Individual illness dynamics: An analysis of children with sepsis admitted to the pediatric intensive care unit

Illness dynamics and patterns of recovery may be essential features in understanding the critical illness course. We propose a method to characterize individual illness dynamics in patients who experienced sepsis in the pediatric intensive care unit. We defined illness states based on illness severity scores generated from a multi-variable prediction model. For each patient, we calculated transition probabilities to characterize movement among illness states. We calculated the Shannon entropy of the transition probabilities. Using the entropy parameter, we determined phenotypes of illness dynamics based on hierarchical clustering. We also examined the association between individual entropy scores and a composite variable of negative outcomes. Entropy-based clustering identified four illness dynamic phenotypes in a cohort of 164 intensive care unit admissions where at least one sepsis event occurred. Compared to the low-risk phenotype, the high-risk phenotype was defined by the highest entropy values and had the most ill patients as defined by a composite variable of negative outcomes. Entropy was significantly associated with the negative outcome composite variable in a regression analysis. Information-theoretical approaches to characterize illness trajectories offer a novel way of assessing the complexity of a course of illness. Characterizing illness dynamics with entropy offers additional information in conjunction with static assessments of illness severity. Additional attention is needed to test and incorporate novel measures representing the dynamics of illness.Author summary: Patterns of illness recovery and decline may be important to understand the illness course during a critical care admission. This paper highlights a novel approach to characterizing these patterns of change in illness severity. We propose that continuous predictive analytic risk scores can be used as a proxy for patient acuity. These scores can be conceptualized as a highly-dimensional time series representing the patient’s illness trajectory during a critical care period. We take an Information-theoretical approach to consider individual illness state transitions. We hypothesize that the entropy associated with illness state transitions is clinically meaningful and represents the complexity of the transitions among states of illness. We found that higher entropy is associated with more negative outcomes during a pediatric critical care admission. This work has implications for expanding methodologies used to characterize heterogeneous illness trajectories in the critical care environment.