Literature DB >> 28190560

Filtering authentic sepsis arising in the ICU using administrative codes coupled to a SIRS screening protocol.

Christopher L Sudduth1, Elizabeth C Overton2, Peter F Lyu3, Ramzy H Rimawi4, Timothy G Buchman5.   

Abstract

PURPOSE: Using administrative codes and minimal physiologic and laboratory data, we sought a high-specificity identification strategy for patients whose sepsis initially appeared during their ICU stay.
MATERIALS AND METHODS: We studied all patients discharged from an academic hospital between September 1, 2013 and October 31, 2014. Administrative codes and minimal physiologic and laboratory criteria were used to identify patients at high risk of developing the onset of sepsis in the ICU. Two clinicians then independently reviewed the patient record to verify that the screened-in patients appeared to become septic during their ICU admission.
RESULTS: Clinical chart review verified sepsis in 437/466 ICU stays (93.8%). Of these 437 encounters, only 151 (34.6%) were admitted to the ICU with neither SIRS nor evidence of infection and therefore appeared to become septic during their ICU stay.
CONCLUSIONS: Selected administrative codes coupled to SIRS criteria and applied to patients admitted to ICU can yield up to 94% authentic sepsis patients. However, only 1/3 of patients thus identified appeared to become septic during their ICU stay. Studies that depend on high-intensity monitoring for description of the time course of sepsis require clinician review and verification that sepsis initially appeared during the monitoring period.
Copyright © 2017 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Administrative codes; Detection; Epidemiology; Intensive care unit; Sepsis; Systemic inflammatory response syndrome

Mesh:

Year:  2017        PMID: 28190560     DOI: 10.1016/j.jcrc.2017.01.012

Source DB:  PubMed          Journal:  J Crit Care        ISSN: 0883-9441            Impact factor:   3.425


  2 in total

1.  Improving transitions and outcomes of sepsis survivors (I-TRANSFER): a type 1 hybrid protocol.

Authors:  Melissa O'Connor; Erin E Kennedy; Karen B Hirschman; Mark E Mikkelsen; Partha Deb; Miriam Ryvicker; Nancy A Hodgson; Yolanda Barrón; Michael A Stawnychy; Patrik A Garren; Kathryn H Bowles
Journal:  BMC Palliat Care       Date:  2022-06-02       Impact factor: 3.113

2.  Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals.

Authors:  Hoyt Burdick; Eduardo Pino; Denise Gabel-Comeau; Carol Gu; Jonathan Roberts; Sidney Le; Joseph Slote; Nicholas Saber; Emily Pellegrini; Abigail Green-Saxena; Jana Hoffman; Ritankar Das
Journal:  BMC Med Inform Decis Mak       Date:  2020-10-27       Impact factor: 2.796

  2 in total

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