Sameera Senanayake1, Nicholas Graves2, Helen Healy3, Keshwar Baboolal3, Adrian Barnett2, Matthew P Sypek4, Sanjeewa Kularatna2. 1. Queensland University of Technology, Australian Center for Health Service Innovation, Brisbane, Australia; Ministry of Health, Colombo, Sri Lanka. Electronic address: sameerajayan.senanayake@hdr.qut.edu.au. 2. Queensland University of Technology, Australian Center for Health Service Innovation, Brisbane, Australia. 3. Royal Brisbane Hospital for Women, Brisbane, Australia; School of Medicine, University of Queensland, Brisbane, Australia. 4. Australia and New Zealand Dialysis and Transplant Registry, Adelaide, South Australia, Australia.
Abstract
OBJECTIVES: The study had two main aims. First, we assessed the cost-effectiveness of transplanting deceased donor kidneys of differing quality levels based on the Kidney Donor Profile Index (KDPI). Second, we assessed the cost-effectiveness of remaining on the waiting list until a high-quality kidney becomes available compared to transplanting a lower-quality kidney. METHODS: A decision analytic model to estimate cost-effectiveness was developed using a Markov process. Separate models were developed for 4 separate KDPI bands, with higher values indicating lower quality. Models were simulated in 1-year cycles for a 20-year time horizon, with transitions through distinct health states relevant to the kidney recipient from the healthcare payer's perspective. Weibull regression was used to calculate the time-dependent transition probabilities in the base analysis. The impact uncertainty arising in model parameters was included by probabilistic sensitivity analysis using the Monte Carlo simulation method. Willingness to pay was considered as Australian $28 000. RESULTS: Transplanting a kidney of any quality is cost-effective compared to remaining on a waitlist. Transplanting a lower KDPI kidney is cost-effective compared to a higher KDPI kidney. Transplanting lower KDPI kidneys to younger patients and higher KDPI kidneys to older patients is also cost-effective. Depending on dialysis in hopes of receiving a lower KDPI kidney is not a cost-effective strategy for any age group. CONCLUSION: Efforts should be made by the health systems to reduce the discard rates of low-quality kidneys with the view of increasing the transplant rates.
OBJECTIVES: The study had two main aims. First, we assessed the cost-effectiveness of transplanting deceased donor kidneys of differing quality levels based on the Kidney Donor Profile Index (KDPI). Second, we assessed the cost-effectiveness of remaining on the waiting list until a high-quality kidney becomes available compared to transplanting a lower-quality kidney. METHODS: A decision analytic model to estimate cost-effectiveness was developed using a Markov process. Separate models were developed for 4 separate KDPI bands, with higher values indicating lower quality. Models were simulated in 1-year cycles for a 20-year time horizon, with transitions through distinct health states relevant to the kidney recipient from the healthcare payer's perspective. Weibull regression was used to calculate the time-dependent transition probabilities in the base analysis. The impact uncertainty arising in model parameters was included by probabilistic sensitivity analysis using the Monte Carlo simulation method. Willingness to pay was considered as Australian $28 000. RESULTS: Transplanting a kidney of any quality is cost-effective compared to remaining on a waitlist. Transplanting a lower KDPI kidney is cost-effective compared to a higher KDPI kidney. Transplanting lower KDPI kidneys to younger patients and higher KDPI kidneys to older patients is also cost-effective. Depending on dialysis in hopes of receiving a lower KDPI kidney is not a cost-effective strategy for any age group. CONCLUSION: Efforts should be made by the health systems to reduce the discard rates of low-quality kidneys with the view of increasing the transplant rates.
Authors: Sameera Senanayake; Nicholas Graves; Helen Healy; Keshwar Baboolal; Adrian Barnett; Sanjeewa Kularatna Journal: Health Econ Rev Date: 2021-04-15
Authors: Sameera Senanayake; Helen Healy; Steven M McPhail; Keshwar Baboolal; Sanjeewa Kularatna Journal: Appl Health Econ Health Policy Date: 2022-07-18 Impact factor: 3.686