Prediction of Critical Illness During Out-of-Hospital Emergency Care
CONTEXT Early identification of nontrauma patients in need of critical care services in the emergency setting may improve triage decisions and facilitate regionalization of critical care. OBJECTIVES To determine the out-of-hospital clinical predictors of critical illness and to characterize the perf...
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description | CONTEXT Early identification of nontrauma patients in need of critical care services in the emergency setting may improve triage decisions and facilitate regionalization of critical care. OBJECTIVES To determine the out-of-hospital clinical predictors of critical illness and to characterize the performance of a simple score for out-of-hospital prediction of development of critical illness during hospitalization. DESIGN AND SETTING Population-based cohort study of an emergency medical services (EMS) system in greater King County, Washington (excluding metropolitan Seattle), that transports to 16 receiving facilities. PATIENTS Nontrauma, non–cardiac arrest adult patients transported to a hospital by King County EMS from 2002 through 2006. Eligible records with complete data (N = 144 913) were linked to hospital discharge data and randomly split into development (n = 87 266 [60%]) and validation (n = 57 647 [40%]) cohorts. MAIN OUTCOME MEASURE Development of critical illness, defined as severe sepsis, delivery of mechanical ventilation, or death during hospitalization. RESULTS Critical illness occurred during hospitalization in 5% of the development (n = 4835) and validation (n = 3121) cohorts. Multivariable predictors of critical illness included older age, lower systolic blood pressure, abnormal respiratory rate, lower Glasgow Coma Scale score, lower pulse oximetry, and nursing home residence during out-of-hospital care (P |
doi_str_mv | 10.1001/jama.2010.1140 |
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OBJECTIVES To determine the out-of-hospital clinical predictors of critical illness and to characterize the performance of a simple score for out-of-hospital prediction of development of critical illness during hospitalization. DESIGN AND SETTING Population-based cohort study of an emergency medical services (EMS) system in greater King County, Washington (excluding metropolitan Seattle), that transports to 16 receiving facilities. PATIENTS Nontrauma, non–cardiac arrest adult patients transported to a hospital by King County EMS from 2002 through 2006. Eligible records with complete data (N = 144 913) were linked to hospital discharge data and randomly split into development (n = 87 266 [60%]) and validation (n = 57 647 [40%]) cohorts. MAIN OUTCOME MEASURE Development of critical illness, defined as severe sepsis, delivery of mechanical ventilation, or death during hospitalization. RESULTS Critical illness occurred during hospitalization in 5% of the development (n = 4835) and validation (n = 3121) cohorts. Multivariable predictors of critical illness included older age, lower systolic blood pressure, abnormal respiratory rate, lower Glasgow Coma Scale score, lower pulse oximetry, and nursing home residence during out-of-hospital care (P < .01 for all). When applying a summary critical illness prediction score to the validation cohort (range, 0-8), the area under the receiver operating characteristic curve was 0.77 (95% confidence interval [CI], 0.76-0.78), with satisfactory calibration slope (1.0). Using a score threshold of 4 or higher, sensitivity was 0.22 (95% CI, 0.20-0.23), specificity was 0.98 (95% CI, 0.98-0.98), positive likelihood ratio was 9.8 (95% CI, 8.9-10.6), and negative likelihood ratio was 0.80 (95% CI, 0.79- 0.82). A threshold of 1 or greater for critical illness improved sensitivity (0.98; 95% CI, 0.97-0.98) but reduced specificity (0.17; 95% CI, 0.17-0.17). CONCLUSIONS In a population-based cohort, the score on a prediction rule using out-of-hospital factors was significantly associated with the development of critical illness during hospitalization. This score requires external validation in an independent population.</description><identifier>ISSN: 0098-7484</identifier><identifier>EISSN: 1538-3598</identifier><identifier>DOI: 10.1001/jama.2010.1140</identifier><identifier>PMID: 20716737</identifier><identifier>CODEN: JAMAAP</identifier><language>eng</language><publisher>Chicago, IL: American Medical Association</publisher><subject>Adult ; Aged ; Aged, 80 and over ; Biological and medical sciences ; Cohort Studies ; Critical Illness - classification ; Critical Illness - mortality ; Decision Support Systems, Clinical ; Emergency medical care ; Emergency Medical Services ; Female ; Forecasting ; General aspects ; Hospitalization ; Humans ; Intubation - statistics & numerical data ; Male ; Medical research ; Medical sciences ; Middle Aged ; Outcome Assessment (Health Care) ; Predictive Value of Tests ; Prognosis ; Retrospective Studies ; Sepsis ; Terminal illnesses ; Triage ; Washington</subject><ispartof>JAMA : the journal of the American Medical Association, 2010-08, Vol.304 (7), p.747-754</ispartof><rights>2015 INIST-CNRS</rights><rights>Copyright American Medical Association Aug 18, 2010</rights><rights>2010 American Medical Association. All rights reserved. 2010</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a464t-c22b11a2ab6397720a2573af6b0552bc84e9534fcec09b56d6cf022229af46d33</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://jamanetwork.com/journals/jama/articlepdf/10.1001/jama.2010.1140$$EPDF$$P50$$Gama$$H</linktopdf><linktohtml>$$Uhttps://jamanetwork.com/journals/jama/fullarticle/10.1001/jama.2010.1140$$EHTML$$P50$$Gama$$H</linktohtml><link.rule.ids>64,230,314,776,780,881,3327,27901,27902,76232,76235</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=23074477$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/20716737$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Seymour, Christopher W</creatorcontrib><creatorcontrib>Kahn, Jeremy M</creatorcontrib><creatorcontrib>Cooke, Colin R</creatorcontrib><creatorcontrib>Watkins, Timothy R</creatorcontrib><creatorcontrib>Heckbert, Susan R</creatorcontrib><creatorcontrib>Rea, Thomas D</creatorcontrib><title>Prediction of Critical Illness During Out-of-Hospital Emergency Care</title><title>JAMA : the journal of the American Medical Association</title><addtitle>JAMA</addtitle><description>CONTEXT Early identification of nontrauma patients in need of critical care services in the emergency setting may improve triage decisions and facilitate regionalization of critical care. OBJECTIVES To determine the out-of-hospital clinical predictors of critical illness and to characterize the performance of a simple score for out-of-hospital prediction of development of critical illness during hospitalization. DESIGN AND SETTING Population-based cohort study of an emergency medical services (EMS) system in greater King County, Washington (excluding metropolitan Seattle), that transports to 16 receiving facilities. PATIENTS Nontrauma, non–cardiac arrest adult patients transported to a hospital by King County EMS from 2002 through 2006. Eligible records with complete data (N = 144 913) were linked to hospital discharge data and randomly split into development (n = 87 266 [60%]) and validation (n = 57 647 [40%]) cohorts. MAIN OUTCOME MEASURE Development of critical illness, defined as severe sepsis, delivery of mechanical ventilation, or death during hospitalization. RESULTS Critical illness occurred during hospitalization in 5% of the development (n = 4835) and validation (n = 3121) cohorts. Multivariable predictors of critical illness included older age, lower systolic blood pressure, abnormal respiratory rate, lower Glasgow Coma Scale score, lower pulse oximetry, and nursing home residence during out-of-hospital care (P < .01 for all). When applying a summary critical illness prediction score to the validation cohort (range, 0-8), the area under the receiver operating characteristic curve was 0.77 (95% confidence interval [CI], 0.76-0.78), with satisfactory calibration slope (1.0). Using a score threshold of 4 or higher, sensitivity was 0.22 (95% CI, 0.20-0.23), specificity was 0.98 (95% CI, 0.98-0.98), positive likelihood ratio was 9.8 (95% CI, 8.9-10.6), and negative likelihood ratio was 0.80 (95% CI, 0.79- 0.82). A threshold of 1 or greater for critical illness improved sensitivity (0.98; 95% CI, 0.97-0.98) but reduced specificity (0.17; 95% CI, 0.17-0.17). CONCLUSIONS In a population-based cohort, the score on a prediction rule using out-of-hospital factors was significantly associated with the development of critical illness during hospitalization. This score requires external validation in an independent population.</description><subject>Adult</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Biological and medical sciences</subject><subject>Cohort Studies</subject><subject>Critical Illness - classification</subject><subject>Critical Illness - mortality</subject><subject>Decision Support Systems, Clinical</subject><subject>Emergency medical care</subject><subject>Emergency Medical Services</subject><subject>Female</subject><subject>Forecasting</subject><subject>General aspects</subject><subject>Hospitalization</subject><subject>Humans</subject><subject>Intubation - statistics & numerical data</subject><subject>Male</subject><subject>Medical research</subject><subject>Medical sciences</subject><subject>Middle Aged</subject><subject>Outcome Assessment (Health Care)</subject><subject>Predictive Value of Tests</subject><subject>Prognosis</subject><subject>Retrospective Studies</subject><subject>Sepsis</subject><subject>Terminal illnesses</subject><subject>Triage</subject><subject>Washington</subject><issn>0098-7484</issn><issn>1538-3598</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpdkc9rFDEUx4Modlu9Cl5kEIqn2b78mGRyKci22kKhPeg5ZLLJmmUmWZMZof-9GXddW3MJ4X3yTd77IPQOwxID4IutHvSSwHzEDF6gBW5oW9NGti_RAkC2tWAtO0GnOW-hLEzFa3RCQGAuqFigq4dk196MPoYqumqV_OiN7qvbvg825-pqSj5sqvtprKOrb2Le-bGUrwebNjaYx2qlk32DXjndZ_v2sJ-h71-uv61u6rv7r7erz3e1ZpyNtSGkw1gT3XEqhSCgSSOodryDpiGdaZmVDWXOWAOya_iaGwekLKkd42tKz9DlPnc3dYNdGxvGpHu1S37Q6VFF7dXzSvA_1Cb-UlQyCSBKwKdDQIo_J5tHNfhsbN_rYOOUVZmV5GU4TSE__kdu45RC6e4PBC0lM7TcQybFnJN1x69gULMeNetRsx416ykXPjxt4Ij_9VGA8wOgc9Hgkg7G538cBcGYmLn3e27OPz7acgZAfwP_cp_l</recordid><startdate>20100818</startdate><enddate>20100818</enddate><creator>Seymour, Christopher W</creator><creator>Kahn, Jeremy M</creator><creator>Cooke, Colin R</creator><creator>Watkins, Timothy R</creator><creator>Heckbert, Susan R</creator><creator>Rea, Thomas D</creator><general>American Medical Association</general><scope>IQODW</scope><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>7QL</scope><scope>7QP</scope><scope>7TK</scope><scope>7TS</scope><scope>7U7</scope><scope>7U9</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>H94</scope><scope>K9.</scope><scope>M7N</scope><scope>NAPCQ</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20100818</creationdate><title>Prediction of Critical Illness During Out-of-Hospital Emergency Care</title><author>Seymour, Christopher W ; Kahn, Jeremy M ; Cooke, Colin R ; Watkins, Timothy R ; Heckbert, Susan R ; Rea, Thomas D</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a464t-c22b11a2ab6397720a2573af6b0552bc84e9534fcec09b56d6cf022229af46d33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Biological and medical sciences</topic><topic>Cohort Studies</topic><topic>Critical Illness - classification</topic><topic>Critical Illness - mortality</topic><topic>Decision Support Systems, Clinical</topic><topic>Emergency medical care</topic><topic>Emergency Medical Services</topic><topic>Female</topic><topic>Forecasting</topic><topic>General aspects</topic><topic>Hospitalization</topic><topic>Humans</topic><topic>Intubation - statistics & numerical data</topic><topic>Male</topic><topic>Medical research</topic><topic>Medical sciences</topic><topic>Middle Aged</topic><topic>Outcome Assessment (Health Care)</topic><topic>Predictive Value of Tests</topic><topic>Prognosis</topic><topic>Retrospective Studies</topic><topic>Sepsis</topic><topic>Terminal illnesses</topic><topic>Triage</topic><topic>Washington</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Seymour, Christopher W</creatorcontrib><creatorcontrib>Kahn, Jeremy M</creatorcontrib><creatorcontrib>Cooke, Colin R</creatorcontrib><creatorcontrib>Watkins, Timothy R</creatorcontrib><creatorcontrib>Heckbert, Susan R</creatorcontrib><creatorcontrib>Rea, Thomas D</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Physical Education Index</collection><collection>Toxicology Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Nursing & Allied Health Premium</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>JAMA : the journal of the American Medical Association</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Seymour, Christopher W</au><au>Kahn, Jeremy M</au><au>Cooke, Colin R</au><au>Watkins, Timothy R</au><au>Heckbert, Susan R</au><au>Rea, Thomas D</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction of Critical Illness During Out-of-Hospital Emergency Care</atitle><jtitle>JAMA : the journal of the American Medical Association</jtitle><addtitle>JAMA</addtitle><date>2010-08-18</date><risdate>2010</risdate><volume>304</volume><issue>7</issue><spage>747</spage><epage>754</epage><pages>747-754</pages><issn>0098-7484</issn><eissn>1538-3598</eissn><coden>JAMAAP</coden><abstract>CONTEXT Early identification of nontrauma patients in need of critical care services in the emergency setting may improve triage decisions and facilitate regionalization of critical care. OBJECTIVES To determine the out-of-hospital clinical predictors of critical illness and to characterize the performance of a simple score for out-of-hospital prediction of development of critical illness during hospitalization. DESIGN AND SETTING Population-based cohort study of an emergency medical services (EMS) system in greater King County, Washington (excluding metropolitan Seattle), that transports to 16 receiving facilities. PATIENTS Nontrauma, non–cardiac arrest adult patients transported to a hospital by King County EMS from 2002 through 2006. Eligible records with complete data (N = 144 913) were linked to hospital discharge data and randomly split into development (n = 87 266 [60%]) and validation (n = 57 647 [40%]) cohorts. MAIN OUTCOME MEASURE Development of critical illness, defined as severe sepsis, delivery of mechanical ventilation, or death during hospitalization. RESULTS Critical illness occurred during hospitalization in 5% of the development (n = 4835) and validation (n = 3121) cohorts. Multivariable predictors of critical illness included older age, lower systolic blood pressure, abnormal respiratory rate, lower Glasgow Coma Scale score, lower pulse oximetry, and nursing home residence during out-of-hospital care (P < .01 for all). When applying a summary critical illness prediction score to the validation cohort (range, 0-8), the area under the receiver operating characteristic curve was 0.77 (95% confidence interval [CI], 0.76-0.78), with satisfactory calibration slope (1.0). Using a score threshold of 4 or higher, sensitivity was 0.22 (95% CI, 0.20-0.23), specificity was 0.98 (95% CI, 0.98-0.98), positive likelihood ratio was 9.8 (95% CI, 8.9-10.6), and negative likelihood ratio was 0.80 (95% CI, 0.79- 0.82). A threshold of 1 or greater for critical illness improved sensitivity (0.98; 95% CI, 0.97-0.98) but reduced specificity (0.17; 95% CI, 0.17-0.17). CONCLUSIONS In a population-based cohort, the score on a prediction rule using out-of-hospital factors was significantly associated with the development of critical illness during hospitalization. This score requires external validation in an independent population.</abstract><cop>Chicago, IL</cop><pub>American Medical Association</pub><pmid>20716737</pmid><doi>10.1001/jama.2010.1140</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Adult Aged Aged, 80 and over Biological and medical sciences Cohort Studies Critical Illness - classification Critical Illness - mortality Decision Support Systems, Clinical Emergency medical care Emergency Medical Services Female Forecasting General aspects Hospitalization Humans Intubation - statistics & numerical data Male Medical research Medical sciences Middle Aged Outcome Assessment (Health Care) Predictive Value of Tests Prognosis Retrospective Studies Sepsis Terminal illnesses Triage Washington |
title | Prediction of Critical Illness During Out-of-Hospital Emergency Care |
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