Individualized Immunosuppression for Kidney Transplant Recipients

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PROJECT SUMMARY/ABSTRACT Dr. Sunjae Bae is an Assistant Professor of Surgery and Population Health at NYU Grossman School of Medicine. He completed his PhD in epidemiology and Master’s in biostatistics at Johns Hopkins Bloomberg School of Public Health. Prior to his research training, he received his medical education at Kyung Hee University and practiced as a clinician. His previous research has focused on immunosuppression and clinical outcomes after kidney transplantation (KT), with the support from the American Society of Nephrology and Mogam Science Scholarship Foundation. Dr. Bae’s long-term goal is to help >230,000 KT recipients live longer and healthier by creating an evidence- based, patient-centered tool for determining the ideal immunosuppression regimen. Lifelong immunosuppression is a defining feature in the care of KT recipients. Immunosuppression is the primary intervention to prevent acute rejection; however, it causes various side effects, notably 2.1- to 6.2-fold risk of infections, cancers, and cardiovascular diseases, which collectively account for >65% of deaths in this population. Since the risks of acute rejection and immunosuppression-related side effects are unique in each recipient, the selection of the immunosuppression regimen should be individualized according to the recipient’s unique risk profile. The transplant community has long recognized the importance of immunosuppression individualization. However, there is little scientific evidence guiding how it should be done, mainly due to 3 methodological challenges. First, traditional analytic methods are ineffective in providing individualized predictions of the risks and benefits after immunosuppression. Second, a method to objectively assess the risk-benefit balance from patients’ perspectives is lacking. Lastly, the new individualization protocol must be clinically relevant. However, given the lack of consensus on how the individualization should be done, the relevance of a new protocol cannot be assessed objectively. This K01 Mentored Research Scientist Development Award will enable Dr. Bae to expand his research skillset and lay the groundwork for addressing these 3 challenges. We propose the following approaches. First, we will create a machine learning-based individualized risk prediction model that can process statistical interactions efficiently and transparently. Second, we will conduct interviews and paired-comparison surveys to quantify the patients’ viewpoints on the risk-benefit balance, e.g., how much reduction in acute rejection is worth risking a 10% increase in type 2 diabetes. Third, we will survey KT clinicians to characterize their clinical practice and perspectives on individualization. Dr. Bae will enhance his analytical skills and expand his domain knowledge through didactic coursework in patient-oriented research methods and implementation science, and research mentorship by a team of multidisciplinary experts committed to Dr. Bae’s career development.