Patients have heart transplant surgery as a life-saving measure after heart failure, and it is crucial to minimize unwanted postoperative outcomes. One of the major negative outcomes following heart transplant surgery is acute rejection, which is currently managed with strong immunosuppression treatment. However, this treatment can lead to other unwanted side effects, such as kidney problems and cancer, experienced by 50% and 30% of patients, respectively. To address this problem, I will analyze biomarkers, or biological molecules found in the blood that can be measured to indicate the various negative outcomes after heart transplantation.
Our research group has measured biomarkers in blood extensively and to build on that work, I will use statistical and machine learning analysis techniques to find the biomarkers that could predict and potentially diagnose complications after heart transplant surgery. Achieving this goal will help develop personalized immunosuppression treatments for patients to reduce adverse health outcomes and improve overall patient care after heart transplantation.