Spectral clustering of risk score trajectories stratifies sepsis patients by clinical outcome and interventions received

Abstract

Sepsis is not a monolithic disease, but a loose collection of symptoms with diverse outcomes. Thus, stratification and subtyping of sepsis patients is of great importance. We examine the temporal evolution of patient state using our previously-published method for computing risk of transition from sepsis into septic shock. Risk trajectories diverge into four clusters following early prediction of septic shock, stratifying by outcome: the highest-risk and lowest-risk groups have a 76.5% and 10.4% prevalence of septic shock, and 43% and 18% mortality, respectively. These clusters differ also in treatments received and median time to shock onset. Analyses reveal the existence of a rapid (30–60 min) transition in risk at the time of threshold crossing. We hypothesize that this transition occurs as a result of the failure of compensatory biological systems to cope with infection, resulting in a bifurcation of low to high risk. Such a collapse, we believe, represents the true onset of septic shock. Thus, this rapid elevation in risk represents a potential new data-driven definition of septic shock.

Prediction of Impending Septic Shock in Children With Sepsis

“…We repeated our analyses of risk score trajectories for stratification of sepsis patients ( 16 ). Spectral clustering ( 27 ) of risk score trajectories in the window surrounding early prediction yielded two clusters ( Fig.…”

“…Previously, we found that entry into preshock was marked by a rapid transition from low to high risk. Prior to entry, patient physiology was indistinguishable between the low- and high-risk clusters ( 16 ). However, after entry into preshock, risk score trajectories diverged and stratified patients by risk of septic shock, mortality, time to septic shock onset, and treatments received.…”

“…Stratification of sepsis patients by risk score trajectories was performed as previously described and is a separate analysis from early prediction ( 16 ). Risk score trajectories for each patient were computed by applying the XGBoost model at each of the 12 hours following time of threshold crossing.…”

“…We predicted impending transition from sepsis to septic shock based on the hypothesis that there exists a physiologically distinct state of sepsis, which we termed “preshock,” and that entry into this state presages the onset of septic shock ( 13 , 15 ). Through analyses of the temporal evolution of patient state, we stratified adult sepsis patients by outcomes and interventions received and discovered that entry into preshock was marked by a rapid shift in both patient physiology and risk occurring within a 30–60 minutes timeframe ( 16 ).…”

A dynamic inflammation model for neutrophil and monocyte responses in sepsis, trauma and surgery patient clusters

“…Clustering methods are one way to try and solve this issue. They have been applied to the evolution of sepsis in very large studies 45 but also on the scale of a few hundred patients 5 . Leijte et al 46 carried out a detailed analysis of mHLA-DR expression kinetics in a large cohort of 241 septic shock patients over the first week after injury.…”