Researchers at Stanford Medicine have developed a machine learning model that significantly enhances the efficiency of liver transplants by predicting the timing of donor death. This advancement is crucial as the demand for liver transplants far exceeds the available donor organs, particularly in cases of donation after circulatory death (DCD), where timing is critical to organ viability. Traditionally, about half of the transplants must be canceled due to the donor’s death occurring too late, which poses a substantial challenge to transplant teams.
The new model outperforms surgeon judgment, reducing the rate of futile procurements by 60%. By accurately predicting whether a donor is likely to die within the critical timeframe for organ viability, the model allows for better allocation of resources, such as normothermic machine perfusion devices, and ultimately increases the number of successful transplants. This innovation not only streamlines the transplant process but also holds the potential to save lives by ensuring that more patients on the waitlist receive the organs they desperately need.
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