Disparities exist in access and outcomes of kidney transplantation. More than half of patients are hospitalized in the year after transplantation, and these patients are more likely AA and have lower socioeconomic status. 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, and develop potential interventions to address surgical disparities in hospitalization following kidney transplantation.
Our specific aims are 1) To develop and validate predictive models to identify transplant recipients at high risk of hospitalization following transplant; 2) To use a community-based participatory research approach to build an electronic hospitalization risk dashboard that will aid in clinical decision-making and guide the use of scarce resources for patients at high risk for hospitalization post-transplant. The overall impact of this proposal is to improve transplant outcomes and reduce disparities among a primarily AA ESRD population in the Southeastern US.
/Relevance: The proposed study seeks to utilize predictive analytics to develop and validate risk prediction models to identify patients at high risk for hospitalization following kidney transplantation, integrate the models into an existing clinical decision support platform for population health management, and use a community-based participatory research approach to inform development of a dashboard to deploy resources that will help improve patient outcomes and reduce disparities in kidney transplantation.
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