Prediction of Kidney Allograft Discard Before Procurement: The Kidney Discard Risk Index
There is an 18.9% discard rate among kidney allografts. Here, we aimed to determine predictors of kidney discard and construct an index to identify high-probability discard kidney allografts prior to procurement.
MATERIALS AND METHODS:A total of 102 246 potential kidney allograft donors from the Organ Procurement and Transplantation Network database were used in this analysis. The cohort was randomized into 2 groups. The training set included 67% of the cohort and was used to derive a predictive index for discard that comprised 21 factors identified by univariate and multivariate logistic regression analysis. The validation set included 33% and was used to internally validate the kidney discard risk index.
RESULTS:In 77.3% of donors, at least 1 kidney was used for transplant, whereas in 22.7% of donors, both kidneys were discarded. The kidney discard risk index was highly predictive of discard with a C statistic of 0.89 (0.88-0.89). The bottom 10th percentile had a discard rate of 0.73%, whereas the top 10th percentile had a discard rate of 83.65%. The 3 most predictive factors for discard were age, creatinine level, and hepatitis C antibody status.
CONCLUSIONS:We identified 21 factors predictive of discard prior to donor procurement and used these to develop a kidney discard risk index with a C statistic of 0.89.
Reviewer: | Mr Simon Knight, Centre for Evidence in Transplantation, Nuffield Department of Surgical Sciences University of Oxford |
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Conclusion: | This paper attempts to derive a risk index for kidney allograft discard using OPTN data from the US. The authors randomly split a cohort of 102,246 kidney offers into training and validation sets and used logistic regression to create an index to predict discard based upon factor available prior to procurement. The final model shows a C-statistic of 0.89, suggesting good performance in this cohort. The three most predictive factors for discard were age, serum creatinine and hepatitis C antibody status. The authors suggest that the risk index couple be used to identify organs at high risk of discard as a basis for interventions or allocation policies to improve utilization. One interesting aspect of this study is that the authors also developed machine learning models based on the same input variables and demonstrated that the logistic regression model achieved similar performance to machine learning approaches whilst maintaining better transparency and explainability. Clearly, the model is only validated in the US allocation system and may not have the same predictive performance in other settings – further research should explore generalizability. |
Aims: | The aim of this study was to establish predictors of kidney discard and to derive a kidney discard risk index (KDSRI) on the basis of these factors. |
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Interventions: | The study population was randomized to the training set and the validation set. |
Participants: | 102,246 potential renal allograft donors. |
Outcomes: | The primary outcome was renal allograft discard. |
Follow Up: | mean follow-up time, 4.5 ± 3.7 years |
Funding: | No funding was received for this study |
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Publication type: | Randomised Controlled Trial |
Organ: | Kidney |
Language: | English |
Author email: | matthew.price@bcm.edu |
MeSH terms: | Allografts; Humans; Kidney; Logistic Models; Multivariate Analysis; Tissue Donors; Tissue and Organ Procurement; Kidney Transplantation |