A predictive model of early mortality in trauma patients
Abstract Background Rapid thrombelastography (rTEG) is a real-time whole-blood viscoelastic coagulation assay. We hypothesized that admission rTEG and clinical data are independent predictors of trauma-related mortality. Methods Prospective observational data (patient demographics, admission vital s...
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Veröffentlicht in: | The American journal of surgery 2014-05, Vol.207 (5), p.642-647 |
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creator | Hampton, David A., M.D., M.Eng Lee, Tim H., M.D Diggs, Brian S., Ph.D McCully, Sean P., M.D Schreiber, Martin A., M.D., F.A.C.S |
description | Abstract Background Rapid thrombelastography (rTEG) is a real-time whole-blood viscoelastic coagulation assay. We hypothesized that admission rTEG and clinical data are independent predictors of trauma-related mortality. Methods Prospective observational data (patient demographics, admission vital signs, laboratory studies, and injury characteristics) from trauma patients enrolled within 6 hours of injury were collected. Mann–Whitney U test and analysis of variance test assessed significance ( P ≤ .05). Logistic regression analyses determined the association of the studied variables with 24-hour mortality. Results Seven hundred ninety-five trauma patients were enrolled, of which 55 died within 24 hours of admission. Admission variables which independently predicted 24-hour mortality were as follows: Glasgow Coma Scale ≤8, hemoglobin 1.5, Ly30 >8%, and penetrating injury ( P < .05). This 5-variable model's area under the receiver operator characteristic curve was .88. The Hosmer–Lemeshow goodness-of-fit test was .90. Conclusions This 5-variable model provides a rapid prediction of 24-hour mortality. The inclusion of rTEG Ly30 demonstrates the association of fibrinolysis with outcome and may support the early use of antifibrinolytic therapies. |
doi_str_mv | 10.1016/j.amjsurg.2013.12.009 |
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We hypothesized that admission rTEG and clinical data are independent predictors of trauma-related mortality. Methods Prospective observational data (patient demographics, admission vital signs, laboratory studies, and injury characteristics) from trauma patients enrolled within 6 hours of injury were collected. Mann–Whitney U test and analysis of variance test assessed significance ( P ≤ .05). Logistic regression analyses determined the association of the studied variables with 24-hour mortality. Results Seven hundred ninety-five trauma patients were enrolled, of which 55 died within 24 hours of admission. Admission variables which independently predicted 24-hour mortality were as follows: Glasgow Coma Scale ≤8, hemoglobin <11 g/dL, international normalized ratio >1.5, Ly30 >8%, and penetrating injury ( P < .05). This 5-variable model's area under the receiver operator characteristic curve was .88. The Hosmer–Lemeshow goodness-of-fit test was .90. Conclusions This 5-variable model provides a rapid prediction of 24-hour mortality. The inclusion of rTEG Ly30 demonstrates the association of fibrinolysis with outcome and may support the early use of antifibrinolytic therapies.</description><identifier>ISSN: 0002-9610</identifier><identifier>EISSN: 1879-1883</identifier><identifier>DOI: 10.1016/j.amjsurg.2013.12.009</identifier><identifier>PMID: 24630907</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Adult ; Age ; Blood pressure ; Coma ; Confidence intervals ; Decision Support Techniques ; Emergency medical care ; Heart rate ; Humans ; Injuries ; Logistic Models ; Logistics ; Middle Aged ; Mortality ; Patients ; Prognosis ; Prospective Studies ; Risk Assessment ; ROC Curve ; Surgery ; Thrombelastography ; Trauma ; Variables ; Wounds and Injuries - blood ; Wounds and Injuries - mortality</subject><ispartof>The American journal of surgery, 2014-05, Vol.207 (5), p.642-647</ispartof><rights>Elsevier Inc.</rights><rights>2014 Elsevier Inc.</rights><rights>Copyright © 2014 Elsevier Inc. All rights reserved.</rights><rights>Copyright Elsevier Limited May 2014</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c448t-79c96a5d006979ed04e85979a8d8a9db511adbd7350f0c2f59401b515a3ca4123</citedby><cites>FETCH-LOGICAL-c448t-79c96a5d006979ed04e85979a8d8a9db511adbd7350f0c2f59401b515a3ca4123</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S000296101400066X$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/24630907$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Hampton, David A., M.D., M.Eng</creatorcontrib><creatorcontrib>Lee, Tim H., M.D</creatorcontrib><creatorcontrib>Diggs, Brian S., Ph.D</creatorcontrib><creatorcontrib>McCully, Sean P., M.D</creatorcontrib><creatorcontrib>Schreiber, Martin A., M.D., F.A.C.S</creatorcontrib><title>A predictive model of early mortality in trauma patients</title><title>The American journal of surgery</title><addtitle>Am J Surg</addtitle><description>Abstract Background Rapid thrombelastography (rTEG) is a real-time whole-blood viscoelastic coagulation assay. We hypothesized that admission rTEG and clinical data are independent predictors of trauma-related mortality. Methods Prospective observational data (patient demographics, admission vital signs, laboratory studies, and injury characteristics) from trauma patients enrolled within 6 hours of injury were collected. Mann–Whitney U test and analysis of variance test assessed significance ( P ≤ .05). Logistic regression analyses determined the association of the studied variables with 24-hour mortality. Results Seven hundred ninety-five trauma patients were enrolled, of which 55 died within 24 hours of admission. Admission variables which independently predicted 24-hour mortality were as follows: Glasgow Coma Scale ≤8, hemoglobin <11 g/dL, international normalized ratio >1.5, Ly30 >8%, and penetrating injury ( P < .05). This 5-variable model's area under the receiver operator characteristic curve was .88. The Hosmer–Lemeshow goodness-of-fit test was .90. Conclusions This 5-variable model provides a rapid prediction of 24-hour mortality. The inclusion of rTEG Ly30 demonstrates the association of fibrinolysis with outcome and may support the early use of antifibrinolytic therapies.</description><subject>Adult</subject><subject>Age</subject><subject>Blood pressure</subject><subject>Coma</subject><subject>Confidence intervals</subject><subject>Decision Support Techniques</subject><subject>Emergency medical care</subject><subject>Heart rate</subject><subject>Humans</subject><subject>Injuries</subject><subject>Logistic Models</subject><subject>Logistics</subject><subject>Middle Aged</subject><subject>Mortality</subject><subject>Patients</subject><subject>Prognosis</subject><subject>Prospective Studies</subject><subject>Risk Assessment</subject><subject>ROC Curve</subject><subject>Surgery</subject><subject>Thrombelastography</subject><subject>Trauma</subject><subject>Variables</subject><subject>Wounds and Injuries - blood</subject><subject>Wounds and Injuries - mortality</subject><issn>0002-9610</issn><issn>1879-1883</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>BENPR</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqFkUuLFDEQgIMo7rj6E5QGL166rUrSj1yUZdFVWPCggreQSaolbT_GJL0w_940M7qwF3NJVfiqknzF2EuECgGbt0NlpiGu4WfFAUWFvAJQj9gOu1aV2HXiMdsBAC9Vg3DBnsU45BRRiqfsgstGgIJ2x7qr4hDIeZv8HRXT4mgslr4gE8ZjTkMyo0_Hws9FCmadTHEwydOc4nP2pDdjpBfn_ZJ9__jh2_Wn8vbLzefrq9vSStmlslVWNaZ2AI1qFTmQ1NU5Mp3rjHL7GtG4vWtFDT1Y3tdKAubT2ghrJHJxyd6c-h7C8nulmPTko6VxNDMta9RYcxRcgVQZff0AHZY1zPl1GwXYNnllqj5RNiwxBur1IfjJhKNG0JtaPeizWr2p1ch1VpvrXp27r_uJ3L-qvy4z8P4EUNZx5ynoaLMqm-0Gskm7xf_3incPOtjRz96a8RcdKd7_RsdcoL9u893GizJHTfND_AH_gZ-u</recordid><startdate>20140501</startdate><enddate>20140501</enddate><creator>Hampton, David A., M.D., M.Eng</creator><creator>Lee, Tim H., M.D</creator><creator>Diggs, Brian S., Ph.D</creator><creator>McCully, Sean P., M.D</creator><creator>Schreiber, Martin A., M.D., F.A.C.S</creator><general>Elsevier Inc</general><general>Elsevier Limited</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>3V.</scope><scope>7QO</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FD</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>MBDVC</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>7X8</scope></search><sort><creationdate>20140501</creationdate><title>A predictive model of early mortality in trauma patients</title><author>Hampton, David A., M.D., M.Eng ; Lee, Tim H., M.D ; Diggs, Brian S., Ph.D ; McCully, Sean P., M.D ; Schreiber, Martin A., M.D., F.A.C.S</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c448t-79c96a5d006979ed04e85979a8d8a9db511adbd7350f0c2f59401b515a3ca4123</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Adult</topic><topic>Age</topic><topic>Blood pressure</topic><topic>Coma</topic><topic>Confidence intervals</topic><topic>Decision Support Techniques</topic><topic>Emergency medical care</topic><topic>Heart rate</topic><topic>Humans</topic><topic>Injuries</topic><topic>Logistic Models</topic><topic>Logistics</topic><topic>Middle Aged</topic><topic>Mortality</topic><topic>Patients</topic><topic>Prognosis</topic><topic>Prospective Studies</topic><topic>Risk Assessment</topic><topic>ROC Curve</topic><topic>Surgery</topic><topic>Thrombelastography</topic><topic>Trauma</topic><topic>Variables</topic><topic>Wounds and Injuries - blood</topic><topic>Wounds and Injuries - mortality</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hampton, David A., M.D., M.Eng</creatorcontrib><creatorcontrib>Lee, Tim H., M.D</creatorcontrib><creatorcontrib>Diggs, Brian S., Ph.D</creatorcontrib><creatorcontrib>McCully, Sean P., M.D</creatorcontrib><creatorcontrib>Schreiber, Martin A., M.D., F.A.C.S</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 Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Research Library (Corporate)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><jtitle>The American journal of surgery</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hampton, David A., M.D., M.Eng</au><au>Lee, Tim H., M.D</au><au>Diggs, Brian S., Ph.D</au><au>McCully, Sean P., M.D</au><au>Schreiber, Martin A., M.D., F.A.C.S</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A predictive model of early mortality in trauma patients</atitle><jtitle>The American journal of surgery</jtitle><addtitle>Am J Surg</addtitle><date>2014-05-01</date><risdate>2014</risdate><volume>207</volume><issue>5</issue><spage>642</spage><epage>647</epage><pages>642-647</pages><issn>0002-9610</issn><eissn>1879-1883</eissn><abstract>Abstract Background Rapid thrombelastography (rTEG) is a real-time whole-blood viscoelastic coagulation assay. We hypothesized that admission rTEG and clinical data are independent predictors of trauma-related mortality. Methods Prospective observational data (patient demographics, admission vital signs, laboratory studies, and injury characteristics) from trauma patients enrolled within 6 hours of injury were collected. Mann–Whitney U test and analysis of variance test assessed significance ( P ≤ .05). Logistic regression analyses determined the association of the studied variables with 24-hour mortality. Results Seven hundred ninety-five trauma patients were enrolled, of which 55 died within 24 hours of admission. Admission variables which independently predicted 24-hour mortality were as follows: Glasgow Coma Scale ≤8, hemoglobin <11 g/dL, international normalized ratio >1.5, Ly30 >8%, and penetrating injury ( P < .05). This 5-variable model's area under the receiver operator characteristic curve was .88. The Hosmer–Lemeshow goodness-of-fit test was .90. Conclusions This 5-variable model provides a rapid prediction of 24-hour mortality. The inclusion of rTEG Ly30 demonstrates the association of fibrinolysis with outcome and may support the early use of antifibrinolytic therapies.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>24630907</pmid><doi>10.1016/j.amjsurg.2013.12.009</doi><tpages>6</tpages></addata></record> |
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subjects | Adult Age Blood pressure Coma Confidence intervals Decision Support Techniques Emergency medical care Heart rate Humans Injuries Logistic Models Logistics Middle Aged Mortality Patients Prognosis Prospective Studies Risk Assessment ROC Curve Surgery Thrombelastography Trauma Variables Wounds and Injuries - blood Wounds and Injuries - mortality |
title | A predictive model of early mortality in trauma patients |
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