Literature DB >> 7789379

Heart rate variability and functional severity of congestive heart failure secondary to coronary artery disease.

G C Casolo1, P Stroder, A Sulla, A Chelucci, A Freni, M Zerauschek.   

Abstract

To investigate the behaviour of heart rate variability (HRV) with the advancing severity of heart failure (CHF) we studied 20 normal subjects and 80 coronary artery disease (CAD) patients in sinus rhythm. CAD patients were selected consecutively in order to form four equal groups of 20 subjects with different degrees of CHF according to the New York Heart Association (NYHA) functional classification. In each subject a 24 h ECG Holter tape was recorded and subsequently analysed to obtain measures of heart rate and HRV. We used several measures of HR and both spectral and non-spectral measures of HRV. Among these we employed the width of the R-R interval distribution over 24 h at three different heights (TV, 10%Var, 50%Var). The CAD group showed significantly lower HRV counts and smaller spectral components than controls. However, these differences were due to the presence of CHF rather than to CAD. Indeed, a progressive and significant increase in heart rate and a contemporary decrease in HRV was observed with the advancing severity of CHF. Class IV patients had the smallest HR variation; the spectral composition in this group was barely detectable. The decrease in time domain measures of HRV followed the increase in NYHA Class in a progressive and regular pattern, while the low frequency and high frequency spectral power showed the largest reduction from NYHA Class I to NYHA Class II patients. No significant change was demonstrated in NYHA Class I patients as compared to Controls.(ABSTRACT TRUNCATED AT 250 WORDS)

Entities:  

Mesh:

Year:  1995        PMID: 7789379     DOI: 10.1093/oxfordjournals.eurheartj.a060919

Source DB:  PubMed          Journal:  Eur Heart J        ISSN: 0195-668X            Impact factor:   29.983


  12 in total

1.  Heart rate variability in patients with the first and recurrent myocardial infarction.

Authors:  T Ristimäe; H V Huikuri; R Teesalu
Journal:  Clin Auton Res       Date:  1998-08       Impact factor: 4.435

2.  Neural control of arterial pressure variability in the neuromuscularly blocked rat.

Authors:  Xiaorui Tang; Tian Hu
Journal:  Eur J Appl Physiol       Date:  2011-09-23       Impact factor: 3.078

3.  Heart rate variability in idiopathic dilated cardiomyopathy: relation to disease severity and prognosis.

Authors:  G Yi; J H Goldman; P J Keeling; M Reardon; W J McKenna; M Malik
Journal:  Heart       Date:  1997-02       Impact factor: 5.994

4.  Prognostic value of ventricular arrhythmias and heart rate variability in patients with unstable angina.

Authors:  G A Lanza; D Cianflone; A G Rebuzzi; G Angeloni; A Sestito; G Ciriello; G La Torre; F Crea; A Maseri
Journal:  Heart       Date:  2005-12-30       Impact factor: 5.994

Review 5.  Autonomic effects of spironolactone and MR blockers in heart failure.

Authors:  Justine I Davies; Miles D Witham; Allan D Struthers
Journal:  Heart Fail Rev       Date:  2005-01       Impact factor: 4.214

Review 6.  Measurement of sympathetic nervous system activity in heart failure: the role of norepinephrine kinetics.

Authors:  M Esler; D Kaye
Journal:  Heart Fail Rev       Date:  2000-03       Impact factor: 4.214

7.  Accurate prediction of coronary artery disease using reliable diagnosis system.

Authors:  Indrajit Mandal; N Sairam
Journal:  J Med Syst       Date:  2012-02-12       Impact factor: 4.460

8.  The dmNTS is not the source of increased blood pressure variability in baroreflex denervated rats.

Authors:  Xiaorui Tang; Barry R Dworkin
Journal:  Auton Neurosci       Date:  2009-03-13       Impact factor: 3.145

9.  Loss of lag-response curvilinearity of indices of heart rate variability in congestive heart failure.

Authors:  Tushar P Thakre; Michael L Smith
Journal:  BMC Cardiovasc Disord       Date:  2006-06-12       Impact factor: 2.298

Review 10.  Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques.

Authors:  Evanthia E Tripoliti; Theofilos G Papadopoulos; Georgia S Karanasiou; Katerina K Naka; Dimitrios I Fotiadis
Journal:  Comput Struct Biotechnol J       Date:  2016-11-17       Impact factor: 7.271

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.