Despite substantial improvements in the knowledge on how to adequately resuscitate the exsanguinating patient, one of the fundamental issues to improve the outcome still remains the early identification of patients in need of transfusion including those requiring MT. Although the criteria that trigger the activation of MTPs remain highly center and provider dependent, the benefits of timely MTP activation have been frequently demonstrated given the identification of the appropriate patient (45, 46). However, not all investigators were able to show improved survival after MTP implementation or activation (47). Allogeneic blood transfusion increased significantly without being associated with mortality. A similar observation was made by Simmons and colleagues (48). Therefore, early and reliable prediction of the need for MT is highly demanded.
The inappropriate use of MTPs in patients not in need of MT may result in a higher incidence of adverse effects of fresh frozen plasma and platelet concentrate transfusions without an improvement in survival (49–51). Although blood transfusion has the obvious benefit of volume restoration and improved oxygen-carrying capacity in the injured patient, there are quite a few risks and immunosuppressive and infectious consequences associated with blood products including transfusion reaction, transmission of blood-borne pathogens, and the impact of limited supply (52–55). For these reasons, there has been a trend to restrict transfusion in nonurgent clinical settings and to limit transfusion to ongoing and imminently life-threatening situations. However, the hazards of transfusion may appear somewhat trivial relative to the need of care for an exsanguinating patient.
Substantial problems in the use of conventional coagulation testing for the early identification of patients in need of transfusion including those requiring MT include delayed turnaround times, incomplete characterization, and their poor predictive nature not accurately reflecting the patient’s true coagulation status (12, 56, 57). Although international normalized ratio (INR) and base deficit (BD) are good predictors of mortality, by themselves, they cannot discriminate between patients to go or not go on for MT (58). Second, surgical relevant bleeding due to thoracic and/or retroperitoneal/intraperitoneal organ injury is difficult to detect and often requires time-consuming diagnostics (59). Thus, significant hemorrhage and coagulopathy may be underestimated or even missed during early resuscitation (14, 60).
The most commonly proposed triggers that were correlated with the need for transfusion including MT in the civilian setting are shown in Table 1 and include systolic blood pressure, which is present in 9/9 scoring systems, followed by heart rate (present in 6/9 scoring systems), hemoglobin/hematocrit (present in 5/9 scoring systems) and positive Focused Assessment Sonography for Trauma (FAST+; present in 4/9 scoring systems). Parameters that can be quickly obtained via point-of-care arterial blood gas analyzers, e.g., BE/BD, lactate, and pH, are included in 6/9 civilian scoring systems. Six of nine systems consider anatomical injury including its magnitude or mechanism of injury as components of their assessment. However, the severity of injury as reflected by the ISS or the overall pattern of the anatomical injury may be difficult to calculate and to assess during initial assessment.
To date, several systems and algorithms have been applied onto other external but also retrospective data sets and have thus been externally validated. In developing their ABC score, Nunez and coworkers (45), for example, have applied both the TASH and the McLaughlin scores onto their local trauma center database including 596 trauma patients for score comparison. In result, all three scores (TASH AUROC [area under receiver operating characteristic] = 0.842, McLaughlin AUROC = 0.846, ABC AUROC = 0.842) were considered as equally good predictors for MT without a statistically significant difference between the scores. In another retrospective study, Cotton and colleagues (75) have applied the ABC score onto adult trauma data sets from three different Level I trauma centers in the United States (n = 513 from trauma center 1, n = 373 from trauma center 2, and n = 133 from trauma center 3) and compared the predictive ability of the score at each institution. The sensitivity and specificity for the ABC score to predict MT ranged from 75% to 90% and from 67% to 88%, respectively. Correctly classified patients and AUROCs, however, were 84% to 87% and 0.83 to 0.90, respectively. Recently, Mitra and coworkers (76) compared the performance of the PWH score (63) to the ABC (45) and TASH scores (61, 62) by a retrospective review of a subgroup of major trauma patients (n = 1.234) derived from the Alfred Trauma Registry (Victoria, Australia). In this analysis, the performance of the TASH score was best with an AUROC of 0.8986, followed by the PWH score (AUROC = 0.8419) and the ABC score.
Our own group has recently applied a total of six scores and algorithms to predict transfusion in trauma patients, i.e., ABC, Larson, PWH, Schreiber, TASH, and Vandromme, onto a large subset of trauma patients derived from the most updated database of the German TR-DGU (n = 5.047; unpublished observation, manuscript in preparation by Brockamp et al.). This extract included data from adult severely injured trauma patients (ISS >16), with all variables present from each patient to calculate all six scores. Although we had initially attempted to validate all scores on our database, the remaining scores had to be excluded from this analysis because of missing or noncaptured data within our registry for model calculation. For the TASH score, this analysis served again as an internal validation, whereas all other scores were externally validated by being subjected onto our data sets. Not surprisingly, the TASH score performed best (AUROC = 0.889) followed by the PWH score (AUROC = 0.860), which is also a weighted score with structure and content variables very similar to the TASH score (Fig. 7). In this analysis, the nonweighted and more simple scores performed less accurate (AUROCs for Vandromme score: 0.840; Larson score: 0.823; Schreiber model: 0.800; and ABC score: 0.763).
Point-of-care viscoelastic testing may offer the unique potential to predict transfusion even faster as compared with scoring systems involving conventional coagulation testing and to activate and guide resuscitations more objectively. A recent retrospective analysis of major trauma patients revealed low FIBTEM amplitudes (<4 mm) and/or low EXTEM amplitudes at 10 min to be highly predictive of MT (79). Independent from the viscoelastic test used, time to effective clot formation, clot strength, and sustained stability of the clot appear to have the highest clinical value. The authors have recently published a comprehensive review on the early and individualized goal-directed therapy for the acute coagulopathy of trauma including their local hospital algorithm for managing this potentially life-threatening disorder based on the use of viscoelastic testing (80). Other algorithms have been published elsewhere (81, 82).
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