From data patterns to mechanistic models in acute critical illness

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 in order 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 physiological 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.

In vivo and systems biology studies implicate IL-18 as a central mediator in chronic pain

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Dynamic Data-Driven Modeling for Ex Vivo Data Analysis: Insights into Liver Transplantation and Pathobiology

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