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Kidney transplantation provides the best outcome for most people with end-stage kidney disease. Transplantation extends and improves quality of life1 and is cost-effective compared with continuing dialysis.2 The greatest impediment to transplantation is that the need far surpasses the availability of donated organs. Many patients will never have the chance of receiving a donor organ because their health deteriorates on the deceased-donor waiting list, and many die while waiting for a donor kidney. The annual mortality rate for dialysis patients on the waiting list exceeds more than 10%, particularly for the older candidates with comorbidities such as diabetes mellitus and vascular disease.3To combat the issue of organ shortage, an approach taken internationally to increase the number of available organs is to include deceased donors who have previously not been accepted because of factors such as age and comorbidity. In Australia, the proportion of expanded-criteria kidneys (or higher Kidney Donor Profile Index [KDPI] kidneys) in the deceased-donor kidney pool has risen from 20.7% in 2005 to 33.6% in 2011, with a similar trend being observed in the United States and United Kingdom.4 Although inclusion of the higher KDPI donors increases the available organs, it also introduces additional complexity into decisions about allocation and acceptance of organs. Transplantation using higher KDPI donor kidneys may lead to survival advantage compared with dialysis, but the incremental gains in graft and patient survival are not universal for all recipients, and the benefits are largely dependent on recipient age and comorbid status.5 As such, the “1 kidney fits all” approach is no longer applicable. Internationally, several strategies have been developed with the prime objective of optimizing the usage and equity in the allocation of deceased-donor kidneys. In the United States, the New Kidney Allocation System (KAS), implemented in 2014, uses the KDPI and the Estimated Posttransplant Survival score for longevity matching of the top percentile of donors and recipients.5 In Europe, the Eurotransplant Senior Program considered age explicitly in giving priority allocation of grafts from donors 65 years or older to recipients in the same age range. At a population level, recent data suggested that implementation of the KAS have led to improved overall transplant volume, acceptance, and transplantation rates for the younger adults and the highly sensitized individuals, but offer acceptance rates have declined for the older candidates, and this may be partly attributed to KDPI differences.6Once the kidneys are allocated, the decision to accept or reject a donor kidney lies at the discretion of the clinicians looking after the potential candidate. Acting as patients' advocates, clinicians have the primary objective of choosing a donor kidney that will ensure maximal gains in posttransplant survival for the individuals while balancing against the time spent waiting on dialysis for the “next” best kidney and the comorbidities the candidate may accrue over time. Whether this decision truly reflects the choice of the patients and whether this will lead to the best outcomes are uncertain. The work presented by Bertsimas et al7 in this issue of the Transplantation provides a unique opportunity to assist clinicians and patients in this very complex decision-making algorithm. Using statistical machine learning techniques, the authors examined one of the analytic tools, random forest, that predicts the probability of a potential candidate receiving a deceased-donor kidney based on some KDPI threshold within a specified time frame. Machine learning is an important feature in data science that focuses on the construction of models or algorithms to find association or make predictions by learning information and structures directly from observed data.