Abstract
Acute inflammation leads to organ failure by engaging catastrophic feedback loops in which stressed tissue evokes an inflammatory response and, in turn, inflammation damages tissue. Manifestations of this maladaptive inflammatory response include cardio-respiratory dysfunction that may be reflected in reduced heart rate and ventilatory pattern variabilities. We have developed signal-processing algorithms that quantify non-linear deterministic characteristics of variability in biologic signals. Now, coalescing under the aegis of the NIH Computational Biology Program and the Society for Complexity in Acute Illness, two research teams performed iterative experiments and computational modeling on inflammation and cardio-pulmonary dysfunction in sepsis as well as on neural control of respiration and ventilatory pattern variability. These teams, with additional collaborators, have recently formed a multi-institutional, interdisciplinary consortium, whose goal is to delineate the fundamental interrelationship between the inflammatory response and physiologic variability. Multi-scale mathematical modeling and complementary physiological experiments will provide insight into autonomic neural mechanisms that may modulate the inflammatory response to sepsis and simultaneously reduce heart rate and ventilatory pattern variabilities associated with sepsis. This approach integrates computational models of neural control of breathing and cardio-respiratory coupling with models that combine inflammation, cardiovascular function, and heart rate variability. The resulting integrated model will provide mechanistic explanations for the phenomena of respiratory sinus-arrhythmia and cardio-ventilatory coupling observed under normal conditions, and the loss of these properties during sepsis. This approach holds the potential of modeling cross-scale physiological interactions to improve both basic knowledge and clinical management of acute inflammatory diseases such as sepsis and trauma.
Highlights
Sepsis is a significant public health concern, accounting for approximately 10% of total U.S deaths annually (Angus et al, 2001; Martin et al, 2003; Vincent et al, 2006; Heron et al, 2009)
Studies from our groups, which coalesced under the U.S National Institute of Health Computational Biology Program1 and the Society for Complexity in Acute Illness2, have led us to hypothesize that these multi-scale inflammatory “tipping points,” subsequent containment failure, and forward feedback to further propagate inflammation are either centrally controlled by neural circuits, or that neural circuits are activated once inflammation is induced in the brain during sepsis
Time-domain signal-processing analysis has correlated alterations in physiologic variability with morbidity and mortality in critically ill patients (Pomeranz et al, 1985; Anonymous, 1996; Godin et al, 1996; Korach et al, 2001; Barnaby et al, 2002; Pontet et al, 2003; Kleiger et al, 2005; Chen and Kuo, 2007; Ahmad et al, 2009; Fairchild et al, 2009). To unify these diverse observations, we hypothesize that the progress of tissue-level failure toward multiple organ dysfunction syndrome (MODS) is accompanied by defined inflammatory networks in different organs, is controlled by inflammation “maps” in the brain, and manifests as decreased physiologic variability (Figure 1A)
Summary
Sepsis is a significant public health concern, accounting for approximately 10% of total U.S deaths annually (Angus et al, 2001; Martin et al, 2003; Vincent et al, 2006; Heron et al, 2009). Studies from our groups, which coalesced under the U.S National Institute of Health Computational Biology Program1 and the Society for Complexity in Acute Illness2, have led us to hypothesize that these multi-scale inflammatory “tipping points,” subsequent containment failure, and forward feedback to further propagate inflammation are either centrally controlled by neural circuits, or that neural circuits are activated once inflammation is induced in the brain during sepsis.
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