title: A computational approach to early sepsis detection.
abstract: To develop high-performance early sepsis prediction technology for the general patient population.
  
  
  
  Retrospective analysis of adult patients admitted to the intensive care unit (from the MIMIC II dataset) who were not septic at the time of admission.
  
  
  
  A sepsis early warning algorithm, InSight, was developed and applied to the prediction of sepsis up to three hours prior to a patient's first five hour Systemic Inflammatory Response Syndrome (SIRS) episode. When applied to a never-before-seen set of test patients, InSight predictions demonstrated a sensitivity of 0.90 (95% CI: 0.89-0.91) and a specificity of 0.81 (95% CI: 0.80-0.82), exceeding or rivaling that of existing biomarker detection methods. Across predictive times up to three hours before a sustained SIRS event, InSight maintained an average area under the ROC curve of 0.83 (95% CI: 0.80-0.86). Analysis of patient sepsis risk showed that contributions from the coevolution of multiple risk factors were more important than the contributions from isolated individual risk factors when making predictions further in advance.
  
  
  
  Sepsis can be predicted at least three hours in advance of onset of the first five hour SIRS episode, using only nine commonly available vital signs, with better performance than methods in standard practice today. High-order correlations of vital sign measurements are key to this prediction, which improves the likelihood of early identification of at-risk patients.
authors.affiliation: Dascena Inc., Hayward, CA, United States.
authors.forename: Jacob S
authors.initials: JS
authors.lastname: Calvert
authors.affiliation: Dascena Inc., Hayward, CA, United States.
authors.forename: Daniel A
authors.initials: DA
authors.lastname: Price
authors.affiliation: Kaiser Permanente South San Francisco Medical Center, South San Francisco, CA, United States; Department of Emergency Medicine, University of California San Francisco, San Francisco, CA, United States.
authors.forename: Uli K
authors.initials: UK
authors.lastname: Chettipally
authors.affiliation: Department of Emergency Medicine, University of California San Francisco, San Francisco, CA, United States.
authors.forename: Christopher W
authors.initials: CW
authors.lastname: Barton
authors.affiliation: Division of General Internal Medicine, Department of Medicine, University of California San Francisco, San Francisco, CA, United States.
authors.forename: Mitchell D
authors.initials: MD
authors.lastname: Feldman
authors.affiliation: Dascena Inc., Hayward, CA, United States. Electronic address: jana@dascena.com.
authors.forename: Jana L
authors.initials: JL
authors.lastname: Hoffman
authors.affiliation: Dascena Inc., Hayward, CA, United States.
authors.forename: Melissa
authors.initials: M
authors.lastname: Jay
authors.affiliation: Dascena Inc., Hayward, CA, United States.
authors.forename: Ritankar
authors.initials: R
authors.lastname: Das
mesh_terms: D000328:Adult; D015415:Biomarkers / Q000378:metabolism; D003422:Critical Care / Q000379:methods; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D012189:Retrospective Studies; D018805:Sepsis / Q000175:diagnosis* / Q000378:metabolism; D013997:Time Factors
chemical_list: D015415:Biomarkers
keywords: Clinical decision support systems; Computer-assisted diagnosis; Early diagnosis; Medical informatics; Sepsis; Severe sepsis