TY - JOUR
T1 - Developing the surveillance algorithm for detection of failure to recognize and treat severe sepsis
AU - Harrison, Andrew M.
AU - Thongprayoon, Charat
AU - Kashyap, Rahul
AU - Chute, Christopher G.
AU - Gajic, Ognjen
AU - Pickering, Brian W.
AU - Herasevich, Vitaly
N1 - Publisher Copyright:
© 2015 Mayo Foundation for Medical Education and Research.
PY - 2015/2/1
Y1 - 2015/2/1
N2 - Objective To develop and test an automated surveillance algorithm (sepsis "sniffer") for the detection of severe sepsis and monitoring failure to recognize and treat severe sepsis in a timely manner. Patients and Methods We conducted an observational diagnostic performance study using independent derivation and validation cohorts from an electronic medical record database of the medical intensive care unit (ICU) of a tertiary referral center. All patients aged 18 years and older who were admitted to the medical ICU from January 1 through March 31, 2013 (N=587), were included. The criterion standard for severe sepsis/septic shock was manual review by 2 trained reviewers with a third superreviewer for cases of interobserver disagreement. Critical appraisal of false-positive and false-negative alerts, along with recursive data partitioning, was performed for algorithm optimization. Results An algorithm based on criteria for suspicion of infection, systemic inflammatory response syndrome, organ hypoperfusion and dysfunction, and shock had a sensitivity of 80% and a specificity of 96% when applied to the validation cohort. In order, low systolic blood pressure, systemic inflammatory response syndrome positivity, and suspicion of infection were determined through recursive data partitioning to be of greatest predictive value. Lastly, 117 alert-positive patients (68% of the 171 patients with severe sepsis) had a delay in recognition and treatment, defined as no lactate and central venous pressure measurement within 2 hours of the alert. Conclusion The optimized sniffer accurately identified patients with severe sepsis that bedside clinicians failed to recognize and treat in a timely manner.
AB - Objective To develop and test an automated surveillance algorithm (sepsis "sniffer") for the detection of severe sepsis and monitoring failure to recognize and treat severe sepsis in a timely manner. Patients and Methods We conducted an observational diagnostic performance study using independent derivation and validation cohorts from an electronic medical record database of the medical intensive care unit (ICU) of a tertiary referral center. All patients aged 18 years and older who were admitted to the medical ICU from January 1 through March 31, 2013 (N=587), were included. The criterion standard for severe sepsis/septic shock was manual review by 2 trained reviewers with a third superreviewer for cases of interobserver disagreement. Critical appraisal of false-positive and false-negative alerts, along with recursive data partitioning, was performed for algorithm optimization. Results An algorithm based on criteria for suspicion of infection, systemic inflammatory response syndrome, organ hypoperfusion and dysfunction, and shock had a sensitivity of 80% and a specificity of 96% when applied to the validation cohort. In order, low systolic blood pressure, systemic inflammatory response syndrome positivity, and suspicion of infection were determined through recursive data partitioning to be of greatest predictive value. Lastly, 117 alert-positive patients (68% of the 171 patients with severe sepsis) had a delay in recognition and treatment, defined as no lactate and central venous pressure measurement within 2 hours of the alert. Conclusion The optimized sniffer accurately identified patients with severe sepsis that bedside clinicians failed to recognize and treat in a timely manner.
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U2 - 10.1016/j.mayocp.2014.11.014
DO - 10.1016/j.mayocp.2014.11.014
M3 - Article
C2 - 25576199
AN - SCOPUS:84922212327
SN - 0025-6196
VL - 90
SP - 166
EP - 175
JO - Mayo Clinic proceedings
JF - Mayo Clinic proceedings
IS - 2
ER -