Resting Heartbeat Complexity Predicts All‐Cause and Cardiorespiratory Mortality in Middle‐ to Older‐Aged Adults From the UK Biobank

Abstract

Background Spontaneous heart rate fluctuations contain rich information related to health and illness in terms of physiological complexity, an accepted indicator of plasticity and adaptability. However, it is challenging to make inferences on complexity from shorter, more practical epochs of data. Distribution entropy (DistEn) is a recently introduced complexity measure that is designed specifically for shorter duration heartbeat recordings. We hypothesized that reduced DistEn predicted increased mortality in a large population cohort. Method and Results The prognostic value of DistEn was examined in 7631 middle‐older–aged UK Biobank participants who had 2‐minute resting ECGs conducted (mean age, 59.5 years; 60.4% women). During a median follow‐up period of 7.8 years, 451 (5.9%) participants died. In Cox proportional hazards models with adjustment for demographics, lifestyle factors, physical activity, cardiovascular risks, and comorbidities, for each 1‐SD decrease in DistEn, the risk increased by 36%, 56%, and 73% for all‐cause, cardiovascular, and respiratory disease–related mortality, respectively. These effect sizes were equivalent to the risk of death from being >5 years older, having been a former smoker, or having diabetes mellitus. Lower DistEn was most predictive of death in those <55 years with a prior myocardial infarction, representing an additional 56% risk for mortality compared with older participants without prior myocardial infarction. These observations remained after controlling for traditional mortality predictors, resting heart rate, and heart rate variability. Conclusions Resting heartbeat complexity from short, resting ECGs was independently associated with mortality in middle‐ to older‐aged adults. These risks appear most pronounced in middle‐aged participants with prior MI, and may uniquely contribute to mortality risk screening.

An Interpretable Population Graph Network to Identify Rapid Progression of Alzheimer’s Disease Using UK Biobank

“…For a rigorous computation of the distance D and edge weight E i,j , we propose a novel approach utilizing the weighted sum of Average Jaccard Similarity Distance D AJS and Chebyshev Distance D Cheby to compute the distance between patients. Here, the D AJS can effectively measure the distance between categorical variables 24 , and the D Cheby can swiftly calculate the maximum distance between numerical variables 25 . We used their average value to calculate a distance for paired patients, the formulas are presented as follows: where C represents the total number of categorical variables, n is the numerical variables.…”

Heart rate response and recovery during exercise predict future delirium risk—A prospective cohort study in middle- to older-aged adults

“…To assess HR, normal R-waves were detected from ECG recordings using the modified Aristotle algorithm of QRS detection 23 , 24 and were consistent with our prior study. 25 Mean resting HR was determined from pre-exercise value, peak HR during exercise was the maximal rate achieved, and the recovery HR was determined at 1-min from cessation of exercise. In order to gauge a participant's HR responses from rest to peak and subsequent recovery, regardless of preset workload, we determined: (1) response ratio = peak HR achieved/resting HR, (2) recovery ratio = peak HR /recovered HR (lowest HR in final rest phase), and (3) their combined product, HRR index = response ratio × recovery ratio.…”

Associations of rest-activity rhythm disturbances with stroke risk and post-stroke adverse outcomes

“…BMI at initial enrollment was calculated as weight We also used a previously described morbidity burden based on the summed presence of any cancers, respiratory, neurological, gastrointestinal, renal, hematological, endocrine, musculoskeletal, connective tissue, and infectious diseases/disorders at the time of actigraphy. 23,[37][38][39]…”