Massive transfusion triggers in severe trauma: Scoping review

Air Medical Blood Transfusion as a Trigger of Massive Transfusion Protocol

Air medical services can improve access to blood products at the point of injury. Studies have shown that early activation of mass transfusion protocols (MTPs) can improve the survival of trauma patients by up to 25%. There are several scoring systems to guide early activation, but the use of a single criterion has been elusive. Our study sought to determine if air medical administration of blood products was a risk factor for massive transfusion activation and utilization of prehospital vital signs for calculation of the shock index. In our retrospective study, we evaluated adult trauma patients transfused by helicopter emergency medical services (HEMS) and as a control all patients in our institution receiving the MTP. Our study found HEMS blood transfusion was not a reliable trigger for MTP, although the sample size may have limited our findings. We found that HEMS care resulted in an overall reduction in the volume of transfusion and an improvement in hemodynamic parameters upon trauma center arrival. HEMS transfusion and a higher rate of tranexamic acid administration may have contributed to these findings. Of note, the assessment of blood consumption score and shock index were nonspecific in the study populations.

Development and Validation of a Dynamic Prediction Model for Massive Hemorrhage in Trauma

Objectives

Early warning prediction of massive hemorrhages can greatly reduce mortality in trauma patients. This study aimed to develop and validate dynamic prediction models for massive hemorrhage in trauma patients.

Methods

Based on vital signs (e.g., heart rate, respiratory rate, pulse pressure, and peripheral oxygen saturation) time-series data and the gated recurrent unit algorithm, we characterized a group of models to flexibly and dynamically predict the occurrence of massive hemorrhages in the subsequent T hours (where T = 1, 2, and 3). Models were evaluated in terms of accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and the area under the curve (AUC).

Results

Results show that of the 2205 trauma patients selected for model development, a total of 265 (12.02%) had a massive hemorrhage. The AUCs of the model in the 1-h-group, 2-h-group, and 3-h-group were 0.763 (95% CI: 0.708-0.820), 0.775 (95% CI: 0.728-0.823), and 0.756 (95% CI: 0.715-0.797), respectively. Finally, the models were used in a web calculator and information system for the hospital emergency department.

Conclusions

This study developed and validated a group of dynamic prediction models based on vital sign time-series data and a deep-learning algorithm to assist medical staff in the early diagnosis and dynamic prediction of a future massive hemorrhage in trauma.

A prediction model for massive hemorrhage in trauma: a retrospective observational study

Background

Massive hemorrhage is the main cause of preventable death after trauma. This study aimed to establish prediction models for early diagnosis of massive hemorrhage in trauma.

Methods

Using the trauma database of Chinese PLA General Hospital, two logistic regression (LR) models were fit to predict the risk of massive hemorrhage in trauma. Sixty-two potential predictive variables, including clinical symptoms, vital signs, laboratory tests, and imaging results, were included in this study. Variable selection was done using the least absolute shrinkage and selection operator (LASSO) method. The first model was constructed based on LASSO feature selection results. The second model was constructed based on the first vital sign recordings of trauma patients after admission. Finally, a web calculator was developed for clinical use.

Results

A total of 2353 patients were included in this study. There were 377 (16.02%) patients with massive hemorrhage. The selected predictive variables were heart rate (OR: 1.01; 95% CI: 1.01–1.02; P<0.001), pulse pressure (OR: 0.99; 95% CI: 0.98–0.99; P = 0.004), base excess (OR: 0.90; 95% CI: 0.87–0.93; P<0.001), hemoglobin (OR: 0.95; 95% CI: 0.95–0.96; P<0.001), displaced pelvic fracture (OR: 2.13; 95% CI: 1.48–3.06; P<0.001), and a positive computed tomography scan or positive focused assessment with sonography for trauma (OR: 1.62; 95% CI: 1.21–2.18; P = 0.001). Model 1, which was developed based on LASSO feature selection results and LR, displayed excellent discrimination (AUC: 0.894; 95% CI: 0.875–0.912), good calibration (P = 0.405), and clinical utility. In addition, the predictive power of model 1 was better than that of model 2 (AUC: 0.718; 95% CI: 0.679–0.757). Model 1 was deployed as a public web tool (http://82.156.217.249:8080/).

Conclusions

Our study developed and validated prediction models to assist medical staff in the early diagnosis of massive hemorrhage in trauma. An open web calculator was developed to facilitate the practical application of the research results.

Prehospital lactate improves prediction of the need for immediate interventions for hemorrhage after trauma

The blood lactate level is used to guide the management of trauma patients with circulatory disturbance. We hypothesized that blood lactate levels at the scene (Lac scene) could improve the prediction for immediate interventions for hemorrhage. We prospectively measured blood lactate levels and assessed retrospectively in 435 trauma patients both at the scene and on arrival at the emergency room (ER) of a level I trauma center. Primary outcome was immediate intervention for hemorrhage defined as surgical/radiological intervention and/or blood transfusion within 24 h. Physiological variables plus Lac scene significantly increased the predictive value for immediate intervention (area under the curve [AUC] 0.882, 95% confidence interval [CI] 0.839-0.925) compared to that using physiological variables only (AUC 0.837, 95% CI 0.787-0.887, P = 0.0073), replicated in the validation cohort (n = 85). There was no significant improvement in predicting value of physiological variables plus Lac scene for massive transfusion compared to physiological variables (AUC 0.903 vs 0.895, P = 0.32). The increased blood lactate level per minute from scene to ER was associated with increased probability for immediate intervention (P < 0.0001). Both adding Lac scene to physiological variables and the temporal elevation of blood lactate levels from scene to ER could improve the prediction of the immediate intervention.