A Predictive Model for Massive Transfusion in Combat Casualty Patients

BACKGROUND:Massive transfusion (MT) is associated with increased morbidity and mortality in severely injured patients. Early and aggressive use of blood products in these patients may correct coagulopathy, control bleeding, and improve outcomes. However, rapid identification of patients at risk for...

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Veröffentlicht in:The journal of trauma 2008-02, Vol.64 (2 Suppl), p.S57-S63
Hauptverfasser: McLaughlin, Daniel F., Niles, Sarah E., Salinas, Jose, Perkins, Jeremy G., Cox, E Darrin, Wade, Charles E., Holcomb, John B.
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container_end_page S63
container_issue 2 Suppl
container_start_page S57
container_title The journal of trauma
container_volume 64
creator McLaughlin, Daniel F.
Niles, Sarah E.
Salinas, Jose
Perkins, Jeremy G.
Cox, E Darrin
Wade, Charles E.
Holcomb, John B.
description BACKGROUND:Massive transfusion (MT) is associated with increased morbidity and mortality in severely injured patients. Early and aggressive use of blood products in these patients may correct coagulopathy, control bleeding, and improve outcomes. However, rapid identification of patients at risk for MT has been difficult. We postulated that evaluation of clinical variables routinely assessed upon admission would allow identification of these patients for earlier, more effective intervention. METHODS:A retrospective cohort study was conducted at a single combat support hospital to identify risk factors for MT in patients with traumatic injuries. Demographic, diagnostic, and laboratory variables obtained upon admission were evaluated. Univariate and multivariate analyses were performed. An algorithm was formulated, validated with an independent dataset and a simple scoring system was devised. RESULTS:Three thousand four hundred forty-two patient records were reviewed. At least one unit of blood was transfused to 680 patients at the combat support hospital. Exclusion criteria included age less than 18 years, transfer from another medical facility, designation as a security internee, or incomplete data fields. The final number of patients was 302, of whom 26.5% (80 of 302) received a MT. Patients with MT had higher mortality (29 vs. 7% [p < 0.001]), and an increased Injury Severity Score (25 ± 11.1 vs. 18 ± 16.2 [p < 0.001]). Four independent risk factors for MT were identifiedheart rate >105 bpm, systolic blood pressure
doi_str_mv 10.1097/TA.0b013e318160a566
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Early and aggressive use of blood products in these patients may correct coagulopathy, control bleeding, and improve outcomes. However, rapid identification of patients at risk for MT has been difficult. We postulated that evaluation of clinical variables routinely assessed upon admission would allow identification of these patients for earlier, more effective intervention. METHODS:A retrospective cohort study was conducted at a single combat support hospital to identify risk factors for MT in patients with traumatic injuries. Demographic, diagnostic, and laboratory variables obtained upon admission were evaluated. Univariate and multivariate analyses were performed. An algorithm was formulated, validated with an independent dataset and a simple scoring system was devised. RESULTS:Three thousand four hundred forty-two patient records were reviewed. At least one unit of blood was transfused to 680 patients at the combat support hospital. Exclusion criteria included age less than 18 years, transfer from another medical facility, designation as a security internee, or incomplete data fields. The final number of patients was 302, of whom 26.5% (80 of 302) received a MT. Patients with MT had higher mortality (29 vs. 7% [p &lt; 0.001]), and an increased Injury Severity Score (25 ± 11.1 vs. 18 ± 16.2 [p &lt; 0.001]). Four independent risk factors for MT were identifiedheart rate &gt;105 bpm, systolic blood pressure &lt;110 mm Hg, pH &lt;7.25, and hematocrit &lt;32.0%. An algorithm was created to analyze the risk of MT (area under the curve [AUC] = 0.839). In an independent data set of 396 patients the ability to accurately identify those requiring MT was 66% (AUC = 0.747). CONCLUSIONS:Independent predictors for MT were identified in a cohort of severely injured patients requiring transfusions. Patients requiring a MT can be identified with variables commonly obtained upon hospital admission.</description><identifier>ISSN: 0022-5282</identifier><identifier>EISSN: 1529-8809</identifier><identifier>DOI: 10.1097/TA.0b013e318160a566</identifier><identifier>PMID: 18376173</identifier><language>eng</language><publisher>United States: Lippincott Williams &amp; Wilkins, Inc</publisher><subject>Adult ; Algorithms ; Blood Pressure - physiology ; Blood Transfusion ; Cohort Studies ; Female ; Heart Rate - physiology ; Hematocrit ; Humans ; Iraq War, 2003-2011 ; Male ; Needs Assessment ; Predictive Value of Tests ; Retrospective Studies ; United States ; Wounds and Injuries - mortality ; Wounds and Injuries - physiopathology ; Wounds and Injuries - therapy</subject><ispartof>The journal of trauma, 2008-02, Vol.64 (2 Suppl), p.S57-S63</ispartof><rights>2008 Lippincott Williams &amp; Wilkins, Inc.</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3480-58d5f09805fd05c8320ee4b6d5a62c3d0352e806412d7948e33cd6eb5f6ef61a3</citedby><cites>FETCH-LOGICAL-c3480-58d5f09805fd05c8320ee4b6d5a62c3d0352e806412d7948e33cd6eb5f6ef61a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27915,27916</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/18376173$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>McLaughlin, Daniel F.</creatorcontrib><creatorcontrib>Niles, Sarah E.</creatorcontrib><creatorcontrib>Salinas, Jose</creatorcontrib><creatorcontrib>Perkins, Jeremy G.</creatorcontrib><creatorcontrib>Cox, E Darrin</creatorcontrib><creatorcontrib>Wade, Charles E.</creatorcontrib><creatorcontrib>Holcomb, John B.</creatorcontrib><title>A Predictive Model for Massive Transfusion in Combat Casualty Patients</title><title>The journal of trauma</title><addtitle>J Trauma</addtitle><description>BACKGROUND:Massive transfusion (MT) is associated with increased morbidity and mortality in severely injured patients. Early and aggressive use of blood products in these patients may correct coagulopathy, control bleeding, and improve outcomes. However, rapid identification of patients at risk for MT has been difficult. We postulated that evaluation of clinical variables routinely assessed upon admission would allow identification of these patients for earlier, more effective intervention. METHODS:A retrospective cohort study was conducted at a single combat support hospital to identify risk factors for MT in patients with traumatic injuries. Demographic, diagnostic, and laboratory variables obtained upon admission were evaluated. Univariate and multivariate analyses were performed. An algorithm was formulated, validated with an independent dataset and a simple scoring system was devised. RESULTS:Three thousand four hundred forty-two patient records were reviewed. At least one unit of blood was transfused to 680 patients at the combat support hospital. Exclusion criteria included age less than 18 years, transfer from another medical facility, designation as a security internee, or incomplete data fields. The final number of patients was 302, of whom 26.5% (80 of 302) received a MT. Patients with MT had higher mortality (29 vs. 7% [p &lt; 0.001]), and an increased Injury Severity Score (25 ± 11.1 vs. 18 ± 16.2 [p &lt; 0.001]). Four independent risk factors for MT were identifiedheart rate &gt;105 bpm, systolic blood pressure &lt;110 mm Hg, pH &lt;7.25, and hematocrit &lt;32.0%. An algorithm was created to analyze the risk of MT (area under the curve [AUC] = 0.839). In an independent data set of 396 patients the ability to accurately identify those requiring MT was 66% (AUC = 0.747). CONCLUSIONS:Independent predictors for MT were identified in a cohort of severely injured patients requiring transfusions. Patients requiring a MT can be identified with variables commonly obtained upon hospital admission.</description><subject>Adult</subject><subject>Algorithms</subject><subject>Blood Pressure - physiology</subject><subject>Blood Transfusion</subject><subject>Cohort Studies</subject><subject>Female</subject><subject>Heart Rate - physiology</subject><subject>Hematocrit</subject><subject>Humans</subject><subject>Iraq War, 2003-2011</subject><subject>Male</subject><subject>Needs Assessment</subject><subject>Predictive Value of Tests</subject><subject>Retrospective Studies</subject><subject>United States</subject><subject>Wounds and Injuries - mortality</subject><subject>Wounds and Injuries - physiopathology</subject><subject>Wounds and Injuries - therapy</subject><issn>0022-5282</issn><issn>1529-8809</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpdkE1Lw0AQhhdRbK3-AkFy8pY6u5vdbI6hWBVa7CGel012QqP5qLuJpf_elBYELzMwPO8L8xByT2FOIYmfsnQOOVCOnCoqwQgpL8iUCpaESkFySaYAjIWCKTYhN95_AkAUcXVNJlTxWNKYT8kyDTYObVX01Q8G685iHZSdC9bG--Mlc6b15eCrrg2qNlh0TW76YGH8YOr-EGxMX2Hb-1tyVZra4915z8jH8jlbvIar95e3RboKCx4pCIWyooREgSgtiEJxBohRLq0wkhXcAhcMFciIMhsnkULOCysxF6XEUlLDZ-Tx1Ltz3feAvtdN5Qusa9NiN3gdjx9GMoER5CewcJ33Dku9c1Vj3EFT0Ed9Okv1f31j6uFcP-QN2r_M2dcIRCdg39U9Ov9VD3t0eoujja0eBYPgMQ8ZgIJx0HC8UOC_kfN6Pw</recordid><startdate>200802</startdate><enddate>200802</enddate><creator>McLaughlin, Daniel F.</creator><creator>Niles, Sarah E.</creator><creator>Salinas, Jose</creator><creator>Perkins, Jeremy G.</creator><creator>Cox, E Darrin</creator><creator>Wade, Charles E.</creator><creator>Holcomb, John B.</creator><general>Lippincott Williams &amp; Wilkins, 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>7X8</scope></search><sort><creationdate>200802</creationdate><title>A Predictive Model for Massive Transfusion in Combat Casualty Patients</title><author>McLaughlin, Daniel F. ; Niles, Sarah E. ; Salinas, Jose ; Perkins, Jeremy G. ; Cox, E Darrin ; Wade, Charles E. ; Holcomb, John B.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3480-58d5f09805fd05c8320ee4b6d5a62c3d0352e806412d7948e33cd6eb5f6ef61a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Adult</topic><topic>Algorithms</topic><topic>Blood Pressure - physiology</topic><topic>Blood Transfusion</topic><topic>Cohort Studies</topic><topic>Female</topic><topic>Heart Rate - physiology</topic><topic>Hematocrit</topic><topic>Humans</topic><topic>Iraq War, 2003-2011</topic><topic>Male</topic><topic>Needs Assessment</topic><topic>Predictive Value of Tests</topic><topic>Retrospective Studies</topic><topic>United States</topic><topic>Wounds and Injuries - mortality</topic><topic>Wounds and Injuries - physiopathology</topic><topic>Wounds and Injuries - therapy</topic><toplevel>online_resources</toplevel><creatorcontrib>McLaughlin, Daniel F.</creatorcontrib><creatorcontrib>Niles, Sarah E.</creatorcontrib><creatorcontrib>Salinas, Jose</creatorcontrib><creatorcontrib>Perkins, Jeremy G.</creatorcontrib><creatorcontrib>Cox, E Darrin</creatorcontrib><creatorcontrib>Wade, Charles E.</creatorcontrib><creatorcontrib>Holcomb, John B.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>The journal of trauma</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>McLaughlin, Daniel F.</au><au>Niles, Sarah E.</au><au>Salinas, Jose</au><au>Perkins, Jeremy G.</au><au>Cox, E Darrin</au><au>Wade, Charles E.</au><au>Holcomb, John B.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Predictive Model for Massive Transfusion in Combat Casualty Patients</atitle><jtitle>The journal of trauma</jtitle><addtitle>J Trauma</addtitle><date>2008-02</date><risdate>2008</risdate><volume>64</volume><issue>2 Suppl</issue><spage>S57</spage><epage>S63</epage><pages>S57-S63</pages><issn>0022-5282</issn><eissn>1529-8809</eissn><abstract>BACKGROUND:Massive transfusion (MT) is associated with increased morbidity and mortality in severely injured patients. Early and aggressive use of blood products in these patients may correct coagulopathy, control bleeding, and improve outcomes. However, rapid identification of patients at risk for MT has been difficult. We postulated that evaluation of clinical variables routinely assessed upon admission would allow identification of these patients for earlier, more effective intervention. METHODS:A retrospective cohort study was conducted at a single combat support hospital to identify risk factors for MT in patients with traumatic injuries. Demographic, diagnostic, and laboratory variables obtained upon admission were evaluated. Univariate and multivariate analyses were performed. An algorithm was formulated, validated with an independent dataset and a simple scoring system was devised. RESULTS:Three thousand four hundred forty-two patient records were reviewed. At least one unit of blood was transfused to 680 patients at the combat support hospital. Exclusion criteria included age less than 18 years, transfer from another medical facility, designation as a security internee, or incomplete data fields. The final number of patients was 302, of whom 26.5% (80 of 302) received a MT. Patients with MT had higher mortality (29 vs. 7% [p &lt; 0.001]), and an increased Injury Severity Score (25 ± 11.1 vs. 18 ± 16.2 [p &lt; 0.001]). Four independent risk factors for MT were identifiedheart rate &gt;105 bpm, systolic blood pressure &lt;110 mm Hg, pH &lt;7.25, and hematocrit &lt;32.0%. An algorithm was created to analyze the risk of MT (area under the curve [AUC] = 0.839). In an independent data set of 396 patients the ability to accurately identify those requiring MT was 66% (AUC = 0.747). CONCLUSIONS:Independent predictors for MT were identified in a cohort of severely injured patients requiring transfusions. Patients requiring a MT can be identified with variables commonly obtained upon hospital admission.</abstract><cop>United States</cop><pub>Lippincott Williams &amp; Wilkins, Inc</pub><pmid>18376173</pmid><doi>10.1097/TA.0b013e318160a566</doi></addata></record>
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source MEDLINE; Journals@Ovid Ovid Autoload
subjects Adult
Algorithms
Blood Pressure - physiology
Blood Transfusion
Cohort Studies
Female
Heart Rate - physiology
Hematocrit
Humans
Iraq War, 2003-2011
Male
Needs Assessment
Predictive Value of Tests
Retrospective Studies
United States
Wounds and Injuries - mortality
Wounds and Injuries - physiopathology
Wounds and Injuries - therapy
title A Predictive Model for Massive Transfusion in Combat Casualty Patients
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