SummaryBackground and objectives Nonlinear measures of heart rate variability (HRV) have gained recent interest as powerful risk predictors in various clinical settings. This study examined whether they improve risk stratification in hemodialysis patients.Design, setting, participants, & measurements To assess heart rate turbulence, deceleration capacity, fractal scaling exponent (a 1 ), and other conventional HRV measures, 281 hemodialysis patients underwent 24-hour electrocardiography between January 2002 and May 2004 and were subsequently followed up.Results During a median 87-month follow-up, 77 patients (27%) died. Age, left ventricular ejection fraction, serum albumin, C-reactive protein, and calcium 3 phosphate independently predicted mortality. Whereas all nonlinear HRV measures predicted mortality, only decreased scaling exponent a 1 remained significant after adjusting for clinical risk factors (hazard ratio per a 0.25 decrement, 1.46; 95% confidence interval [95% CI], 1.16-1.85). The inclusion of a 1 into a prediction model composed of clinical risk factors increased the C statistic from 0.84 to 0.87 (P=0.03), with 50.8% (95% CI, 20.2-83.7) continuous net reclassification improvement for 5-year mortality. The predictive power of a 1 showed an interaction with age (P=0.02) and was particularly strong in patients aged ,70 years (n=208; hazard ratio, 1.87; 95% CI, 1.38-2.53), among whom a 1 increased the C statistic from 0.85 to 0.89 (P=0.01), with a 93.1% (95% CI, 59.3-142.0) continuous net reclassification improvement.Conclusions Scaling exponent a 1 that reflects fractal organization of short-term HRV improves risk stratification for mortality when added to the prediction model by conventional risk factors in hemodialysis patients, particularly those aged ,70 years.
BackgroundHeart rate variability (HRV) and heart rate (HR) dynamics are used to predict the survival probability of patients after acute myocardial infarction (AMI), but the association has been established in patients with mixed levels of left ventricular ejection fraction (LVEF).ObjectiveWe investigated whether the survival predictors of HRV and HR dynamics depend on LVEF after AMI.MethodsWe studied 687 post-AMI patients including 147 with LVEF ≤35% and 540 with LVEF >35%, of which 23 (16%) and 22 (4%) died during the 25 month follow-up period, respectively. None had an implanted cardioverter-defibrillator. From baseline 24 h ECG, the standard deviation (SDNN), root mean square of successive difference (rMSSD), percentage of successive difference >50 ms (pNN50) of normal-to-normal R-R interval, ultra-low (ULF), very-low (VLF), low (LF), and high (HF) frequency power, deceleration capacity (DC), short-term scaling exponent (α1), non-Gaussianity index (λ25s), and the amplitude of cyclic variation of HR (Acv) were calculated.ResultsThe predictors were categorized into three clusters; DC, SDNN, α1, ULF, VLF, LF, and Acv as Cluster 1, λ25s independently as Cluster 2, and rMSSD, pNN50, and HF as Cluster 3. In univariate analyses, mortality was best predicted by indices belonging to Cluster 1 regardless of LVEF. In multivariate analyses, however, mortality in patients with low LVEF was best predicted by the combinations of Cluster 1 predictors or Cluster 1 and 3 predictors, whereas in patients without low LVEF, it was best predicted by the combinations of Cluster 1 and 2 predictors.ConclusionThe mortality risk in post-AMI patients with low LVEF is predicted by indices reflecting decreased HRV or HR responsiveness and cardiac parasympathetic dysfunction, whereas in patients without low LVEF, the risk is predicted by a combination of indices that reflect decreased HRV or HR responsiveness and indicator that reflects abrupt large HR changes suggesting sympathetic involvement.
Heart rate fragmentation (HRF) is a type of sinoatrial instability characterized by frequent (often every beat) appearance of inflection in the R-R interval time series, despite the electrocardiograms appearing to be sinus rhythm. Because the assessment of parasympathetic function by heart rate variability (HRV) analysis depends on the assumption that the high-frequency component (HF, 0.15–0.4 Hz) of HRV is mediated solely by the cardiac parasympathetic nerve, HRF that is measured as a part of HF power confounds the parasympathetic functional assessment by HRV. In this study, we analyzed HRF in a 24-h electrocardiogram big data and investigated the changes in HRF with age and sex and its influence on the assessment of HRV. We observed that HRF is often observed during childhoods (0–20 year) and increased after 75 year, but it has a large impact on individual differences in HF power at ages 60–90.
Objectives: Senility death is defined as natural death in the elderly who do not have a cause of death to be described otherwise and, if human life is finite, it may be one of the ultimate goals of medicine and healthcare. A recent survey in Japan reports that municipalities with a high senility death ratio have lower healthcare costs per late-elderly person. However, the causes of regional differences in senility death ratio and their biomedical determinants were unknown. In this study, we examined the relationships of the regional difference in senility death ratio with the regional differences in heart rate variability and physical activity. Methods: We compared the age-adjusted senility death ratio of all Japanese prefectures with the regional averages of heart rate variability and actigraphic physical activity obtained from a physiological big data of Allostatic State Mapping by Ambulatory ECG Repository (ALLSTAR). Results: The age-adjusted senility death ratio of 47 Japanese prefectures in 2015 ranged from 1.2% to 3.6% in men and from 3.5% to 7.8% in women. We compared these ratios with the age-adjusted indices of heart rate variability in 108,865 men and 136,536 women and of physical activity level in 16,661 men and 21,961 women. Heart rate variability indices and physical activity levels that are known to be associated with low mortality risk were higher in prefectures with higher senility death ratio. Conclusion: The regional senility death ratio in Japan may be associated with regional health status as reflected in heart rate variability and physical activity levels.
Background Many indices of heart rate variability (HRV) and heart rate dynamics have been proposed as cardiovascular mortality risk predictors, but the redundancy between their predictive powers is unknown. Methods From the Allostatic State Mapping by Ambulatory ECG Repository project database, 24‐hr ECG data showing continuous sinus rhythm were extracted and SD of normal‐to‐normal R‐R interval (SDNN), very‐low‐frequency power (VLF), scaling exponent α1, deceleration capacity (DC), and non‐Gaussianity λ25s were calculated. The values were dichotomized into high‐risk and low‐risk values using the cutoffs reported in previous studies to predict mortality after acute myocardial infarction. The rate of multiple high‐risk predictors accumulating in the same person was examined and was compared with the rate expected under the assumption that these predictors are independent of each other. Results Among 265,291 ECG data from the ALLSTAR database, the rates of subjects with high‐risk SDNN, DC, VLF, α1, and λ25s values were 2.95, 2.75, 5.89, 15.75, and 18.82%, respectively. The observed rate of subjects without any high‐risk value was 66.68%, which was 1.10 times the expected rate (60.74%). The ratios of observed rate to the expected rate at which one, two, three, four, and five high‐risk values accumulate in the same person were 0.73 times (24.10 and 32.82%), 1.10 times (6.56 and 5.99%), 4.26 times (1.87 and 0.44%), 47.66 times (0.63 and 0.013%), and 1,140.66 times (0.16 and 0.00014%), respectively. Conclusions High‐risk predictors of HRV and heart rate dynamics tend to cluster in the same person, indicating a high degree of redundancy between them.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.