Survival predictors of heart rate variability after myocardial infarction with and without low left ventricular ejection fraction

Background: Heart 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). Objective: We investigated whether the survival predictors of HRV and HR dynamics depend on LVEF after AMI. Methods: We studied 687 post-AMI patients including 147 with LVEF ≤35% and 540 with LVEF \u3e35%, 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 \u3e50 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 (α Results: The predictors were categorized into three clusters; DC, SDNN, α Conclusion: The 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

Association of heart rate variability with regional difference in senility death ratio: ALLSTAR big data analysis

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

Non-REM Sleep Marker for Wearable Monitoring: Power Concentration of Respiratory Heart Rate Fluctuation

A variety of heart rate variability (HRV) indices have been reported to estimate sleep stages, but the associations are modest and lacking solid physiological basis. Non-REM (NREM) sleep is associated with increased regularity of respiratory frequency, which results in the concentration of high frequency (HF) HRV power into a narrow frequency range. Using this physiological feature, we developed a new HRV sleep index named Hsi to quantify the degree of HF power concentration. We analyzed 11,636 consecutive 5-min segments of electrocardiographic (ECG) signal of polysomnographic data in 141 subjects and calculated Hsi and conventional HRV indices for each segment. Hsi was greater during NREM (mean [SD], 75.1 [8.3]%) than wake (61.0 [10.3]%) and REM (62.0 [8.4]%) stages. Receiver-operating characteristic curve analysis revealed that Hsi discriminated NREM from wake and REM segments with an area under the curve of 0.86, which was greater than those of heart rate (0.642), peak HF power (0.75), low-to-high frequency ratio (0.77), and scaling exponent α (0.77). With a cutoff >70%, Hsi detected NREM segments with 77% sensitivity, 80% specificity, and a Cohen’s kappa coefficient of 0.57. Hsi may provide an accurate NREM sleep maker for ECG and pulse wave signals obtained from wearable sensors

Identification of a small thrombus in the left ventricle identified on iodine maps derived from dual-source photon-counting detector CT

Transthoracic echocardiography is the main imaging modality to diagnose left ventricular thrombus (LVT), but its efficacy in certain cases is suboptimal. We report a patient in whom an LVT, initially unidentified by transthoracic echocardiography, was successfully diagnosed with iodine maps derived from dual-source photon-counting detector CT (DS-PCD-CT). The 64-year-old male was admitted to our institution following myocardial infarction. Although TTE failed to detect this small LVT, iodine maps derived from CT angiography (which was conducted to evaluate the coronary artery stenosis) revealed its presence. Iodine maps derived from DS-PCD-CT collecting data with high temporal resolution are beneficial to diagnose LVTs