title: High-performance detection and early prediction of septic shock for alcohol-use disorder patients.
abstract: BACKGROUND
  The presence of Alcohol Use Disorder (AUD) complicates the medical conditions of patients and increases the difficulty of detecting and predicting the onset of septic shock for patients in the ICU.
  
  
  METHODS
  We have developed a high-performance sepsis prediction algorithm, InSight, which outperforms existing methods for AUD patient populations. InSight analyses a combination of singlets, doublets, and triplets of clinical measurements over time to generate a septic shock risk score. AUD patients obtained from the MIMIC III database were used in this retrospective study to train InSight and compare performance with the Modified Early Warning Score (MEWS), the Simplified Acute Physiology Score (SAPS II), and the Systemic Inflammatory Response Syndrome (SIRS) for septic shock prediction and detection.
  
  
  RESULTS
  From 4-fold cross validation, InSight performs particularly well on diagnostic odds ratio and demonstrates a relatively high Area Under the Receiver Operating Characteristic (AUROC) metric. Four hours prior to onset, InSight had an average AUROC of 0.815, and at the time of onset, InSight had an average AUROC value of 0.965. When applied to patient populations where AUD may complicate prediction methods of sepsis, InSight outperforms existing diagnostic tools.
  
  
  CONCLUSIONS
  Analysis of the higher order correlations and trends between relevant clinical measurements using the InSight algorithm leads to more accurate detection and prediction of septic shock, even in cases where diagnosis may be confounded by AUD.
authors.affiliation: Dascena Inc., Hayward, CA, USA.
authors.forename: Jacob
authors.initials: J
authors.lastname: Calvert
authors.affiliation: Dascena Inc., Hayward, CA, USA.
authors.forename: Thomas
authors.initials: T
authors.lastname: Desautels
authors.affiliation: Kaiser Permanente South San Francisco Medical Center, South San Francisco, CA, USA; Department of Emergency Medicine, University of California San Francisco, San Francisco, CA, USA.
authors.forename: Uli
authors.initials: U
authors.lastname: Chettipally
authors.affiliation: Department of Emergency Medicine, University of California San Francisco, San Francisco, CA, USA.
authors.forename: Christopher
authors.initials: C
authors.lastname: Barton
authors.affiliation: Dascena Inc., Hayward, CA, USA.
authors.forename: Jana
authors.initials: J
authors.lastname: Hoffman
authors.affiliation: Dascena Inc., Hayward, CA, USA.
authors.forename: Melissa
authors.initials: M
authors.lastname: Jay
authors.affiliation: Dascena Inc., Hayward, CA, USA.
authors.forename: Qingqing
authors.initials: Q
authors.lastname: Mao
authors.affiliation: Dascena Inc., Hayward, CA, USA.
authors.forename: Hamid
authors.initials: H
authors.lastname: Mohamadlou
authors.affiliation: Dascena Inc., Hayward, CA, USA.
authors.forename: Ritankar
authors.initials: R
authors.lastname: Das
keywords: Alcohol use disorder; Clinical decision support systems; Electronic health records; Sepsis; Septic shock