Reappraising the concept of massive transfusion in trauma

Simon J Stanworth, Timothy P Morris, Christine Gaarder, J Carel Goslings, Marc Maegele, Mitchell J Cohen, Thomas C König, Ross A Davenport, Jean-Francois Pittet, Pär I Johansson, Shubha Allard, Tony Johnson, Karim Brohi, Simon J Stanworth, Timothy P Morris, Christine Gaarder, J Carel Goslings, Marc Maegele, Mitchell J Cohen, Thomas C König, Ross A Davenport, Jean-Francois Pittet, Pär I Johansson, Shubha Allard, Tony Johnson, Karim Brohi

Abstract

Introduction: The massive-transfusion concept was introduced to recognize the dilutional complications resulting from large volumes of packed red blood cells (PRBCs). Definitions of massive transfusion vary and lack supporting clinical evidence. Damage-control resuscitation regimens of modern trauma care are targeted to the early correction of acute traumatic coagulopathy. The aim of this study was to identify a clinically relevant definition of trauma massive transfusion based on clinical outcomes. We also examined whether the concept was useful in that early prediction of massive transfusion requirements could allow early activation of blood bank protocols.

Methods: Datasets on trauma admissions over a 1 or 2-year period were obtained from the trauma registries of five large trauma research networks. A fractional polynomial was used to model the transfusion-associated probability of death. A logistic regression model for the prediction of massive transfusion, defined as 10 or more units of red cell transfusions, was developed.

Results: In total, 5,693 patient records were available for analysis. Mortality increased as transfusion requirements increased, but the model indicated no threshold effect. Mortality was 9% in patients who received none to five PRBC units, 22% in patients receiving six to nine PRBC units, and 42% in patients receiving 10 or more units. A logistic model for prediction of massive transfusion was developed and validated at multiple sites but achieved only moderate performance. The area under the receiver operating characteristic curve was 0.81, with specificity of only 50% at a sensitivity of 90% for the prediction of 10 or more PRBC units. Performance varied widely at different trauma centers, with specificity varying from 48% to 91%.

Conclusions: No threshold for definition exists at which a massive transfusion specifically results in worse outcomes. Even with a large sample size across multiple trauma datasets, it was not possible to develop a transportable and clinically useful prediction model based on available admission parameters. Massive transfusion as a concept in trauma has limited utility, and emphasis should be placed on identifying patients with massive hemorrhage and acute traumatic coagulopathy.

Figures

Figure 1
Figure 1
Transfusion-related mortality. Mortality by packed red blood cells (PRBCs) administered during the first 24 hours of admission.
Figure 2
Figure 2
Estimated probability of death per unit of packed red blood cells (PRBCs) administered (95% confidence interval in grey). Dots are deviance residuals. The band of dots above the line represents patients who died; the band below is those who survived.
Figure 3
Figure 3
Scatterplots showing admission parameters and injury severity associated with transfusion requirements. Where covariates are missing for patient data, an average of imputed values has been substituted. (a) Packed red blood cells (PRBCs) transfusions by admission systolic blood pressure. (b) PRBC transfusions by admission base deficit. (c) PRBC transfusions by admission prothrombin time. (d) PRBC transfusions by injury-severity score.
Figure 4
Figure 4
Performance of the massive-transfusion prediction tool. The performance of the model developed on non-German TR-DGU centers and validated on German TR-DGU registry data (see text). (a) Receiver operating characteristic plot. Area under the ROC curve, 0.81. (b) Calibration plot.

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Source: PubMed

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