Predicting Progression to Septic Shock in the Emergency Department Using an Externally Generalizable Machine-Learning Algorithm-Reference-Cited by-同舟云学术

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Predicting Progression to Septic Shock in the Emergency Department Using an Externally Generalizable Machine-Learning Algorithm

Published:2021-04 Issue:4 Volume:77 Page:395-406
ISSN:0196-0644
Container-title:Annals of Emergency Medicine
language:en
Short-container-title:Annals of Emergency Medicine

Author:

Wardi Gabriel^ORCID,Carlile Morgan,Holder Andre,Shashikumar Supreeth,Hayden Stephen R.,Nemati Shamim

Publisher

Elsevier BV

Subject

Emergency Medicine

Reference42 articles.

1. Incidence and trends of sepsis in US hospitals using clinical vs claims data, 2009-2014;Rhee;JAMA,2017

2. Many emergency department patients with severe sepsis and septic shock do not meet diagnostic criteria within 3 hours of arrival;Villar;Ann Emerg Med,2014

3. Predictors of patients who present to the emergency department with sepsis and progress to septic shock between 4 and 48 hours of emergency department arrival;Capp;Crit Care Med,2015

4. Unexpected intensive care transfer of admitted patients with severe sepsis;Wardi;J Intensive Care,2017

5. Early- versus late-onset shock in European intensive care units;Sakr;Shock,2007

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