058.32 3515.70
Lung 2.80 14.73 38.80 171.58 41.18 108.62 480.37
Pancreas 6.70 3.23 10.82 122.18 21.78 72.61 818.54
Overall 469.24 999.50 2591.23 2973.63 6291.44 16 005.75
Standardizing the rate of ineligible donor use could result in approximately 2974 to 16 006 life-years gained annually. To calculate the percentile match results, we first calculated the ineligible donor use rates across OPOs. We then found a given percentile of ineligible donor use. All OPOs with ineligible donor use rates below the given percentile had their rates increased to match the percentile. We then calculated the projected increase in transplants and life-years gained using this simulated increase in donations.
OPO, organ procurement organization.

Figure 3 maps the increase in life-years gained by OPO if all OPOs <75th percentile rose to meet that use rate. At this percentile match, the overall increase in life-years gained ranged from 0 to 8284 across the study period, depending on the current ineligible use rate and the volume of transplants. OPOs shown with no increase in life-years gained were either at or above the 75th percentile of ineligible donor use.

F3
FIGURE 3.:
Map showing the estimated annual increase in life-years gained by OPO, if all OPOs with ineligible donation use rates <75th percentile increased their use to meet the 75th percentile. The increase shown is across all organs, based on annual use from January 2008 to November 2020. OPO, organ procurement organization.

DISCUSSION

An immediate method to reduce the morbidity and mortality of patients awaiting an organ transplant is to increase the number of donated organs. Reducing regional heterogeneity in the use of ineligible organ donations is one strategy to address the current donation shortage. For heart, lung, and pancreas transplants, using an organ from an ineligible donor had no difference in either graft or patient survival. In contrast, kidney and liver transplants had statistically lower survival rates when using ineligible donors. The rate of ineligible donor use varies from 5% to 39% across OPOs,5 suggesting that measures to better standardize ineligible donor use could be taken nationally. Our modeling showed that increasing use of ineligible donors could result in an additional 469 to 2591 organ transplants annually, providing an additional 2974 to 16 006 life-years for waitlisted patients each year.

In December 2020, the Centers for Medicare and Medicaid Services issued a final rule that would update the eligibility definition to be based on standardized data from the Centers for Disease Control and Preventions mortality files (ie, International Classification of Diseases Tenth Revision COD codes consistent with organ donation).19 This update occurred after our study period but could be an opportunity to reduce regional heterogeneity in ineligible donor use. As this update is designed to reduce subjectivity in OPO performance metrics,20 it also provides an opportunity to evaluate differences in ineligible donor use across OPOs, with close attention and efforts to improve performance of underperforming OPOs.

Survival Differences for Ineligible Donations

The lack of significant detriment in graft survival for recipients of heart, lung, and pancreas ineligible donations may be related to low use rates (3%–5%) of ineligible donations for these organs and stringent selection. Kidney and liver recipients, however, did see small but statistically significant decreases in graft survival when using ineligible donations. Notably, the median graft and patient survival for recipients of ineligible donations are still significantly higher than those of waitlisted patients who never receive a transplant,18 suggesting that there is a benefit to using select ineligible donations compared with no transplant at all. We also note that a decrease in graft survival could have an impact on the timing of retransplantation for recipients of ineligible donations. This suggests that perhaps the use of ineligible donations might be more appropriate in certain recipient populations. For example, older patients who may have a lower overall likelihood of requiring retransplantation would also have a lower likelihood of being affected by any graft survival detriment derived from using an ineligible donation. However, the graft and patient survival loss from using ineligible donations were significantly higher for older recipients, so careful consideration would be required to balance these competing effects.

The data suggest that the graft survival detriment associated using ineligible kidney donations is primarily from the use of DBD donations. When looking exclusively at DBD donations across all organs, the 10-y graft survival probability loss associated with ineligible donors increased by 6.90% when compared with all donors. Additionally, although DCD donations were associated with worse graft survival for kidney and liver donations overall, this effect was actually reversed when considering only ineligible donations. Kidney and pancreas DCD donations actually corresponded with higher survival probabilities than ineligible DBD donations. Liver, heart, and lung DCD donations did not see a significantly lower graft survival probability than ineligible DBD donations. This could reflect a more stringent selection process of DCD donors.

Association of Recipient Demographics With Survival

Our Cox regression model offers insight into the association of eligibility with graft survival when controlling for a recipient’s age, ethnicity, sex, BMI, and OPO of donation. After controlling for these demographics, donor ineligibility or its interaction with recipient ethnicity was associated with lower graft survival for kidney, liver, and lung transplants.

Our Cox regression found higher hazard rates for Black recipients of heart, kidney, and liver donations, when compared with White recipients. Research suggests several potential sources for this disparity. Across organs, research suggests that a correlation between Black ethnicity and low socioeconomic status reduces access to medical care, and may result in difficulty adhering to treatment regimens.21-23 However, prior studies have shown that socioeconomic status and adherence do not fully describe this disparity and suggest that there are other systemic factors also contributing to this problem that are hard to define.21,22,24 Regarding kidney and liver transplants, Black recipients had a higher median time on dialysis before transplant, and a higher median model for end-stage liver disease score at the time of listing, which are also correlated with decreased graft survival.21,23,25

Our model also found that the survival loss for kidney and liver transplants associated with the use of ineligible donors was increased for older recipients. However, recipients with higher BMI values did not experience an increased survival loss. This suggests that although older recipients have less reserve to handle marginal organs, high BMI patients might not have this same issue with reserve.

Potential Benefit of Increased Ineligible Donor Use

In many OPOs, the use of ineligible donations is already a common tool for addressing the need to increase organ donation.5 However, the use of ineligible donations varies drastically by OPO. Our results suggest that the use of ineligible donations can provide a significant benefit to patients on the waiting list and is a viable method for reducing patient morbidity and mortality. Regulatory changes in the definitions of eligibility, improved benchmarking of minimum standards of ineligible donor use, or sharing best practices to increase ineligible donor usage could improve ineligible donor utilization.

Contextual Factors

This study benefitted from a large sample size via the use of a national, multiorgan data set containing nearly 13 y of data. Previous studies on the survival effects of ineligibility have focused on individual organs or a limited number of transplant centers. By including all adult solid organ transplants from January 2008 to November 2020, we were able to provide a holistic view of the impact of ineligible donor use in the United States. Our use of recipient demographics and Cox regression allowed us to better understand the specific association of ineligibility after controlling for known correlates with survival. The broad scope of our analysis prevented us from including more detailed patient health characteristics, which might better describe ineligible donor use patterns. Specific reasons for donor ineligibility by organ were not readily available for a more granular analysis of which factors that determine ineligibility lead to worse outcomes. Additionally, the eligibility definition was changed in January 2017, which directly affected OPO performance metrics and could have impacted ineligible donor use.5 Although the data do not suggest a sudden shift in use associated with this specific change, we used an eligibility definition based on a donor’s eligibility at the time of donation to be as consistent as possible in our analyses. Small sample sizes for ineligible heart, lung, and pancreas in certain OPOs also made parameter estimation challenging in our Cox regression. As a result, we were unable to estimate interaction effects between donor eligibility and some recipient characteristics for these organs.

We only considered graft and patient survival as quality outcomes when comparing eligible and ineligible donations. There are many other metrics that also provide valuable information on organ quality, such as delayed graft function, graft loss, readmission rates, etc, but were not within the scope of this analysis. Additionally, like most elements of the organ donation process, ineligible donor use depends on decisions made by individual clinicians, recipients, and other stakeholders and is not fully dependent on OPOs. However, the granularity of our data limited our analysis to the OPO level. We also note that there may be heterogeneity in eligibility reporting standards across OPOs, which could cause misclassification in terms of eligibility and affect our calculation of donation rates and survival estimates by OPO.

CONCLUSIONS

In this study, we performed a national-level review of graft and patient survival outcomes for ineligible versus eligible solid organ donations. We found that transplants using ineligible donations offer significant benefit for waitlisted patients who might otherwise never receive a transplant. Increasing the use of ineligible donors is an immediate method of reducing the current organ donation shortage and could significantly reduce patient morbidity and mortality.

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