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Education, Teaching, and Quality Improvement |

Validation of an Electronic Surveillance Tool for Identifying Hospital Inpatients With Severe Sepsis

Bristol Brandt, BS; Amanda Gartner, MSN; Michael Moncure, MD; Chad Cannon, MD; Liz Carlton, MSN; Steven Simpson, MD
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University of Kansas Medical Center, Kansas City, KS


Chest. 2013;144(4_MeetingAbstracts):560A. doi:10.1378/chest.1704562
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Abstract

SESSION TITLE: Outcomes/Quality Control Posters

SESSION TYPE: Original Investigation Poster

PRESENTED ON: Wednesday, October 30, 2013 at 01:30 PM - 02:30 PM

PURPOSE: To assess the value of CareVeillance (CV), an electronic surveillance tool that continuously samples data from the electronic medical record (EMR) for early identification of severe sepsis.

METHODS: This is a prospective and retrospective descriptive analysis of CV, developed at the University of Kansas Hospital (KUH). Patients with severe sepsis were identified in 3 ways: 11 day real-time pilot of CV software in 2/12, comparison of hospital discharge (administrative) data with a retrospective application of CV software to all KUH patients discharged in 2/12, and retrospective adjudication of patients in both categories (gold standard). Random number generation was used to determine a statistical sample of the CV-identified severe sepsis patients for adjudication.

RESULTS: During the real-time pilot, CV identified 19 patients who were concurrently determined to have severe sepsis by trained CV monitoring personnel; 16 were discharged in 2/12 and were evaluated in the retrospective CV application where all 16 were, likewise, identified. Of this 16, adjudication diagnosis agreed with CV diagnosis 100% of the time. Only 13 of the 16 (81.3%) were diagnosed by their care team with severe sepsis during their hospitalization; 2 of the remaining 3 had severe sepsis on adjudication. Administrative data identified 104 patients with severe sepsis who were discharged in 2/12 (not limited to the pilot period). CV retrospectively identified all 104 of these patients and an additional 110 patients as having severe sepsis. Adjudication of 22 (20%) of these additional patients revealed that 8 (36.4%) had severe sepsis, suggesting that of the 110 patients, 40 had severe sepsis but were not diagnosed during their hospitalization. CV falsely identified 70 patients as having severe sepsis.

CONCLUSIONS: CV identified severe sepsis patients when it was applied to EMR data either prospectively or retrospectively; it was more sensitive than hospital care teams. CV identified all patients with severe sepsis according to the administrative data but also identified patients the care team and therefore the administrative data missed. However, CV has relatively low specificity.

CLINICAL IMPLICATIONS: CV identifies patients with severe sepsis that may otherwise go unrecognized. Applied prospectively and judiciously, CV can bring this information to the attention of the patient's care team and has the ability to reduce these patients' length of ICU and hospital stay, overall cost of care, and mortality.

DISCLOSURE: The following authors have nothing to disclose: Bristol Brandt, Amanda Gartner, Michael Moncure, Chad Cannon, Liz Carlton, Steven Simpson

No Product/Research Disclosure Information


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