Spontaneous baroreceptor reflex sensitivity for risk stratification of heart failure patients: optimal cut-off and age effects

Abstract

Baroreceptor reflex sensitivity (BRS) is an important prognostic factor because a reduced BRS has been associated with an adverse cardiovascular outcome. The threshold for a ‘reduced’ BRS was established by the ATRAMI study at BRS <3 ms/mmHg in patients with a previous myocardial infarction, and has been shown to improve risk assessment in many other cardiac dysfunctions. The successful application of this cut-off to other populations suggests that it may reflect an inherent property of baroreflex functioning, so our goal is to investigate whether it represents a ‘natural’ partition of BRS values. As reduced baroreflex responsiveness is also associated with ageing, we investigated whether a BRS estimate <3 ms/mmHg could be the result of a process of physiological senescence as well as a sign of BRS dysfunction. This study involved 228 chronic heart failure patients and 60 age-matched controls. Our novel method combined transfer function BRS estimation and automatic clustering of BRS probability distributions, to define indicative levels of different BRS activities. The analysis produced a fit clustering (cophenetic correlation coefficient 0.9 out of 1) and identified one group of homogeneous patients (well separated from the others by 3 ms/mmHg) with an increased BRS-based mortality risk [hazard ratio (HR): 3.19 (1.73, 5.89), P<0.001]. The age-dependent BRS cut-off, estimated by 5% quantile regression of log (BRS) with age (considering the age-matched controls), provides a similar mortality value [HR: 2.44 (1.37, 4.43), P=0.003]. In conclusion, the 3 ms/mmHg cut-off identifies two large clusters of homogeneous heart failure (HF) patients, thus supporting the hypothesis of a natural cut-off in the HF population. Furthermore, age was found to have no statistical impact on risk assessment, suggesting that there is no need to establish age-based cut-offs because 3 ms/mmHg optimally identifies patients at high mortality risk.