Search for disease-specific cardiovascular reactivity patterns: developing the methodology

Abstract

Aberrations of CVR (cardiovascular reactivity), an expression of autonomic function, lack specificity for a particular disorder. Recently, a CVR pattern particular to chronic fatigue syndrome has been observed. In the present study, we aimed to develop methodologies for assessing disease-specific CVR patterns. As a prototype, a population of 50 consecutive patients with FMF (familial Mediterranean fever) was studied and compared with control populations. A 10 min supine/30 min head-up tilt test with recording of the heart rate and blood pressure or the pulse transit time was performed. Five studies were conducted applying different methods. In each study, statistical analysis identified independent predictors of CVR in FMF. Based on regression coefficients of these predictors, a linear DS (discriminant score) was computed for every subject. Each study established an equation to assess CVR, calculate DS for FMF and determine the sensitivity and specificity of the DS cut-off. In each of the five studies, abnormal CVR was observed in FMF patients. The best accuracy (88% sensitivity and 90.1% specificity for FMF) was obtained by a method based on beat-to-beat heart rate and pulse transit time recordings. Data was processed by fractal and recurrence quantitative analysis with recordings in FMF patients compared with a mixed control population. Identification of disease-specific CVR patterns was possible with the methodologies described in the present study. In FMF, disease-specific CVR may be explained by the interplay between neuroendocrine loops specific to FMF with cardiovascular homoeostatic mechanisms. Recognition of disease-specific CVR patterns may advance the understanding of homoeostatic mechanisms and have implications in clinical practice.

How random is your heart beat?

“…In the case of a highly correlated stochastic process, our analysis may underestimate the stochastic component of the variability. This is because the method is sensitive to the occurrence a strong autocorrelation in the data which results in the appearance of deterministic lines in the recurrence plots [22,23,24]. This affects the resultant K 2 entropy.…”

Association of cardiovascular risk using non-linear heart rate variability measures with the framingham risk score in a rural population

“…The endocrine system via a diverse set of hormones and the ANS also modulate HRV (Naschitz et al, 2005). HRV is also influenced by the same factors as incorporated in the Framingham risk equation including age, gender, cholesterol, blood pressure, diabetes status and smoking.…”