This K23 proposal will provide Xingxing S. Cheng, MD, MS with the protected time, mentorship, training, and research experience to become an independent clinical investigator. Dr. Cheng is a board-certified nephrologist and accredited transplant nephrologist, with a long-term vision of improving the effectiveness of health care for kidney transplant patients. She seeks to combine patient-oriented research and decision science to find innovative solutions to clinical problems. Skills she will acquire in this grant, under the guidance of a strong mentorship team, include 1) integration of patient-oriented research and decision analytic models; 2) assessing the heterogeneity of response in different patient subgroups; and 3) advanced modelling skills. This grant proposes to refine the process of screening of coronary artery disease in patients with chronic kidney disease on the wait-list awaiting kidney transplantation. Currently, patients undergo repeat cardiac screening tests at frequent intervals, few of which result in interventions. These tests impose a high treatment burden on patients and potentially delay time to transplant, while bringing unclear benefit to the patient. This grant proposes to refine the cardiac screening strategy and personalize it for specific patient subgroups (e.g. elderly patients with diabetes mellitus). It will examine strategies that vary the frequency of cardiac testing and risk stratification for cardiac testing based on a simple, inexpensive, and non-invasive test that can be performed in routine clinical settings, a 6-minute walk test (6MWT). This project will achieve this broad aim by three specific aims: 1) to characterize the 6MWT in identifying low-risk patients who do not additional cardiac testing; 2) to model strategies varying the frequency of cardiac testing and use of 6MWT; 3) to design strategies personalized for specific patient subgroups and model them in the entire kidney transplant candidate population of the United States (US). The first two aims will arise from a well-characterized cohort of patients at Dr. Cheng?s institution. Machine-learning systems will be used to identify which patient subgroups are best served by which strategies (i.e. the heterogeneity of response).
The third aim will leverage the US Renal Data System, which Dr. Cheng?s mentors and institution has a track record of leveraging for innovative research. The proposed work has high potential to make a significant clinical impact. Its completion will enable the identification and characterization of rational strategies for pre-transplant screening of coronary artery disease that may be incorporated into clinical practice or pave the way for a future multi-center comparative effectiveness trial. The proposed work is realistic and feasible within the award period, and will allow Dr. Cheng to build research skills, advance and disseminate scientific knowledge, create additional collaborative networks, and compete for R01 or equivalent funding. In summary, the K23 award will provide the support to enable Dr. Cheng to become a successful independent clinical investigator.
Currently, patients on the kidney transplant waitlist undergo repeat cardiac screening tests at frequent intervals, but relatively few interventions result. This project proposes to refine the cardiac screening strategy and personalize it for specific patient subgroups (e.g. elderly patients without diabetes mellitus), by examining strategies that reduce the frequency of cardiac testing and use performance-based risk stratification to reduce the number of patients who need cardiac testing. If successful, this project will reduce the cardiac testing burden and facilitate activation for transplant for patients on the kidney transplant waitlist.
