AI model may identify high-risk patients for post-transplant complications

• An AI-based tool may be able to predict the risk of developing chronic graft-versus-host disease (GVHD) and transplant-related death after stem cell or bone marrow transplant.
• Combining biomarkers with clinical factors, the AI tool predicted outcomes more accurately than clinical data alone, particularly for transplant-related mortality.
• The tool arranged patients into low- and high-risk groups, with clear differences in outcomes up to 18 months post-transplant, and was validated in an independent patient cohort.
• The machine learning model is available as a free, web-based application to support risk assessment and research.

Stem cell and bone marrow transplants are procedures that replace diseased, damaged, or destroyed blood-forming cells with healthy tissue. They are a common treatment for leukemia, lymphoma, and blood disorders.

These procedures involve harvesting cells from a donor (allogenic) or using the patient’s own cells (autologous). For many people, transplantation can be lifesaving. However, recovery does not end after leaving the hospital.

Potential complications can result in treatment-related mortality, typically driven by GVHD. Although advances in transplant care have improved survival rates, GVHD is the leading cause of late morbidity and mortality after an allogenic stem cell transplant.

It is difficult to predict who will experience GVHD and who will not. However, evidence suggests that between half to a third of all people who have an allogeneic transplant develop some symptoms of GvHD.

It can occur shortly after the transplant, known as acute GVHD, or can arise months after the transplant, called chronic GVHD (cGCHD).

Preventing GVHD can be challenging, as this typically involves balancing immune suppression to prevent GVHD without increasing infection risk and preventing adverse reactions to these treatments.

A new study, published in the Journal of Clinical Investigation, describes a machine-learning model that estimates a patient’s risk of developing cGVHD and dying from transplant-related causes before symptoms appear.

Researchers suggest the tool could give clinicians an early warning and open a window for closer monitoring or preventive strategies.