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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.