Infection and cardiovascular disease are two main sources of mortality in the dialysis population. Even though acute infections have been associated with an increased risk of myocardial infarction and stroke in the general population, the extent to which infection is a contributing factor to increased risk of cardiovascular events longitudinally in the dialysis population is largely unknown. The largest source of research data for the dialysis population is the United States Renal Data System database, which contains hospitalization records of nearly all patients on maintenance dialysis. Our long-term goal is to study the dynamic association of cardiovascular events and various contributing risk factors, particularly infection. Towards this goal, we will develop generalized semiparametric regression models to study trends over time generally, over time (years) on dialysis and over age, specifically. Determining the age- and time-dependent association between infection and the occurrence of cardiovascular events and obtaining the predicted subject- specific risk trajectory (probability) of cardiovascular events based on predictors, for instance, from the previous one to three months (i.e., time-lagged prediction) are critical steps towards the development of targeted intervention strategies in the US dialysis population. Innovation. The main challenge towards this goal is the lack of methods able to handle the extreme/ challenging structure of the longitudinal data available for analysis, characterized by extreme- (ultra-) sparsity, unsynchronized measurements, and imprecision/measurement error. This results from data collected on patient hospitalization records, which is extremely irregular and infrequent. In addition, longitudinal clinical inflammatory markers data (available for a subset of the USRDS cohort) are at unsynchronized time points with the outcome, possibly contaminated with measurement error. Currently there are no existing methods for generalized semiparametric regression modeling of longitudinal binary outcome (e.g., occurrence of cardiovascular events) or modeling of count/rate outcome that can handle 1) irregular, 2) infrequent, 3) unsynchronized and 4) error-prone longitudinal data.
Aims. The proposed research will fill this gap, by developing new estimation &inference procedures for generalized semiparametric regression models (GSRMs) for longitudinal data under these emerging challenges using functional data analysis (FDA). This will be achieved through the following specific aims: 1) Develop a unified functional analysis framework for estimation and inference for GSRMs, including generalized and generalized partial linear varying coefficient models, for highly irregular, infrequent, unsynchronized and noise-contaminated longitudinal data;2) Develop methods to predict subject-specific response trajectories;3) Characterize the efficiency of our proposed FDA approach. Furthermore, these methods will be used to determine, for the first time, the cardiovascular-infection risk longitudinal dynamics in the dialysis population.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Research Project (R01)
Project #
5R01DK092232-03
Application #
8547059
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Flessner, Michael Francis
Project Start
2011-09-15
Project End
2014-08-31
Budget Start
2013-09-01
Budget End
2014-08-31
Support Year
3
Fiscal Year
2013
Total Cost
$186,409
Indirect Cost
$30,080
Name
University of California Los Angeles
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
092530369
City
Los Angeles
State
CA
Country
United States
Zip Code
90095
Estes, Jason P; Nguyen, Danh V; Chen, Yanjun et al. (2018) Time-dynamic profiling with application to hospital readmission among patients on dialysis. Biometrics :
Li, Yihao; Nguyen, Danh V; Chen, Yanjun et al. (2018) Modeling time-varying effects of multilevel risk factors of hospitalizations in patients on dialysis. Stat Med 37:4707-4720
Estes, Jason P; Nguyen, Danh V; Chen, Yanjun et al. (2018) Rejoinder: Time-dynamic profiling with application to hospital readmission among patients on dialysis. Biometrics :
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Rhee, Connie M; Kalantar-Zadeh, Kamyar; Ravel, Vanessa et al. (2018) Thyroid Status and Death Risk in US Veterans With Chronic Kidney Disease. Mayo Clin Proc 93:573-585
Kalantar-Zadeh, Kamyar; Kovesdy, Csaba P; Streja, Elani et al. (2017) Transition of care from pre-dialysis prelude to renal replacement therapy: the blueprints of emerging research in advanced chronic kidney disease. Nephrol Dial Transplant 32:ii91-ii98
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Campos, Luis Fernando; ?entürk, Damla; Chen, Yanjun et al. (2017) Bias and estimation under misspecification of the risk period in self-controlled case series studies. Stat (Int Stat Inst) 6:373-389
Rhee, Connie M; You, Amy S; Nguyen, Danh V et al. (2017) Thyroid Status and Mortality in a Prospective Hemodialysis Cohort. J Clin Endocrinol Metab 102:1568-1577
Rhee, Connie M; Kovesdy, Csaba P; Ravel, Vanessa A et al. (2017) Association of Glycemic Status During Progression of Chronic Kidney Disease With Early Dialysis Mortality in Patients With Diabetes. Diabetes Care 40:1050-1057

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