Supplemental Digital Content – Text 3 1
DyBN
Dynamic Bayesian Network (DyBN) inference was used to model the evolution of probabilistic dependencies within a system over time, and to suggest possible feedback interactions among inflammatory mediators. This analysis was carried out using MATLAB™
(The Math Works, Inc., Natick, Ma) as previously described by our group [13, 17]. Inflammatory mediators were represented at multiple time points within the same network structure. In this approach, time was modeled discretely as in a discrete Markov chain. Each mediator was given a time index subscript indicating the time slice to which it belonged. Additional temporal dependencies were represented in a DyBN by edges between time slices. Each node in the network was associated with a conditional probability distribution of a variable that is conditioned upon its parents (upstream nodes).
DyNA
Dynamic Network Analysis (DyNA) was utilized to gain insights into dynamic changes in network connectivity of the post-traumatic inflammatory response leading to survival versus mortality over time, based on stringent network identification method described previously in the setting of nosocomial infection post-trauma [14, 18]. The mathematical formulation of this method is essentially to calculate the correlation among variables by which we can examine their dependence. To do so, inflammatory mediator networks were created in adjacent 8h time periods (0-8h, 8-16h, and 16-24h) for the first 24h followed by 1-day time periods for seven days (D1- D7) using MATLAB™ (The MathWorks, Inc., Natick, MA). Connections in the network were created if the correlation coefficient between two nodes (inflammatory mediators) was greater or equal to a threshold of 0.9. For the network complexity calculations, to account for network sizes
References are numbered as per main manuscript.
Supplemental Digital Content – Text 3 2
(number of significantly altered nodes) in the adjacent time periods detailed above, we utilized the following formula
Total number of edges * Number of total nodes Maximum possible edges among total nodes
References are numbered as per main manuscript.
