Assessing The Predictive Value of Clinical Factors Used to Determine The Presence of Sepsis Causing Shock in the Emergency Department

Shock. 2016 Jul;46(1):27-32. doi: 10.1097/SHK.0000000000000558.


Introduction: Differentiating shock etiologies is a challenging task in the Emergency Department (ED); even the strongest clinical predictors leave some diagnostic uncertainty. This study sought to establish an evidence base for using clinical covariates in the diagnostic evaluation of septic shock.

Methods: We conducted a prospective, observational study of consecutive ED patients with shock from November 11, 2012 to September 23, 2013. We included all patients at least 18 years old with shock, defined as new vasopressor requirement, systolic blood pressure less than 90 mmHg after at least 1 L of crystalloid or 2 units packed red blood cells, or systolic blood pressure less than 90 mmHg and fluids withheld due to concern for fluid overload. Multivariate logistic regression and recursive partitioning models were constructed, predicting septic cause of shock. The logistic regression model was derived using first 500 patients, and validated with the subsequent 200 patients.

Results: In the derivation cohort, 55.6% (95% confidence interval: 51.2%-60.0%) were septic, and 20.8% (17.2%-24.4%) died during hospitalization. The multivariate model (derivation area under the curve = 0.88, validation area under the curve = 0.89) identified predictors of septic shock, including temperature more than 100.4°F (odds ratio 4.6, 2.3-9.2) and history of fever (odds ratio 9.2, 4.4-19.2); however, only 153 of 277 (55.3%, 49.5%-61.2%) patients with septic shock had either of these findings. In the recursive partitioning model, if all predictors were absent, the probability of sepsis causing shock was 21% (16.6%-25.6%).

Conclusions: Clinical data can predict the presence of sepsis causing shock in the ED in most patients. The remaining diagnostic uncertainty provides an opportunity for adding novel diagnostic testing.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Aged, 80 and over
  • Emergency Service, Hospital / statistics & numerical data
  • Female
  • Humans
  • Logistic Models
  • Male
  • Middle Aged
  • Predictive Value of Tests
  • Prospective Studies
  • Sepsis / complications*
  • Sepsis / diagnosis*
  • Shock, Septic / diagnosis*
  • Shock, Septic / etiology*