Cardiovascular Variability as a Measure of Inflammation* T im o th y G. B u c h m a n , P h D , M D , M C C M
Center for Critical Care Emory University Atlanta, GA
n 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-a—while continuously telemeter ing 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 pos sibly 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 variabil ity (1 I f 0) is independent of frequency and is called “white noise.” Audible white noise is familiar when a radio receiver
I
is on but not tuned to a transmitting frequency. Brown vari ability (1 I f 2) derives its name from Brownian motion. It has greater density at lower frequencies; it sounds like a water fall or heavy rainfall. Between white and brown lies pink variability (1//), which has a flickering quality to it. Healthy heart rate variability tends to display mostly 1If and per haps some \ l f 2 characteristics (4). Vandendriessche et al (1) noted that inflammation erodes the structure of the variability, reducing it toward white (1 I f 0) noise, and further that the ero sion in variability structure precedes the changes in vital signs that we associate with shock. Still, the journey from experimental m urine data to clinical utility remains a long one. The authors are under standably hopeful that the m ediator-induced changes in cardiac physiology are m irrored in clinical sepsis. Unfor tunately, 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 vari ability. We probably need more sophisticated pattern recog nition 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.
‘ See also p. e560. Key Words: cardiovascular variability; complex systems; entropy; inflammation Dr. Buchman consulted for the National Institutes of Health (reviews for several study sections; money subsequently donated to Emory University) and the James S. McDonnell Foundation (reviews for several grant pro grams; money subsequently donated to Emory University), is employed by Emory University, and received support for travel from The Data Ware house Institute (Speaker on Big Data in Medicine). His institution received support for travel from IBM (Keynote Speaker at Information on Demand 2013 conference) and received grant support from Center for Medicare and Medicaid Services (Healthcare Innovation Award) and from the De partment of Defense (Surgical Critical Care Initiative). Copyright © 2014 by the Society of Critical Care Medicine and Lippincott Williams & Wilkins
DOI: 10.1097/CCM.0000000000000460
1964
w w w .c c m jo u r n a l.o r g
REFERENCES 1. Vandendriessche B, Peperstraete H, Rogge E, et al: A Multiscale Entropy-Based Tool for Scoring Severity of Systemic Inflammation. Crit Care Med 2014; 4 2 :e 5 6 0 -e 5 6 9 2. Pincus SM, Goldberger AL: Physiological time-series analysis: What does regularity quantify? Am J Physiol 1994; 266:H1 64 3-H 1 656 3. Costa M, Goldberger AL, Peng CK: Multiscale entropy analysis of complex physiologic time series. Phys Rev Lett 2002; 89 :068 10 2 4. Ivanov PC, Nunes Amaral LA, Goldberger AL, et al: From 1/f noise to multifractal cascades in heartbeat dynamics. Chaos 2001; 1 1 :6 4 1 -6 5 2 5. Seely AJ, Macklem PT: Complex systems and the technology of vari ability analysis. Crit Care 2004; 8 :R 3 67 -R 38 4
August 2014 • Volume 42 • Number 8
Copyright of Critical Care Medicine is the property of Lippincott Williams & Wilkins and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Center for Critical Care Emory University Atlanta, GA
n 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-a—while continuously telemeter ing 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 pos sibly 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 variabil ity (1 I f 0) is independent of frequency and is called “white noise.” Audible white noise is familiar when a radio receiver
I
is on but not tuned to a transmitting frequency. Brown vari ability (1 I f 2) derives its name from Brownian motion. It has greater density at lower frequencies; it sounds like a water fall or heavy rainfall. Between white and brown lies pink variability (1//), which has a flickering quality to it. Healthy heart rate variability tends to display mostly 1If and per haps some \ l f 2 characteristics (4). Vandendriessche et al (1) noted that inflammation erodes the structure of the variability, reducing it toward white (1 I f 0) noise, and further that the ero sion in variability structure precedes the changes in vital signs that we associate with shock. Still, the journey from experimental m urine data to clinical utility remains a long one. The authors are under standably hopeful that the m ediator-induced changes in cardiac physiology are m irrored in clinical sepsis. Unfor tunately, 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 vari ability. We probably need more sophisticated pattern recog nition 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.
‘ See also p. e560. Key Words: cardiovascular variability; complex systems; entropy; inflammation Dr. Buchman consulted for the National Institutes of Health (reviews for several study sections; money subsequently donated to Emory University) and the James S. McDonnell Foundation (reviews for several grant pro grams; money subsequently donated to Emory University), is employed by Emory University, and received support for travel from The Data Ware house Institute (Speaker on Big Data in Medicine). His institution received support for travel from IBM (Keynote Speaker at Information on Demand 2013 conference) and received grant support from Center for Medicare and Medicaid Services (Healthcare Innovation Award) and from the De partment of Defense (Surgical Critical Care Initiative). Copyright © 2014 by the Society of Critical Care Medicine and Lippincott Williams & Wilkins
DOI: 10.1097/CCM.0000000000000460
1964
w w w .c c m jo u r n a l.o r g
REFERENCES 1. Vandendriessche B, Peperstraete H, Rogge E, et al: A Multiscale Entropy-Based Tool for Scoring Severity of Systemic Inflammation. Crit Care Med 2014; 4 2 :e 5 6 0 -e 5 6 9 2. Pincus SM, Goldberger AL: Physiological time-series analysis: What does regularity quantify? Am J Physiol 1994; 266:H1 64 3-H 1 656 3. Costa M, Goldberger AL, Peng CK: Multiscale entropy analysis of complex physiologic time series. Phys Rev Lett 2002; 89 :068 10 2 4. Ivanov PC, Nunes Amaral LA, Goldberger AL, et al: From 1/f noise to multifractal cascades in heartbeat dynamics. Chaos 2001; 1 1 :6 4 1 -6 5 2 5. Seely AJ, Macklem PT: Complex systems and the technology of vari ability analysis. Crit Care 2004; 8 :R 3 67 -R 38 4
August 2014 • Volume 42 • Number 8
Copyright of Critical Care Medicine is the property of Lippincott Williams & Wilkins and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
