Understanding Cardiovascular Event Risk Dynamics Over Time in Older Patients on Dialysis: A Generalized Multiple-Index Varying Coefficient Model Approach

• Activity Number: 279
• Type: Invited
• Date/Time: Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
• Sponsor: WNAR
• Author(s): Damla Senturk*+ and Jason Estes and Lorien Dalrymple and Yi Mu and Danh Nguyen
• Companies: University of California, Los Angeles and University of California, Los Angeles and University of California, Sacramento and University of California, Davis and University of California, Davis
• Keywords: Cardiovascular outcomes ; End stage renal disease ; Generalized linear models ; Time-varying effects ; United States Renal Data System ; Infection

Abstract

Among patients on dialysis, cardiovascular disease and infection are leading causes of hospitalization and death. Recent studies have found that the risk of cardiovascular events is higher after an infection-related hospitalization. In this work, we characterize the dynamics of cardiovascular event risk trajectories for patients on dialysis via multiple time indices, including (1) time since the start of dialysis, (2) time since the pivotal initial infection-related hospitalization and (3) patient's age at the start of dialysis, by developing generalized multiple-index varying coefficient (GM-IVC) models. The proposed GM-IVC models utilize a multiplicative structure and one-dimensional varying coefficient functions along each time and age index to capture the cardiovascular risk dynamics before and after the initial infection-related hospitalization. We develop a two-step estimation procedure for the GM-IVC models based on local maximum likelihood. We report new insights on the dynamics of cardiovascular events risk over multiple indices using the United States Renal Data System database, which collects data on nearly all patients with end-stage renal disease in the U.S.