Autonomic response to cardiac dysfunction in chronic heart failure

A risk predictor based on autonomic information flow

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

Background: Chronic heart failure (CHF) is associated with a complex dysfunction of cardiac, cardiovascular, autonomic, and other mechanisms. Autonomic information flow (AIF) characteristics calculated from heart rate patterns were recently found as promising predictors of outcome in several cardiovascular diseases. Aim: To assess the prognostic value of AIF indices in CHF patients. Methods: We analyzed 24-hour Holter recordings from 200 consecutive CHF patients in sinus rhythm and computed AIF over the shortest possible interval of an interbeat series, namely over one heart beat interval (BDnn), and over longer intervals (12.5-166.7 seconds, PDmVLF), which reflect slower heart rate modulations. End-point for survival analysis over three years (Cox model) was total cardiac death. A prognostic model was built (backward elimination) considering known clinical and functional risk factors, and the ability of AIF indices to add prognostic information to this model assessed. Results: Out of candidate predictors, New York Heart Association class, left ventricular ejection fraction, peak VO2, and systolic pressure were selected as the variables with the highest joint predictive value. When entered into this model, both BDnn and PDmVLF added prognostic information (HR (95%CI): 1.76 (1.00-3.09), P = 0.05, 1.73 (1.05-2.85), P = 0.031 respectively). High risk was associated with reduced fast AIF and increased slower AIF. Conclusion: In CHF patients, AIF indices provide prognostic information independent of known risk factors.

Original languageEnglish
Pages (from-to)214-220
Number of pages7
JournalPACE - Pacing and Clinical Electrophysiology
Volume31
Issue number2
DOIs
Publication statusPublished - Feb 2008

Fingerprint

Heart Failure
Heart Rate
Survival Analysis
Proportional Hazards Models
Stroke Volume
Cardiovascular Diseases
Joints
Blood Pressure

Keywords

  • Autonomic information flow
  • Autonomic nervous system
  • Cardiovascular control
  • Chronic heart failure
  • Heart rate variability

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine

Cite this

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title = "Autonomic response to cardiac dysfunction in chronic heart failure: A risk predictor based on autonomic information flow",
abstract = "Background: Chronic heart failure (CHF) is associated with a complex dysfunction of cardiac, cardiovascular, autonomic, and other mechanisms. Autonomic information flow (AIF) characteristics calculated from heart rate patterns were recently found as promising predictors of outcome in several cardiovascular diseases. Aim: To assess the prognostic value of AIF indices in CHF patients. Methods: We analyzed 24-hour Holter recordings from 200 consecutive CHF patients in sinus rhythm and computed AIF over the shortest possible interval of an interbeat series, namely over one heart beat interval (BDnn), and over longer intervals (12.5-166.7 seconds, PDmVLF), which reflect slower heart rate modulations. End-point for survival analysis over three years (Cox model) was total cardiac death. A prognostic model was built (backward elimination) considering known clinical and functional risk factors, and the ability of AIF indices to add prognostic information to this model assessed. Results: Out of candidate predictors, New York Heart Association class, left ventricular ejection fraction, peak VO2, and systolic pressure were selected as the variables with the highest joint predictive value. When entered into this model, both BDnn and PDmVLF added prognostic information (HR (95{\%}CI): 1.76 (1.00-3.09), P = 0.05, 1.73 (1.05-2.85), P = 0.031 respectively). High risk was associated with reduced fast AIF and increased slower AIF. Conclusion: In CHF patients, AIF indices provide prognostic information independent of known risk factors.",
keywords = "Autonomic information flow, Autonomic nervous system, Cardiovascular control, Chronic heart failure, Heart rate variability",
author = "Dirk Hoyer and Roberto Maestri and {Teresa La Rovere}, Maria and {Domenico Pinna}, Gian",
year = "2008",
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doi = "10.1111/j.1540-8159.2007.00971.x",
language = "English",
volume = "31",
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T1 - Autonomic response to cardiac dysfunction in chronic heart failure

T2 - A risk predictor based on autonomic information flow

AU - Hoyer, Dirk

AU - Maestri, Roberto

AU - Teresa La Rovere, Maria

AU - Domenico Pinna, Gian

PY - 2008/2

Y1 - 2008/2

N2 - Background: Chronic heart failure (CHF) is associated with a complex dysfunction of cardiac, cardiovascular, autonomic, and other mechanisms. Autonomic information flow (AIF) characteristics calculated from heart rate patterns were recently found as promising predictors of outcome in several cardiovascular diseases. Aim: To assess the prognostic value of AIF indices in CHF patients. Methods: We analyzed 24-hour Holter recordings from 200 consecutive CHF patients in sinus rhythm and computed AIF over the shortest possible interval of an interbeat series, namely over one heart beat interval (BDnn), and over longer intervals (12.5-166.7 seconds, PDmVLF), which reflect slower heart rate modulations. End-point for survival analysis over three years (Cox model) was total cardiac death. A prognostic model was built (backward elimination) considering known clinical and functional risk factors, and the ability of AIF indices to add prognostic information to this model assessed. Results: Out of candidate predictors, New York Heart Association class, left ventricular ejection fraction, peak VO2, and systolic pressure were selected as the variables with the highest joint predictive value. When entered into this model, both BDnn and PDmVLF added prognostic information (HR (95%CI): 1.76 (1.00-3.09), P = 0.05, 1.73 (1.05-2.85), P = 0.031 respectively). High risk was associated with reduced fast AIF and increased slower AIF. Conclusion: In CHF patients, AIF indices provide prognostic information independent of known risk factors.

AB - Background: Chronic heart failure (CHF) is associated with a complex dysfunction of cardiac, cardiovascular, autonomic, and other mechanisms. Autonomic information flow (AIF) characteristics calculated from heart rate patterns were recently found as promising predictors of outcome in several cardiovascular diseases. Aim: To assess the prognostic value of AIF indices in CHF patients. Methods: We analyzed 24-hour Holter recordings from 200 consecutive CHF patients in sinus rhythm and computed AIF over the shortest possible interval of an interbeat series, namely over one heart beat interval (BDnn), and over longer intervals (12.5-166.7 seconds, PDmVLF), which reflect slower heart rate modulations. End-point for survival analysis over three years (Cox model) was total cardiac death. A prognostic model was built (backward elimination) considering known clinical and functional risk factors, and the ability of AIF indices to add prognostic information to this model assessed. Results: Out of candidate predictors, New York Heart Association class, left ventricular ejection fraction, peak VO2, and systolic pressure were selected as the variables with the highest joint predictive value. When entered into this model, both BDnn and PDmVLF added prognostic information (HR (95%CI): 1.76 (1.00-3.09), P = 0.05, 1.73 (1.05-2.85), P = 0.031 respectively). High risk was associated with reduced fast AIF and increased slower AIF. Conclusion: In CHF patients, AIF indices provide prognostic information independent of known risk factors.

KW - Autonomic information flow

KW - Autonomic nervous system

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KW - Heart rate variability

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