Developing the surveillance algorithm for detection of failure to recognize and treat severe sepsis

Andrew M. Harrison, Charat Thongprayoon, Rahul Kashyap, Christopher G. Chute, Ognjen Gajic, Brian W Pickering, Vitaly D Herasevich

Research output: Contribution to journalArticle

21 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)166-175
Number of pages10
JournalMayo Clinic Proceedings
Volume90
Issue number2
DOIs
StatePublished - Feb 1 2015

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Sepsis
Systemic Inflammatory Response Syndrome
Intensive Care Units
Central Venous Pressure
Electronic Health Records
Septic Shock
Infection
Tertiary Care Centers
Hypotension
Shock
Lactic Acid
Databases
Blood Pressure
Therapeutics

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Developing the surveillance algorithm for detection of failure to recognize and treat severe sepsis. / Harrison, Andrew M.; Thongprayoon, Charat; Kashyap, Rahul; Chute, Christopher G.; Gajic, Ognjen; Pickering, Brian W; Herasevich, Vitaly D.

In: Mayo Clinic Proceedings, Vol. 90, No. 2, 01.02.2015, p. 166-175.

Research output: Contribution to journalArticle

Harrison, Andrew M. ; Thongprayoon, Charat ; Kashyap, Rahul ; Chute, Christopher G. ; Gajic, Ognjen ; Pickering, Brian W ; Herasevich, Vitaly D. / Developing the surveillance algorithm for detection of failure to recognize and treat severe sepsis. In: Mayo Clinic Proceedings. 2015 ; Vol. 90, No. 2. pp. 166-175.
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