Transplantation · 2018
Survival and Quality of Life Impact of a Risk-based Allocation Algorithm for Deceased Donor Kidney Transplantation.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Calisa Vaishnavi, Craig Jonathan C, Howard Kirsten, Howell Martin, Alexander Stephen, Chadban Steven J, Clayton Philip, Lim Wai H, Kanellis John, Wyburn Kate, Johnson David W, McDonald Stephen P, Opdam Helen, Chapman Jeremy R, Yang Jean, Wong Germaine
Аннотация
BACKGROUND: To determine the incremental gains in graft and patient survival under a risk-based, deceased donor kidney allocation compared with the current Australian algorithm.
METHODS: Risk-based matching algorithms were applied to first graft, kidney only recipients (n = 7513) transplanted in Australia between 1994 and 2013. Probabilistic models were used to compare the waiting time, life, and QALYs and graft years between the 8 risk-based allocation strategies against current practice.
RESULTS: Compared with current practice, Kidney Donor Risk Index-Estimated Posttransplant Survival matching of the lowest 20% of scores reduced median waiting time by 0.64 years (95% confidence interval [CI], 0.52-0.73) for recipients aged 30 years or younger, but increased waiting time by 0.94 years (95% CI, 0.79-1.09) for recipients older than 60 years. Among all age groups, the greatest gains occurred if Kidney Donor Risk Index-Estimated Posttransplant Survival matching of the lowest 30% of scores was used, incurring a median overall gain of 0.63 (95% CI, 0.03-1.25) life years and 0.78 (95% CI, 0.30-1.26) graft years compared with the current practice. A median gain in survival of 1.91 years for younger recipients (aged 30-45 years) was offset by a median reduction in survival (by 0.95 life years) among the older recipients. Prioritization of lower-quality donor kidneys for older candidates reduced the waiting time for recipients older than 45 years, but no changes in graft and patient survivals were observed.
CONCLUSIONS: Risk-based matching engendered a moderate, overall increase in graft and patient survivals, accrued through benefits for recipients 45 years or younger but disadvantage to recipients older than 60 years.
METHODS: Risk-based matching algorithms were applied to first graft, kidney only recipients (n = 7513) transplanted in Australia between 1994 and 2013. Probabilistic models were used to compare the waiting time, life, and QALYs and graft years between the 8 risk-based allocation strategies against current practice.
RESULTS: Compared with current practice, Kidney Donor Risk Index-Estimated Posttransplant Survival matching of the lowest 20% of scores reduced median waiting time by 0.64 years (95% confidence interval [CI], 0.52-0.73) for recipients aged 30 years or younger, but increased waiting time by 0.94 years (95% CI, 0.79-1.09) for recipients older than 60 years. Among all age groups, the greatest gains occurred if Kidney Donor Risk Index-Estimated Posttransplant Survival matching of the lowest 30% of scores was used, incurring a median overall gain of 0.63 (95% CI, 0.03-1.25) life years and 0.78 (95% CI, 0.30-1.26) graft years compared with the current practice. A median gain in survival of 1.91 years for younger recipients (aged 30-45 years) was offset by a median reduction in survival (by 0.95 life years) among the older recipients. Prioritization of lower-quality donor kidneys for older candidates reduced the waiting time for recipients older than 45 years, but no changes in graft and patient survivals were observed.
CONCLUSIONS: Risk-based matching engendered a moderate, overall increase in graft and patient survivals, accrued through benefits for recipients 45 years or younger but disadvantage to recipients older than 60 years.
Предметные рубрики MeSH
AdolescentAdultAge FactorsAgedAged, 80 and overAlgorithmsAustraliaChildDecision Support TechniquesDonor SelectionGraft SurvivalHumansKidney TransplantationMarkov ChainsMiddle AgedPredictive Value of TestsQuality of LifeQuality-Adjusted Life YearsRisk AssessmentRisk FactorsTime FactorsTissue DonorsTreatment OutcomeYoung Adult