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Enregistrement W2094957687 · doi:10.1097/tp.0b013e318181fe3b

Ingredients for a Successful Living Donor Kidney Exchange Program

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

Notice bibliographique

RevueTransplantation · 2008
Typeletter
Langueen
DomaineMedicine
ThématiqueOrgan Donation and Transplantation
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésDonationTransplantationPopulationMedicineABO blood group systemKidney transplantationMatching (statistics)Family medicineDemographyPolitical scienceSurgeryLawSociologyEnvironmental healthInternal medicine

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,010
score de la tête « metaresearch » (Gemma)0,017
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,038
Score d'incertitude au seuil0,126

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0100,017
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,000
Études des sciences et des technologies0,0030,001
Communication savante0,0030,004
Science ouverte0,0020,007
Intégrité de la recherche0,0040,006
Charge utile insuffisante (le modèle a refusé de juger)0,0380,008

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,025
Tête enseignante GPT0,290
Écart entre enseignants0,265 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations27
Publié2008
Routes d'admission1
Résumé présentoui

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