Redundancy among risk predictors derived from heart rate variability and dynamics: ALLSTAR big data analysis

Abstract

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.

Age and gender differences in objective sleep properties using large-scale body acceleration data in a Japanese population

“…Our study has several limitations. One is selection bias, since almost all data stored in the Holter database were collected from subjects who were suspected of having some form of cardiovascular disease 21 , such as arrhythmia. Furthermore, the study may have included subjects with conditions that severely disturb sleep, such as depression, insomnia, and sleep apnea.…”

“…We used a database of 24-h electrocardiography (ECG) and tri-axial acceleration data constructed by the ALLSTAR research project 18 – 21 to examine age- and gender-related variations in parameters of habitual sleep in a Japanese population. The database of the ALLSTAR research project has been explained in detail elsewhere 18 – 21 . Briefly, the project was started in 2010, with the cooperation of Suzuken Co., Ltd (Nagoya, Japan), the owner of the data, and researchers from seven universities across Japan (including J.H.…”

“…Recently, we gathered large-scale 24-h body acceleration data recorded from > 80,000 individuals residing in Japan, including those recorded during sleep in real-life settings 18 – 21 . Since recordings were continued even during sleep, an analysis of the database provided an opportunity to conduct sleep studies using objective measures based on body acceleration (i.e., actigraphy) in a Japanese population.…”

Risk stratification after acute myocardial infarction by amplitude–frequency mapping of cyclic variation of heart rate

“…To analyze the relationship between Acv and Fcv, we used big data of 24‐hr ECG from the Allostatic State Mapping by Ambulatory ECG Repository (ALLSTAR) project (Hayano et al., 2020; Yuda et al., 2021). The ALLSTAR database contained 430,169 Holter ECG data recorded from patients throughout Japan between November 2007 and March 2014.…”

“…ToanalyzetherelationshipbetweenAcvandFcv,weusedbigdata of 24-hr ECG from the Allostatic State Mapping by Ambulatory ECG Repository (ALLSTAR) project (Hayano et al, 2020;Yuda et al, 2021 utilized in this project were made publicly available through the project's homepages (http://www.suzuk en.co.jp/produ ct/holte r/ detai l/ and http://www.med.nagoy a-cu.ac.jp/mededu.dir/allst ar/), in which opportunities to refuse the use of personal data were ensured for all research participants.…”