There are more candidates on the waitlist for a liver transplant than there are available organs, yet about half the time a match is found with a donor who dies after cardiac arrest following the removal of life support, the transplant must be cancelled. For this type of organ donation, called donation after circulatory death (DCD), the time between the removal of life support and death must not exceed 30–45 minutes, or the surgeons will often reject the liver because of the increased risk of complications to the recipient.
Stanford Medicine researchers have now developed a machine learning (ML)-based model that predicts whether a donor is likely to die within the time frame during which their organs remain viable for transplantation. The model was found to outperform surgeon judgment and reduced by 60% the rate of futile procurements, which occur when transplant preparations have begun but death happens too late.
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