Data-driven discovery of a novel sepsis pre-shock state predicts impending septic shock in the ICU

Abstract

Septic shock is a life-threatening condition in which timely treatment substantially reduces mortality. Reliable identification of patients with sepsis who are at elevated risk of developing septic shock therefore has the potential to save lives by opening an early window of intervention. We hypothesize the existence of a novel clinical state of sepsis referred to as the “pre-shock” state, and that patients with sepsis who enter this state are highly likely to develop septic shock at some future time. We apply three different machine learning techniques to the electronic health record data of 15,930 patients in the MIMIC-III database to test this hypothesis. This novel paradigm yields improved performance in identifying patients with sepsis who will progress to septic shock, as defined by Sepsis- 3 criteria, with the best method achieving a 0.93 area under the receiver operating curve, 88% sensitivity, 84% specificity, and median early warning time of 7 hours. Additionally, we introduce the notion of patient-specific positive predictive value, assigning confidence to individual predictions, and achieving values as high as 91%. This study demonstrates that early prediction of impending septic shock, and thus early intervention, is possible many hours in advance.

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

“…Consequently, a number of computational approaches for early prediction of sepsis and septic shock using electronic health record (EHR) data have been developed (8)(9)(10)(11). We recently developed a method for predicting patients with sepsis who are likely to transition to septic shock based on the hypothesis that there exists a detectable physiologically distinct state of sepsis, which we identify as the "pre-shock state," and that entry into this state presages the impending onset of septic shock (11). The pre-shock state was characterized by fitting a regression model to classify data from patients with sepsis who never develop septic shock from those who do.…”

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

“…Previously, we hypothesized that there exists a physiologically distinct state of sepsis that precedes the imminent transition into septic shock ( Liu et al, 2019 ). We referred to this state as the pre-shock state.…”

“…Figures 1 and 2 show that prior to entry into the pre-shock state, patients are indistinguishable on the basis of their risk scores or physiological variables. Risk trajectories diverge only upon entry into the pre-shock state ( Liu et al, 2019 ). Entry into this state is rapid, with a large change in risk score occurring over a time window of 30–60 min.…”

“…More recent studies have corroborated this finding, though delayed treatment remains common in current practice ( Kumar et al, 2006 ; Martin-loeches et al, 2015 ; Ferrer et al, 2014 ; Levy et al, 2010 ; Kalil et al, 2017 ). Consequently, a number of computational approaches for early prediction of sepsis and septic shock using electronic health record (EHR) data have been developed ( Henry et al, 2015 ; Nemati et al, 2018 ; Mao et al, 2018 ; Liu et al, 2019 ). We recently developed a method for predicting patients with sepsis who are likely to transition to septic shock based on the hypothesis that there exists a detectable physiologically distinct state of sepsis, which we identify as the ‘pre-shock state,’ and that entry into this state presages the impending onset of septic shock ( Liu et al, 2019 ).…”

“…Consequently, a number of computational approaches for early prediction of sepsis and septic shock using electronic health record (EHR) data have been developed ( Henry et al, 2015 ; Nemati et al, 2018 ; Mao et al, 2018 ; Liu et al, 2019 ). We recently developed a method for predicting patients with sepsis who are likely to transition to septic shock based on the hypothesis that there exists a detectable physiologically distinct state of sepsis, which we identify as the ‘pre-shock state,’ and that entry into this state presages the impending onset of septic shock ( Liu et al, 2019 ). The pre-shock state was characterized by fitting a regression model to classify data from patients with sepsis who never develop septic shock from those who do.…”