APPLICATION OF THE POINCARE PLOT TO HEART-RATE-VARIABILITY - A NEW MEASURE OF FUNCTIONAL STATUS IN HEART-FAILURE

被引:153
作者
KAMEN, PW
TONKIN, AM
机构
[1] Department of Cardiology, Austin Hospital, Melbourne, Victoria
[2] Department of Cardiology, Austin Hospital, Melbourne, Victoria
来源
AUSTRALIAN AND NEW ZEALAND JOURNAL OF MEDICINE | 1995年 / 25卷 / 01期
关键词
POINCARE PLOT; HEART RATE VARIABILITY; HEART FAILURE;
D O I
10.1111/j.1445-5994.1995.tb00573.x
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background: Conventional methods of quantifying heart rate variability using summary statistics have shown that decreased variability is associated with increased mortality in heart failure. However, many patients with heart failure have arrhythmias which make the 'raw' heart rate variability data less suitable for the use of summary statistical measures. Aims: To examine the clinical potential of a new measure of heart rate variability data, presented by the Poincare plot pattern, as an adjunct to the summary statistical measures of R-R interval variability. Methods: We used the Poincare plot pattern to display beat-to-beat heart rate variability data from a group of 23 patients with heart failure and compared them with data collected from 20 healthy age-matched control subjects. The data, which consisted of 2000 consecutive R-R intervals, were gathered over 20-40 minutes while the subjects rested supine in a quiet darkened room. Results: The morphological classification scheme proposed reflected the functional status of patients in heart failure. There was a significant difference (chi-square=27.5, df=6, p<0.0001) in the different pattern types between patients with NYHA Class I and II compared to patients with NYHA Class III and IV. All healthy subjects displayed a 'cluster' type of pattern characterised by normally distributed data. Sixteen of the 23 patients in heart failure also produced data which were normally distributed but the remaining seven produced data which required careful filtering to make them suitable for analysis using summary statistics, but which could be analysed by the Poincare plot. Conclusions: The Poincare plot pattern is a semi-quantitative tool which can be applied to the analysis of R-R interval data. It has potential advantages in that it allows assessment of data which are grossly non-Gaussian in distribution, and is a simple and easily implemented method which can be used in a clinical setting to augment the standard electrocardiogram to provide 'real time' visualisation of data.
引用
收藏
页码:18 / 26
页数:9
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