Heart Rate Fragmentation as a Novel Biomarker of Adverse Cardiovascular Events: The Multi-Ethnic Study of Atherosclerosis

Costa, Marisa da; Redline, Susan; Davis, Roger B.; Heckbert, Susan R.; Soliman, Elsayed Z.; Goldberger, Ary L.

doi:10.3389/fphys.2018.01117

Cited by 34 publications

(56 citation statements)

References 45 publications

(77 reference statements)

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“…Accumulating evidence indicates that autonomic cardiac disfunction is implicated as a pathophysiological link between sleep disorders and their physiological consequences (25)(26)(27)(28). Polysomnogram HRV as a measure of cardiac autonomic modulation is a noninvasive, automatable biomarker of cardiovascular risk (29), and is independently associated with carotid atherosclerosis (30). The HRV metrics mostly correlate with clinical improvement (31).…”

Section: Discussionmentioning

confidence: 99%

Melatonin Therapy Improves Cardiac Autonomic Modulation in Pinealectomized Patients

Campos¹,

Bueno

Barcelos

et al. 2020

Front. Endocrinol.

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The purpose of this investigational study was to assess the effects of melatonin replacement therapy on cardiac autonomic modulation in pinealectomized patients. This was an open-label, single-arm, single-center, proof-of-concept study consisting of a screening period, a 3-month treatment period with melatonin (3 mg/day), and a 6-month washout period. The cardiac autonomic function was determined through heart rate variability (HRV) measures during polysomnography. Pinealectomized patients (n = 5) with confirmed absence of melatonin were included in this study. Melatonin treatment increased vagal-dominated HRV indices including root mean square of the successive R-R interval differences (RMSSD) (39.7 ms, 95% CI 2.0-77.4, p = 0.04), percentage of successive R-R intervals that differ by more than 50 ms (pNN50) (17.1%, 95% CI 9.1-25.1, p = 0.003), absolute power of the high-frequency band (HF power) (1,390 ms 2 , 95% CI 511.9-2,267, p = 0.01), and sympathetic HRV indices like standard deviation of normal R-R wave interval (SDNN) (57.6 ms, 95% CI 15.2-100.0, p = 0.02), and absolute power of the low-frequency band (LF power) (4,592 ms 2 , 95% CI 895.6-8,288, p = 0.03). These HRV indices returned to pretreatment values when melatonin treatment was discontinued. The HRV entropy-based regularity parameters were not altered in this study, suggesting that there were no significant alterations of the REM-NREM ratios between the time stages of the study. These data show that 3 months of melatonin treatment may induce an improvement in cardiac autonomic modulation in melatonin-non-proficient patients. ClinicalTrials.gov Identifier: NCT03885258.

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Section: Discussionmentioning

confidence: 99%

Melatonin Therapy Improves Cardiac Autonomic Modulation in Pinealectomized Patients

Campos¹,

Bueno

Barcelos

et al. 2020

Front. Endocrinol.

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“… 15 Our group has shown that DFA is an efficient detector of ectopy, and was useful as part of a strategy for atrial fibrillation detection. 12 The DFA slope was calculated every 10 minutes over box sizes 4 to 12 (consistent with Costa and Goldberger 6 , 7 , 9 ) and averaged over the 24-hour period.…”

Section: Methodsmentioning

confidence: 99%

“…Semi-quantitative analyses of erratic sinus rhythm have pointed to an adverse clinical effect of these fluctuations, and the metrics of Costa and Goldberger indeed are associated with worse prognosis in the MESA study. 9 The new measures number four: the percentage of inflection points (PIP), or points at which the first difference changes sign, the percentage of short segments (PSS), the inverse of the average segment length (IALS) and the percentage of alternating segments (PAS). For each, a larger value denotes a more fragmented heart rate structure.…”

Section: Introductionmentioning

confidence: 99%

Heart rate fragmentation gives novel insights into non-autonomic mechanisms governing beat-to-beat control of the heart’s rhythm

Lensen

Monfredi

Andris

et al. 2020

JRSM Cardiovascular Disease

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Objectives To demonstrate how heart rate fragmentation gives novel insights into non-autonomic mechanisms of beat-to-beat variability in cycle length, and predicts survival of cardiology clinic patients, over and above traditional clinical risk factors and measures of heart rate variability. Approach: We studied 2893 patients seen by cardiologists with clinical data including 24-hour Holter monitoring. Novel measures of heart rate fragmentation alongside canonical time and frequency domain measures of heart rate variability, as well as an existing local dynamics score were calculated. A proportional hazards model was utilized to relate the results to survival. Main results: The novel heart rate fragmentation measures were validated and characterized with respect to the effects of age, ectopy and atrial fibrillation. Correlations between parameters were determined. Critically, heart rate fragmentation results could not be accounted for by undersampling respiratory sinus arrhythmia. Increased heart rate fragmentation was associated with poorer survival (p ≪ 0.01 in the univariate model). In multivariable analyses, increased heart rate fragmentation and more abnormal local dynamics (p 0.045), along with increased clinical risk factors (age (p ≪ 0.01), tobacco use (p ≪ 0.01) and history of heart failure (p 0.019)) and lower low- to high-frequency ratio (p 0.022) were all independent predictors of 2-year mortality. Significance: Analysis of continuous ECG data with heart rate fragmentation indices yields information regarding non-autonomic control of beat-to-beat variability in cycle length that is independent of and additive to established parameters for investigating heart rate variability, and predicts mortality in concert with measures of local dynamics, frequency content of heart rate, and clinical risk factors.

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“…It is worth noting that the fragmentation features (PIP and IALS) were useful in this study. This could be correlated to the fact that has been found in [48] that suggests that any decrease in the fluctuations of these features are hardly to be detected in a recorded ECG data. In addition, a paradoxical increase in these fluctuations comes as a results of a reduced vagal tone that would suggest the occurrence of heart diseases [49].…”

Section: Clinical Relevancementioning

confidence: 81%

Predicting Hypertensive Patients With Higher Risk of Developing Vascular Events Using Heart Rate Variability and Machine Learning

et al. 2020

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The prognosis of cardiovascular and cerebrovascular events for patients suffering from hypertension is considered of a high importance in preventing any further development of cardiac diseases. Despite of the ability of current gold standard techniques in predicting vascular events risks, they still lack the required clinical efficiency. In this vein, the study proposed herein provides an investigation on the feasibility of using heart rate variability (HRV) utilized through a machine learning approach to predict hypertensive patients at higher risk of developing vascular events. Initially, HRV features were extracted from all patient's data using time-domain, frequency-domain, non-linear, and fragmentation metrics. The extraction of features was based on a 24-hour cycle analysis segmented into four time periods; namely late-night, early-morning, afternoon, and evening. Analysis of all features was performed using a one-way analysis of variance (ANOVA) test on period by period basis. Furthermore, the selection of best features was performed following a Chi-squared test for demographic and HRV features. Then, a model based on decision trees and random under-sampling boosting (RUSBOOST) was trained using demographic features, HRV features, and a combination of both features. The performance of the trained model achieved a maximum accuracy of 97.08% using the combined set of features during the afternoon time period. In addition, the precision and F1-score in predicting high risk patients reached 81.25% and 86.67%, respectively. The overall area under the curve for the model was at 0.98, suggesting a high performance in the sensitivity and specificity measures. This study paves the way towards utilizing machine learning models and heart rate variability for the prognosis of vascular events in hypertensive patient. Furthermore, it assists clinicians in decision making by providing a simple, yet effective, and continuous prediction approach when compared to other available techniques. INDEX TERMS hypertension, cardiovascular events, cerebrovascular events, heart rate variability, ANOVA, machine learning, feature selection, RUSBOOST

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Heart Rate Fragmentation as a Novel Biomarker of Adverse Cardiovascular Events: The Multi-Ethnic Study of Atherosclerosis

Cited by 34 publications

References 45 publications

Melatonin Therapy Improves Cardiac Autonomic Modulation in Pinealectomized Patients

Melatonin Therapy Improves Cardiac Autonomic Modulation in Pinealectomized Patients

Heart rate fragmentation gives novel insights into non-autonomic mechanisms governing beat-to-beat control of the heart’s rhythm

Predicting Hypertensive Patients With Higher Risk of Developing Vascular Events Using Heart Rate Variability and Machine Learning

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