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 Poincaré plot pattern, as an adjunct to the summary statistical measures of R-R interval variability.
Methods: We used the Poincaré 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 consists 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, p < 0.0001) in the different pattern types between patients with NYHA Class I and II compared to patients with NYHA Class II 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 Poincaré plot.
Conclusions: The Poincaré 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.