A predictive model for massive transfusion in combat casualty patients.

A predictive model for massive transfusion in combat casualty patients. Research Abstract Details 

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A predictive model for massive transfusion in combat casualty patients. Abstract Text:

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 identified: heart rate >105 bpm, systolic blood pressure <110 mm Hg, pH <7.25, and hematocrit <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.

A predictive model for massive transfusion in combat casualty patients. Publishing Authors By Initials

For similar disorders of environmental origin: wounds and injuries research abstracts see: disorders of environmental origin: wounds and injuries research

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A predictive model for massive transfusion in combat casualty patients. Journal Published:

PUBLICATION TYPE: Journal Article

Journal: The Journal of trauma

VOLUME: 64

Page Numbers: S57-63; discussion S63

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ISSN: 1529-8809

DAY: 14

MONTH: Feb

YEAR: 2008

A predictive model for massive transfusion in combat casualty patients. Information

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LANGUAGE: eng

NlmUniqueID: 376373

A predictive model for massive transfusion in combat casualty patients. Keywords Mesh Terms:

KEYWORDS: Wounds and Injuries

MESH TERMS: therapy

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Grant and Affiliation Information for A predictive model for massive transfusion in combat casualty patients.

AFFILIATION: United States Institute of Surgical Research, Fort Sam Houston, TX 78234, USA. daniel.mclaughlin@amedd.army.mil

Country: United States

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MEDLINETA: J Trauma

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