BACKGROUND: Many kidneys are discarded every year, with 3631 kidneys discarded in 2016 alone. Identifying kidneys at high risk of discard could facilitate "rescue" allocation to centers more likely to transplant them. The Probability of Delay or Discard (PODD) model was developed to identify marginal kidneys at risk of discard or delayed allocation beyond 36 hours of cold ischemia time. However, PODD has not been prospectively validated, and patterns of discard may have changed after policy changes such as the introduction of Kidney Donor Profile Index and implementation of the Kidney Allocation System (KAS). METHODS: We prospectively validated the PODD model using Scientific Registry of Transplant Recipients data in the KAS era (January 1, 2015, to March 1, 2018). C statistic was calculated to assess accuracy in predicting kidney discard. We assessed clustering in centers' utilization of kidneys with PODD >0.6 ("high-PODD") using Gini coefficients. Using match run data from January 1, 2015, to December 31, 2016, we examined distribution of these high-PODD kidneys offered to centers that never accepted a high-PODD kidney. RESULTS: The PODD model predicted discard accurately under KAS (C-statistic, 0.87). Compared with utilization of low-PODD kidneys (Gini coefficient = 0.41), utilization of high-PODD kidneys was clustered more tightly among a few centers (Gini coefficient, 0.84 with >60% of centers never transplanted a high-PODD kidneys). In total, 11684 offers (35.0% of all high-PODD offers) were made to centers that never accepted a high-PODD kidney. CONCLUSIONS: Prioritizing allocation of high-PODD kidneys to centers that are more likely to transplant them might help reduce kidney discard.
BACKGROUND: Many kidneys are discarded every year, with 3631 kidneys discarded in 2016 alone. Identifying kidneys at high risk of discard could facilitate "rescue" allocation to centers more likely to transplant them. The Probability of Delay or Discard (PODD) model was developed to identify marginal kidneys at risk of discard or delayed allocation beyond 36 hours of cold ischemia time. However, PODD has not been prospectively validated, and patterns of discard may have changed after policy changes such as the introduction of Kidney Donor Profile Index and implementation of the Kidney Allocation System (KAS). METHODS: We prospectively validated the PODD model using Scientific Registry of Transplant Recipients data in the KAS era (January 1, 2015, to March 1, 2018). C statistic was calculated to assess accuracy in predicting kidney discard. We assessed clustering in centers' utilization of kidneys with PODD >0.6 ("high-PODD") using Gini coefficients. Using match run data from January 1, 2015, to December 31, 2016, we examined distribution of these high-PODD kidneys offered to centers that never accepted a high-PODD kidney. RESULTS: The PODD model predicted discard accurately under KAS (C-statistic, 0.87). Compared with utilization of low-PODD kidneys (Gini coefficient = 0.41), utilization of high-PODD kidneys was clustered more tightly among a few centers (Gini coefficient, 0.84 with >60% of centers never transplanted a high-PODD kidneys). In total, 11684 offers (35.0% of all high-PODD offers) were made to centers that never accepted a high-PODD kidney. CONCLUSIONS: Prioritizing allocation of high-PODD kidneys to centers that are more likely to transplant them might help reduce kidney discard.
Authors: Peter P Reese; Meera N Harhay; Peter L Abt; Matthew H Levine; Scott D Halpern Journal: J Am Soc Nephrol Date: 2015-09-14 Impact factor: 10.121
Authors: Melissa J Azur; Elizabeth A Stuart; Constantine Frangakis; Philip J Leaf Journal: Int J Methods Psychiatr Res Date: 2011-03 Impact factor: 4.035
Authors: J M Garonzik-Wang; N T James; K C Weatherspoon; N A Deshpande; J A Berger; E C Hall; R A Montgomery; D L Segev Journal: Am J Transplant Date: 2011-10-12 Impact factor: 8.086
Authors: B Tanriover; S Mohan; D J Cohen; J Radhakrishnan; T L Nickolas; P W Stone; D S Tsapepas; R J Crew; G K Dube; P R Sandoval; B Samstein; E Dogan; R S Gaston; J N Tanriover; L E Ratner; M A Hardy Journal: Am J Transplant Date: 2014-02 Impact factor: 8.086
Authors: Allan B Massie; Xun Luo; Bonnie E Lonze; Niraj M Desai; Adam W Bingaman; Matthew Cooper; Dorry L Segev Journal: J Am Soc Nephrol Date: 2015-12-17 Impact factor: 10.121