Evidence-based modeling of critical illness: an initial consensus from the Society for Complexity in Acute Illness

Abstract

Introduction: Given the complexity of biological systems, understanding their dynamic behaviors, such as the Acute Inflammatory Response (AIR), requires a formal synthetic process. Dynamic Mathematical Modeling (DMM) represents a suite of methods intended for inclusion within the required synthetic framework. The DMM, however, is a relatively novel approach in the practice of biomedical research. The Society for Complexity in Acute Illness (SCAI) was formed in 2004 from the leading research groups using DMM in the study of acute inflammation. This society believes that it is important to offer guidelines for the design, development, and use of DMM in the setting of AIR research to avoid the "garbage in, garbage out" problem. Accordingly, SCAI identified a need for and carried out a critical appraisal of DMM as currently used in the setting of acute illness.

Methods: The SCAI annual meeting in 2005, the Fourth International Conference on Complexity in Acute Illness (Cologne, Germany), was structured with the intent of developing a consensus statement on the methods and execution of DMM in AIR research. The conference was organized to include a series of interactive breakout sessions that included thought leaders from both the DMM and acute illness fields, the results of which were then presented in summary form to the entire group for discussion and consensus. The information in this article represents the concatenation of those presentations.

Results: The output from the Fourth International Conference on Complexity in Acute Illness involved consensus statements for the following topics: (1) the need for DMM; (2) a suggested approach for the process of establishing a modeling project; (3) the type of "wet" laboratory experiments and data needed to establish a modeling project; (4) general quality measures for data to be input to a modeling project; and (5) a descriptive list of several types of DMM to provide guidance in selection of a method for a project.

Conclusion: We believe that the complexity of biological systems requires that DMM needs to be among the methods used to improve understanding and make progress with attempts to characterize and manipulate the AIR. We believe that this consensus statement will help guide the integration, rational implementation, and standardization of DMM into general biomedical research.