AI Breakthrough Predicts Viable Donor Livers, Reducing Waste by 60%

Researchers at Stanford Medicine have developed a groundbreaking machine learning model that predicts the viability of donor livers for transplantation. This innovation has the potential to reduce the number of canceled transplants by up to 60%. The study, led by Rintaro Yanagawa from Kyoto University, addresses a critical challenge in organ transplantation: the timing of death in donors after circulatory death.

Currently, there is a significant discrepancy between the number of patients awaiting liver transplants and the available organs. Many potential donors are declared unsuitable for transplantation because the time from the removal of life support to death exceeds the optimal window of 30 to 45 minutes. If this window is exceeded, the risk of complications for the transplant recipient increases significantly.

The newly developed model predicts whether a donor is likely to die within the acceptable timeframe for organ viability. According to Kazunari Sasaki, MD, clinical professor of abdominal transplantation and senior author of the study, “By identifying when an organ is likely to be useful before any preparations for surgery have started, this model could make the transplant process more efficient.”

Transforming Liver Donation Efficiency

For individuals suffering from end-stage liver disease, a transplant is often the only effective treatment. The gap between the number of patients needing a liver and the available donors is slowly narrowing, in part due to advances in organ preservation techniques such as normothermic machine perfusion. This process maintains the organ at optimal temperatures and oxygen levels while in transit, making it feasible to use livers from donors who have experienced circulatory death.

The number of liver transplants is on the rise, particularly due to increasing donations after circulatory death. Sasaki noted, “The waitlist is getting smaller. In the future, it might be possible for everyone who needs a liver transplant to get one from a deceased donor.”

Despite advancements in organ donation, significant challenges remain. After life support is removed, the blood supply to the organs can become compromised, leading to potential liver damage. If death occurs more than 30 minutes after blood flow begins to decline, the liver may no longer be suitable for transplantation.

Machine Learning Outperforms Conventional Methods

The innovative model utilizes a range of clinical data points, including gender, age, body mass index, and vital signs, along with neurological assessments to predict the time of death accurately. The research team tested various machine-learning algorithms to identify the one most effective at forecasting death within the critical window for successful organ transplantation.

The winning algorithm demonstrated a prediction accuracy of 75%, surpassing both existing computerized tools and the average judgment of surgeons, which stood at 65%. It also proved effective even when medical records were incomplete, marking a significant advancement in the field.

Researchers have designed the model for customization, allowing it to adapt to different hospital protocols and surgeon preferences. A natural language interface is also in development, which will facilitate easier integration of donor medical records into the predictive model.

The study published in The Lancet Digital Health highlights the importance of using artificial intelligence to minimize missed opportunities in organ transplantation. While both the model and surgeon judgment currently have a missed opportunity rate of just over 15%, ongoing refinements aim to reduce this further.

“We are now working on decreasing the missed opportunity rate because it is in the patients’ best interest that those who need transplants receive them,” Sasaki emphasized. The research team is also exploring applications for heart and lung transplants, indicating a broad potential impact for this technology across various organ donation scenarios.

The collaborative research involved contributions from leading institutions, including the International University of Health and Welfare, Duke University School of Medicine, and Cleveland Clinic, among others. This pioneering work could reshape the landscape of organ transplantation, ultimately saving more lives and making the process more efficient for both healthcare providers and patients in need.