Cited by 489Heart Rate Variability and Autonomic ControlECG Monitoring and AnalysisCardiac electrophysiology and arrhythmias

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Predicting Survival in Heart Failure Case and Control Subjects by Use of Fully Automated Methods for Deriving Nonlinear and Conventional Indices of Heart Rate Dynamics

Ho, Kalon K. L., Moody, George B., Peng, Chung-Kang, Mietus, Joseph E., Larson, Martin G., Levy, Daniel, Goldberger, Ary L.·1997·Circulation

DOI 10.1161/01.cir.96.3.842

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Abstract

Background Despite much recent interest in quantification of heart rate variability (HRV), the prognostic value of conventional measures of HRV and of newer indices based on nonlinear dynamics is not universally accepted. Methods and Results We have designed algorithms for analyzing ambulatory ECG recordings and measuring HRV without human intervention, using robust methods for obtaining time-domain measures (mean and SD of heart rate), frequency-domain measures (power in the bands of 0.001 to 0.01 Hz [VLF], 0.01 to 0.15 Hz [LF], and 0.15 to 0.5 Hz [HF] and total spectral power [TP] over all three of these bands), and measures based on nonlinear dynamics (approximate entropy [ApEn], a measure of complexity, and detrended fluctuation analysis [DFA], a measure of long-term correlations). The study population consisted of chronic congestive heart failure (CHF) case patients and sex- and age-matched control subjects in the Framingham Heart Study. After exclusion of technically inadequate studies and those with atrial fibrillation, we used these algorithms to study HRV in 2-hour ambulatory ECG recordings of 69 participants (mean age, 71.7±8.1 years). By use of separate Cox proportional-hazards models, the conventional measures SD ( P <.01), LF ( P <.01), VLF ( P <.05), and TP ( P <.01) and the nonlinear measure DFA ( P <.05) were predictors of survival over a mean follow-up period of 1.9 years; other measures, including ApEn ( P >.3), were not. In multivariable models, DFA was of borderline predictive significance ( P =.06) after adjustment for the diagnosis of CHF and SD. Conclusions These results demonstrate that HRV analysis of ambulatory ECG recordings based on fully automated methods can have prognostic value in a population-based study and that nonlinear HRV indices may contribute prognostic value to complement traditional HRV measures.

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Heart Rate Variability: Standards of Measurement, Physiological Interpretation, and Clinical Use
Electrophysiology, T. F. O. T. E. S. O. C. T. N. A. (1996) · Circulation · doi:10.1161/01.cir.93.5.1043 · Cited by 17,159
Heart rate variability: Standards of measurement, physiological interpretation, and clinical use
Malik, M., Bigger, J. T., Camm, A. J., Kleiger, R. E., Malliani, A., Moss, A. J., & Schwartz, P. J. (1996) · European Heart Journal · doi:10.1093/oxfordjournals.eurheartj.a014868 · Cited by 15,289
Analysis of Survival Data
Cox, D. R., & Oakes, D. (2018) · Chapman & Hall/CRC eBooks · doi:10.1201/9781315137438 · Cited by 6,269
Decreased heart rate variability and its association with increased mortality after acute myocardial infarction
Kleiger, R. E., Miller, J. P., Bigger, J. T., & Moss, A. J. (1987) · The American Journal of Cardiology · doi:10.1016/0002-9149(87)90795-8 · Cited by 3,995
Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series
Peng, C., Havlin, S., Stanley, E., & Goldberger, A. L. (1995) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.166141 · Cited by 3,865
Self-citation
The natural history of congestive heart failure: the Framingham study
McKee, P. A., Castelli, W. P., McNamara, P. M., & Kannel, W. B. (1971) · New England Journal of Medicine · doi:10.1056/nejm197112232852601 · Cited by 3,448
Assessment of autonomic function in humans by heart rate spectral analysis
Pomeranz, B., Macaulay, R. J., Caudill, M. A., Kutz, I., Adam, D., Gordon, D., Kilborn, K. M., Barger, A. C., Shannon, D. C., Cohen, R. J., et al. (1985) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.1985.248.1.h151 · Cited by 2,876
Epidemiological approaches to heart disease: the Framingham study
Dawber, T. R., Meadors, G. F., & Moore, F. E. (1951) · American Journal of Public Health and the Nations Health · doi:10.2105/ajph.41.3.279 · Cited by 2,148
Survival after the onset of congestive heart failure in Framingham Heart Study subjects.
Ho, K. K. L., Anderson, K. M., Kannel, W. B., Grossman, W., & Levy, D. A. (1993) · Circulation · doi:10.1161/01.cir.88.1.107 · Cited by 1,865
Self-citation
Frequency domain measures of heart period variability and mortality after myocardial infarction
Bigger, J. T., Fleiss, J. L., Steinman, R. C., Rolnitzky, L. M., Kleiger, R. E., & Rottman, J. N. (1992) · Circulation · doi:10.1161/01.cir.85.1.164 · Cited by 1,712

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10.1161/circ.90.2.8044959 · DOI
10.1161/circ.93.5.1043 · DOI
Moody GB Mark RG. Development and evaluation of a 2-lead ECG analysis program. Computers in Cardiology 1982. Los Alamitos Calif: IEEE Computer Society Press; 1983:33-38.
10.1161/circ.81.6.2344676 · DOI
The REG Procedure. SAS/STAT→ User’s Guide Version 6 Fourth Edition Volume 2. Cary NC: SAS Institute Inc; 1989:1351-1456.
10.1161/circ.81.4.2317904 · DOI
10.1161/circ.85.1.1728446 · DOI
10.1161/circ.88.1.8319323 · DOI

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Self-citation
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Self-citation
Determinants and interindividual variation of R-R interval dynamics in healthy middle-aged subjects
Pikkujämsä, S. M., Mäkikallio, T. H., Airaksinen, J. K. E., & Huikuri, H. V. (2001) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.2001.280.3.h1400 · Cited by 85
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Authors: Ho, K. K. L., Moody, G. B., Peng, C.-K., Mietus, J. E., Larson, M. G., Levy, D., & Goldberger, A. L.
Affiliations: Beth Israel Deaconess Medical Center (US)
ORCID: Ho, Kalon K. L.
Moody, George B.
Peng, Chung-Kang
Larson, Martin G.
Levy, Daniel
Venue: Circulation
Publisher: Ovid Technologies (Wolters Kluwer Health)
Published: 1997-08-05
Volume: 96
Issue: 3
Pages: 842-848
ISSN: 0009-7322 (ISSN-L)
1524-4539
DOI: 10.1161/01.cir.96.3.842
OpenAlex: W2006524496
Cited by: 489
References: 26
Citations by year: 2026: 2 · 2025: 5 · 2024: 3 · 2023: 9 · 2022: 6 · 2021: 12 · 2020: 24 · 2019: 19 · 2018: 17 · 2017: 17 · 2016: 18 · 2015: 18 · 2014: 13 · 2013: 21 · 2012: 26
FWCI: 9.37
Type: journal article
Language: en
Open access: closed
Locations: Circulation (publishedVersion)
PubMed (publishedVersion)
Funders: National Institutes of Health (5r29mh054081-03, n01hc038038-022)
National Heart, Lung, and Blood Institute (N01-HC-38038)
National Institute of Mental Health (MH-54081)
Topics: Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac electrophysiology and arrhythmias
Keywords: heart rate variability, congestive heart failure, survival prediction, prognostic factors, nonlinear HRV indices, detrended fluctuation analysis, approximate entropy, autonomic nervous system, Holter monitoring, frequency-domain HRV measures
MeSH terms: Aged, Aged, 80 and over, Algorithms, Automation, Feasibility Studies, Female, Forecasting, Heart Failure, Heart Rate, Humans, Male, Middle Aged, Models, Cardiovascular, Reference Values, Electrocardiography, Ambulatory, Survival Analysis, Nonlinear Dynamics, Entropy

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Predicting Survival in Heart Failure Case and Control Subjects by Use of Fully Automated Methods for Deriving Nonlinear and Conventional Indices of Heart Rate Dynamics

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