Background Evidence on the utility of ambulatory BP monitoring for risk prediction has been scarce and inconclusive in patients on hemodialysis. In addition, in cardiac diseases such as heart failure and atrial fibrillation (common among patients on hemodialysis), studies have found that parameters such as systolic BP (SBP) and pulse pressure (PP) have inverse or nonlinear (U-shaped) associations with mortality. Methods In total, 344 patients on hemodialysis (105 with atrial fibrillation, heart failure, or both) underwent ambulatory BP monitoring for 24 hours, starting before a dialysis session. The primary end point was allcause mortality; the prespecified secondary end point was cardiovascular mortality. We performed linear and nonlinear Cox regression analyses for risk prediction to determine the associations between BP and study end points. Results During the mean 37.6-month follow-up, 115 patients died (47 from a cardiovascular cause). SBP and PP showed a U-shaped association with all-cause and cardiovascular mortality in the cohort. In linear subgroup analysis, SBP and PP were independent risk predictors and showed a significant inverse relationship to all-cause and cardiovascular mortality in patients with atrial fibrillation or heart failure. In patients without these conditions, these associations were in the opposite direction. SBP and PP were significant independent risk predictors for cardiovascular mortality; PP was a significant independent risk predictor for all-cause mortality. Conclusions This study provides evidence for the U-shaped association between peripheral ambulatory SBP or PP and mortality in patients on hemodialysis. Furthermore, it suggests that underlying cardiac disease can explain the opposite direction of associations.

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.

Background: Excess mortality in hemodialysis patients is mostly of cardiovascular origin. We examined the association of heart rate turbulence (HRT), a marker of baroreflex sensitivity, with cardiovascular mortality in hemodialysis patients. Methods: A population of 290 prevalent hemodialysis patients was followed up for a median of 3 years. HRT categories 0 (both turbulence onset [TO] and slope [TS] normal), 1 (TO or TS abnormal), and 2 (both TO and TS abnormal) were obtained from 24 h Holter recordings. The primary end-point was cardiovascular mortality. Associations of HRT categories with the endpoints were analyzed by multivariable Cox regression models including HRT, age, albumin, and the improved Charlson Comorbidity Index for hemodialysis patients. Multivariable linear regression analysis identified factors associated with TO and TS. Results: During the follow-up period, 20 patients died from cardiovascular causes. In patients with HRT categories 0, 1 and 2, cardiovascular mortality was 1, 10, and 22%, respectively. HRT category 2 showed the strongest independent association with cardiovascular mortality with a hazard ratio of 19.3 (95% confidence interval: 3.69-92.03; P < 0.001). Age, calcium phosphate product, and smoking status were associated with TO and TS. Diabetes mellitus and diastolic blood pressure were only associated with TS.

Heart Rate Variability (HRV), i. e., the variation of time intervals between consecutive heart beats, is a marker of the health status, since it unveils changes in beat-to-beat variation of the heart, even before there is a remarkable change in heart rate itself. HRV reflects the balance between the sympathetic and the parasympathetic nervous system. The heart rate itself is nonstationary and the structure generating the signal involves nonlinear contributions. Thus, nonlinear methods to quantify the variability of the heart rate gained interest over the last years. In this work, two nonlinear indices, i. e., Correlation Dimension (CD) and Fractal Dimension (FD), to quantify HRV derived from mathematical models are presented. The implemented methods are tested on their ability to differentiate between healthy and pathological subjects. The databases used for the test are retrieved from PhysioNet. The results show that the FD is able to differentiate between nonpathological and pathological subjects, while the other implemented method, i. e., CD, shows no significant difference. In summary, this paper shows that fractal descriptors are an appropriate support for analyzing the HRV, and therefore help to prevent or detect cardiovascular diseases. Especially Higuchi's Fractal Dimension, well established in the analysis of time series, should get more attention in analyzing biomedical signals, such as HRV.

Heart rate variability (HRV) analysis is a non-invasive tool for assessing cardiac health. Entropy measures quantify the chaotic properties of HRV, but they are sensitive to the choice of their required parameters. Previous studies therefore have performed parameter optimization, targeting solely their particular patient cohort. In contrast, this work aimed to challenge entropy measures with recently published parameter sets, without time-consuming optimization, for risk prediction in end-stage renal disease patients. Approximate entropy, sample entropy, fuzzy entropy, fuzzy measure entropy, and corrected approximate entropy were examined. In total, 265 hemodialysis patients from the ISAR (rISk strAtification in end-stage Renal disease) study were analyzed. Throughout a median follow-up time of 43 months, 70 patients died. Fuzzy entropy and corrected approximate entropy (CApEn) provided significant hazard ratios, which remained significant after adjustment for clinical risk factors from literature if an entropy maximizing threshold parameter was chosen. Revealing results were seen in the subgroup of patients with heart disease (HD) when setting the radius to a multiple of the data's standard deviation (r = 0.2 · σ); all entropies, except CApEn, predicted mortality significantly and remained significant after adjustment. Therefore, these two parameter settings seem to reflect different cardiac properties. This work shows the potential of entropy measures for cardiovascular risk stratification in cohorts the parameters were not optimized for, and it provides additional insights into the parameter choice.