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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 |
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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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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) < 100 mm Hg (OR = 1.9, 95% CI = 1.2 to 2.9), heart rate > 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</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 & 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</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 & 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) < 100 mm Hg (OR = 1.9, 95% CI = 1.2 to 2.9), heart rate > 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</description><subject>Adult</subject><subject>Age Factors</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Bacteremia - diagnosis</subject><subject>Bacteremia - mortality</subject><subject>Bacteremia - therapy</subject><subject>Clinical medicine</subject><subject>Cohort Studies</subject><subject>Confidence Intervals</subject><subject>critical care, sepsis</subject><subject>Critical Illness - mortality</subject><subject>Critical Illness - therapy</subject><subject>Decision Making</subject><subject>Emergency medical care</subject><subject>Emergency Medicine - standards</subject><subject>Emergency Medicine - trends</subject><subject>emergency service, hospital</subject><subject>Emergency Service, Hospital - organization & administration</subject><subject>Female</subject><subject>Health risk assessment</subject><subject>Hospital Mortality - trends</subject><subject>Hospital Units - organization & 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 & 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 & 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 ; Joyce, Nina ; Howell, Michael D. ; Lawrence Mottley, J. ; Shapiro, Nathan I.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5002-10526a41c6d32480154d7205c4ab51ee981453d094f9e22d363cebcbd1b4bab93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Adult</topic><topic>Age Factors</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Bacteremia - diagnosis</topic><topic>Bacteremia - mortality</topic><topic>Bacteremia - therapy</topic><topic>Clinical medicine</topic><topic>Cohort Studies</topic><topic>Confidence Intervals</topic><topic>critical care, sepsis</topic><topic>Critical Illness - mortality</topic><topic>Critical Illness - therapy</topic><topic>Decision Making</topic><topic>Emergency medical care</topic><topic>Emergency Medicine - standards</topic><topic>Emergency Medicine - trends</topic><topic>emergency service, hospital</topic><topic>Emergency Service, Hospital - organization & administration</topic><topic>Female</topic><topic>Health risk assessment</topic><topic>Hospital Mortality - trends</topic><topic>Hospital Units - organization & 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 & 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 & 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 & Medical Complete (Alumni)</collection><collection>Nursing & 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) < 100 mm Hg (OR = 1.9, 95% CI = 1.2 to 2.9), heart rate > 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</abstract><cop>Oxford, UK</cop><pub>Blackwell Publishing Ltd</pub><pmid>21040109</pmid><doi>10.1111/j.1553-2712.2010.00872.x</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record> |
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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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