Cardiovascular variability as a measure of inflammation*.

In this issue of Critical Care Medicine, Vandendriessche et al (1) report on a multiscale entropy-based tool that may provide insight into the severity of systemic inflammation. Here, entropy is used in the sense of informatics and pattern matching: loss of entropy can be thought of a reduction in the richness and complexity of a signal (2). Using mice as a model system, the investigators injected familiar mediators of inflammation—lipopolysaccharide and tumor necrosis factor-α—while continuously telemetering blood pressure and heart rate in the animals. Their data suggest that changes in beat-to-beat cardiovascular variability revealed in analysis of multiscale entropy—specifically erosion of the richness and structure of the variability—herald lethal physiologic decompensation in advance of frank collapse (3). If their data eventually translate to the human condition, then such noninvasive monitoring could be widely deployed and proved useful in early identification of septic patients and possibly in monitoring of their responses to treatment. There is an intuitive description of changes in beatto-beat variability in the article. Physicists speak about three basic “flavors” of variability that vary according to their intensity across frequencies. So-called white variability (1/f ) is independent of frequency and is called “white noise. ” Audible white noise is familiar when a radio receiver is on but not tuned to a transmitting frequency. Brown variability (1/f ) derives its name from Brownian motion. It has greater density at lower frequencies; it sounds like a waterfall or heavy rainfall. Between white and brown lies pink variability (1/f ), which has a flickering quality to it. Healthy heart rate variability tends to display mostly 1/f and perhaps some 1/f 2 characteristics (4). Vandendriessche et al (1) noted that inflammation erodes the structure of the variability, reducing it toward white (1/f ) noise, and further that the erosion in variability structure precedes the changes in vital signs that we associate with shock. Still, the journey from experimental murine data to clinical utility remains a long one. The authors are understandably hopeful that the mediator-induced changes in cardiac physiology are mirrored in clinical sepsis. Unfortunately, dozens (if not hundreds) of metrics have been proposed to describe the changes in physiologic variability, and yet none have yet emerged as especially useful (much less definitive) (5). The fact is that each metric proposed represents an implicit guess as to the importance of one or more aspects of that variability. We probably need more sophisticated pattern recognition tools. We certainly need a better understanding of the mechanisms that give rise to healthy variability. Shrewd guesses can nevertheless be valuable. Fortunately, the authors have generously offered to make their analytic code available to clinicians whose access to datastreams from physiologic monitors is both simpler and more efficient than just a few years ago. We should quickly learn whether this new metric proves itself an effective tool in the clinical environment.