Rationale and study design of the prospective, longitudinal, observational cohort study "rISk strAtification in end-stage renal disease" (ISAR) study.

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: ClinicalTrials.gov NCT01152892 (June 28, 2010).

Implementation and verification of an enhanced algorithm for the automatic computation of RR-interval series derived from 24 h 12-lead ECGs.

An important tool in early diagnosis of cardiac dysfunctions is the analysis of electrocardiograms (ECGs) obtained from ambulatory long-term recordings. Heart rate variability (HRV) analysis became a significant tool for assessing the cardiac health. The usefulness of HRV assessment for the prediction of cardiovascular events in end-stage renal disease patients was previously reported. The aim of this work is to verify an enhanced algorithm to obtain an RR-interval time series in a fully automated manner. The multi-lead corrected R-peaks of each ECG lead are used for RR-series computation and the algorithm is verified by a comparison with manually reviewed reference RR-time series. Twenty-four hour 12-lead ECG recordings of 339 end-stage renal disease patients from the ISAR (rISk strAtification in end-stage Renal disease) study were used. Seven universal indicators were calculated to allow for a generalization of the comparison results. The median score of the indicator of synchronization, i.e. intraclass correlation coefficient, was 96.4% and the median of the root mean square error of the difference time series was 7.5 ms. The negligible error and high synchronization rate indicate high similarity and verified the agreement between the fully automated RR-interval series calculated with the AIT Multi-Lead ECGsolver and the reference time series. As a future perspective, HRV parameters calculated on this RR-time series can be evaluated in longitudinal studies to ensure clinical benefit.