Prospective evaluation of an automated method to identify patients with severe sepsis or septic shock in the emergency department
Identifieur interne : 000283 ( Pmc/Checkpoint ); précédent : 000282; suivant : 000284Prospective evaluation of an automated method to identify patients with severe sepsis or septic shock in the emergency department
Auteurs : Samuel M. Brown [États-Unis] ; Jason Jones [États-Unis] ; Kathryn Gibb Kuttler [États-Unis] ; Roger K. Keddington [États-Unis] ; Todd L. Allen [États-Unis] ; Peter Haug [États-Unis]Source :
- BMC Emergency Medicine [ 1471-227X ] ; 2016.
Abstract
Sepsis is an often-fatal syndrome resulting from severe infection. Rapid identification and treatment are critical for septic patients. We therefore developed a probabilistic model to identify septic patients in the emergency department (ED). We aimed to produce a model that identifies 80 % of sepsis patients, with no more than 15 false positive alerts per day, within one hour of ED admission, using routine clinical data.
We developed the model using retrospective data for 132,748 ED encounters (549 septic), with manual chart review to confirm cases of severe sepsis or septic shock from January 2006 through December 2008. A naïve Bayes model was used to select model features, starting with clinician-proposed candidate variables, which were then used to calculate the probability of sepsis. We evaluated the accuracy of the resulting model in 93,733 ED encounters from April 2009 through June 2010.
The final model included mean blood pressure, temperature, age, heart rate, and white blood cell count. The area under the receiver operating characteristic curve (AUC) for the continuous predictor model was 0.953. The binary alert achieved 76.4 % sensitivity with a false positive rate of 4.7 %.
We developed and validated a probabilistic model to identify sepsis early in an ED encounter. Despite changes in process, organizational focus, and the H1N1 influenza pandemic, our model performed adequately in our validation cohort, suggesting that it will be generalizable.
Url:
DOI: 10.1186/s12873-016-0095-0
PubMed: 27549755
PubMed Central: 4994262
Affiliations:
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PMC:4994262Le document en format XML
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<series><title level="j">BMC Emergency Medicine</title>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>Sepsis is an often-fatal syndrome resulting from severe infection. Rapid identification and treatment are critical for septic patients. We therefore developed a probabilistic model to identify septic patients in the emergency department (ED). We aimed to produce a model that identifies 80 % of sepsis patients, with no more than 15 false positive alerts per day, within one hour of ED admission, using routine clinical data.</p>
</sec>
<sec><title>Methods</title>
<p>We developed the model using retrospective data for 132,748 ED encounters (549 septic), with manual chart review to confirm cases of severe sepsis or septic shock from January 2006 through December 2008. A naïve Bayes model was used to select model features, starting with clinician-proposed candidate variables, which were then used to calculate the probability of sepsis. We evaluated the accuracy of the resulting model in 93,733 ED encounters from April 2009 through June 2010.</p>
</sec>
<sec><title>Results</title>
<p>The final model included mean blood pressure, temperature, age, heart rate, and white blood cell count. The area under the receiver operating characteristic curve (AUC) for the continuous predictor model was 0.953. The binary alert achieved 76.4 % sensitivity with a false positive rate of 4.7 %.</p>
</sec>
<sec><title>Conclusions</title>
<p>We developed and validated a probabilistic model to identify sepsis early in an ED encounter. Despite changes in process, organizational focus, and the H1N1 influenza pandemic, our model performed adequately in our validation cohort, suggesting that it will be generalizable.</p>
</sec>
</div>
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<title-group><article-title>Prospective evaluation of an automated method to identify patients with severe sepsis or septic shock in the emergency department</article-title>
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<contrib-group><contrib contrib-type="author" corresp="yes" equal-contrib="yes"><name><surname>Brown</surname>
<given-names>Samuel M.</given-names>
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<address><phone>(801) 507-6529</phone>
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<given-names>Jason</given-names>
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</contrib>
<aff id="Aff1"><label>1</label>
Pulmonary and Critical Care Medicine, Intermountain Medical Center, Murray, USA</aff>
<aff id="Aff2"><label>2</label>
Pulmonary and Critical Care Medicine, University of Utah School of Medicine, Salt Lake City, USA</aff>
<aff id="Aff3"><label>3</label>
Clinical Intelligence and Decision Support, Kaiser Permanente Southern California, Pasadena, USA</aff>
<aff id="Aff4"><label>4</label>
Homer Warner Center for Informatics Research, Intermountain Healthcare, Murray, USA</aff>
<aff id="Aff5"><label>5</label>
Intensive Medicine Clinical Program, Intermountain Healthcare, Salt Lake City, USA</aff>
<aff id="Aff6"><label>6</label>
Institute for Healthcare Delivery Research, Intermountain Healthcare, Salt Lake City, USA</aff>
<aff id="Aff7"><label>7</label>
Shock Trauma ICU, Intermountain Medical Center, 5121 S. Cottonwood Street, Murray, UT 84107 USA</aff>
</contrib-group>
<pub-date pub-type="epub"><day>22</day>
<month>8</month>
<year>2016</year>
</pub-date>
<pub-date pub-type="pmc-release"><day>22</day>
<month>8</month>
<year>2016</year>
</pub-date>
<pub-date pub-type="collection"><year>2016</year>
</pub-date>
<volume>16</volume>
<issue>1</issue>
<elocation-id>31</elocation-id>
<history><date date-type="received"><day>5</day>
<month>3</month>
<year>2016</year>
</date>
<date date-type="accepted"><day>12</day>
<month>8</month>
<year>2016</year>
</date>
</history>
<permissions><copyright-statement>© The Author(s). 2016</copyright-statement>
<license license-type="OpenAccess"><license-p><bold>Open Access</bold>
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>
), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/publicdomain/zero/1.0/">http://creativecommons.org/publicdomain/zero/1.0/</ext-link>
) applies to the data made available in this article, unless otherwise stated.</license-p>
</license>
</permissions>
<abstract id="Abs1"><sec><title>Background</title>
<p>Sepsis is an often-fatal syndrome resulting from severe infection. Rapid identification and treatment are critical for septic patients. We therefore developed a probabilistic model to identify septic patients in the emergency department (ED). We aimed to produce a model that identifies 80 % of sepsis patients, with no more than 15 false positive alerts per day, within one hour of ED admission, using routine clinical data.</p>
</sec>
<sec><title>Methods</title>
<p>We developed the model using retrospective data for 132,748 ED encounters (549 septic), with manual chart review to confirm cases of severe sepsis or septic shock from January 2006 through December 2008. A naïve Bayes model was used to select model features, starting with clinician-proposed candidate variables, which were then used to calculate the probability of sepsis. We evaluated the accuracy of the resulting model in 93,733 ED encounters from April 2009 through June 2010.</p>
</sec>
<sec><title>Results</title>
<p>The final model included mean blood pressure, temperature, age, heart rate, and white blood cell count. The area under the receiver operating characteristic curve (AUC) for the continuous predictor model was 0.953. The binary alert achieved 76.4 % sensitivity with a false positive rate of 4.7 %.</p>
</sec>
<sec><title>Conclusions</title>
<p>We developed and validated a probabilistic model to identify sepsis early in an ED encounter. Despite changes in process, organizational focus, and the H1N1 influenza pandemic, our model performed adequately in our validation cohort, suggesting that it will be generalizable.</p>
</sec>
</abstract>
<kwd-group xml:lang="en"><title>Keywords</title>
<kwd>Sepsis</kwd>
<kwd>Automated detection</kwd>
<kwd>Bayesian classifier</kwd>
</kwd-group>
<funding-group><award-group><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/100000057</institution-id>
<institution>National Institute of General Medical Sciences</institution>
</institution-wrap>
</funding-source>
<award-id>k23gm094465</award-id>
<principal-award-recipient><name><surname>Brown</surname>
<given-names>Samuel M.</given-names>
</name>
</principal-award-recipient>
</award-group>
</funding-group>
<custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name>
<meta-value>© The Author(s) 2016</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
</pmc>
<affiliations><list><country><li>États-Unis</li>
</country>
<region><li>Utah</li>
</region>
</list>
<tree><country name="États-Unis"><noRegion><name sortKey="Brown, Samuel M" sort="Brown, Samuel M" uniqKey="Brown S" first="Samuel M." last="Brown">Samuel M. Brown</name>
</noRegion>
<name sortKey="Allen, Todd L" sort="Allen, Todd L" uniqKey="Allen T" first="Todd L." last="Allen">Todd L. Allen</name>
<name sortKey="Brown, Samuel M" sort="Brown, Samuel M" uniqKey="Brown S" first="Samuel M." last="Brown">Samuel M. Brown</name>
<name sortKey="Brown, Samuel M" sort="Brown, Samuel M" uniqKey="Brown S" first="Samuel M." last="Brown">Samuel M. Brown</name>
<name sortKey="Haug, Peter" sort="Haug, Peter" uniqKey="Haug P" first="Peter" last="Haug">Peter Haug</name>
<name sortKey="Jones, Jason" sort="Jones, Jason" uniqKey="Jones J" first="Jason" last="Jones">Jason Jones</name>
<name sortKey="Keddington, Roger K" sort="Keddington, Roger K" uniqKey="Keddington R" first="Roger K." last="Keddington">Roger K. Keddington</name>
<name sortKey="Kuttler, Kathryn Gibb" sort="Kuttler, Kathryn Gibb" uniqKey="Kuttler K" first="Kathryn Gibb" last="Kuttler">Kathryn Gibb Kuttler</name>
<name sortKey="Kuttler, Kathryn Gibb" sort="Kuttler, Kathryn Gibb" uniqKey="Kuttler K" first="Kathryn Gibb" last="Kuttler">Kathryn Gibb Kuttler</name>
</country>
</tree>
</affiliations>
</record>
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