Published in:
Open Access
01-12-2016 | Study protocol
Rationale and study design of the prospective, longitudinal, observational cohort study “rISk strAtification in end-stage renal disease” (ISAR) study
Authors:
Christoph Schmaderer, Susanne Tholen, Anna-Lena Hasenau, Christine Hauser, Yana Suttmann, Siegfried Wassertheurer, Christopher C. Mayer, Axel Bauer, Kostantinos D. Rizas, Stephan Kemmner, Konstantin Kotliar, Bernhard Haller, Johannes Mann, Lutz Renders, Uwe Heemann, Marcus Baumann
Published in:
BMC Nephrology
|
Issue 1/2016
Login to get access
Abstract
Background
The ISAR study is a prospective, longitudinal, observational cohort study to improve the cardiovascular risk stratification in endstage renal disease (ESRD). The major goal is to characterize the cardiovascular phenotype of the study subjects, namely alterations in micro- and macrocirculation and to determine autonomic function.
Methods/design
We intend to recruit 500 prevalent dialysis patients in 17 centers in Munich and the surrounding area. Baseline examinations include: (1) biochemistry, (2) 24-h Holter Electrocardiography (ECG) recordings, (3) 24-h ambulatory blood pressure measurement (ABPM), (4) 24 h pulse wave analysis (PWA) and pulse wave velocity (PWV), (5) retinal vessel analysis (RVA) and (6) neurocognitive testing. After 24 months biochemistry and determination of single PWA, single PWV and neurocognitive testing are repeated. Patients will be followed up to 6 years for (1) hospitalizations, (2) cardiovascular and (3) non-cardiovascular events and (4) cardiovascular and (5) all-cause mortality.
Discussion/conclusion
We aim to create a complex dataset to answer questions about the insufficiently understood pathophysiology leading to excessively high cardiovascular and non-cardiovascular mortality in dialysis patients. Finally we hope to improve cardiovascular risk stratification in comparison to the use of classical and non-classical (dialysis-associated) risk factors and other models of risk stratification in ESRD patients by building a multivariable Cox-Regression model using a combination of the parameters measured in the study.
Clinical trials identifier