A multiscale entropy-based tool for scoring severity of systemic inflammation.
OBJECTIVE
Early detection and start of appropriate treatment are highly correlated with survival of sepsis and septic shock, but the currently available predictive tools are not sensitive enough to identify patients at risk.


METHODS
Linear (time and frequency domain) and nonlinear (unifractal and multiscale complexity dynamics) measures of beat-to-beat interval variability were analyzed in two mouse models of inflammatory shock to determine if they are sensitive enough to predict outcome.


METHODS
University research laboratory.


METHODS
Blood pressure transmitter-implanted female C57BL/6J mice.


METHODS
IV administration of tumor necrosis factor (n = 11) or lipopolysaccharide (n = 14).


RESULTS
Contrary to linear indices of variability, unifractal dynamics, and absolute heart rate or blood pressure, quantification of complex beat-to-beat dynamics using multiscale entropy was able to predict survival outcome starting as early as 40 minutes after induction of inflammatory shock. Based on these results, a new and clinically relevant index of multiscale entropy was developed that scores the key features of a multiscale entropy profile. Contrary to multiscale entropy, multiscale entropy scoring can be followed as a function of time to monitor disease progression with limited loss of information.


CONCLUSIONS
Analysis of multiscale complexity of beat-to-beat dynamics at high temporal resolution has potential as a sensitive prognostic tool with translational power that can predict survival outcome in systemic inflammatory conditions such as sepsis and septic shock.
1Inflammation Research Center, VIB, Ghent, Belgium. 2Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium. 3Department of Intensive Care Medicine, Ghent University Hospital, Ghent, Belgium. 4Department of Management, Technology and Economics, ETH Zurich, Zurich, Switzerland. 5Center for Neurogenomics and Cognitive Research, VU University Amsterdam, Amsterdam, The Netherlands.
Benjamin
B
Vandendriessche
Harlinde
H
Peperstraete
Elke
E
Rogge
Peter
P
Cauwels
Eric
E
Hoste
Oliver
O
Stiedl
Peter
P
Brouckaert
Anje
A
Cauwels
D000818:Animals; D001794:Blood Pressure; D042241:Early Diagnosis; D019277:Entropy; D005260:Female; D006339:Heart Rate; D016014:Linear Models; D008070:Lipopolysaccharides; D051379:Mice; D008810:Mice, Inbred C57BL; D017711:Nonlinear Dynamics; D011379:Prognosis; D012720:Severity of Illness Index; D015996:Survival Rate; D018746:Systemic Inflammatory Response Syndrome / Q000139:chemically induced / Q000175:diagnosis*; D016896:Treatment Outcome; D014409:Tumor Necrosis Factor-alpha
D008070:Lipopolysaccharides; D014409:Tumor Necrosis Factor-alpha