(The Guardian) – Machine learning model predicts whether donor is likely to die within the timeframe that liver remains viable

Recently, in cases where people need a liver transplant, access has been expanded by using donors who die after cardiac arrest. However, in about half of these donations after circulatory death (DCD) cases, the transplant ends up being cancelled.

That is because the time between the removal of life support and death must not exceed 45 minutes. If the donor does not die within the timeframe needed to preserve organ quality, surgeons often reject the liver because of the increased risk of complications to the recipient.

Now doctors, scientists and researchers at Stanford University have developed a machine learning model that predicts whether a donor is likely to die within the timeframe during which their organs are viable for transplantation. (Read More)