Development and validation of a pre-hospital “Red Flag” alert for activation of intra-hospital haemorrhage control response in blunt trauma

Haemorrhage remains the leading cause of early preventable death in severe trauma [1, 2]. A multidisciplinary analysis showed that approximately 2.5% of the deaths in a trauma centre are preventable or potentially preventable. Among the main causes were haemorrhage (39%) and multiple organ failure (28%), often a consequence of haemorrhagic shock. The main reasons for preventable death due to haemorrhage were delayed recognition and management [3]. Organizational optimization is essential to control bleeding as quickly as possible and to reduce patient mortality [4‐6]. It is therefore crucial to identify during the pre-hospital phase those patients at high risk of severe haemorrhage (SH) to quickly activate a specific intra-hospital standardized haemorrhage control response, connecting the multispecialty trauma team, blood bank, transfusion protocols, interventional radiology and surgery [7].

Thus, to address efficiently the challenge of SH and shape the response, the design of a haemorrhage specific alert is necessary. Standard triage algorithms are designed to guide severe trauma patients to appropriate trauma centres [8, 9] and trigger trauma team activation [10]. The pre-hospital MGAP score (mechanism, Glasgow coma scale, age and arterial pressure) [11] was developed to predict mortality but showed a proper ability to predict SH (area under the curve 0.7, 95% CI 0.66–0.73) [12]. Established haemorrhage scores predict the need for massive transfusion [13‐15]. The TASH score is probably one of the most widely cited scores to predict massive transfusion [16]. However, massive transfusion only applies to a minority of patients, whereas a timely integrative haemostatic strategy could decrease overall transfusion requirements. The aforementioned massive transfusion scores are only validated with intra-hospital data, which renders their application questionable during the pre-hospital phase. A “Code Red” policy has been implemented in trauma centres across the UK [17, 18] with the pre-arrival organization seen as an integral part of the severe haemorrhage pathway [19]. This activation code consists of three criteria (suspicion or evidence of active haemorrhage, systolic arterial blood pressure < 90 mmHg, failure to respond to a fluid bolus) but its predictive accuracy has not yet been evaluated. On a pragmatic standpoint, this type of alert is of utmost importance for trauma centres since emergency care may compete with elective care because of common facilities and workforces.

The aim of our study was to develop and validate an easy-to-use pre-hospital prediction tool for SH in blunt trauma patients derived from a prediction model. This tool is meant to be used as a binary Red Flag alert to activate a specific intra-hospital severe haemorrhage response. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement was followed to report its results [20].

The flowchart of both the derivation (n = 4339) and validation (n = 3606) cohorts is presented in Fig. 1. Patients presenting with initial cardiac arrest, penetrating trauma or no pre-hospital data available were excluded, leaving 3675 (85%) patients for analysis in the derivation cohort and 2999 (83%) in the validation cohort. The distribution of patient across centres is described and illustrated in Additional file 1.

The main characteristics of both cohorts are presented in Table 1. The observed mortality was lower in both groups than the expected mortality according to the TRISS. The two cohorts significantly differed mainly in terms of haemostatic strategies with less surgery (58 vs 51%), more angio-embolization (3% vs 6%) and a shorter intensive care unit length of stay in the validation cohort.

In this study, a Red Flag binary alert derived from an efficient combination of pre-hospital criteria was identified with high predictive performances to detect patients at risk of SH. Its high predictive performances were confirmed in internal and external validations. To our knowledge this is the first report of a validated pre-hospital triggered haemorrhage pre-alert. In practice, the presence of any combination of at least two criteria during the pre-hospital care phase among patients with SI (HR/SBP) ≥ 1, unstable pelvic fracture, intubation, point of care haemoglobin ≤ 13 g/dl or MBP ≤ 70 mmHg activates the Red Flag and provides a powerful signal to initiate an adequate intra-hospital standardized haemorrhage control response (massive transfusion protocol and/or immediate haemostatic procedures).

The criteria identified in this study as associated with SH share similarities with some used in previously described haemorrhage control pathways or massive transfusion scores. Unstable pelvic fracture is part of the TASH score, and part of numerous existing scores predicting ongoing haemorrhage [16, 41]. It is a source of internal bleeding that is difficult to control especially in the case of arterial bleeding (20%) but also in the case of venous bleeding, despite pelvic binding. In the TASH score, unstable pelvic fracture accounts for about 20% of the total score (6 points from 28), as in our study (1 from 5). The Shock Index has been demonstrated as a useful sign to diagnose acute hypovolemia and as a good marker of severe haemorrhagic shock [42]. The threshold used in the Red Flag is 1, while the most frequently suggested SI cut-off value to predict massive transfusion is 0.9 in the literature [43]. Also, the threshold of haemoglobin concentration identified in our study was 1 point higher than the threshold used in the TASH score (13 g/dl vs 12 g/dl) [13]. The timing and the technique of measurement used in our study may explain the difference. Indeed, in the present study, haemoglobin concentration was assessed with a point-of-care technique on scene, thus at a very early stage, whereas the TASH score uses the haemoglobin laboratory concentration at hospital admission. Blood pressure is also a key variable in almost all existing predictive scores for severe haemorrhage [16, 41]. Nevertheless, only SBP is used, while the oscillometric sphygmomanometer, used in many EMSs, measures a MBP and extrapolates SBP and DBP via an algorithm [44]. For this reason, MBP was chosen in the Red Flag and the information carried by this variable was found independent of that carried by the SI. Intubation by the pre-hospital team, however, has never been suggested as being associated with severe haemorrhage in previous studies. In our pre-hospital system, physicians are involved in the pre-hospital setting and this may explain this association as it is usually the most severe patients who are intubated during pre-hospital care [45].

The major advantage of this Red Flag alert is its simplicity of use and pragmatism as it is computed with routinely assessed variables and thus directly available criteria for the pre-hospital care team. It allows the rapid identification of patients who require mobilization of important human and material resources to control haemorrhage (advanced immediate resuscitation and/or haemostatic procedures, early and sustained transfusion, etc.). The predictive performances of the previously described “Code Red” have not yet been extensively evaluated [17, 18]. The other existing simple scores are not based on pre-hospital variables [41], or were built to predict outcome such as mortality [11, 46]. The Red Flag could not be compared to the TASH or ABC score. Those latter scores were validated for an intra-hospital setting and include variables such as ultrasound use or blood gas results, variables that are not systematically available in the pre-hospital environment.

The present work is the first to attempt an extensive assessment of the predictive performance of routine pre-hospital data and to include a validation. A code should be easy to remember and the criteria routinely available; both apply to the Red Flag. Indeed, any prediction tool requires evaluation and validation in the very specific setting it will be implemented in. In the case of an inappropriate activation, the complete set of the haemorrhage control infrastructure may be activated and disorganize programmed care for a while. So, any activation code requires a delicate balance between sensitivity and specificity; that is, between the risk of not activating the haemorrhage control pathway when it is needed (false negative, potentially detrimental to the patient) and over-activation (increase in false positive). On the one hand, it is crucial not to miss any haemorrhagic patients and get activated for their arrival, but, on the other, over-activation can generate waste of precious resources and induces team fatigue leading to further non-compliance. Unjustified activation may even reduce the chance of other patients to benefit from the resources inadequately put on standby. An appropriate number of activations, however, maintains team readiness and training. The clinical consequences of this alert will have to be assessed (times, process, outcomes, etc.).

Our study obviously has some limitations. The first is its retrospective design, as it usually precludes the ad hoc choice of the data studied. It might have been interesting to investigate the contribution of other criteria which were not collected in our study: pre-hospital ultrasonography, described as a bleeding characterization criterion in the ABC score [15], or pre-hospital blood lactate dosage described as a predictor of trauma severity [47]. However, this is actually a strength of our study, as it allowed the analysis of pre-hospital data that are routinely collected in practice by the EMS which reinforces the interest in Red Flag as a pragmatic, easy-to-use tool. Moreover, beside this retrospective analysis, data collection was prospective as this study used data from the Traumabase®, and this has limited data loss and biases inherent to retrospective data collection [48]. Furthermore, this study is the first to evaluate the question in a physician-staffed EMS, whereas existing work has been generated in a paramedic-staffed EMS. Transposition of experiences and data from one system to another can be difficult. The external validity of the study could only be assessed by testing and validating it outside the original centres. The characteristics of these centres, however, are quite different with regard to equipment, internal organization and case mix as there is a lot of contrast in demographic characteristics of the different area covered by each centre within the region. So, results from this study are thus likely to be transposable to other centres. Furthermore, demographic and clinical characteristics of our cohort, as well as mortality, were similar to those in the trauma literature [14, 49]. It is noteworthy that this alert does not apply to penetrating trauma and has not been validated for children. Finally, the impact of this Red Flag alert on patient care has not been evaluated and requires a separate prospective study.

We have constructed and validated a simple Red Flag alert for identifying severe blunt trauma patients during the pre-hospital care phase and activating a specific immediate intra-hospital haemorrhage control response prior to arrival. The impact of its use on severe trauma patient care and on resource utilization remains to be determined.