Each node represents an incompatible donor/recipient pair. A, The most
links (13) are seen by pair 22, a blood type A recipient and a blood type
O donor who are both willing to travel. Pairs 31 and 38 have the same donor
and recipient types as pair 22, but see only 7 links each because these pairs
are unwilling to travel outside of their region. Pair 5 sees 3 links with
a type O recipient (who is restricted to only type O donors) and a type A
donor. Pairs 6 and 35 also have the same blood type configuration as pair
5, but see only 1 link each because pair 6 is unwilling to trade with older
donors and pair 35 is unwilling to travel. B, By matching the strategically
wrong pairs without first considering all possible combinations of matches,
many future possibilities are lost. The same nodes and links from panel A
are shown here after matching pair 22 with pair 38.
Segev DL, Gentry SE, Warren DS, Reeb B, Montgomery RA. Kidney Paired Donation and Optimizing the Use of Live Donor Organs. JAMA. 2005;293(15):1883-1890. doi:10.1001/jama.293.15.1883
Author Affiliations: Department of Surgery,
Johns Hopkins University School of Medicine, Baltimore, Md (Drs Segev, Warren,
and Montgomery and Ms Reeb); and Laboratory for Information and Decision Systems,
Massachusetts Institute of Technology, Cambridge (Ms Gentry).
Context Blood type and crossmatch incompatibility will exclude at least one
third of patients in need from receiving a live donor kidney transplant. Kidney
paired donation (KPD) offers incompatible donor/recipient pairs the opportunity
to match for compatible transplants. Despite its increasing popularity, very
few transplants have resulted from KPD.
Objective To determine the potential impact of improved matching schemes on the
number and quality of transplants achievable with KPD.
Design, Setting, and Population We developed a model that simulates pools of incompatible donor/recipient
pairs. We designed a mathematically verifiable optimized matching algorithm
and compared it with the scheme currently used in some centers and regions.
Simulated patients from the general community with characteristics drawn from
distributions describing end-stage renal disease patients eligible for renal
transplantation and their willing and eligible live donors.
Main Outcome Measures Number of kidneys matched, HLA mismatch of matched kidneys, and number
of grafts surviving 5 years after transplantation.
Results A national optimized matching algorithm would result in more transplants
(47.7% vs 42.0%, P<.001), better HLA concordance
(3.0 vs 4.5 mismatched antigens; P<.001), more
grafts surviving at 5 years (34.9% vs 28.7%; P<.001),
and a reduction in the number of pairs required to travel (2.9% vs 18.4%; P<.001) when compared with an extension of the currently
used first-accept scheme to a national level. Furthermore, highly sensitized
patients would benefit 6-fold from a national optimized scheme (2.3% vs 14.1%
successfully matched; P<.001). Even if only 7%
of patients awaiting kidney transplantation participated in an optimized national
KPD program, the health care system could save as much as $750 million.
Conclusions The combination of a national KPD program and a mathematically optimized
matching algorithm yields more matches with lower HLA disparity. Optimized
matching affords patients the flexibility of customizing their matching priorities
and the security of knowing that the greatest number of high-quality matches
will be found and distributed equitably.
Renal transplantation has emerged as the treatment of choice for medically
suitable patients with end-stage renal disease.1 More
than 60 000 patients await kidney transplantation and are listed on the
United Network for Organ Sharing (UNOS) recipient registry.2 Live
donor renal transplantation represents the most promising solution for closing
the gap between organ supply and demand.
Unfortunately, many patients with willing live donors will be excluded
from live donor renal transplantation because of blood type incompatibility
or positive donor-specific crossmatch. Based on blood type frequencies in
the United States, there is a 35% chance that any 2 individuals will be ABO
incompatible. Furthermore, 30% of the patients awaiting donation from the
UNOS recipient registry are sensitized to allo-HLA due to previous transplants,
pregnancies, or blood transfusions. While successful desensitization techniques
have been developed to overcome incompatibilities, these have been limited
to specialized programs and are very resource intensive.3- 10
Kidney paired donation (KPD) offers an incompatible donor/recipient
pair the opportunity to match with another donor and recipient in a similar
situation.11 In the United States, these transplantations
are currently performed at few institutions, with matches identified through
local or regional patient databases.2,4,12 However,
even with the increasing popularity of KPD, only 51 patients have received
transplants via paired donation, with nearly half of them performed at Johns
Hopkins University.2 UNOS has recently proposed
a national live donor KPD program through the Organ Procurement and Transplantation
Network, but regulatory obstacles to a national program still exist (including
the question of “valuable consideration”); therefore, no data
exist regarding the impact of national vs regional programs.12,13 Because
it is critical to find the most effective method of matching patients and
donors at the outset, before any national strategy is implemented, we investigated
virtual paired donation programs on simulated patient populations.
We believe that KPD is a cost-effective and underused method of providing
transplants to the large number of patients with incompatible donors. Centers
that perform KPD currently use a “first-accept” matching scheme.
Using local/regional databases, an incompatible donor/recipient pair is matched
with the first compatible pair identified, the individuals’ listings
are removed from the pool, and they are provided with transplants. The pairing
identified and removed from the pool might not be the best solution for either
the 2 pairs involved or the other pairs in the KPD program pool. Inefficient
matching algorithms are likely limiting the number and quality of matches
that can be identified.
We developed a model that uses simulated pools of incompatible donor/recipient
pairs to determine if alternative matching algorithms might increase the number
and quality of matches that can be found in a small (regional) or large (national)
Since there are no direct data regarding the incompatible donor/recipient
pool that would enter a national KPD program, we simulated patient pools using
probability models and UNOS data (Table 1)
(Box 1). According to a model
reported by Zenios, at least 884 new incompatible donor/recipient pairings
will occur yearly.15,16 This model
incorporates the genetic linkage of potential related pairs, the social network
of unrelated pairs, blood type distributions, blood type compatibility, and
predicted rates of positive crossmatch. Assuming 15% of incompatible pairs
will seek transplantation by other modalities, approximately 750 patients
could enter a KPD program yearly. Assuming the average waiting times for identifying
an appropriate deceased donor, there are approximately 4000 recipients with
incompatible donors listed on the UNOS recipient registry who could enter
a KPD program initially and then 750 each subsequent year.2
Region and Percentages of Patients*†
1 (3.6%): Connecticut, Maine, Massachusetts,
New Hampshire, Rhode Island
2 (15.0%): Delaware, District of Columbia,
Maryland, New Jersey, Pennsylvania, West Virginia
3 (12.3%): Alabama, Arkansas, Florida, Georgia,
Louisiana, Mississippi, Puerto Rico
4 (7.2%): Oklahoma, Texas
5 (23.1%): Arizona, California, Nevada, New
6 (2.3%): Alaska, Hawaii, Idaho, Montana,
7 (8.8%): Illinois, Minnesota, North Dakota,
South Dakota, Wisconsin
8 (3.9%): Colorado, Iowa, Kansas, Missouri,
9 (8.5%): New York, Vermont
10 (6.7%): Indiana, Michigan, Ohio
11 (8.6%): Kentucky, North Carolina, South Carolina,
Recipient: conditioned on ABO type
Donor: race is identical to recipient race with 90%
probability; otherwise donor race distributed according to UNOS waiting
Race-specific distribution of HLA antigens among
recipients in the UNOS registry
14% highly sensitized
Donor age, y∥
Abbreviation: UNOS, United Network for Organ Sharing.
*Detail of regional boundaries acquired from UNOS Web site.
†Based on UNOS waiting list.2
‡Based on UNOS waiting list2 and
§Based on Leffell et al.14
∥Based on UNOS living donors.2
Recipients were simulated by blood type, race, and region according
to the model reported by Zenios15 and distribution
of data from the UNOS deceased donor kidney waiting list.2 Two
HLA-A, B, and DR antigens were assigned to each recipient based on the race-specific
distribution among recipients in the UNOS registry.14 Finally,
consistent with the incidence of highly sensitized patients (panel reactive
antibodies ≥ 80%) on the current UNOS waiting list, 14% of recipients
were marked as highly sensitized.2
Donors were similarly assigned with blood type determined by the Zenios
model.15 For the analyses described here, we
assumed each donor and recipient were from the same region and that 90% of
donors were of the same race as the recipient. This is consistent with our
institutional experience of live donor renal transplantation. The remaining
donors (10%) were assigned other races based on blood-type specific distribution
of race in the UNOS registry of live kidney donors.2 Donors
were given an age grouping based on age distributions from the UNOS registry,
and HLA antigens were assigned based on the race-specific distribution among
donors in the UNOS registry, as determined by Leffell et al.14
An optimized algorithm based on the Edmonds algorithm from graph theory17,18 (Figure
1) was implemented using a personal computer and compared with the
current first-accept method of kidney matching.12 In
brief, the first-accept scheme scans a database of donor/recipient pairs and
identifies 1 feasible solution (Box 2). An optimized algorithm considers every feasible solution from
the donor/recipient pool, compares these solutions, and picks the one that
best meets a set of individualized optimization priorities, modified by a
predefined optimization bonus. For example, with a pool of 1000 donor/recipient
pairs, the currently used first-accept method evaluates only 1 solution, while
the optimized algorithm considers approximately 10250 feasible
solutions before it picks the best one. The optimized algorithm used in this
study has been mathematically proven to yield the best possible solution for
any given sets of priorities.17,18 (For
additional information and a demonstration, see http://www.optimizedmatch.com)
A set of matches within a given pool of donor/recipient
pairs, with patient-specified acceptance criteria and the obvious restriction
that no pair can be involved in more than 1 match.
Criteria required for a match between 2 donor/recipient
pairs to be plausible. These can be selected by each donor/recipient pair.
For the experiments in this report:
(1) For all pairs, blood type compatibility would be required.
(2) For pairs with unsensitized recipients, donors would be accepted
only of the same age donor group or younger, while for pairs with sensitized
recipients, donors of any age would be accepted.
(3) Pairs unwilling to travel would only be considered for matches
within the same region.
(4) Highly sensitized recipients would only accept exchanges with
0 or 1 HLA antigen mismatch.
In some experiments certain criteria, such as willingness
to travel, were individualized based on different estimates of patient preference.
When the optimized match algorithm searched for the
best feasible solution, the highest priority was given to paired matches with
the fewest HLA mismatches. These priorities could be customized to the wishes
of each individual donor/recipient pair. For example, in some experiments
the priority was modified to emphasize regional considerations. In these experiments,
simulated pairs preferred matches within their region even if the number of
HLA mismatches increased.
Bonus points could be awarded in the optimized algorithm
to any disadvantaged group. For example, a bonus would be given in some experiments
to highly sensitized patients to maximize donor availability for hard-to-match
This is the matching scheme that has been used by
transplant centers currently performing kidney paired donation. A first-accept
match starts with the first incompatible donor/recipient pair on the list.
The database is searched for any donor/recipient pair that meets acceptance
criteria as defined above. Both pairs involved in the first acceptable match
are then removed from the database and transplantation is arranged. Notably,
these pairs are no longer available for consideration in other combinations,
even though they might have yielded better matches to other pairs. The next
incompatible pair is then matched and removed from the pool. This process
is repeated until all identified matches are performed and no match opportunities
remain in the database. Optimization priorities and optimization bonuses are
not applicable to a first-accept match.
In our optimized algorithm, all possible combinations
from the entire data set are considered at once. Each match in a particular
feasible solution is given a score based on the optimization priorities of
the donor/recipient pairs and any optimization bonuses. The scores for the
entire combination of matches are summed. This process is repeated for every
different feasible solution (every different combination of matches possible)
for the pool of donor/recipient pairs. The combination of matches that yields
the highest summed score is chosen, these matches are removed from the database,
and the transplants are arranged.
An optimized match is used to identify the combination
of matches with the highest number and quality. Crossmatch tests are performed
on all identified matches. Negative crossmatch matches are removed from the
database and transplantation is arranged. The optimized match is run again
on the pairs remaining in the database after adding back any positive crossmatch
pairings. This is repeated until all possible matches have been tested and
Although many groups have tried, predicting crossmatch outcomes is difficult.19- 22 The
published probability of a positive crossmatch between 2 strangers is 11%.16 Unsensitized patients in this model were given an
11% chance of a positive crossmatch with any random donor; highly sensitized
patients are much more likely to have a positive crossmatch and were considered
only if there were 0 or 1 HLA mismatches with the proposed donor.
Since a paired donation is not ultimately plausible unless a crossmatch
is performed, and since this information is not available before a KPD algorithm
is run, we report on a method for incorporating the results of those crossmatches
into the algorithm in real-time (Box 2).
For every experiment, unless otherwise indicated, we generated random
databases of 4000 donor/recipient pairs, based on the simulated patient characteristics
described previously. Each experiment was executed 30 times, each time using
a different simulated patient database. Statistical significance between numbers
of pairs matched and numbers of surviving grafts was calculated using the
Wilcoxon paired sign-rank test. Because HLA mismatches are nonnormal, statistical
significance between HLA mismatches was calculated using the Wilcoxon rank-sum
test. Statistical significance was defined as P<.05.
A sensitivity analysis was performed to assess the impact of altered
patient characteristics on our projections. The simulated patient characteristics
were varied and the effect on differences between optimized and first-accept
matches was analyzed. We varied the incidence of highly sensitized patients
(5%-20%), the racial makeup of the pool, the percentage of donors who were
of the same race as their recipients (60%-100%), and the regional distribution.
Finally, the effect of donor and recipient blood types was analyzed by applying
4 normal (mean, 1; SD, 0.25) perturbations to the Zenios model.15
We compared performance of the first-accept and optimized algorithms
using a wide range of static database sizes, ranging from 100 to 5000 pairs
(Figure 2). Optimization afforded a
statistically significant advantage with regard to the number of matches recognized
for all database sizes (P<.001). We also studied
the recognized matches in terms of HLA antigen mismatch, which has been shown
in multiple studies to correlate with crossmatch likelihood and allograft
outcomes.2,21,23- 26 Matches
identified by the optimized algorithm had significantly fewer HLA mismatches
when compared with first-accept (P<.001).
Furthermore, we calculated the number of transplanted kidneys predicted
to survive 5 years following KPD using HLA mismatch–based live donor
transplantation data from Opelz.23 Although
HLA mismatch is not the only predictor of 5-year graft survival, it is the
only predictor that can be improved by better matching algorithms. A significantly
higher number of recipients matched through the optimized algorithm were predicted
to have functioning kidneys at 5 years when compared with first-accept (34.9%
vs 28.7%; P<.001).
With a national database of 5000 pairs, a mean of 2394 pairings were
possible using mathematical optimization with an average of 3 mismatches,
whereas only 2110 pairs with an average of 4.5 mismatches resulted from the
currently used first-accept scheme (P<.001). At
5 years, this would result in 1666 predicted transplants surviving with optimized
algorithm vs 1414 from first-accept (P<.001).
The improvement in overall quality is highlighted by a 10-fold increase in
0 mismatch (423 vs 41) transplants identified.
We estimated that at least 4000 donor/recipient pairs would initially
participate in a KPD, as calculated by waiting times for patients modeled
by Zenios.15 We calculated the benefit of using
an optimized algorithm for the initial accrued pool, with a significantly
greater predicted number of grafts surviving at 5 years (1150 vs 1397; P<.001).
According to the Zenios model, we estimated that at least 750 new donor/recipient
pairs would participate in KPD per year, or 250 every 4 months. To calculate
the recurring advantage afforded by an optimized algorithm, we first eliminated
the maximum number of kidneys that could be matched from the initial pool.
Every 4 months, 5% of the pool was assumed to seek transplantation by means
other than KPD and 250 new pairs were added to the pool. Optimized and first-accept
algorithms were compared over a 5-year period, ie, 15 iterations of dropouts
and new pairs. The optimized algorithm again outperformed first-accept with
significantly more matches (P = .02), better
HLA concordance (P<.001), and more grafts predicted
to survive at 5 years (P<.001).
Local and regional KPD programs are already in practice at a limited
number of centers. A larger national pool offers the possibility of a greater
number and quality of matches. However, this would require that the donor
or recipient of each incompatible pair receive the transplant at the same
hospital as the matched partner or that kidneys get transported between institutions.
Local/regional programs would reduce the distance that patients or organs
would have to travel. Transporting kidneys may reduce some of the benefits
of live donor transplantation by increasing cold ischemia time. Currently,
no data are available on the trade off between a larger national pool and
the requirement of a greater travel distance for patients or organs.
To identify the number of patients who could benefit if matching were
performed using a national database, we varied the number of pairs willing
to travel outside of their region between 0% and 100% (Table 2). A simulation with no patients willing to travel represented
a strictly regional KPD program. Patients who were willing to travel were
considered for all paired donations, but matches within the region were preferred
for all patients. The patients required to travel might be unmatchable within
their regions or might receive an advantage in HLA matching quality.
More pairs would be matched if some were willing to travel (P<.001). This was observed with both strategies, but the difference
was more pronounced with the optimized algorithm (mean, 1712 regional vs 1891
national; a gain of 179) when compared with first-accept (mean, 1544 regional
vs 1673 national; a gain of 129). The improvement derived by mathematical
optimization when compared with the current first-accept scheme was statistically
significant with regard to number of pairs matched, HLA mismatch, and predicted
number of grafts surviving at 5 years, no matter how many pairs were willing
to travel (P<.001). In fact, the role of optimization
was important enough that greater benefit would be derived from a regional
optimized algorithm than a national first-accept scheme (P = .002).
The benefit to the overall pool of patients of a national KPD program
was clearly shown. However, concerns regarding feasibility of a national KPD
program focus in part on the willingness of donors and/or recipients to travel
outside of their regions.
To evaluate the potential individual benefit of traveling to the unsensitized
patient, we compared match likelihood and quality between unsensitized pairs
willing to travel and those unwilling to travel outside of their region. No
matter what fraction of pairs was willing to travel, the optimized solution
always found more and better matches for those willing to travel (P<.05).
Furthermore, the optimized solution could profoundly reduce the subset
of pairs that are actually required to travel when compared with a first-accept
scheme (Table 2). Even if 100% of pairs
were willing to travel, only 2.9% of the pool would actually need to travel
in order to achieve the maximum benefit from an optimized algorithm, as compared
with 18.4% of the pool required to travel in a first-accept scheme (P<.001). In all cases, mathematical optimization would
yield more and better matches than first-accept but require fewer pairs to
The most challenging recipients are the 14% of UNOS registrants who
have become highly sensitized. Because these patients harbor anti-HLA antibody,
they are harder to match, resulting in a median waiting time of 6.73 years
for the highly sensitized patient (panel reactive antibodies ≥ 80%).2 This group of patients would arguably derive the greatest
benefit from KPD due to the increased chance of finding a negative crossmatch
donor. The cost of implementing a transplant in 1 highly sensitized patient
after 6.73 years of waiting and undergoing dialysis until a negative crossmatch
deceased donor organ can be obtained is approximately $485 038. Alternatively,
immediate transplantation with KPD followed by 6.73 years of immunosuppression
costs only $204 738 (Table 3).
Maximizing matches for these patients would produce the greatest benefit to
the individual patient and reduce the burden to the health care system. We
tested 2 modalities for improving matches for this subgroup.
First, the effects of the method of matching and willingness to travel
were evaluated with regards to highly sensitized patients. In the current
system of first-accept regional matches, only 2.3% of highly sensitized patients
received transplants in our simulation. However, if highly sensitized patients
were willing to travel nationally and mathematical optimization was used,
14.1% of highly sensitized patients would be successfully matched, a 6-fold
increase (P<.001). This increase in pairs matched
was lost with national first-accept (8.4%) or regional optimized searches
Second, the effect of giving an optimization bonus to highly sensitized
patients was evaluated. This type of optimization is not possible using the
first-accept scheme. Using a national optimized search as previously described,
90 highly sensitized patients found matches. This number rose to 132 when
highly sensitized patients were favored with a bonus in the optimized algorithm
(P<.001). No statistical difference was seen in
the number of unsensitized or overall matches found. These findings demonstrate
that highly sensitized patients can benefit from prioritization without a
negative impact on the overall pool.
Another criticism of KPD relates to feasibility of crossmatching a national
pool of patients while executing a matching algorithm. We evaluated our optimized
crossmatch handling algorithm with regards to number of matches identified,
the quality of these matches, as well as the predicted number of crossmatch
and iterations required. No reduction was seen in the number of transplants
performed or HLA mismatch when crossmatch was taken into consideration. Furthermore,
only 1.23 crossmatch tests were required for every transplant performed, and
an average of 6 (range, 5-8) iterations were required to complete the algorithm.
This shows that optimization is still logistically plausible in the context
of real-world crossmatch testing.
For all of the variations in input data described in the “Methods”
section, the difference between number of matches, HLA quality, and number
of transplanted kidneys predicted to survive 5 years following KPD was significantly
better using the optimized algorithm when compared with a first-accept scheme
When compared with dialysis while awaiting a transplant from the deceased
donor transplantation list, a live donor kidney transplant offered through
a KPD provides a cost advantage as well as an improvement in graft and patient
survival. We have shown that KPD is less expensive than dialysis or desensitization
for each type of recipient with a willing incompatible live donor (Table 3). The costs of various treatment options
for a pool of simulated incompatible donor/recipient pairs were calculated
for only the patients matched using KPD (Table
4) and for the entire pool (Table 5). A mathematically optimized algorithm not only increases the number
of potential matches, but also saves nearly $48 million over the currently
utilized first-accept schemes.
For a pool of 4000 potential recipients (< 7% of the current
UNOS registry), nearly $750 million would be saved by KPD compared with the
cost of dialysis and deceased donor transplantation. The greatest cost savings
would be realized by a national system that offered optimized KPD and desensitization
of all unmatched recipients.
Kidney paired donation is no longer just a concept. The ethical and
legal concerns that once dominated the discussion of KPD have given way to
administrative and logistical challenges inherent in organizing complex cooperative
programs between transplant centers.11,28,29 Local,
state, and regional programs are being introduced around the United States.
Despite this, only a relatively small number of patients have benefited from
KPD to date. It is critical to the success and public perception of KPD that
careful consideration be given to what impact a local/regional vs national
scheme will have on the ability to make transplants available to the greatest
number of patients both equitably and cost-efficiently. Determining optimal
allocation priorities and algorithms is absolutely crucial to the smart proliferation
of KPD in the United States and the prevention of a haphazard system that
diminishes the impact of this promising approach to the organ shortage.
To study the effects of algorithmic decisions and priorities on both
local/regional and national matching outcomes, we created a computer program
to simulate databases of recipients and their donors. First, the data show
that a national KPD program would provide a greater number and quality of
matches than local/regional schemes. Even more significantly, these simulations
have shown that mathematically verifiable optimization would further increase
the number and quality of matches identified in any KPD cohort. In fact, greater
benefit would be derived from adopting an optimized algorithm on a regional
basis than expanding the currently used first-accept algorithm to a national
level. In terms of outcome and cost, it is critical to optimize matching for
We have also shown that an optimized national KPD scheme would result
in significant rewards for those who are willing to travel, as well as for
the remaining pool of patients. Furthermore, only 2.9% of the donor/recipient
pairs would actually need to travel to achieve the maximum benefit from an
optimized algorithm. This finding discredits one of the most widely perceived
barriers to implementation of a national KPD program and greatly reduces the
need to transport patients or organs between regions.
The advantages of optimization are not in quantity and quality alone.
Resistance to KPD is a patient-specific issue, and includes concerns such
as reluctance to travel, worries about donor age and quality and concerns
that others will benefit most from the scheme. Optimization can be individualized
on a patient-by-patient basis, with priorities regarding travel, HLA matching,
donor age, transport of donor kidneys, and other parameters left in the hands
of the patient and the transplant center entering the data. Adding an optimization
bonus allows the flexibility to maximize transplantation of highly sensitized
patients or other disadvantaged groups without hampering the overall outcome
of the match.
We believe that KPD should be the preferred treatment for patients who
have incompatibilities with their intended donors who wish to participate,
as KPD is less expensive than desensitization and requires less immunosuppression.
Our simulations suggest that approximately 47% of incompatible pairs could
be matched through an optimized national KPD program. Those who do not match
can either await the next round of matching or undergo desensitization with
their cross-match-positive and/or ABO-incompatible intended donor. Some patients
will favor desensitization because of timing issues, travel concerns, and
desire to receive a kidney from a loved one. Furthermore, due to the breadth
of their HLA reactivity, only a modest percentage of the highly sensitized
patients will find an ABO-compatible donor with whom they have a negative
crossmatch. Patients who are difficult to match can be paired with donors
that do not completely eliminate incompatibility but provide better immunologic
conditions for desensitization. We have combined KPD with desensitization
at our institution: patients not eligible for antibody reduction protocols
due to high donor-specific antigen titers can be matched with a donor with
whom their donor-specific antigen titer is lower and thus amenable to desensitization.30
Public perception is critical to the future success of KPD. If a national
system does not use an algorithm that yields the best matches for a given
pool, taking into account the priorities of individual patients, the public
will be uneasy about paired donation. Concerns about equity are best addressed
within an optimized framework where priority can be assigned to matches that
help vulnerable populations. We believe that a national optimized match would
best utilize this new source of live donor organs.
Corresponding Author: Dorry L. Segev, MD,
Division of Transplantation, Department of Surgery, Johns Hopkins University
School of Medicine, 720 Rutland Ave, Ross 765, Baltimore, MD 21287 (firstname.lastname@example.org).
Author Contributions: Dr Segev and Ms Gentry
had full access to all of the data in the study and take responsibility for
the integrity of the data and the accuracy of the data analysis.
Study concept and design: Segev, Gentry, Montgomery.
Acquisition of data: Segev, Gentry, Reeb.
Analysis and interpretation of data: Segev,
Gentry, Warren, Montgomery.
Drafting of the manuscript: Segev, Gentry.
Critical revision of the manuscript for important
intellectual content: Segev, Gentry, Warren, Reeb, Montgomery.
Statistical analysis: Segev, Gentry.
Administrative, technical, or material support:
Study supervision: Montgomery.
Financial Disclosures: None reported.
Funding/Support: Dr Segev is funded by an American
Society of Transplant Surgeons Fellowship in transplantation. Ms Gentry is
funded by a US Department of Energy Computational Science Graduate Fellowship.