Areas of Focus

Project Funding and Areas of Focus. Our USRDS modeling research is funded by National Institutes of Health (NIH) NIDDK since 2011. A brief history of the project (funded by grant R01 DK092232) and focus in each project period is described below.

Project funding renewal 2021-2026: Multilevel Time-Dynamic Modeling of Hospitalization and Survival in Patients on Dialysis

• Project narrative: Over 726,000 individuals in the U.S. have end-stage kidney disease (ESKD) with about 87% of patients on the life-sustaining treatment of dialysis at over 6,000 dialysis facilities across U.S. (2017).  The proposal involves developing new methods for studying time-dynamic effects of patient-, facility- and region-level factors (across the United States) of hospitalization and mortality risk among dialysis patients, a cohort with frequent hospitalizations and very high mortality rates. The overarching goal is to provide the knowledge base for improving patient care by identifying modifiable multilevel risk factors and time periods of elevated risks after transition to dialysis.

 Project funding renewal 2015-2021: Modeling Time Dynamic Multilevel Outcomes in Patients on Dialysis

• Project narrative: Understanding time-dynamic effects of risk factors on outcomes of patients with chronic diseases, such as cardiovascular events and infections in patients on dialysis, is important for effective disease management and prevention. The proposal involves developing new methods for multilevel time-dynamic modeling of patient outcomes using the United States Renal Data System. The goal is to provide guidance in identifying modifiable patient-level and facility-level risk factors and approaches to quality improvement of dialysis care providers.

Initial funding period 2011 - 2015: Effective Semiparametric Models for Ultra-sparse, Unsynchronized, Imprecise Data

• Project narrative: The public health burden directly related to infection and cardiovascular disease in the dialysis population is substantial. The proposal involves developing the necessary estimation and inference framework to use the United States Renal Data System database in modeling age- and time-varying dynamics of the association between cardiovascular events and various contributing risk factors including infection. Understanding this cardiovascular-infection risk dynamics in patients over time is important to the development of targeted intervention strategies in the US dialysis population.