Kidney transplant (KTx) professionals have many choices of immunosuppressive (IS) medications and regimens for their patients. With early acute rejection (AR) rates now low and short term graft survival high, individualized long-term graft survival depends on patients'underlying co-morbidities or complications of IS medications. Yet few studies or data exist on how to balance the trade-offs between IS efficacy (in preventing AR) versus complications, including infections, cancers and new onset diabetes after transplant (NODAT). Randomized trials or FDA-sanctioned Risk Evaluation and Mitigation Strategies help with very specific situations;but along with prior analyses of large national databases, also have many limitations. In this study, we propose to use a novel, three-database linkage of the a) U.S.A. national Organ Procurement and Transplant Network (OPTN) registry: recording initial data on all KTx and subsequent survival outcomes, b) a Medicare billing claims database: covers the first 3 years post-KTx, and c) a national Pharmacy Clearinghouse Database (PCD) that covers 60% of all medication fills in the U.S.A. Using this integration, we can minimize limitations of prior approaches to develop accurate, longitudinal, national level data on the transplant procedures, IS use, survival and non-fatal morbidity. Our investigative team of KTx specialists, economists and statisticians, all with transplant database and outcomes research expertise, can then assess in a more rigorous way than previously possible, the efficacy and morbidity tradeoffs of the IS regimens and doses. In the proposed CISTEM study (Choosing Immune Suppression in Renal Transplantation by Efficacy and Morbidity), we will complete the following three aims: 1) To construct a novel linkage of data from the OPTN registry, an updated Medicare claims dataset and the PCD to quantify the associations of KTx IS with outcome metrics of efficacy and morbidity, adjusted for demographic, medical and immunologic parameters;through propensity-score and covariate-adjusted survival models that will quantify the association between IS regimen or dose and specific measures of efficacy (AR) or morbidity (major infections, cancers, NODAT) that contribute to the hard outcomes of graft survival and patient survival, with sub-analyses for key racial and high-risk subgroups;2) To use the transition probabilities of events, generated in aim 1, to develop Markov models and calculate the overall cost-effectiveness, including trade-off costs, for each of the major IS regimens in use, by overall group and in the key sub-groups mentioned above;and 3) To use the decision analytics from Aims 1 and 2 to generate individualized and real-time reports of the predicted efficacy/complications outcomes and costs of different KTx IS regimens through a free and updateable patient-focused web- or mobile phone-based risk engine and communication tool. The long-term significance is that physicians and patients will be able to make IS choices in a more cost-effective and better informed manner than any other strategy currently available.

Public Health Relevance

Kidney transplantation is an expensive treatment for end-stage kidney failure, but less expensive over the long-term than dialysis or a return to dialysis i the transplant fails. Therefore, maintaining the longest transplant and patient survival is highly desirable, but depends now on a balance between immunosuppressive medication efficacy and morbidity. This project will quantify the adjusted risks and additional costs for key efficacy and morbidity events with many commonly used immunosuppressive medications regimens and doses, then use the medical and cost data to develop a web- and mobile-phone based tool for individualized predictions that physicians and patients can use to make more informed decisions.

National Institute of Health (NIH)
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Research Project (R01)
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Study Section
Health Services Organization and Delivery Study Section (HSOD)
Program Officer
Abbott, Kevin C
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Washington University
Schools of Medicine
Saint Louis
United States
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