Kernel-based Characterization of Dynamics in a Heterogeneous Population of Septic Patients Under Therapy

Sepsis is a medical condition characterized as a systemic inflammatory response to an infection. The high level of heterogeneity among sepsis patients is one of the main reasons of unsuccessful clinical trials. A more careful targeting of specific therapeutic strategies to more biologically homogeneous groups of patients is essential to developing effective sepsis treatment. We propose a kernel-based approach to characterize dynamics of inflammatory response in a heterogeneous population of septic patients. Our method utilizes Linear State Space Control (LSSC) models to take into account dynamics of inflammatory response over time as well as the effect of therapy applied to the patient. We use a similarity measure defined on kernels of LSSC models to find homogeneous groups of patients. An application of the proposed method to analysis of dynamics of inflammatory response to sepsis therapy in 64 virtual patients identified four biologically relevant homogeneous groups providing the initial evidence that patient-specific sepsis treatment based on several treatment protocols is feasible.