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Identifying Infected Emergency Department Patients Admitted to the Hospital Ward at Risk of Clinical Deterioration and Intensive Care Unit Transfer

Objectives:  An important challenge faced by emergency physicians (EPs) is determining which patients should be admitted to an intensive care unit (ICU) and which can be safely admitted to a regular ward. Understanding risk factors leading to undertriage would be useful, but these factors are not we...

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Published in:Academic emergency medicine 2010-10, Vol.17 (10), p.1080-1085
Main Authors: Kennedy, Maura, Joyce, Nina, Howell, Michael D., Lawrence Mottley, J., Shapiro, Nathan I.
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description Objectives:  An important challenge faced by emergency physicians (EPs) is determining which patients should be admitted to an intensive care unit (ICU) and which can be safely admitted to a regular ward. Understanding risk factors leading to undertriage would be useful, but these factors are not well characterized. Methods:  The authors performed a secondary analysis of two prospective, observational studies of patients admitted to the hospital with clinically suspected infection from an urban university emergency department (ED). Inclusion criteria were as follows: adult ED patient (age 18 years or older), ward admission, and suspected infection. The primary outcome was transfer to an ICU within 48 hours of admission. Using multiple logistic regression, independent predictors of early ICU transfer were identified, and the area under the curve for the model was calculated. Results:  Of 5,365 subjects, 93 (1.7%) were transferred to an ICU within 48 hours. Independent predictors of ICU transfer included respiratory compromise (odds ratio [OR] = 2.5, 95% confidence interval [CI] = 1.4 to 4.3), congestive heart failure (CHF; OR = 2.2, 95% CI = 1.4 to 3.6), peripheral vascular disease (OR = 2.0, 95% CI = 1.1 to 3.7), systolic blood pressure (sBP)  90 beats/min (OR = 1.8, 95% CI = 1.1 to 2.8), and creatinine > 2.0 (OR = 1.8, 95% CI = 1.1 to 2.8). Cellulitis was associated with a lower likelihood of ICU transfer (OR = 0.33, 95% CI = 0.15 to 0.72). The area under the curve for the model was 0.73, showing moderate discriminatory ability. Conclusions:  In this preliminary study, independent predictors of ICU transfer within 48 hours of admission were identified. While somewhat intuitive, physicians should consider these factors when determining patient disposition. ACADEMIC EMERGENCY MEDICINE 2010; 17:1080–1085 © 2010 by the Society for Academic Emergency Medicine
doi_str_mv 10.1111/j.1553-2712.2010.00872.x
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Understanding risk factors leading to undertriage would be useful, but these factors are not well characterized. Methods:  The authors performed a secondary analysis of two prospective, observational studies of patients admitted to the hospital with clinically suspected infection from an urban university emergency department (ED). Inclusion criteria were as follows: adult ED patient (age 18 years or older), ward admission, and suspected infection. The primary outcome was transfer to an ICU within 48 hours of admission. Using multiple logistic regression, independent predictors of early ICU transfer were identified, and the area under the curve for the model was calculated. Results:  Of 5,365 subjects, 93 (1.7%) were transferred to an ICU within 48 hours. Independent predictors of ICU transfer included respiratory compromise (odds ratio [OR] = 2.5, 95% confidence interval [CI] = 1.4 to 4.3), congestive heart failure (CHF; OR = 2.2, 95% CI = 1.4 to 3.6), peripheral vascular disease (OR = 2.0, 95% CI = 1.1 to 3.7), systolic blood pressure (sBP) &lt; 100 mm Hg (OR = 1.9, 95% CI = 1.2 to 2.9), heart rate &gt; 90 beats/min (OR = 1.8, 95% CI = 1.1 to 2.8), and creatinine &gt; 2.0 (OR = 1.8, 95% CI = 1.1 to 2.8). Cellulitis was associated with a lower likelihood of ICU transfer (OR = 0.33, 95% CI = 0.15 to 0.72). The area under the curve for the model was 0.73, showing moderate discriminatory ability. Conclusions:  In this preliminary study, independent predictors of ICU transfer within 48 hours of admission were identified. While somewhat intuitive, physicians should consider these factors when determining patient disposition. ACADEMIC EMERGENCY MEDICINE 2010; 17:1080–1085 © 2010 by the Society for Academic Emergency Medicine</description><identifier>ISSN: 1069-6563</identifier><identifier>EISSN: 1553-2712</identifier><identifier>DOI: 10.1111/j.1553-2712.2010.00872.x</identifier><identifier>PMID: 21040109</identifier><language>eng</language><publisher>Oxford, UK: Blackwell Publishing Ltd</publisher><subject>Adult ; Age Factors ; Aged ; Aged, 80 and over ; Bacteremia - diagnosis ; Bacteremia - mortality ; Bacteremia - therapy ; Clinical medicine ; Cohort Studies ; Confidence Intervals ; critical care, sepsis ; Critical Illness - mortality ; Critical Illness - therapy ; Decision Making ; Emergency medical care ; Emergency Medicine - standards ; Emergency Medicine - trends ; emergency service, hospital ; Emergency Service, Hospital - organization &amp; administration ; Female ; Health risk assessment ; Hospital Mortality - trends ; Hospital Units - organization &amp; administration ; Hospitalization ; Hospitals, University ; Humans ; Intensive care ; intensive care units ; Intensive Care Units - organization &amp; administration ; Kaplan-Meier Estimate ; Length of Stay ; Logistic Models ; Male ; Middle Aged ; Odds Ratio ; Patient Admission ; Patient admissions ; Patient Selection ; patient transfer ; Patient Transfer - organization &amp; administration ; prognosis ; Prospective Studies ; risk assessment ; Risk Factors ; Sex Factors ; Survival Analysis ; Time Factors ; triage</subject><ispartof>Academic emergency medicine, 2010-10, Vol.17 (10), p.1080-1085</ispartof><rights>2010 by the Society for Academic Emergency Medicine</rights><rights>2010 by the Society for Academic Emergency Medicine.</rights><rights>Copyright Hanley &amp; Belfus, Inc. Oct 2010</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5002-10526a41c6d32480154d7205c4ab51ee981453d094f9e22d363cebcbd1b4bab93</citedby><cites>FETCH-LOGICAL-c5002-10526a41c6d32480154d7205c4ab51ee981453d094f9e22d363cebcbd1b4bab93</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/21040109$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Kennedy, Maura</creatorcontrib><creatorcontrib>Joyce, Nina</creatorcontrib><creatorcontrib>Howell, Michael D.</creatorcontrib><creatorcontrib>Lawrence Mottley, J.</creatorcontrib><creatorcontrib>Shapiro, Nathan I.</creatorcontrib><title>Identifying Infected Emergency Department Patients Admitted to the Hospital Ward at Risk of Clinical Deterioration and Intensive Care Unit Transfer</title><title>Academic emergency medicine</title><addtitle>Acad Emerg Med</addtitle><description>Objectives:  An important challenge faced by emergency physicians (EPs) is determining which patients should be admitted to an intensive care unit (ICU) and which can be safely admitted to a regular ward. Understanding risk factors leading to undertriage would be useful, but these factors are not well characterized. Methods:  The authors performed a secondary analysis of two prospective, observational studies of patients admitted to the hospital with clinically suspected infection from an urban university emergency department (ED). Inclusion criteria were as follows: adult ED patient (age 18 years or older), ward admission, and suspected infection. The primary outcome was transfer to an ICU within 48 hours of admission. Using multiple logistic regression, independent predictors of early ICU transfer were identified, and the area under the curve for the model was calculated. Results:  Of 5,365 subjects, 93 (1.7%) were transferred to an ICU within 48 hours. Independent predictors of ICU transfer included respiratory compromise (odds ratio [OR] = 2.5, 95% confidence interval [CI] = 1.4 to 4.3), congestive heart failure (CHF; OR = 2.2, 95% CI = 1.4 to 3.6), peripheral vascular disease (OR = 2.0, 95% CI = 1.1 to 3.7), systolic blood pressure (sBP) &lt; 100 mm Hg (OR = 1.9, 95% CI = 1.2 to 2.9), heart rate &gt; 90 beats/min (OR = 1.8, 95% CI = 1.1 to 2.8), and creatinine &gt; 2.0 (OR = 1.8, 95% CI = 1.1 to 2.8). Cellulitis was associated with a lower likelihood of ICU transfer (OR = 0.33, 95% CI = 0.15 to 0.72). The area under the curve for the model was 0.73, showing moderate discriminatory ability. Conclusions:  In this preliminary study, independent predictors of ICU transfer within 48 hours of admission were identified. While somewhat intuitive, physicians should consider these factors when determining patient disposition. 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administration</subject><subject>Hospitalization</subject><subject>Hospitals, University</subject><subject>Humans</subject><subject>Intensive care</subject><subject>intensive care units</subject><subject>Intensive Care Units - organization &amp; administration</subject><subject>Kaplan-Meier Estimate</subject><subject>Length of Stay</subject><subject>Logistic Models</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Odds Ratio</subject><subject>Patient Admission</subject><subject>Patient admissions</subject><subject>Patient Selection</subject><subject>patient transfer</subject><subject>Patient Transfer - organization &amp; administration</subject><subject>prognosis</subject><subject>Prospective Studies</subject><subject>risk assessment</subject><subject>Risk Factors</subject><subject>Sex Factors</subject><subject>Survival Analysis</subject><subject>Time Factors</subject><subject>triage</subject><issn>1069-6563</issn><issn>1553-2712</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><recordid>eNqNkc1uEzEUhUcIRNvAKyCLDasJ_hnPzwKkaBpopCIQasXS8th3UocZO7Wd0jxHXxgPKRGwwpt75fPdY1-dLEMEz0k6bzdzwjnLaUXonOJ0i3Fd0fn9k-z0KDxNPS6bvOQlO8nOQthgjHnVVM-zE0pwkcaa0-xhpcFG0--NXaOV7UFF0Gg5gl-DVXt0Dlvp45gY9EVGk2pACz2aOGHRoXgD6MKFrYlyQN-k10hG9NWE78j1qB2MNSoJ5xDBG-eTg7NIWp2eimCDuQPUSg_o2pqIrry0oQf_InvWyyHAy8c6y64_LK_ai_zy88dVu7jMFceY5gRzWsqCqFIzWtSY8EJXFHNVyI4TgKYmBWcaN0XfAKWalUxBpzpNuqKTXcNm2fuD73bXjaBVWs7LQWy9GaXfCyeN-Fux5kas3Z1gmNc1rpLBm0cD7253EKIYTVAwDNKC2wVRlZRWbApglr3-h9y4nbdpO1HxOmGswQmqD5DyLgQP_fErBIspd7ERU7xiildMuYtfuYv7NPrqz1WOg7-DTsC7A_DDDLD_b2OxaJefUsd-Au4yvh0</recordid><startdate>201010</startdate><enddate>201010</enddate><creator>Kennedy, Maura</creator><creator>Joyce, Nina</creator><creator>Howell, Michael D.</creator><creator>Lawrence Mottley, J.</creator><creator>Shapiro, Nathan I.</creator><general>Blackwell Publishing Ltd</general><general>Wiley Subscription Services, Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>K9.</scope><scope>NAPCQ</scope><scope>U9A</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>201010</creationdate><title>Identifying Infected Emergency Department Patients Admitted to the Hospital Ward at Risk of Clinical Deterioration and Intensive Care Unit Transfer</title><author>Kennedy, Maura ; 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administration</topic><topic>Female</topic><topic>Health risk assessment</topic><topic>Hospital Mortality - trends</topic><topic>Hospital Units - organization &amp; administration</topic><topic>Hospitalization</topic><topic>Hospitals, University</topic><topic>Humans</topic><topic>Intensive care</topic><topic>intensive care units</topic><topic>Intensive Care Units - organization &amp; administration</topic><topic>Kaplan-Meier Estimate</topic><topic>Length of Stay</topic><topic>Logistic Models</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Odds Ratio</topic><topic>Patient Admission</topic><topic>Patient admissions</topic><topic>Patient Selection</topic><topic>patient transfer</topic><topic>Patient Transfer - organization &amp; administration</topic><topic>prognosis</topic><topic>Prospective Studies</topic><topic>risk assessment</topic><topic>Risk Factors</topic><topic>Sex Factors</topic><topic>Survival Analysis</topic><topic>Time Factors</topic><topic>triage</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kennedy, Maura</creatorcontrib><creatorcontrib>Joyce, Nina</creatorcontrib><creatorcontrib>Howell, Michael D.</creatorcontrib><creatorcontrib>Lawrence Mottley, J.</creatorcontrib><creatorcontrib>Shapiro, Nathan I.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>Nursing &amp; Allied Health Premium</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Academic emergency medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kennedy, Maura</au><au>Joyce, Nina</au><au>Howell, Michael D.</au><au>Lawrence Mottley, J.</au><au>Shapiro, Nathan I.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identifying Infected Emergency Department Patients Admitted to the Hospital Ward at Risk of Clinical Deterioration and Intensive Care Unit Transfer</atitle><jtitle>Academic emergency medicine</jtitle><addtitle>Acad Emerg Med</addtitle><date>2010-10</date><risdate>2010</risdate><volume>17</volume><issue>10</issue><spage>1080</spage><epage>1085</epage><pages>1080-1085</pages><issn>1069-6563</issn><eissn>1553-2712</eissn><abstract>Objectives:  An important challenge faced by emergency physicians (EPs) is determining which patients should be admitted to an intensive care unit (ICU) and which can be safely admitted to a regular ward. Understanding risk factors leading to undertriage would be useful, but these factors are not well characterized. Methods:  The authors performed a secondary analysis of two prospective, observational studies of patients admitted to the hospital with clinically suspected infection from an urban university emergency department (ED). Inclusion criteria were as follows: adult ED patient (age 18 years or older), ward admission, and suspected infection. The primary outcome was transfer to an ICU within 48 hours of admission. Using multiple logistic regression, independent predictors of early ICU transfer were identified, and the area under the curve for the model was calculated. Results:  Of 5,365 subjects, 93 (1.7%) were transferred to an ICU within 48 hours. Independent predictors of ICU transfer included respiratory compromise (odds ratio [OR] = 2.5, 95% confidence interval [CI] = 1.4 to 4.3), congestive heart failure (CHF; OR = 2.2, 95% CI = 1.4 to 3.6), peripheral vascular disease (OR = 2.0, 95% CI = 1.1 to 3.7), systolic blood pressure (sBP) &lt; 100 mm Hg (OR = 1.9, 95% CI = 1.2 to 2.9), heart rate &gt; 90 beats/min (OR = 1.8, 95% CI = 1.1 to 2.8), and creatinine &gt; 2.0 (OR = 1.8, 95% CI = 1.1 to 2.8). Cellulitis was associated with a lower likelihood of ICU transfer (OR = 0.33, 95% CI = 0.15 to 0.72). The area under the curve for the model was 0.73, showing moderate discriminatory ability. Conclusions:  In this preliminary study, independent predictors of ICU transfer within 48 hours of admission were identified. While somewhat intuitive, physicians should consider these factors when determining patient disposition. 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subjects Adult
Age Factors
Aged
Aged, 80 and over
Bacteremia - diagnosis
Bacteremia - mortality
Bacteremia - therapy
Clinical medicine
Cohort Studies
Confidence Intervals
critical care, sepsis
Critical Illness - mortality
Critical Illness - therapy
Decision Making
Emergency medical care
Emergency Medicine - standards
Emergency Medicine - trends
emergency service, hospital
Emergency Service, Hospital - organization & administration
Female
Health risk assessment
Hospital Mortality - trends
Hospital Units - organization & administration
Hospitalization
Hospitals, University
Humans
Intensive care
intensive care units
Intensive Care Units - organization & administration
Kaplan-Meier Estimate
Length of Stay
Logistic Models
Male
Middle Aged
Odds Ratio
Patient Admission
Patient admissions
Patient Selection
patient transfer
Patient Transfer - organization & administration
prognosis
Prospective Studies
risk assessment
Risk Factors
Sex Factors
Survival Analysis
Time Factors
triage
title Identifying Infected Emergency Department Patients Admitted to the Hospital Ward at Risk of Clinical Deterioration and Intensive Care Unit Transfer
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