Background: Renal transplantation is a lifesaving intervention for end-stage renal disease. The demand for renal transplantation outweighs the availability of organs; however, up to 20% of recovered kidneys are discarded before transplantation. We aimed to better characterize the risk factors for deceased donor kidney discard.
Methods: We performed a secondary analysis of the Organ Procurement and Transplantation Network database from 2000 to 2012 of all solid organ donors. The cohort was split into training (80%) and validation (20%) subsets. We performed a stepwise logistic regression to develop a multivariate risk prediction model for kidney graft discard and validated the model. The performance of the models was evaluated with respect to calibration, and area under the curve (AUC) of receiver operating characteristic curves.
Results: There were no significant baseline differences between the training (n = 57 474) and validation (n = 14 368) cohorts. The multivariate model validation showed very good discriminant function in predicting kidney discard (AUC = 0.84). Predictors of increased discard included age older than 50 years, performance of a kidney biopsy, cytomegalovirus seropositive status, donation after cardiac death, hepatitis B and C seropositive status, cigarette use, diabetes, hypertension, terminal creatinine greater than 1.5 mg/dL and AB blood type. The model outperformed the Kidney Donor Risk Index in predicting discard (P < 0.001). Subgroup analysis of expanded criteria donor kidneys demonstrated good discrimination with an AUC of 0.70.
Conclusions: We have characterized several important predictors of deceased donor kidney discard. Better understanding of factors that lead to increased deceased donor kidney discard can allow for targeted interventions to reduce discard.