Vaishnavi Calisa1,2, Jonathan C Craig1,2, Kirsten Howard1, Martin Howell1,2, Stephen Alexander2, Steven J Chadban3,4,5, Philip Clayton3,6,7, Wai H Lim8,9, John Kanellis10, Kate Wyburn4,5, David W Johnson11,12,13, Stephen P McDonald3,6,7, Helen Opdam14, Jeremy R Chapman15, Jean Yang16, Germaine Wong1,2,15. 1. Sydney School of Public Heath, The University of Sydney, Camperdown, NSW, Australia. 2. Centre for Kidney Research, Kid's Research Institute, The Children's Hospital at Westmead. 3. ANZDATA Registry, SA Health and Medical Research Institute, Adelaide, Australia. 4. Renal Medicine, Royal Prince Alfred Hospital, Sydney, Australia. 5. Kidney Node, Charles Perkins Centre, University of Sydney, Australia. 6. School of Medicine, University of Adelaide, Adelaide, Australia. 7. Central Northern Adelaide Renal and Transplantation Service, Royal Adelaide Hospital, Adelaide, Australia. 8. Department of Renal Medicine, Sir Charles Gairdner Hospital, Perth, Australia. 9. School of Medicine and Pharmacology, University of Western Australia, Perth, Australia. 10. Department of Nephrology, Monash Health and Centre for Inflammatory Diseases, Department of Medicine, Monash University, Clayton, VIC, Australia. 11. Department of Nephrology, Princess Alexandra Hospital, Brisbane, Australia. 12. Centre for Kidney Disease Research, University of Queensland, Brisbane, Australia. 13. Translational Research Institute, Brisbane, Australia. 14. Austin Hospital, Melbourne, Australia. 15. Centre for Transplant and Renal Research, Westmead Hospital. 16. School of Mathematics and Statistics, University of Sydney, Australia.
Abstract
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.
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.
Authors: Sameera Senanayake; Nicholas Graves; Helen Healy; Keshwar Baboolal; Adrian Barnett; Matthew P Sypek; Sanjeewa Kularatna Journal: BMC Health Serv Res Date: 2020-10-09 Impact factor: 2.655