A Simple Bacteremia Score for Predicting Bacteremia in Patients with Suspected Infection in the Emergency Department: A Cohort Study
Bacteremia is a life-threatening condition that has increased in prevalence over the past two decades. Prompt recognition of bacteremia is important; however, identification of bacteremia requires 1 to 2 days. This retrospective cohort study, conducted from 10 November 2014 to November 2019, among p...
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creator | Han, Hyelin Kim, Da Seul Kim, Minha Heo, Sejin Chang, Hansol Lee, Gun Tak Lee, Se Uk Kim, Taerim Yoon, Hee Hwang, Sung Yeon Cha, Won Chul Sim, Min Sub Jo, Ik Joon Park, Jong Eun Shin, Tae Gun |
description | Bacteremia is a life-threatening condition that has increased in prevalence over the past two decades. Prompt recognition of bacteremia is important; however, identification of bacteremia requires 1 to 2 days. This retrospective cohort study, conducted from 10 November 2014 to November 2019, among patients with suspected infection who visited the emergency department (ED), aimed to develop and validate a simple tool for predicting bacteremia. The study population was randomly divided into derivation and development cohorts. Predictors of bacteremia based on the literature and logistic regression were assessed. A weighted value was assigned to predictors to develop a prediction model for bacteremia using the derivation cohort; discrimination was then assessed using the area under the receiver operating characteristic curve (AUC). Among the 22,519 patients enrolled, 18,015 were assigned to the derivation group and 4504 to the validation group. Sixteen candidate variables were selected, and all sixteen were used as significant predictors of bacteremia (model 1). Among the sixteen variables, the top five with higher odds ratio, including procalcitonin, neutrophil-lymphocyte ratio (NLR), lactate level, platelet count, and body temperature, were used for the simple bacteremia score (model 2). The proportion of bacteremia increased according to the simple bacteremia score in both cohorts. The AUC for model 1 was 0.805 (95% confidence interval [CI] 0.785-0.824) and model 2 was 0.791 (95% CI 0.772-0.810). The simple bacteremia prediction score using only five variables demonstrated a comparable performance with the model including sixteen variables using all laboratory results and vital signs. This simple score is useful for predicting bacteremia-assisted clinical decisions. |
doi_str_mv | 10.3390/jpm14010057 |
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Prompt recognition of bacteremia is important; however, identification of bacteremia requires 1 to 2 days. This retrospective cohort study, conducted from 10 November 2014 to November 2019, among patients with suspected infection who visited the emergency department (ED), aimed to develop and validate a simple tool for predicting bacteremia. The study population was randomly divided into derivation and development cohorts. Predictors of bacteremia based on the literature and logistic regression were assessed. A weighted value was assigned to predictors to develop a prediction model for bacteremia using the derivation cohort; discrimination was then assessed using the area under the receiver operating characteristic curve (AUC). Among the 22,519 patients enrolled, 18,015 were assigned to the derivation group and 4504 to the validation group. Sixteen candidate variables were selected, and all sixteen were used as significant predictors of bacteremia (model 1). Among the sixteen variables, the top five with higher odds ratio, including procalcitonin, neutrophil-lymphocyte ratio (NLR), lactate level, platelet count, and body temperature, were used for the simple bacteremia score (model 2). The proportion of bacteremia increased according to the simple bacteremia score in both cohorts. The AUC for model 1 was 0.805 (95% confidence interval [CI] 0.785-0.824) and model 2 was 0.791 (95% CI 0.772-0.810). The simple bacteremia prediction score using only five variables demonstrated a comparable performance with the model including sixteen variables using all laboratory results and vital signs. This simple score is useful for predicting bacteremia-assisted clinical decisions.</description><identifier>ISSN: 2075-4426</identifier><identifier>EISSN: 2075-4426</identifier><identifier>DOI: 10.3390/jpm14010057</identifier><identifier>PMID: 38248758</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Antibiotics ; Bacteremia ; Blood ; Body temperature ; Cohort analysis ; Emergency medical care ; Emergency service ; Health aspects ; Hospitals ; Infection ; Infections ; Leukocytes (neutrophilic) ; Lymphocytes ; Medical research ; Medicine, Experimental ; Neutrophils ; Patients ; Population studies ; Prediction models ; Procalcitonin ; Regression analysis ; Sepsis ; Statistical analysis ; Variables</subject><ispartof>Journal of personalized medicine, 2024-01, Vol.14 (1), p.57</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c379t-ff655a7229878b84f2bdad417c507e635e9361028e307c7f026cba31f48bee613</cites><orcidid>0000-0002-2624-0008 ; 0000-0002-1297-9813 ; 0000-0001-9657-1040 ; 0000-0002-1352-3009 ; 0000-0001-8340-9511 ; 0000-0003-1714-1400</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38248758$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Han, Hyelin</creatorcontrib><creatorcontrib>Kim, Da Seul</creatorcontrib><creatorcontrib>Kim, Minha</creatorcontrib><creatorcontrib>Heo, Sejin</creatorcontrib><creatorcontrib>Chang, Hansol</creatorcontrib><creatorcontrib>Lee, Gun Tak</creatorcontrib><creatorcontrib>Lee, Se Uk</creatorcontrib><creatorcontrib>Kim, Taerim</creatorcontrib><creatorcontrib>Yoon, Hee</creatorcontrib><creatorcontrib>Hwang, Sung Yeon</creatorcontrib><creatorcontrib>Cha, Won Chul</creatorcontrib><creatorcontrib>Sim, Min Sub</creatorcontrib><creatorcontrib>Jo, Ik Joon</creatorcontrib><creatorcontrib>Park, Jong Eun</creatorcontrib><creatorcontrib>Shin, Tae Gun</creatorcontrib><title>A Simple Bacteremia Score for Predicting Bacteremia in Patients with Suspected Infection in the Emergency Department: A Cohort Study</title><title>Journal of personalized medicine</title><addtitle>J Pers Med</addtitle><description>Bacteremia is a life-threatening condition that has increased in prevalence over the past two decades. Prompt recognition of bacteremia is important; however, identification of bacteremia requires 1 to 2 days. This retrospective cohort study, conducted from 10 November 2014 to November 2019, among patients with suspected infection who visited the emergency department (ED), aimed to develop and validate a simple tool for predicting bacteremia. The study population was randomly divided into derivation and development cohorts. Predictors of bacteremia based on the literature and logistic regression were assessed. A weighted value was assigned to predictors to develop a prediction model for bacteremia using the derivation cohort; discrimination was then assessed using the area under the receiver operating characteristic curve (AUC). Among the 22,519 patients enrolled, 18,015 were assigned to the derivation group and 4504 to the validation group. Sixteen candidate variables were selected, and all sixteen were used as significant predictors of bacteremia (model 1). Among the sixteen variables, the top five with higher odds ratio, including procalcitonin, neutrophil-lymphocyte ratio (NLR), lactate level, platelet count, and body temperature, were used for the simple bacteremia score (model 2). The proportion of bacteremia increased according to the simple bacteremia score in both cohorts. The AUC for model 1 was 0.805 (95% confidence interval [CI] 0.785-0.824) and model 2 was 0.791 (95% CI 0.772-0.810). The simple bacteremia prediction score using only five variables demonstrated a comparable performance with the model including sixteen variables using all laboratory results and vital signs. This simple score is useful for predicting bacteremia-assisted clinical decisions.</description><subject>Antibiotics</subject><subject>Bacteremia</subject><subject>Blood</subject><subject>Body temperature</subject><subject>Cohort analysis</subject><subject>Emergency medical care</subject><subject>Emergency service</subject><subject>Health aspects</subject><subject>Hospitals</subject><subject>Infection</subject><subject>Infections</subject><subject>Leukocytes (neutrophilic)</subject><subject>Lymphocytes</subject><subject>Medical research</subject><subject>Medicine, Experimental</subject><subject>Neutrophils</subject><subject>Patients</subject><subject>Population studies</subject><subject>Prediction models</subject><subject>Procalcitonin</subject><subject>Regression analysis</subject><subject>Sepsis</subject><subject>Statistical analysis</subject><subject>Variables</subject><issn>2075-4426</issn><issn>2075-4426</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNptkd1rFDEUxQdRbKl98l0CvgiybT4mH-PbulYtFCysPg-ZzM1ulplkTDLIvvuHm6VVt6XJQy65v3O43FNVrwm-YKzBl7tpJDUmGHP5rDqlWPJFXVPx_Kg-qc5T2uFyFKdU4JfVCVO0VpKr0-r3Eq3dOA2APmqTIcLoNFqbEAHZENFthN6Z7PzmuO88utXZgc8J_XJ5i9ZzmqC0e3TtbSlc8AcobwFdjRA34M0efYJJxzwW1Qe0RKuwDTGjdZ77_avqhdVDgvP796z68fnq--rr4ubbl-vV8mZhmGzywlrBuZaUNkqqTtWWdr3uayINxxIE49AwQTBVwLA00mIqTKcZsbXqAARhZ9W7O98php8zpNyOLhkYBu0hzKmlDZG8OGBR0LeP0F2Yoy_THSglJRWU_Kc2eoDWeRty1OZg2i6lwgVrpCzUxRNUuX3ZpgkerCv_DwTv7wQmhpQi2HaKbtRx3xLcHmJvj2Iv9Jv7UeduhP4f-zdk9gfN_aXV</recordid><startdate>20240101</startdate><enddate>20240101</enddate><creator>Han, Hyelin</creator><creator>Kim, Da Seul</creator><creator>Kim, Minha</creator><creator>Heo, Sejin</creator><creator>Chang, Hansol</creator><creator>Lee, Gun Tak</creator><creator>Lee, Se Uk</creator><creator>Kim, Taerim</creator><creator>Yoon, Hee</creator><creator>Hwang, Sung Yeon</creator><creator>Cha, Won Chul</creator><creator>Sim, Min Sub</creator><creator>Jo, Ik Joon</creator><creator>Park, Jong Eun</creator><creator>Shin, Tae Gun</creator><general>MDPI AG</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FH</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>LK8</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-2624-0008</orcidid><orcidid>https://orcid.org/0000-0002-1297-9813</orcidid><orcidid>https://orcid.org/0000-0001-9657-1040</orcidid><orcidid>https://orcid.org/0000-0002-1352-3009</orcidid><orcidid>https://orcid.org/0000-0001-8340-9511</orcidid><orcidid>https://orcid.org/0000-0003-1714-1400</orcidid></search><sort><creationdate>20240101</creationdate><title>A Simple Bacteremia Score for Predicting Bacteremia in Patients with Suspected Infection in the Emergency Department: A Cohort Study</title><author>Han, Hyelin ; Kim, Da Seul ; Kim, Minha ; Heo, Sejin ; Chang, Hansol ; Lee, Gun Tak ; Lee, Se Uk ; Kim, Taerim ; Yoon, Hee ; Hwang, Sung Yeon ; Cha, Won Chul ; Sim, Min Sub ; Jo, Ik Joon ; Park, Jong Eun ; Shin, Tae Gun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c379t-ff655a7229878b84f2bdad417c507e635e9361028e307c7f026cba31f48bee613</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Antibiotics</topic><topic>Bacteremia</topic><topic>Blood</topic><topic>Body temperature</topic><topic>Cohort analysis</topic><topic>Emergency medical care</topic><topic>Emergency service</topic><topic>Health aspects</topic><topic>Hospitals</topic><topic>Infection</topic><topic>Infections</topic><topic>Leukocytes (neutrophilic)</topic><topic>Lymphocytes</topic><topic>Medical research</topic><topic>Medicine, Experimental</topic><topic>Neutrophils</topic><topic>Patients</topic><topic>Population studies</topic><topic>Prediction models</topic><topic>Procalcitonin</topic><topic>Regression analysis</topic><topic>Sepsis</topic><topic>Statistical analysis</topic><topic>Variables</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Han, Hyelin</creatorcontrib><creatorcontrib>Kim, Da Seul</creatorcontrib><creatorcontrib>Kim, Minha</creatorcontrib><creatorcontrib>Heo, Sejin</creatorcontrib><creatorcontrib>Chang, Hansol</creatorcontrib><creatorcontrib>Lee, Gun Tak</creatorcontrib><creatorcontrib>Lee, Se Uk</creatorcontrib><creatorcontrib>Kim, Taerim</creatorcontrib><creatorcontrib>Yoon, Hee</creatorcontrib><creatorcontrib>Hwang, Sung Yeon</creatorcontrib><creatorcontrib>Cha, Won Chul</creatorcontrib><creatorcontrib>Sim, Min Sub</creatorcontrib><creatorcontrib>Jo, Ik Joon</creatorcontrib><creatorcontrib>Park, Jong Eun</creatorcontrib><creatorcontrib>Shin, Tae Gun</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Biological Science Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of personalized medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Han, Hyelin</au><au>Kim, Da Seul</au><au>Kim, Minha</au><au>Heo, Sejin</au><au>Chang, Hansol</au><au>Lee, Gun Tak</au><au>Lee, Se Uk</au><au>Kim, Taerim</au><au>Yoon, Hee</au><au>Hwang, Sung Yeon</au><au>Cha, Won Chul</au><au>Sim, Min Sub</au><au>Jo, Ik Joon</au><au>Park, Jong Eun</au><au>Shin, Tae Gun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Simple Bacteremia Score for Predicting Bacteremia in Patients with Suspected Infection in the Emergency Department: A Cohort Study</atitle><jtitle>Journal of personalized medicine</jtitle><addtitle>J Pers Med</addtitle><date>2024-01-01</date><risdate>2024</risdate><volume>14</volume><issue>1</issue><spage>57</spage><pages>57-</pages><issn>2075-4426</issn><eissn>2075-4426</eissn><abstract>Bacteremia is a life-threatening condition that has increased in prevalence over the past two decades. Prompt recognition of bacteremia is important; however, identification of bacteremia requires 1 to 2 days. This retrospective cohort study, conducted from 10 November 2014 to November 2019, among patients with suspected infection who visited the emergency department (ED), aimed to develop and validate a simple tool for predicting bacteremia. The study population was randomly divided into derivation and development cohorts. Predictors of bacteremia based on the literature and logistic regression were assessed. A weighted value was assigned to predictors to develop a prediction model for bacteremia using the derivation cohort; discrimination was then assessed using the area under the receiver operating characteristic curve (AUC). Among the 22,519 patients enrolled, 18,015 were assigned to the derivation group and 4504 to the validation group. Sixteen candidate variables were selected, and all sixteen were used as significant predictors of bacteremia (model 1). Among the sixteen variables, the top five with higher odds ratio, including procalcitonin, neutrophil-lymphocyte ratio (NLR), lactate level, platelet count, and body temperature, were used for the simple bacteremia score (model 2). The proportion of bacteremia increased according to the simple bacteremia score in both cohorts. The AUC for model 1 was 0.805 (95% confidence interval [CI] 0.785-0.824) and model 2 was 0.791 (95% CI 0.772-0.810). The simple bacteremia prediction score using only five variables demonstrated a comparable performance with the model including sixteen variables using all laboratory results and vital signs. 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subjects | Antibiotics Bacteremia Blood Body temperature Cohort analysis Emergency medical care Emergency service Health aspects Hospitals Infection Infections Leukocytes (neutrophilic) Lymphocytes Medical research Medicine, Experimental Neutrophils Patients Population studies Prediction models Procalcitonin Regression analysis Sepsis Statistical analysis Variables |
title | A Simple Bacteremia Score for Predicting Bacteremia in Patients with Suspected Infection in the Emergency Department: A Cohort Study |
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