The prognostic value of nonlinear indices of blood pressure variability in mouse models of sepsis

Abstract

Sepsis is associated with high mortality despite decades of research. A lack of validated biomarkers prevents the extrapolation of experimental data to the clinic. It has been well established that the sympathetic/parasympathetic balance is altered in critically ill patients, and that assessment of autonomic function has prognostic value that can be used to direct treatment.We used implantable telemetry devices to monitor blood pressure (BP) in conscious mice and developed a system in Matlab that implements a number of analysis algorithms, including multiscale entropy (MSE). These algorithms model the dataset as a nonlinear system and are capable of uncovering underlying regulating patterns. This approach allows us to do semiautomatic analysis of large amounts of data. In order to overcome the fact that interpretation of MSE results is time consuming, we developed a scoring algorithm that summarizes the output in a single numerical value, which can be plotted in function of time to follow disease progression.As proof of principle, BP measurements from mice that received a lethal or sublethal dose of TNF (tumor necrosis factor), a simple model for inflammatory shock, were analyzed. We found that the MSE parameter can distinguish between lethal/non‐lethal outcome already in the first 2 hours post‐TNF. Preliminary data indicates that this observation can be expanded towards other sepsis models and ICU patients.