AI-Enhanced Cell-Based Blood Tests to Improve Predictive Accuracy for Complex Transplant Outcomes

Managing immunosuppressive drugs in transplant patients is a delicate challenge. Insufficient medication can lead to rejection, which may be cell- or antibody-mediated, while excess immunosuppression raises the risk of infection and complications such as Epstein-Barr virus (EBV)-associated lymphoma. Current diagnostic tools provide limited insight into a patient’s immune status, relying on reactive, binary results. Clinicians often must combine various sources of clinical data to make critical decisions, making a timely and accurate diagnosis difficult. Now, the integration of artificial intelligence (AI) and machine learning (ML) capabilities with cell-based blood tests could significantly improve predictive accuracy for complex transplant outcomes.

Plexision (Pittsburgh, PA, USA) is advancing personalized transplant medicine using AI-enhanced cell-based testing. Plexision’s suite of cell-based blood tests predicts the most common rejection subtypes and post-transplant infections, all using a common platform of immune cell function. The company is further enhancing the platform’s predictive accuracy, enabling it to rank the likelihood of several outcomes such as stable graft function, T-cell-mediated and antibody-mediated rejection, and infection-related lymphoma. The platform delivers results in as little as 6 to 24 hours and transforms test results from binary outputs to ranked, patient-specific predictions, allowing for more tailored clinical decisions.

Image: The cell-based tests bring artificial intelligence to transplant outcome care (Photo courtesy of iStock)

The platform has demonstrated strong clinical performance in multi-center studies. For instance, PlexABMR, one of the company’s tests, predicted antibody-mediated rejection in kidney transplant recipients with 81% positive predictive value and 75% negative predictive value. Another test accurately predicted EBV infection, a leading cause of life-threatening post-transplant lymphoma. Moving forward, Plexision aims to continue advancing its AI-driven capabilities and expand its impact in precision transplant care by delivering timely, individualized insights to clinicians managing high-risk transplant patients.

“Plexision is redefining transplant diagnostics by predicting rejection subtypes and infection risks using blood tests derived from a single immune cell function platform,” said Dr. Rakesh Sindhi, a transplant surgeon, Co-founder and Chief Scientific Officer, Plexision. "By expanding our artificial intelligence and machine learning capabilities, we are transforming how clinicians will interpret immunologic signals, moving from singular binary ‘yes/no’ answers to ranked predictions tailored to each patient.”

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