Technology Advances Improve Organ Transplant Success Rates

Organ transplants are risky, and a dearth of available organs make success rates that much more critical. Artificial intelligence and cloud computing are pushing innovation in donor-matching techniques, predicting organ graft success, and reducing transplant rejection rates.

 

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Image credit: Paris Transplant Group

More than 106,000 people are on the national organ transplant waiting list, but last year only 40,000 received transplants, according to the U.S. Health Resources & Services Administration. Even the lucky recipients aren’t out of the woods as transplanted organs have an overall rejection rate of 10-15 percent.

Compounding the problem of too few donors is an inadequate donor-match process. The current computerized donor-matching system ranks potential recipients by objective criteria, such as blood and tissue type, medical urgency, time on the waiting list, and distance between donor and potential recipient. 

However, those factors are considered individually, and any one of them could eliminate a potential recipient. For example, an urgent-need patient who lives far away from the hospital frequently is bounced in favor of a closer patient with a medium need.

A newer artificial intelligence-based system is in the works, with the goal of making better matches and saving more lives. Using the Analytic Hierarchy Process (AHP), the Organ Procurement and Transplantation Network (OPTN) is developing a methodology that assesses multiple criteria and assigns potential candidates a points-based score. It prioritizes recipients based on the combination of all their factors, rather than individual rankings of each one. Priorities can also be adjusted depending on organ type. AHP makes transplant allocation more transparent and equitable.

Predicting Success

One of the most difficult factors to establish is the predicted organ acceptance rate. Will the patient’s body reject the donated organ? It’s difficult to know for certain, but researchers at the Paris Transplant Group are tapping artificial intelligence to better predict patient outcomes.

The PTC has developed the iBox, a universal tool that uses an AI-based prediction system to determine the long-term survival of transplanted kidneys. Using data available during post-transplant patient follow-up, the iBox predicts the probability of success up to seven years after surgery. The algorithms consider patient, donor, and graft parameters along with long-term patient data to calculate the risk of failure. Tested on more than 7,500 patients in multiple countries over several years, the iBox predictions largely matched real-life outcomes.

Because the data is easily available after transplantation, the iBox can be used in standard clinical settings worldwide. It also can re-evaluate the risk at multiple time points, 3 or 5 or 7 years after transplant. The updated risk level reflects physical changes to the body and the graft, including factors such as scarring, damage, inflammation, and donor-specific antibody levels. The iBox tool has been integrated into Predigraft, patient monitoring software from Cibiltech.

Combatting Rejection

Predicting success is highly valuable, but preventing rejection is even better. Researchers from Boston University startup INIA Biosciences are developing the INIA, a non-invasive ultrasound device that uses AI and cloud computing to reduce the likelihood of rejection. It modulates specific nerve signals to suppress the body’s immune response to attack the newly transplanted organ. The INIA would replace the current practice of placing patients on a cocktail of anti-rejection drugs for the remainder of their lives, at a cost of about $30,000 per year.

Even with the drugs, about 30 percent of kidney transplants are rejected. The INIA device can detect failure and help preserve the donated organs. If a patient’s body begins to reject the transplanted organ, INIA will notify medical staff early—before the organ breaks down, potentially allowing it to be retransplanted in a different patient. The INIA solution is still in development, but it has the potential to change patient monitoring and to lessen patient reliance on drugs. That will reduce costs for patients, hospitals, and insurance companies.

 

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