Predicting Hospital Readmissions Among Kidney Transplant Recipients
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More than half of kidney transplant recipients are rehospitalized in the year after kidney transplantation, placing a large burden on the hospital system and placing patients at higher risk for poor health outcomes, including infection, graft failure and/or death. Several studies have identified risk factors for hospital admission using traditional statistical methods, but models have been limited by moderate predictive accuracy, the use of static (rather than dynamic) models, and the use of administrative data that may not capture the changing risk factors pre- to post-transplant. The overall goal of this research is to develop and validate rigorous, dynamic risk prediction models, integrate these models within a clinician dashboard to allow for real-time decision making and appropriate interventions for high risk patients. This presentation will discuss the importance of the clinical problem, as well as preliminary data analyses from national surveillance data models and local Emory Transplant Center models. The long term objective of this project is to improve health outcomes of patients and improve the efficiency of clinical care for transplant recipients.