Case Series Design, Inference, and Analysis of Infection-Cardiovascular Risk in Patients on Dialysis

• Activity Number: 279
• Type: Invited
• Date/Time: Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
• Sponsor: WNAR
• Author(s): Danh Nguyen*+
• Companies: University of California, Davis
• Keywords: case series method ; cardiovascular event ; exposure error ; infection ; Non-homogenous Poisson process ; Risk misspecification

Abstract

Cardiovascular disease and infection are major factors for morbidity and mortality in patients on dialysis. Hospitalization data from United States Renal Data System (USRDS) captures nearly all (> 95%) patients with end-stage renal disease in the U.S., the largest source of research data available for this population. Although the precise mechanisms by which infection may affect cardiovascular events are not fully known, infections may affect vascular endothelium, create a chronic sub-clinical inflammatory state that affects atherosclerosis, or may create a procoagulant state. Thus, we hypothesize that the time period following infection are associated with increased cardiovascular event risk. Case series design/mehod and analysis of infection-cardiovascular risk in patients on dialysis using USRDS data presents several unique challenges, including (1) infection (exposure) onset measurement error since the time of infection is not known precisely, (2) misspecification of risk period, (3) and other inferential challenges, such as formal hypothesis testing. Current challenges, resolutions/development and future challenges will be discussed.