*Purpose: To better predict outcomes of graft survival in kidney donation and ultimately save more lives for those waiting on the list.

*Methods: Machine learning methods (MLM) were compared to kidney donor risk index (KDRI aka KDPI) for the ability to predict graft failure by 12, 24, and 36 months after deceased donor kidney transplantation (DDKT). The MLM model, an ensemble of thousands of randomly generated decision trees, was trained with the same data initially used to develop KDRI.

*Results: An MLM trained with the readily available recipient and donor variables performs significantly better than KDRI/KDPI when predicting graft failure by 12, 24, and 36 months after DDKT. When comparing equal prediction failure rates of 10%, MLM successfully predicted 126% more successful DDKTs (an additional 2,148) than KDRI/KDPI from 1995-2005. Over the entire ROC curve, the MLM performed statistically significantly better c-statistic than KDRI/KDPI in all predictions. CONCLUSION: Using MLM, many high-KDRI kidney offers resulted in thousands of successful patient outcomes without increasing risk of predicted graft failure. The MLM provided a significant improvement over KDRI for the assessment of kidney offers and give clinical professionals an improved basis for making the critical decisions. This work lays the foundation for future MLM in organ transplantation and describes the steps to measure, analyze, and validate future models.

*Conclusions: Kidney transplantation is an effective cure for patients suffering from end-stage renal disease. Kidney transplantation is cost-effective, provides a significant survival benefit, and improves the quality of life for patients. Using our research, we believe we will not only improve the quality of life for the transplant recipient, but also improve the workflows of the Organ Procurement Organizations and Transplant Centers.

« Back to 2019 American Transplant Congress