CAMBRIDGE, Mass., Sept. 2017––The demand for kidney transplantation outpaces supply, yet when an organ is available, there is high discard rate of up to 50% for certain types of organs. This is partly due to the complexity of the decision to accept or reject an organ, which is based on well-intentioned “what if” calculations by physicians. In a recent study, MIT Sloan School of Management Professors Dimitris Bertsimas and Prof. Nikos Trichakis created a data-based model to improve the decision-making process, aspiring to relieve some of the disease burden of over 650,000 end-stage renal disease patients in the U.S.
“The decision about whether to accept a kidney from a deceased donor is very challenging,” explains Bertsimas, codirector of MIT Sloan’s Operations Research Center. “Since 2002, the number of candidates on the waitlist has nearly doubled, from just over 50,000 to more than 96,000 by 2013. During that time, living donation rates have decreased. Deciding whether to accept a deceased donor organ – or wait for a potentially higher quality organ offer later – can be a life or death decision. That decision is made by physicians, who rely on experience and intuition rather than on data and patient outcomes.”
Trichakis says, “The risk of relying on human calculations and gut instinct is that a very sick patient may underestimate how long it will take to get the next offer and then become too sick for transplantation. Or a young person with a good chance for getting a healthier organ offer next month may prematurely accept the first offer. If that organ fails, they may require additional surgery and care. The model could help make the entire donation system more efficient, as the tool would facilitate better matching of organs overall and contribute to fewer discarded kidneys.”
The analytics tool, which they designed with Jerry Kung of the MIT Sloan Operations Research Center, Dr. Parsia Vagefi of Massachusetts General Hospital (MGH), and Dr. David Wojciechowski of MGH, aims to calculate the probability of a patient being offered a deceased-donor kidney of a certain quality level within a specific time frame, given their individual characteristics. It looks at 10 years of data and millions of prior decisions to estimate a patient’s waiting time in the context of a current active organ offer until the time to the next offer for a higher quality kidney.
“The tool understands how decisions are made and uses machine learning to make a prediction based on those millions of past observations it has access to,” says Trichakis. “As for accuracy, the AUC is 87%, which illustrates that the model produces credible predictions.”
If widely accepted, the professors say that the tool could not only assist practitioners in their organ acceptance decisions, but also serve as an educational tool for candidates awaiting transplantation. “In defining the future organ offer landscape in a patient-specific format, we hope to not only provide transplant practitioners the ability to achieve expedited, evidence-based, decision-making for organ selection, but also to provide an interactive educational tool for transplant candidates to further their understanding of an additional aspect of the risk/benefit ratio associated with organ offer acceptance – specifically the factor of additional waiting time,” they wrote.
While they are in the process of finalizing the study, Bertsimas and Trichakis hope to make the tool available for hospitals to use in the near future. Developing an app-form of the tool for doctors and patients is also in their plans.
Bertsimas adds, “This tool could have a very significant impact not only on kidney transplants, but also for other types of transplants like liver transplants. It could be of great help in situations where physicians and patients must make decisions without knowing what the future holds -- and having to balance a current offer with potential future offers.”Bertsimas, Trichakis, Kung, Vagefi, and Wojciechowski are coauthors of “Accept of Decline? An Analytics-Based Decision Tool for Kidney Offer Evaluation.”
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