Prospective evaluation of an automated method to identify patients with severe sepsis or septic shock in the emergency department
Identifieur interne : 000849 ( Main/Exploration ); précédent : 000848; suivant : 000850Prospective 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.
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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Brown</name><affiliation wicri:level="1"><nlm:aff id="Aff1">Pulmonary and Critical Care Medicine, Intermountain Medical Center, Murray, USA</nlm:aff><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><affiliation wicri:level="1"><nlm:aff id="Aff2">Pulmonary and Critical Care Medicine, University of Utah School of Medicine, Salt Lake City, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Pulmonary and Critical Care Medicine, University of Utah School of Medicine, Salt Lake City</wicri:regionArea><wicri:noRegion>Salt Lake City</wicri:noRegion></affiliation><affiliation wicri:level="2"><nlm:aff id="Aff7">Shock Trauma ICU, Intermountain Medical Center, 5121 S. 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Brown</name><affiliation wicri:level="1"><nlm:aff id="Aff1">Pulmonary and Critical Care Medicine, Intermountain Medical Center, Murray, USA</nlm:aff><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><affiliation wicri:level="1"><nlm:aff id="Aff2">Pulmonary and Critical Care Medicine, University of Utah School of Medicine, Salt Lake City, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Pulmonary and Critical Care Medicine, University of Utah School of Medicine, Salt Lake City</wicri:regionArea><wicri:noRegion>Salt Lake City</wicri:noRegion></affiliation><affiliation wicri:level="2"><nlm:aff id="Aff7">Shock Trauma ICU, Intermountain Medical Center, 5121 S. Cottonwood Street, Murray, UT 84107 USA</nlm:aff><country xml:lang="fr">États-Unis</country><placeName><region type="state">Utah</region></placeName><wicri:cityArea>Shock Trauma ICU, Intermountain Medical Center, 5121 S. Cottonwood Street, Murray</wicri:cityArea></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:aff id="Aff3">Clinical Intelligence and Decision Support, Kaiser Permanente Southern California, Pasadena, USA</nlm:aff><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:aff id="Aff1">Pulmonary and Critical Care Medicine, Intermountain Medical Center, Murray, USA</nlm:aff><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><affiliation wicri:level="1"><nlm:aff id="Aff4">Homer Warner Center for Informatics Research, Intermountain Healthcare, Murray, USA</nlm:aff><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><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:aff id="Aff5">Intensive Medicine Clinical Program, Intermountain Healthcare, Salt Lake City, USA</nlm:aff><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:aff id="Aff6">Institute for Healthcare Delivery Research, Intermountain Healthcare, Salt Lake City, USA</nlm:aff><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:aff id="Aff4">Homer Warner Center for Informatics Research, Intermountain Healthcare, Murray, USA</nlm:aff><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">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"><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></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Angus, Dc" uniqKey="Angus D">DC Angus</name></author><author><name sortKey="Linde Zwirble, Wt" uniqKey="Linde Zwirble W">WT Linde-Zwirble</name></author><author><name sortKey="Lidicker, J" uniqKey="Lidicker J">J Lidicker</name></author><author><name sortKey="Clermont, G" uniqKey="Clermont G">G Clermont</name></author><author><name sortKey="Carcillo, J" uniqKey="Carcillo J">J Carcillo</name></author><author><name sortKey="Pinsky, Mr" uniqKey="Pinsky M">MR Pinsky</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Martin, Gs" uniqKey="Martin G">GS Martin</name></author><author><name sortKey="Mannino, Dm" uniqKey="Mannino D">DM Mannino</name></author><author><name 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sortKey="Friedrichs, Md" uniqKey="Friedrichs M">MD Friedrichs</name></author><author><name sortKey="Mayer, J" uniqKey="Mayer J">J Mayer</name></author><author><name sortKey="Hirshberg, El" uniqKey="Hirshberg E">EL Hirshberg</name></author><author><name sortKey="Conklin, J" uniqKey="Conklin J">J Conklin</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Harrell, Fe" uniqKey="Harrell F">FE Harrell</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Efron, B" uniqKey="Efron B">B Efron</name></author><author><name sortKey="Tibshirani, R" uniqKey="Tibshirani R">R Tibshirani</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Van Der Sijs, H" uniqKey="Van Der Sijs H">H van der Sijs</name></author><author><name sortKey="Aarts, J" uniqKey="Aarts J">J Aarts</name></author><author><name sortKey="Vulto, A" uniqKey="Vulto A">A Vulto</name></author><author><name sortKey="Berg, M" uniqKey="Berg M">M Berg</name></author></analytic></biblStruct></listBibl></div1></back></TEI><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>Pour manipuler ce document sous Unix (Dilib)
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