Prospective evaluation of an automated method to identify patients with severe sepsis or septic shock in the emergency department

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...
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Autor*in:	Brown, Samuel M. [verfasserIn] Jones, Jason Kuttler, Kathryn Gibb Keddington, Roger K. Allen, Todd L. Haug, Peter

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	Sepsis Automated detection Bayesian classifier

Anmerkung:	© The Author(s). 2016

Übergeordnetes Werk:	Enthalten in: BMC emergency medicine - London : BioMed Central, 2001, 16(2016), 1 vom: 22. Aug.
Übergeordnetes Werk:	volume:16 ; year:2016 ; number:1 ; day:22 ; month:08

Links:	Volltext

DOI / URN:	10.1186/s12873-016-0095-0

Katalog-ID:	SPR027355357

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520			\|a 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. 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. 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 %. 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.
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allfields	10.1186/s12873-016-0095-0 doi (DE-627)SPR027355357 (SPR)s12873-016-0095-0-e DE-627 ger DE-627 rakwb eng Brown, Samuel M. verfasserin aut Prospective evaluation of an automated method to identify patients with severe sepsis or septic shock in the emergency department 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2016 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. 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. 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 %. 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. Sepsis (dpeaa)DE-He213 Automated detection (dpeaa)DE-He213 Bayesian classifier (dpeaa)DE-He213 Jones, Jason aut Kuttler, Kathryn Gibb aut Keddington, Roger K. aut Allen, Todd L. aut Haug, Peter aut Enthalten in BMC emergency medicine London : BioMed Central, 2001 16(2016), 1 vom: 22. Aug. (DE-627)33101873X (DE-600)2050431-7 1471-227X nnns volume:16 year:2016 number:1 day:22 month:08 https://dx.doi.org/10.1186/s12873-016-0095-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2016 1 22 08
spelling	10.1186/s12873-016-0095-0 doi (DE-627)SPR027355357 (SPR)s12873-016-0095-0-e DE-627 ger DE-627 rakwb eng Brown, Samuel M. verfasserin aut Prospective evaluation of an automated method to identify patients with severe sepsis or septic shock in the emergency department 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2016 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. 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. 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 %. 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. Sepsis (dpeaa)DE-He213 Automated detection (dpeaa)DE-He213 Bayesian classifier (dpeaa)DE-He213 Jones, Jason aut Kuttler, Kathryn Gibb aut Keddington, Roger K. aut Allen, Todd L. aut Haug, Peter aut Enthalten in BMC emergency medicine London : BioMed Central, 2001 16(2016), 1 vom: 22. Aug. (DE-627)33101873X (DE-600)2050431-7 1471-227X nnns volume:16 year:2016 number:1 day:22 month:08 https://dx.doi.org/10.1186/s12873-016-0095-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2016 1 22 08
allfields_unstemmed	10.1186/s12873-016-0095-0 doi (DE-627)SPR027355357 (SPR)s12873-016-0095-0-e DE-627 ger DE-627 rakwb eng Brown, Samuel M. verfasserin aut Prospective evaluation of an automated method to identify patients with severe sepsis or septic shock in the emergency department 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2016 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. 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. 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 %. 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. Sepsis (dpeaa)DE-He213 Automated detection (dpeaa)DE-He213 Bayesian classifier (dpeaa)DE-He213 Jones, Jason aut Kuttler, Kathryn Gibb aut Keddington, Roger K. aut Allen, Todd L. aut Haug, Peter aut Enthalten in BMC emergency medicine London : BioMed Central, 2001 16(2016), 1 vom: 22. Aug. (DE-627)33101873X (DE-600)2050431-7 1471-227X nnns volume:16 year:2016 number:1 day:22 month:08 https://dx.doi.org/10.1186/s12873-016-0095-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2016 1 22 08
allfieldsGer	10.1186/s12873-016-0095-0 doi (DE-627)SPR027355357 (SPR)s12873-016-0095-0-e DE-627 ger DE-627 rakwb eng Brown, Samuel M. verfasserin aut Prospective evaluation of an automated method to identify patients with severe sepsis or septic shock in the emergency department 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2016 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. 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. 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 %. 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. Sepsis (dpeaa)DE-He213 Automated detection (dpeaa)DE-He213 Bayesian classifier (dpeaa)DE-He213 Jones, Jason aut Kuttler, Kathryn Gibb aut Keddington, Roger K. aut Allen, Todd L. aut Haug, Peter aut Enthalten in BMC emergency medicine London : BioMed Central, 2001 16(2016), 1 vom: 22. Aug. (DE-627)33101873X (DE-600)2050431-7 1471-227X nnns volume:16 year:2016 number:1 day:22 month:08 https://dx.doi.org/10.1186/s12873-016-0095-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2016 1 22 08
allfieldsSound	10.1186/s12873-016-0095-0 doi (DE-627)SPR027355357 (SPR)s12873-016-0095-0-e DE-627 ger DE-627 rakwb eng Brown, Samuel M. verfasserin aut Prospective evaluation of an automated method to identify patients with severe sepsis or septic shock in the emergency department 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2016 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. 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. 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 %. 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. Sepsis (dpeaa)DE-He213 Automated detection (dpeaa)DE-He213 Bayesian classifier (dpeaa)DE-He213 Jones, Jason aut Kuttler, Kathryn Gibb aut Keddington, Roger K. aut Allen, Todd L. aut Haug, Peter aut Enthalten in BMC emergency medicine London : BioMed Central, 2001 16(2016), 1 vom: 22. Aug. (DE-627)33101873X (DE-600)2050431-7 1471-227X nnns volume:16 year:2016 number:1 day:22 month:08 https://dx.doi.org/10.1186/s12873-016-0095-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2016 1 22 08
language	English
source	Enthalten in BMC emergency medicine 16(2016), 1 vom: 22. Aug. volume:16 year:2016 number:1 day:22 month:08
sourceStr	Enthalten in BMC emergency medicine 16(2016), 1 vom: 22. Aug. volume:16 year:2016 number:1 day:22 month:08
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institution	findex.gbv.de
topic_facet	Sepsis Automated detection Bayesian classifier
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author	Brown, Samuel M.
spellingShingle	Brown, Samuel M. misc Sepsis misc Automated detection misc Bayesian classifier Prospective evaluation of an automated method to identify patients with severe sepsis or septic shock in the emergency department
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topic_title	Prospective evaluation of an automated method to identify patients with severe sepsis or septic shock in the emergency department Sepsis (dpeaa)DE-He213 Automated detection (dpeaa)DE-He213 Bayesian classifier (dpeaa)DE-He213
topic	misc Sepsis misc Automated detection misc Bayesian classifier
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title	Prospective evaluation of an automated method to identify patients with severe sepsis or septic shock in the emergency department
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title_full	Prospective evaluation of an automated method to identify patients with severe sepsis or septic shock in the emergency department
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author_browse	Brown, Samuel M. Jones, Jason Kuttler, Kathryn Gibb Keddington, Roger K. Allen, Todd L. Haug, Peter
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title_sort	prospective evaluation of an automated method to identify patients with severe sepsis or septic shock in the emergency department
title_auth	Prospective evaluation of an automated method to identify patients with severe sepsis or septic shock in the emergency department
abstract	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. 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. 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 %. 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. © The Author(s). 2016
abstractGer	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. 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. 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 %. 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. © The Author(s). 2016
abstract_unstemmed	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. 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. 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 %. 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. © The Author(s). 2016
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title_short	Prospective evaluation of an automated method to identify patients with severe sepsis or septic shock in the emergency department
url	https://dx.doi.org/10.1186/s12873-016-0095-0
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author2	Jones, Jason Kuttler, Kathryn Gibb Keddington, Roger K. Allen, Todd L. Haug, Peter
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up_date	2024-07-04T01:30:11.126Z
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Prospective evaluation of an automated method to identify patients with severe sepsis or septic shock in the emergency department

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