Literature DB >> 27270843

Predicting Sepsis Risk Using the "Sniffer" Algorithm in the Electronic Medical Record.

Evelyn M Olenick1, Kathie S Zimbro, Gabrielle M DʼLima, Patricia Ver Schneider, Danielle Jones.   

Abstract

The Sepsis "Sniffer" Algorithm (SSA) has merit as a digital sepsis alert but should be considered an adjunct to versus an alternative for the Nurse Screening Tool (NST), given lower specificity and positive predictive value. The SSA reduced the risk of incorrectly categorizing patients at low risk for sepsis, detected sepsis high risk in half the time, and reduced redundant NST screens by 70% and manual screening hours by 64% to 72%. Preserving nurse hours expended on manual sepsis alerts may translate into time directed toward other patient priorities.

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Year:  2017        PMID: 27270843     DOI: 10.1097/NCQ.0000000000000198

Source DB:  PubMed          Journal:  J Nurs Care Qual        ISSN: 1057-3631            Impact factor:   1.597


  2 in total

Review 1.  Information Technology and Acute Kidney Injury: Alerts, Alarms, Bells, and Whistles.

Authors:  F Perry Wilson
Journal:  Adv Chronic Kidney Dis       Date:  2017-07       Impact factor: 3.620

2.  On classifying sepsis heterogeneity in the ICU: insight using machine learning.

Authors:  Zina M Ibrahim; Honghan Wu; Ahmed Hamoud; Lukas Stappen; Richard J B Dobson; Andrea Agarossi
Journal:  J Am Med Inform Assoc       Date:  2020-03-01       Impact factor: 4.497

  2 in total

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