TY - JOUR
T1 - Prospective validation of prediction model for kidney discard
AU - Zhou, Sheng
AU - Massie, Allan B.
AU - Holscher, Courtenay M.
AU - Waldram, Madeleine M.
AU - Ishaque, Tanveen
AU - Thomas, Alvin G.
AU - Segev, Dorry L.
N1 - Funding Information:
This work was supported by grants K24DK101882 (Segev) and F32DK109662 (Holscher) from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) and an American College of Surgeons Resident Research Scholarship (Holscher). The analyses described here are the responsibility of the authors alone and do not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. The data reported here have been supplied by the
Funding Information:
This work was supported by grants K24DK101882 (Segev) and F32DK109662 (Holscher) from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) and an American College of Surgeons Resident Research Scholarship (Holscher). The analyses described here are the responsibility of the authors alone and do not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. The data reported here have been supplied by the Minneapolis Medical Research Foundation (MMRF) as the contractor for the Scientific Registry of Transplant Recipients (SRTR). The interpretation and reporting of these data are the responsibility of the author(s) and in no way should be seen as an official policy of or interpretation by the SRTR or the U.S. Government.
Publisher Copyright:
Copyright © 2018 Wolters Kluwer Health, Inc. All rights reserved.
PY - 2019/4/1
Y1 - 2019/4/1
N2 - 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.
AB - 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.
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U2 - 10.1097/TP.0000000000002362
DO - 10.1097/TP.0000000000002362
M3 - Article
C2 - 30015701
AN - SCOPUS:85063712662
VL - 103
SP - 764
EP - 771
JO - Transplantation
JF - Transplantation
SN - 0041-1337
IS - 4
ER -