from data patterns to biological mechanism in critical illness: the role of engineering
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From data patterns to biological mechanism in critical illness: the role ofengineering
Timothy George Buchman Ph.D., M.D.
PII: S0883-9441(14)00137-3DOI: doi: 10.1016/j.jcrc.2014.04.001Reference: YJCRC 51491
To appear in: Journal of Critical Care
Received date: 1 April 2014Accepted date: 1 April 2014
Please cite this article as: Buchman Timothy George, From data patterns to biologicalmechanism in critical illness: the role of engineering, Journal of Critical Care (2014), doi:10.1016/j.jcrc.2014.04.001
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From data patterns to biological mechanism in critical illness: the role of engineering Dr. Timothy George Buchman, Ph.D., M.D. Emory University Hospital
In this issue of the Journal of Critical Care, Aerts and colleagues meditate on a troubling
fact about pattern-based approaches to critical physiology: they yield few details about
the mechanisms that generate the patterns. For example, while it is correct to say that
worsening illness is typically associated with loss of variability in a (such as heart rate
variability), the metrics explain neither the reason that variability is normal nor the basis
of its erosion in critical illnesses. Hypotheses exist, such as the failure of coupling-
recoupling among the cyclic processes that characterize biology at many scales.(1)
Examples exist to show that such coupling failure is associated with critical illness.(2-4)
But none of that explains the “how and why” of the coupling-recoupling behavior.
The authors assert that in order to discover the “how and why” things fall apart in critical
illness, there should be a synthesis between pattern analysis and “mechanistic
mathematical and computational modeling”. The statement is surely correct, but equally
surely it is incomplete. Evolution is about engineering: reusing and modifying both
structures and controllers in ways that optimize performance tradeoffs. For example, we
know that mammalian life depends on sustaining three processes—oxygenation,
perfusion and ventilation—across a very wide range of internal (illness) and external
(environmental) conditions and physiologic demands. Keeping target tissues functioning
means keeping variation in blood flow, oxygen delivery and carbon dioxide removal
minimal even when there are wide swings in those physiologic demand.
Engineers typically frame these questions in terms of control theory and how
disturbances must be handled, leading to a set of specifications and a set of options that
can potentially meet those specifications. Tradeoffs in (for example) speed and precision
of responses to disturbances are evaluated and compromises reached. In biological terms,
engineers ask how homeostasis is achieved while the organism meets the challenges of
the external world.
In biology, we deal with the reverse engineering problem: what produces the healthy
behaviors and what is it that “fails” during critical illness?(5) The engineers are ahead of
us on this. Toolsets such as “system identification” use statistical methods to build
models of systems from inputs and outputs. While some models are opaque (“black
box”), increasingly “grey box” models are employed where the details are not entirely
known but some insight into what affects what already exists. Such approaches are
conspicuous by their relative paucity in the laboratory and in the literature.
It’s fair to ask why there has been so little emphasis on the use of engineering insights
and strategies in critical illness and critical care. We speculate that the last couple of
decades have been focused not only on the acquisition of data at all levels of resolution
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(from nucleic acid sequence to organism monitoring) but also confronting the challenges
associated with managing big data: volume, variety, velocity and validity.
Computers can now automate pattern detection and even suggest relationships in the
generating systems. Yet patterns and data relationships are not models. It will take
partnerships among clinicians, biologists and engineers to realize the potential of the
data; to propose and test model systems; and to return the insights gained to the bedside.
1. Godin PJ, Buchman TG. Uncoupling of biological oscillators: a complementary
hypothesis concerning the pathogenesis of multiple organ dysfunction syndrome. Crit
Care Med. 1996 Jul;24(7):1107-16.
2. Norris PR, Ozdas A, Cao H, Williams AE, Harrell FE, Jenkins JM, et al. Cardiac
uncoupling and heart rate variability stratify ICU patients by mortality: a study of 2088
trauma patients. Ann Surg. 2006 Jun;243(6):804-12; discussion 12-4.
3. Ahmad S, Ramsay T, Huebsch L, Flanagan S, McDiarmid S, Batkin I, et al.
Continuous multi-parameter heart rate variability analysis heralds onset of sepsis in
adults. PLoS One. 2009;4(8):e6642.
4. Ellenby MS, McNames J, Lai S, McDonald BA, Krieger D, Sclabassi RJ, et al.
Uncoupling and recoupling of autonomic regulation of the heart beat in pediatric septic
shock. Shock. 2001 Oct;16(4):274-7.
5. Jopling J, Buchman TG. Sepsis through the eyes of an engineer -- why treatments
have succeeded and failed. Crit Rev Biomed Eng. 2012;40(4):353-61.