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.
Keywords: ALLSTAR; big data; heart rate variability; mortality; redundancy; relationship mapping.
© 2020 The Authors. Annals of Noninvasive Electrocardiology published by Wiley Periodicals LLC.