A data-driven model of the role of energy in sepsis. (21st January 2022) - Ramirez-Zuniga, Ivan; Rubin, Jonathan. E.; Swigon, David; Redl, Heinz; Clermont, Gilles
Highlights: A computational data-driven model links inflammation to host energy processing. Model was calibrated to non-human primate data associated with pathogenic infection. Logistic regression reveals parameter differences between survivor and non-survivor subjects, including energy-dependent factors. The model can be used to predict patient outcome from new data. These results suggest that the quality of early measurements can be critical for prediction accuracy. Abstract: Exposure to pathogens elicits a complex immune response involving multiple interdependent pathways. This response may mitigate detrimental effects and restore health but, if imbalanced, can lead to negative outcomes including sepsis. This complexity and need for balance pose a challenge for clinicians and have attracted attention from modelers seeking to apply computational tools to guide therapeutic approaches. In this work, we address a shortcoming of such past efforts by incorporating the dynamics of energy production and consumption into a computational model of the acute immune response. With this addition, we performed fits of model dynamics to data obtained from non–human primates exposed to Escherichia coli . Our analysis identifies parameters that may be crucial in determining survival outcomes and also highlights energy-related factors that modulate the immune response across baseline and altered glucose conditions.