Though the all-time high of 17878 kidney transplants performed in the United States in 2015 represents a significant accomplishment, over 100000 patients remain on the kidney transplant waiting list1 and an additional 600000 Americans have end-stage renal disease.2 The transplant community continues to express concern about both the stagnation in living kidney donation3 and underutilization of deceased donor kidneys, including those that have been recovered but discarded4-6 as well as unrealized potential for donation.7
Nearly one fifth of kidneys recovered with intent to transplant are not used, totaling 3159 discarded kidneys in 2015 and over 14 000 during the past 5 years. Clearly, some of these discarded kidneys should have been discarded due to medical contraindications to transplantation, such as anatomical defects, trauma, cancer, and infectious disease.4,8 However, a substantial number of kidneys with characteristics similar to transplanted kidneys are discarded,9 possibly due to risk aversion among transplant hospitals10,11 or inefficiencies in the allocation system.12
From the late 1980s to 2009, the kidney discard rate (KDR) more than tripled from 5% to nearly 20% (Figure 1). It has been hypothesized that this rise was caused by an expansion of the types of kidney donors recovered by Organ Procurement Organizations (OPOs). Efforts to further expand the donor pool, such as through the extended criteria donor13 policy and the “every organ, every time” philosophy promoted by Health and Human Services' (HHS) Organ Donation Breakthrough Collaborative, successfully led to more transplants14 but with a byproduct of more discards.15,16
It may be the case that the likelihood of discard for a kidney recovered today is no different than for a similar kidney recovered in the early 1990s. In other words, the entire decades-long rise in the KDR may have been exclusively driven by the recovery of a broader range of kidneys due to changing demographics of the US donor pool and explicit efforts to expand it. On the other hand, clinician behavior changes or other systemic factors may have caused a kidney recovered today to have a higher chance of discard than an identical kidney recovered in the past.
In this retrospective study of national registry data, we sought to determine whether the long-term increasing trend in the KDR could be entirely explained by the recovery of kidneys from older donors with more comorbidities, or if a residual increase over time was still evident after accounting for these changes. Though the KDR has remained relatively stable since 2009, clues from the preceding decades may point to future strategies for driving down the KDR and increasing transplant opportunities for end-stage renal disease patients.
MATERIALS AND METHODS
This study used data from the Organ Procurement and Transplantation Network (OPTN). The OPTN data system includes data on all donors, waitlisted candidates, and transplant recipients in the United States, submitted by the members of the OPTN, and has been described elsewhere.17 The Health Resources and Services Administration, US Department of Health and Human Services provides oversight to the activities of the OPTN contractor.
All deceased kidney donors with at least 1 kidney recovered for the purpose of transplantation between October 1, 1987, and December 31, 2015, were included. The kidney discard “rate” was defined as the proportion of kidneys recovered for transplantation but not transplanted. October 25, 1999, to December 31, 2009, was chosen for multivariable regression analysis due to the availability of a broader array of donor factors starting in late 1999 and the plateauing of the KDR after 2009. The Kidney Donor Risk Index (KDRI) was calculated as per Rao18 but ignoring nondonor factors.
The probability of discard was modeled using logistic regression, progressively adding donor factors to understand their role in mitigating the time effect. Model variable selection was based on literature review. Year (time effect), body mass index (BMI), bilirubin, blood urea nitrogen, creatinine, height, and weight were parameterized as continuous, linear terms. Missing values for bilirubin (1.5%), blood urea nitrogen (0.2%), and serum creatinine (0.2%) were median-imputed, whereas those for BMI (0.3%), height (0.1%), and weight (0.1%) were median-imputed by integer donor age. For binary variables (“Yes” vs. “other”), “other” included no, negative, missing, and unknown values. No variable had more than 6% missing or unknown; most were less than 0.5%. Clustering due to 2 kidneys from the same donor was accounted for using generalized estimating equations. The notation odds ratio (OR)10 reflects the estimated odds ratio over a 10-year period.
To visualize the mitigating effect of changes in donor factors on the KDR trend, we used an analogue to the “least squares means” concept referred to as the “mean of the predicted values” approach.19 Per this method, a predicted KDR for each year—assuming the distribution of donor factors had remained stable throughout the entire ten-year period—was derived by averaging the predicted probabilities of discard for all kidneys recovered in the entire period but with the time coefficient(s) multiplied by the respective year being predicted. For example, the 18.0% KDR predicted for the end of 2009 was derived by averaging the model-estimated discard probability for all kidneys recovered from October 25, 1999, to December 31, 2009, with the year predictor set equal to December 31, 2009.
To complement and validate the regression modeling results, we developed a propensity score20 to estimate the probability each kidney was recovered in subperiod 1 (October 25, 1999 to December 31, 2000) versus subperiod 2 (January 1, 2009 to December 31, 2009) as a function of donor characteristics. One-to-one nearest neighbor matching was implemented using a caliper width of 0.025, based on the standard deviation of the logit of the propensity score.21 To ensure matching was adequate, the distribution of donor factors was reported as well as absolute standardized differences22 expressed as percent (% absolute standardized differences) and P values.
OPO-specific estimates of OR10 were derived using logistic regression for each OPO on kidneys recovered from 1987 to 2009, with year modeled as a continuous, linear term, and clustering of kidneys accounted for by generalized estimating equation. Mergers between OPOs were accounted for by mapping data from subsumed OPOs to the corresponding, currently operating OPO.
Analyses were performed using SAS/STAT Version 12.2, SAS Institute Inc. Cary, NC.
The KDR in the United States rose from just 5.1% in 1988 to 19.2% in 2009 and subsequently stabilized at 18% to 19% between 2010 and 2015 (Figure 1). The number of kidneys recovered for transplant nearly doubled, from 7705 in 1988 to 14 394 in 2009. Just 393 kidneys were discarded in 1988 compared with 2763 in 2009.
Trends in the Characteristics of Recovered Kidney Donors
The characteristics of deceased kidney donors changed substantially from the late 1980s to 2009. Median donor age jumped sharply from 26 to 43 years. Between 1994 and 2009, median KDRI rose from 1.1 to 1.3, and median BMI increased from 23.9 to 26.1 kg/m,2 while median serum creatinine (terminal) did not change (Figure 2).
In 1987, 7.2% of kidney donors were black compared with 15.2% in 2009. The percentage of donors of Hispanic origin increased from 4.8% to 13.9%. The percentage of diabetic donors increased fourfold, from just 2.3% in 1994 to 10% by 2009. Fewer donors were HCV+ in 1994 (2.1%) compared with 2009 (3.7%) (Figure 3).
Donation after circulatory death donors increased from 1.3% of kidney donors in 1994 to 12.4% in 2009. The percentage of kidneys that were machine perfused (ie, pumped) declined from 13.4% in 1994 to 8.7% by 1999, but subsequently rose sharply to over 30% by 2009. The percentage of kidneys reported by the OPO as biopsied more than doubled, from 23.1% in 1999 to 48.9% in 2009. Of kidneys biopsied, the percentage with reported glomerulosclerosis exceeding 20% declined slightly from 13.6% to 10.3% (Figure 4). The combined effect of a sharp increase in biopsies and only a small change in glomerulosclerosis resulted in an increase from 3.1% to 5.0% in the percent of kidneys with reported glomerulosclerosis exceeding 20%.
A higher percentage of donors were women in 2009 (41.1%) compared with the late 1980s (35.5%). Just 3% of donors in 2009 had a history of cancer, a slight increase from 2% in earlier years.
The Aging Donor Pool Partially Explains the Rise in Discard Rates
We first built an elementary statistical model to predict the KDR as a smooth function of time, without adjusting for changes in donor characteristics (Figure 5, blue curve). Both extreme predictions (13.3% for October 25, 1999, and 19.0% for December 31, 2009) were very close to the respective observed KDRs, suggesting reasonable model fit. The model-derived, absolute increase in the KDR over the 10-year period was 5.7% (19.0%-13.3%), and the increasing trend was statistically significant (P < 0.0001).
The model was then augmented to include donor age which was a significant predictor of discard (P < 0.0001) and partially attenuated the observed effect of time on KDRs. To show this, we used the modified least squares means approach to predict the KDR over time, had the distribution donor age remained unchanged. After adjusting for the increasing age of the donor pool, 10-year rise in KDRs would have been just 3.9% instead of 5.7% (Figure 5, panel B—red curve), revealing that the increasing age of the donor pool accounted for approximately 31.4% of the KDR increase (Table 1).
This leaves 68.6% of the increasing KDR trend, which was still statistically significant (P < 0.0001), unexplained.
Changes in Other Donor Characteristics as an Explanation for the Rise in Discard Rates
We progressively expanded the model to include other donor factors in an attempt to learn which factors may further mitigate the apparent time effect.
All factors in KDRI as well as other donor factors associated with discard, such as blood type, hepatitis C serostatus, and history of cancer23,24 (Table 1, model C) were added to the model. In panel C (Figure 5), the red curve estimates what the KDR trend would have been had the distribution of each of these donor factors remained stable during the entire 10-year period. Adjusting for these factors explained 91.2% of the increasing KDR trend, which was no longer statistically significant (P = 0.19), suggesting temporal shifts in donor factors were responsible for the vast majority if not all of the KDR trend.
This analysis would be incomplete, however, without accounting for 2 key factors which have been shown to be highly associated with the likelihood of discard but which are heavily influenced by changes in clinical practices: (a) whether a procurement biopsy is performed, and (b) whether the kidney is put on a pulsatile perfusion pump.23-25
Changes in Biopsy and Pumping Practice Further Explain the Rise in Discard Rates
After adjusting for donor factors and the sharply increasing trend in the percentage of kidneys biopsied (Figure 4), a factor independently associated with a higher likelihood of discard (Table 2), the 10-year KDR rise was more than entirely explained (>100%, Table 1) and again not statistically significant (P = 0.24; Figure 6, panel D). After further adjusting for the percentage of kidneys with glomerulosclerosis greater than 20%, the residual KDR trend is negligible and still not statistically significant (P = 0.88, panel E). In other words, it is estimated that the KDR would have remained essentially flat had the distribution of recovered kidneys by donor age, race, comorbidities, and so on, proportion biopsied, and proportion with glomerulosclerosis greater than 20% remained constant during these 10 years.
However, models A to E did not account for whether each kidney was pumped, a practice that more than tripled from 1999 to 2009. Panel F (Figure 5) shows that after adjusting for the increased use of pumping throughout the 2000s (Figure 4), a statistically significant (P = 0.007), residual KDR trend re-emerged. In other words, if more kidneys had not been pumped during this decade, it is estimated that the observed KDR trend would have risen even more sharply than the observed 5.7% rise.
The full model (Figure 5, panel F), which included all available donor factors, the use of biopsies, % glomerulosclerosis, and the percent of kidneys pumped, explained 82.5% of the 10-year increase in the KDR. Had the distribution of all modeled factors remained stable throughout this period, this method estimates that the KDR would still have increased, but only by about 1% (from 15.8% to 16.8%). On the odds scale, this translates into a 12% increased odds had these factors remained stable, compared with the observed 54% rise in the odds of discard from 1999 to 2009 (OR10, Table 1).
The results of this progressive model-building (steps A-F) are summarized in Table 1. Factor-specific odds ratios from the Model F appear in Table 2.
Propensity Analysis (October 1999-2000 vs 2009)
To complement and validate the multivariable regression findings above, we performed a propensity analysis comparing matched samples of recovered kidneys from the beginning (subperiod 1: October 1999 to December 2000) and end (subperiod 2: January 2009 to December 2009) of the 10-year period. Of the 12 911 and 14 394 kidneys recovered in subperiods 1 and 2, respectively, 7276 from each subperiod were matched based on propensity score (Table 3).
Characteristics of the matched samples were nearly identical, with all absolute standardized differences falling well below the customary 10% threshold26 and none reaching statistical significance (Table 3). Before matching, the 2009 KDR was 19.2% compared with 14.7% for the 1999 to 2000 subperiod, an odds ratio of 1.38. After matching, the 2009 KDR was 16.5% compared with 16.2% for the 1999 to 2000 subperiod, a residual odds ratio of 1.07 after accounting for same-donor clustering. This finding of a small, residual KDR increase after adjusting for changes in the available characteristics of recovered kidneys is consistent with the results from regression model F.
The residual odds ratio of 1.07 represents an 82.1% reduction from 1.38. In terms of both relative risk and risk difference, the proportion explained is also about 80%, which is very similar to the 82.5% explained and estimated by the regression approach. The lack of statistical significance (P = 0.24) in this residual difference could be a byproduct of the reduced sample sizes attributable to matching; it may also suggest that, all else being equal, the likelihood of discard for a kidney recovered in 2009 was no different than for an identical kidney recovered in 2000.
Together, the regression and propensity analyses suggest that at least 80% of the long-run increase in KDRs can be attributed to changes in the available characteristics of kidneys offered for transplantation. However, a residual increase was evident in both analyses.
OPO-Recorded Discard Reasons Over Time (1996-2015)
Biopsy findings has consistently been the most common reason recorded by OPOs for the discard of kidneys. Despite the proportion of kidneys biopsied more than doubling during 1999 to 2009 (Figure 4), the percent of discards with biopsy findings cited as the primary reason remained relatively stable at approximately 40% during this period (Figure 6).
Starting in 2008, the percentage of discards attributed to no recipient located—list exhausted (indicating the OPO attempted but was unable to find a transplant center willing to accept the kidney) began to rise steadily, increasing from less than 10% to nearly 30% in 2015. And although biopsy findings was still the most often-cited discard reason in 2015, the percentage has declined to 32.9%.
The 3 other most common discard reasons have been poor organ function (10.9%), anatomical abnormalities (8.0%), and diseased organ (4.9%).
OPO-Specific Trends in the Kidney Discard Rate (1987-2009)
Most OPOs experienced a similar rising trend in KDRs during this 22-year period (Figure 7A). OPO-specific odds ratios (OR) (OR10), quantifying the increased odds of discard per decade, ranged from 0.90 to 3.94, with the vast majority (78%) between 1.25 and 2.25 (Figure 7B). Three OPOs saw notably larger increases than others, with OR10 values near 4.0. No OPOs experienced a statistically significant decline in KDRs during this period.
Several outliers were apparent. Compared with other OPOs, a far higher percentage of kidney donors recovered in OPO 1 and OPO 2 had Kidney Donor Profile Index exceeding 85%, which appears to explain their sharp rise in KDR during the late 2000s. The increasing slopes for these 2 OPOs were statistically significant at the 0.01 level even after adjustment for multiple comparisons (Bonferroni method).27 Despite a sharp increase in less-than-ideal kidney donors recovered in the 2000s (mirroring the national trend), the KDR for OPO 3 remained stable (OR10 = 0.94, P = 0.45). The abnormally high KDR for OPO 3 in the 1980s and 1990s does not appear to be explained by recovery of kidneys from older donors (Figure 7).
The vast majority (at least 80%) of the KDR rise in the 2000s can be explained by a changing donor pool and practices related to biopsy and pumping. A small, residual increase in the KDR cannot be explained by changes in available factors and hence derives from other cause(s). The regression modeling results suggest that the odds of discard for a kidney recovered in 2009 were 12% higher than an identical kidney (including whether biopsied or pumped) recovered in 1999.
It is conceivable that changes in other, unreported donor characteristics could be responsible for the residual increase in the KDR. Increased risk status (according to the Public Health Services28,29) and pumping resistance, for example, were not available for the entire time period and thus excluded from the analysis. However, the proportion of pumped kidneys with resistance exceeding 0.4 mm Hg/mL per minute and donors labeled Public Health Services increased risk changed a little from 2004 to 2000; still, it is possible that the underlying reasons donors received the increased risk label changed over time. Furthermore, though kidney biopsies and pumping may be done routinely, they may also be performed based on indication; as such, observed effects associated with these factors may be partially attributable to unmeasured factors triggering the decision to biopsy or pump. And though we were able to adjust for history of hypertension, we could not account for potentially varying degrees of severity among donors.
Despite these limitations, the veracity of our central findings is strengthened by the use of 2 complementary statistical methods that produced similar results. Furthermore, to alter our key findings, an unaccounted-for factor would have to be highly associated with the likelihood of discard, predominantly uncorrelated with all factors we did include, and have had a substantial distributional change over time among recovered kidney donors.
Other study limitations include the fact that OPOs can only record a single discard reason even though the underlying cause may be multifactorial. Reporting of these reasons is subjective and varies across OPOs, so although the sharp changes in reporting patterns may reflect a changing dynamic in the underlying causes of kidney discards, these findings should not be overinterpreted.
A residual, unexplained increase of 1% in the KDR translates into an excess of 938 discards during 2010 to 2015. This estimate is derived by hypothesizing a KDR of 17.5% instead of the observed 18.5% during this period.
With a growing gap between supply and demand during the 2000s, why would a kidney donated in 2009 be more likely to be discarded than an identical kidney donated in 1999? Increased risk aversion, manifested in transplant programs lowering their acceptance rates for less-than-ideal kidneys, is possible. In an effort to promote quality improvement, the OPTN, Centers for Medicare and Medicaid Services, and private insurers have increasingly relied on program-specific reports that grade performance based on transplant graft and recipient survival rates.30,31 Though these outcome measures have been shown to be well risk-adjusted,32 some centers may still have concerns about unadjusted confounders.33,34 Since 2007, when CMS issued its conditions of participation,35 which deemed transplant hospitals with lower than expected posttransplant survival rates to be out of compliance, some hospitals have become more selective in accepting kidneys36 or reduced their transplant volume.11
Because of these concerns, the OPTN's Membership and Professional Standards Committee is exploring revisions to routine performance monitoring to avoid discouraging the acceptance and utilization of imperfect but transplantable kidneys. A Health Resources and Services Administration–sponsored initiative led by UNOS—the Collaborative Innovation and Improvement Network—is investigating an entirely new paradigm for measuring, monitoring, and improving quality in transplant programs.37 Reese et al4 effectively highlight other potential program oversight, financial reimbursement, and allocation policy levers for maximizing kidney utilization. Ex vivo organ repair or other donor management interventions38 that improve organ quality may also hold promise for optimizing the supply of kidneys.
We have shown that most of the long-term rise in kidney KDRs can be explained by expansion of the donor pool. The push to identify and recover more donors in the early 2000s may have increased discards,13-16 but it also spurred a sharp rise in transplants. Compared with the 8540 deceased donor kidney transplants in 2002, 10 660 were performed in 2006, a 25% rise in just 4 years. More transplants at the cost of more discards is a better reality for patients than a stagnant transplant practice.
Still, these and others' results suggest that a significant number of opportunities for kidney transplant are missed.4 Not only has the donor pool gradually expanded, but the pool of candidates added to the kidney waiting list has also become older and increasingly comorbid.39 Such patients may realize a survival advantage from a less-than-ideal kidney compared with remaining on dialysis.40,41 Despite more waiting list candidates who stand to benefit from such kidneys, center practices on whether to even receive such offers vary substantially.42
Our findings suggest that the increase in the percent of kidneys pumped from 10% to 30% during the 2000s prevented the KDR from rising even higher than the observed 19.2%. Had pumping rates not increased, our model predicts the KDR would have risen by an additional 1%, translating into 878 kidneys spared from discard and instead transplanted due to pumping during 2010 to 2015. Though pumping may on occasion cause discard by revealing concerning performance measures (eg, high vascular resistance), the therapeutic43 and logistical (allowing more flexibility in scheduling surgery) benefits appear to more than compensate for any negative impact on utilization.
Consequently, future changes in pumping practice may have potential to reverse the kidney discard trend. In 2015, the percentage of recovered kidneys pumped ranged from 0% to 74% across the 58 OPOs, revealing marked disparities in practice. KDRs may have increased in the wake of the new kidney allocation system,44 and a decline in pumping may have contributed.45 The cost savings associated with minimizing delayed graft function can exceed the cost of pumping,46-48 suggesting expanded use of pumping nationwide for less-than-ideal kidneys with a high likelihood of discard is not untenable.
By contrast, the decision to perform a biopsy is associated with markedly increased risk of discard,23,24 despite questionable reliability and diagnostic value.49 Our results suggest that increased biopsy rates contributed to the long-term rise in the KDR. Kidney transplantation without routine use of biopsies has proven successful in Europe,50 suggesting reduced reliance on biopsy findings in clinical decision making might increase kidney utilization in the United States. Alternatively, biopsy interpretation could be centralized and performed by expert pathologists.
Why did the long-term rise in the KDR abruptly stop after 2009? The long-term rising trends in donor age and KDRI came to a halt at that same time, and in fact, median donor age actually fell from 43 to 39 from 2009 to 2015 (Figure 2). Any potential drop in the KDRs due to this trend reversal may have been offset by a continuation of the residual KDR increase that we have found, possibly due to increased risk aversion or allocation inefficiency.
We have found that though the long-term rise in the KDR is largely explained by expansion of the donor pool, a residual increasing trend is evident. This increase may be due to behavioral and/or other systemic factors, such as heightened aversion among transplant programs to accepting imperfect kidneys and allocation system inefficiencies. Our and others' findings also suggest that more routine pumping of these kidneys, which has been shown to improve outcomes and increase utilization, may be a potent and cost-effective way to increase the organ supply by reducing discards. Overcoming logistical barriers to pumping in an era of increased kidney shipping44 may be particularly important.
The authors would like to acknowledge Frank Delmonico, whose ideas for related work spawned this study, Heather Neil for article preparation and submission, and the rest of the UNOS Research department for their behind-the-scene contributions.
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