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Record W2094957687 · doi:10.1097/tp.0b013e318181fe3b

Ingredients for a Successful Living Donor Kidney Exchange Program

2008· letter· en· W2094957687 on OpenAlexaboutno aff
Marry de Klerk, Willem Weimar

Bibliographic record

VenueTransplantation · 2008
Typeletter
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsnot available
Fundersnot available
KeywordsDonationTransplantationPopulationMedicineABO blood group systemKidney transplantationMatching (statistics)Family medicineDemographyPolitical scienceSurgeryLawSociologyEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

A simple solution for all kidney patients with a willing but incompatible living donor, because of a positive cross match or an ABO blood type incompatibility, is living donor kidney exchange (1). Over the last two decades, several centers in various countries embarked on these programs (2). In South Korean the first procedure was performed in 1991, in Switzerland a single exchange took place in 1999, while individual centers in the United States, The Netherlands, and Canada followed in 2000, 2003, and 2005, respectively. However, not all these initiatives have resulted in regular programs. Most single centers are not able to accrue enough candidates for efficient exchange of both easy and difficult-to-match pairs on a permanent basis. For that purpose collaboration with other centers is essential. Indeed, Segev et al. in this issue (3) calculated that by expansion of a paired kidney exchange (PKE) program to a national level more difficult to match couples could be helped. Along the same line, Basu et al. proposed PKE to improve HLA-matching and thus transplantation outcomes in India (4). In the Netherlands with a population of 16.4×106 inhabitants we started a national exchange program in 2004. All seven transplant centers work according to a common protocol for the evaluation of the donor, while we agreed on only four simple rules. Registration of donor/acceptor combinations can take place four times a year only if donor and acceptor are medical suitable for donation and transplantation. Allocation is the responsibility of an independent organization, the Dutch Transplant Foundation. Because computerized matching will result in huge numbers of possible combinations of donor-recipient pairs, allocation criteria are necessary. Thus, our computer program selects the best possible combinations on six preset conditions: (1) a maximum number of matched couples, (2) blood type identical before blood type compatible matches, (3) most difficult to match patients (highly sensitized recipients) first, based on HLA match probability, (4) short chains preferred (e.g. rather two doublets than one quartet), (5) couples distributed over multiple centers, (6) wait time calculated from the first day of dialysis. PKE is for many patients the best if not the only option and to be preferred over dialysis. Therefore, we kept our algorithm as simple as possible without too many complicating parameters, for example, donor age, gender, CMV sera status, renal function, or number of HLA mismatches. The third rule is that the national HLA reference laboratory performs all the cross matches between the new donor and the recipient. And finally surgical procedures will take place on the same moment and not the kidney will be transported but the donor travels to the recipient center to ensure minimal logistic problems and to keep ischemia times as short as possible. With these four important rules we now run a national program for four and half years. From January 2004 to June 2008, we registered 143 pairs with blood type incompatibility and 133 pairs with a positive cross-match. We have now performed 18 match runs with a median input of 14 new pairs (range, 7-22 pairs) per match run and a median number of couples participating per match run of 47 (range, 16-66 pairs) which is still increasing (Fig. 1). In the ABO blood type incompatible group almost 70% (99 of 143) had blood type O recipients. Median PRA in the positive cross match group was 46% (2-100). We found new match combinations for 159 of 276 (58%) registered couples. Easy-to-match couples with blood type A recipients and blood type B donors or vice versa had a 85% success rate. However, also the positive cross match couples with a median PRA of 46% could successfully be matched in 74% of the cases. Combining blood type incompatible and positive cross match donor-recipient pairs in one program increased our success rate and made it possible that 26 of 99 (27%) of the ABO blood type incompatible pairs with blood type O recipients could be matched. Our experience with a national PKE program underscores the proposals of Segev et al. and Basu et al. that enlarging the pool of participants in an exchange program will result in high number of match possibilities. Our Dutch matching algorithm was adapted to create doublets in 2004, triplets in 2005 and for a maximum possible chain length in 2007. This resulted in an exponential growth in possible combinations, for example, in a pool with 57 pairs we found 3,427,603 combinations. By changing the sort order of the algorithm the outcome of the matching process can be influenced. We chose for the maximum number of combinations, but other options are also possible. Basu et al. might give priority to HLA matching, while Segev et al. could prefer difficult to match, sensitized patients, and travel requirements. In conclusion, PKE programs can be successful but relative high numbers of candidates and thus cooperation between centers, and trust in each other is essential. Independent organizations should be responsible for the allocation and the final cross matches, while computerized matching programs should be flexible enough to fulfill the needs of a particular transplant community. To optimize match combinations, compatible donor-recipient pairs may be enrolled and altruistic Good Samaritan donors may be helpful in a domino-paired program for unsuccessful PKE couples (5, 6).FIGURE 1.: Input of donor-recipient pairs per match run from January 2004 until May 2008.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.038
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0030.001
Scholarly communication0.0030.004
Open science0.0020.007
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0380.008

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.290
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations27
Published2008
Admission routes1
Has abstractyes

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