Prospective evaluation of an automated method to identify patients with severe sepsis or septic shock in the emergency department.
Identifieur interne : 000474 ( PubMed/Checkpoint ); précédent : 000473; suivant : 000475Prospective 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.
Descripteurs français
- KwdFr :
- Adulte, Adulte d'âge moyen, Choc septique (diagnostic), Courbe ROC, Facteurs de l'âge, Femelle, Humains, Mâle, Numération des leucocytes, Pression sanguine, Rythme cardiaque, Sensibilité et spécificité, Sepsie (diagnostic), Service hospitalier d'urgences (), Température du corps, Théorème de Bayes, Triage (), Études rétrospectives.
- MESH :
- diagnostic : Choc septique, Sepsie.
- Adulte, Adulte d'âge moyen, Courbe ROC, Facteurs de l'âge, Femelle, Humains, Mâle, Numération des leucocytes, Pression sanguine, Rythme cardiaque, Sensibilité et spécificité, Service hospitalier d'urgences, Température du corps, Théorème de Bayes, Triage, Études rétrospectives.
English descriptors
- KwdEn :
- Adult, Age Factors, Bayes Theorem, Blood Pressure, Body Temperature, Emergency Service, Hospital (statistics & numerical data), Female, Heart Rate, Humans, Leukocyte Count, Male, Middle Aged, ROC Curve, Retrospective Studies, Sensitivity and Specificity, Sepsis (diagnosis), Shock, Septic (diagnosis), Triage (methods).
- MESH :
- diagnosis : Sepsis, Shock, Septic.
- methods : Triage.
- statistics & numerical data : Emergency Service, Hospital.
- Adult, Age Factors, Bayes Theorem, Blood Pressure, Body Temperature, Female, Heart Rate, Humans, Leukocyte Count, Male, Middle Aged, ROC Curve, Retrospective Studies, Sensitivity and Specificity.
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.
DOI: 10.1186/s12873-016-0095-0
PubMed: 27549755
Affiliations:
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Links to Exploration step
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Brown</name><affiliation wicri:level="1"><nlm:affiliation>Pulmonary and Critical Care Medicine, Intermountain Medical Center, Murray, USA. samuel.brown@imail.org.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Pulmonary and Critical Care Medicine, Intermountain Medical Center, Murray</wicri:regionArea><wicri:noRegion>Murray</wicri:noRegion></affiliation></author><author><name sortKey="Jones, Jason" sort="Jones, Jason" uniqKey="Jones J" first="Jason" last="Jones">Jason Jones</name><affiliation wicri:level="1"><nlm:affiliation>Clinical Intelligence and Decision Support, Kaiser Permanente Southern California, Pasadena, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Clinical Intelligence and Decision Support, Kaiser Permanente Southern California, Pasadena</wicri:regionArea><wicri:noRegion>Pasadena</wicri:noRegion></affiliation></author><author><name sortKey="Kuttler, Kathryn Gibb" sort="Kuttler, Kathryn Gibb" uniqKey="Kuttler K" first="Kathryn Gibb" last="Kuttler">Kathryn Gibb Kuttler</name><affiliation wicri:level="1"><nlm:affiliation>Pulmonary and Critical Care Medicine, Intermountain Medical Center, Murray, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Pulmonary and Critical Care Medicine, Intermountain Medical Center, Murray</wicri:regionArea><wicri:noRegion>Murray</wicri:noRegion></affiliation></author><author><name sortKey="Keddington, Roger K" sort="Keddington, Roger K" uniqKey="Keddington R" first="Roger K" last="Keddington">Roger K. 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Brown</name><affiliation wicri:level="1"><nlm:affiliation>Pulmonary and Critical Care Medicine, Intermountain Medical Center, Murray, USA. samuel.brown@imail.org.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Pulmonary and Critical Care Medicine, Intermountain Medical Center, Murray</wicri:regionArea><wicri:noRegion>Murray</wicri:noRegion></affiliation></author><author><name sortKey="Jones, Jason" sort="Jones, Jason" uniqKey="Jones J" first="Jason" last="Jones">Jason Jones</name><affiliation wicri:level="1"><nlm:affiliation>Clinical Intelligence and Decision Support, Kaiser Permanente Southern California, Pasadena, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Clinical Intelligence and Decision Support, Kaiser Permanente Southern California, Pasadena</wicri:regionArea><wicri:noRegion>Pasadena</wicri:noRegion></affiliation></author><author><name sortKey="Kuttler, Kathryn Gibb" sort="Kuttler, Kathryn Gibb" uniqKey="Kuttler K" first="Kathryn Gibb" last="Kuttler">Kathryn Gibb Kuttler</name><affiliation wicri:level="1"><nlm:affiliation>Pulmonary and Critical Care Medicine, Intermountain Medical Center, Murray, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Pulmonary and Critical Care Medicine, Intermountain Medical Center, Murray</wicri:regionArea><wicri:noRegion>Murray</wicri:noRegion></affiliation></author><author><name sortKey="Keddington, Roger K" sort="Keddington, Roger K" uniqKey="Keddington R" first="Roger K" last="Keddington">Roger K. Keddington</name><affiliation wicri:level="1"><nlm:affiliation>Intensive Medicine Clinical Program, Intermountain Healthcare, Salt Lake City, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Intensive Medicine Clinical Program, Intermountain Healthcare, Salt Lake City</wicri:regionArea><wicri:noRegion>Salt Lake City</wicri:noRegion></affiliation></author><author><name sortKey="Allen, Todd L" sort="Allen, Todd L" uniqKey="Allen T" first="Todd L" last="Allen">Todd L. Allen</name><affiliation wicri:level="1"><nlm:affiliation>Institute for Healthcare Delivery Research, Intermountain Healthcare, Salt Lake City, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Institute for Healthcare Delivery Research, Intermountain Healthcare, Salt Lake City</wicri:regionArea><wicri:noRegion>Salt Lake City</wicri:noRegion></affiliation></author><author><name sortKey="Haug, Peter" sort="Haug, Peter" uniqKey="Haug P" first="Peter" last="Haug">Peter Haug</name><affiliation wicri:level="1"><nlm:affiliation>Homer Warner Center for Informatics Research, Intermountain Healthcare, Murray, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Homer Warner Center for Informatics Research, Intermountain Healthcare, Murray</wicri:regionArea><wicri:noRegion>Murray</wicri:noRegion></affiliation></author></analytic><series><title level="j">BMC emergency medicine</title><idno type="eISSN">1471-227X</idno><imprint><date when="2016" type="published">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adult</term><term>Age Factors</term><term>Bayes Theorem</term><term>Blood Pressure</term><term>Body Temperature</term><term>Emergency Service, Hospital (statistics & numerical data)</term><term>Female</term><term>Heart Rate</term><term>Humans</term><term>Leukocyte Count</term><term>Male</term><term>Middle Aged</term><term>ROC Curve</term><term>Retrospective Studies</term><term>Sensitivity and Specificity</term><term>Sepsis (diagnosis)</term><term>Shock, Septic (diagnosis)</term><term>Triage (methods)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Adulte</term><term>Adulte d'âge moyen</term><term>Choc septique (diagnostic)</term><term>Courbe ROC</term><term>Facteurs de l'âge</term><term>Femelle</term><term>Humains</term><term>Mâle</term><term>Numération des leucocytes</term><term>Pression sanguine</term><term>Rythme cardiaque</term><term>Sensibilité et spécificité</term><term>Sepsie (diagnostic)</term><term>Service hospitalier d'urgences ()</term><term>Température du corps</term><term>Théorème de Bayes</term><term>Triage ()</term><term>Études rétrospectives</term></keywords><keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Sepsis</term><term>Shock, Septic</term></keywords><keywords scheme="MESH" qualifier="diagnostic" xml:lang="fr"><term>Choc septique</term><term>Sepsie</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Triage</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Emergency Service, Hospital</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adult</term><term>Age Factors</term><term>Bayes Theorem</term><term>Blood Pressure</term><term>Body Temperature</term><term>Female</term><term>Heart Rate</term><term>Humans</term><term>Leukocyte Count</term><term>Male</term><term>Middle Aged</term><term>ROC Curve</term><term>Retrospective Studies</term><term>Sensitivity and Specificity</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Adulte</term><term>Adulte d'âge moyen</term><term>Courbe ROC</term><term>Facteurs de l'âge</term><term>Femelle</term><term>Humains</term><term>Mâle</term><term>Numération des leucocytes</term><term>Pression sanguine</term><term>Rythme cardiaque</term><term>Sensibilité et spécificité</term><term>Service hospitalier d'urgences</term><term>Température du corps</term><term>Théorème de Bayes</term><term>Triage</term><term>Études rétrospectives</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">27549755</PMID><DateCompleted><Year>2017</Year><Month>11</Month><Day>27</Day></DateCompleted><DateRevised><Year>2018</Year><Month>11</Month><Day>13</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1471-227X</ISSN><JournalIssue CitedMedium="Internet"><Volume>16</Volume><Issue>1</Issue><PubDate><Year>2016</Year><Month>08</Month><Day>22</Day></PubDate></JournalIssue><Title>BMC emergency medicine</Title><ISOAbbreviation>BMC Emerg Med</ISOAbbreviation></Journal><ArticleTitle>Prospective evaluation of an automated method to identify patients with severe sepsis or septic shock in the emergency department.</ArticleTitle><Pagination><MedlinePgn>31</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/s12873-016-0095-0</ELocationID><Abstract><AbstractText Label="BACKGROUND">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.</AbstractText><AbstractText Label="METHODS">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.</AbstractText><AbstractText Label="RESULTS">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 %.</AbstractText><AbstractText Label="CONCLUSIONS">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.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Brown</LastName><ForeName>Samuel M</ForeName><Initials>SM</Initials><AffiliationInfo><Affiliation>Pulmonary and Critical Care Medicine, Intermountain Medical Center, Murray, USA. samuel.brown@imail.org.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Pulmonary and Critical Care Medicine, University of Utah School of Medicine, Salt Lake City, USA. samuel.brown@imail.org.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Shock Trauma ICU, Intermountain Medical Center, 5121 S. Cottonwood Street, Murray, UT, 84107, USA. samuel.brown@imail.org.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Jones</LastName><ForeName>Jason</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Clinical Intelligence and Decision Support, Kaiser Permanente Southern California, Pasadena, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kuttler</LastName><ForeName>Kathryn Gibb</ForeName><Initials>KG</Initials><AffiliationInfo><Affiliation>Pulmonary and Critical Care Medicine, Intermountain Medical Center, Murray, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Homer Warner Center for Informatics Research, Intermountain Healthcare, Murray, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Keddington</LastName><ForeName>Roger K</ForeName><Initials>RK</Initials><AffiliationInfo><Affiliation>Intensive Medicine Clinical Program, Intermountain Healthcare, Salt Lake City, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Allen</LastName><ForeName>Todd L</ForeName><Initials>TL</Initials><AffiliationInfo><Affiliation>Institute for Healthcare Delivery Research, Intermountain Healthcare, Salt Lake City, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Haug</LastName><ForeName>Peter</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>Homer Warner Center for Informatics Research, Intermountain Healthcare, Murray, USA.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>K23 GM094465</GrantID><Acronym>GM</Acronym><Agency>NIGMS NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2016</Year><Month>08</Month><Day>22</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>BMC Emerg Med</MedlineTA><NlmUniqueID>100968543</NlmUniqueID><ISSNLinking>1471-227X</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000328" MajorTopicYN="N">Adult</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000367" MajorTopicYN="N">Age Factors</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D001499" MajorTopicYN="N">Bayes Theorem</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D001794" MajorTopicYN="N">Blood Pressure</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D001831" MajorTopicYN="N">Body Temperature</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D004636" MajorTopicYN="N">Emergency Service, Hospital</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006339" MajorTopicYN="N">Heart Rate</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007958" MajorTopicYN="N">Leukocyte Count</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008297" MajorTopicYN="N">Male</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008875" MajorTopicYN="N">Middle Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012372" MajorTopicYN="N">ROC Curve</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012189" MajorTopicYN="N">Retrospective Studies</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012680" MajorTopicYN="N">Sensitivity and Specificity</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D018805" MajorTopicYN="N">Sepsis</DescriptorName><QualifierName UI="Q000175" MajorTopicYN="Y">diagnosis</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D012772" MajorTopicYN="N">Shock, Septic</DescriptorName><QualifierName UI="Q000175" MajorTopicYN="N">diagnosis</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D014218" MajorTopicYN="N">Triage</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">Automated detection</Keyword><Keyword MajorTopicYN="Y">Bayesian classifier</Keyword><Keyword MajorTopicYN="Y">Sepsis</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2016</Year><Month>03</Month><Day>05</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2016</Year><Month>08</Month><Day>12</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2016</Year><Month>8</Month><Day>24</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2016</Year><Month>8</Month><Day>24</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2017</Year><Month>11</Month><Day>29</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">27549755</ArticleId><ArticleId IdType="doi">10.1186/s12873-016-0095-0</ArticleId><ArticleId IdType="pii">10.1186/s12873-016-0095-0</ArticleId><ArticleId IdType="pmc">PMC4994262</ArticleId></ArticleIdList><ReferenceList><Reference><Citation>N Engl J Med. 2014 May 1;370(18):1683-93</Citation><ArticleIdList><ArticleId IdType="pubmed">24635773</ArticleId></ArticleIdList></Reference><Reference><Citation>J Am Med Inform Assoc. 2006 Mar-Apr;13(2):138-47</Citation><ArticleIdList><ArticleId 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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="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></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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