title: Physiologically-based, predictive analytics using the heart-rate-to-Systolic-Ratio significantly improves the timeliness and accuracy of sepsis prediction compared to SIRS.
abstract: BACKGROUND
  Enhancing the efficiency of diagnosis and treatment of severe sepsis by using physiologically-based, predictive analytical strategies has not been fully explored. We hypothesize assessment of heart-rate-to-systolic-ratio significantly increases the timeliness and accuracy of sepsis prediction after emergency department (ED) presentation.
  
  
  METHODS
  We evaluated the records of 53,313 ED patients from a large, urban teaching hospital between January and June 2015. The HR-to-systolic ratio was compared to SIRS criteria for sepsis prediction. There were 884 patients with discharge diagnoses of sepsis, severe sepsis, and/or septic shock.
  
  
  RESULTS
  Variations in three presenting variables, heart rate, systolic BP and temperature were determined to be primary early predictors of sepsis with a 74% (654/884) accuracy compared to 34% (304/884) using SIRS criteria (p < 0.0001)in confirmed septic patients.
  
  
  CONCLUSIONS
  Physiologically-based predictive analytics improved the accuracy and expediency of sepsis identification via detection of variations in HR-to-systolic ratio. This approach may lead to earlier sepsis workup and life-saving interventions.
authors.affiliation: Morehouse School of Medicine, Atlanta, GA, USA. Electronic address: odanner@msm.edu.
authors.forename: Omar K
authors.initials: OK
authors.lastname: Danner
authors.affiliation: Performametrics, Boston, MA, USA.
authors.forename: Sandra
authors.initials: S
authors.lastname: Hendren
authors.affiliation: Grady Memorial Hospital, Atlanta, GA, USA.
authors.forename: Ethel
authors.initials: E
authors.lastname: Santiago
authors.affiliation: Grady Memorial Hospital, Atlanta, GA, USA.
authors.forename: Brittany
authors.initials: B
authors.lastname: Nye
authors.affiliation: Grady Memorial Hospital, Atlanta, GA, USA.
authors.forename: Prasad
authors.initials: P
authors.lastname: Abraham
mesh_terms: D000465:Algorithms; D001831:Body Temperature; D042241:Early Diagnosis; D004636:Emergency Service, Hospital; D006339:Heart Rate; D006801:Humans; D000069550:Machine Learning; D012680:Sensitivity and Specificity; D018805:Sepsis / Q000175:diagnosis*; D018746:Systemic Inflammatory Response Syndrome / Q000175:diagnosis*; D013599:Systole
keywords: Big data; Heart rate to systolic ratio; Machine learning; Predictive analytics; Sepsis; Sepsis predictive probability index; Severe sepsis