Elsevier

Journal of Critical Care

Volume 29, Issue 4, August 2014, Pages 604-610
Journal of Critical Care

Monitoring/Outcomes
From data patterns to mechanistic models in acute critical illness☆

https://doi.org/10.1016/j.jcrc.2014.03.018Get rights and content

Abstract

The complexity of the physiologic and inflammatory response in acute critical illness has stymied the accurate diagnosis and development of therapies. The Society for Complex Acute Illness was formed a decade ago with the goal of leveraging multiple complex systems approaches to address this unmet need. Two main paths of development have characterized the society’s approach: (i) data pattern analysis, either defining the diagnostic/prognostic utility of complexity metrics of physiologic signals or multivariate analyses of molecular and genetic data and (ii) mechanistic mathematical and computational modeling, all being performed with an explicit translational goal. Here, we summarize the progress to date on each of these approaches, along with pitfalls inherent in the use of each approach alone. We suggest that the next decade holds the potential to merge these approaches, connecting patient diagnosis to treatment via mechanism-based dynamical system modeling and feedback control and allowing extrapolation from physiologic signals to biomarkers to novel drug candidates. As a predicate example, we focus on the role of data-driven and mechanistic models in neuroscience and the impact that merging these modeling approaches can have on general anesthesia.

Section snippets

Equal but separate: The state of complexity in acute critical illness

Acute critical illness can be defined as the constellation of acute inflammatory and pathophysiologic consequences that occur subsequent to sepsis, trauma/hemorrhage, and other acute events such as pancreatitis that can be differentiated from acute critical illnesses that do not require critical care (such as acute psychiatric illness). Sepsis alone is responsible for more than 215 000 deaths in the United States per year and an annual health care cost of more than $16 billion [1], whereas

Data patterns: From molecules to physiology to models

The responses to severe infection and trauma/hemorrhage involve a generalized activation and systemic expression of the host’s inflammatory pathways—the so-called systemic inflammatory response syndrome (SIRS). In parallel to, and at least in part driven by SIRS, a profound physiologic dysfunction accompanies acute critical illness. At the genomic level, it is now clear that most cell types and a plethora of biological pathways are induced in acutely ill patients [16]. This dysfunction can be

Applications of mechanistic models to acute critical illness

The ultimate translational goal of biomedical research is to be able to affect control on the biosystem to positively affect human health, and this requires the construction of mechanistic knowledge-based models. Dynamical systems modeling predicated on mechanistic models, wherein an internal state model is used to describe the system dynamics using biological and physiologic laws and system interconnections, is of fundamental importance in the description of physical dynamical systems. Toward

Conceptualizing data with mechanism: An example from neuroscience and general anesthesia

The foregoing sections have delineated the benefits and challenges inherent in purely data-driven and mechanistic modeling in the setting of acute critical illness. Thus, neither method is ideal, although it may be argued that both approaches offer complementary value to a purely reductionist approach. In multiple fields of biomedical science, there is a growing recognition of the need to link purely data-driven models with mechanistic models to retain the advantages while minimizing the

Conclusions and future prospects

The unmet need for new treatments and diagnostic modalities for acute critical illness is, in a word, acute. Although decades of work have led to many novel insights from the molecular to the physiologic level, the net result has been disappointing. We suggest that this is not because the effort has not been worthwhile or because promising candidate approaches were not pursued. Rather, it is our contention that what has not taken place is the process of synthesis of these insights into a larger

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    ☆

    This work was supported in part by the National Institutes of Health grants R01GM67240, P50GM53789, R33HL089082, R01HL080926, R01AI080799, R01HL76157, R01DC008290, and UO1DK072146; the National Institute on Disability and Rehabilitation Research grant H133E070024; the National Science Foundation grant 0830-370-V601; the QNRF under NPRP grant 4-187-2-060; the Agency for Innovation by Science and TechnologyIWT SBO 080040; a Shared University Research Award from IBM, Inc; and grants from the Commonwealth of Pennsylvania, the Pittsburgh Life Sciences Greenhouse, and the Pittsburgh Tissue Engineering Initiative/Department of Defense.

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