Spectral Clustering of Risk Score Trajectories Stratifies Sepsis Patients by Clinical Outcome and Interventions Received

Abstract

AbstractSepsis is not a monolithic disease, but a loose collection of symptoms with a diverse range of outcomes. The diverse patterns of sepsis make guideline-driven treatment difficult, as guidelines are based on the needs of the “average” patient. Thus, stratification and subtyping of sepsis patients is of interests, with the ultimate goal of identifying groups of patients who respond similarly to treatment. To do this, we examine the temporal evolution of patient state using our previously-published method for computing patient risk of transition into septic shock. The application of spectral clustering to risk score trajectories reveals that these trajectories diverge into four distinct clusters in the time window following early prediction of septic shock. Patients in these clusters stratify by outcome: the highest-risk group has a 76.5% prevalence of septic shock and 43% mortality, whereas the lowest-risk group has a 10.4% prevalence of septic shock and 18% mortality. These clusters differ also in treatments received, as well as median time to septic shock onset. Data analyses reveal the existence of a rapid (30-60 min) transition in risk at the time of threshold crossing. We hypothesize that this rapid transition occurs as a result of an abrupt failure of compensatory biological systems to cope with infection, resulting in a bifurcation of low to high risk. Such a collapse in compensation, we believe, represents the true onset of septic shock. Thus, what we previously referred to as the pre-shock state represents a potential new data-driven definition of septic shock.