Benchmarking Heart Rate Variability to Overcome Sex-Related Bias

Since the seminal studies by Sayers (Ergonomics 16:17-32, 1973) and Akselrod et al. (Science 213:220-222, 1981) a few decades ago, it became clear that beat-by-beat oscillations in RR interval length (i.e. heart-rate variability [HRV]) contain information on underlying neural-control mechanisms based on the instantaneous balance between parasympathetic and sympathetic innervation. Over the years, the number of studies addressing HRV has increased markedly and now outnumbers 23,000. Despite such a large interest, there is still a continuing debate about interpretation of indices produced by computer analysis of HRV.The main part of studies relies on spectral techniques to extract parameters that are linked to hidden information. The general idea is that these proxies of autonomic regulation can be useful to clinical applications in various conditions in which autonomic dysregulation may play a role. There are, however, serious shortcomings related to algorithms, interpretation, and the hidden value of individual indices. In particular, it appears that specific training is necessary to interpret the hidden informational value of HRV. This technical complexity represents a severe barrier to large-scale clinical applications. Moreover, important differences in HRV separate the sexes, and age plays an additional confounding role.We present here a preliminary application of a novel unitary index of RR variability (Autonomic Nervous System Index of cardiac regulation) capable of providing information on the performance of autonomic regulation using a percentile rank position as projected on a large benchmark population. A summary of the underlying sympatho-vagal model is also presented.