Prediction of massive blood transfusion in battlefield trauma: Development and validation of the Military Acute Severe Haemorrhage (MASH) score

A Systematic Review of Tranexamic Acid-Associated Venous Thromboembolic Events in Combat Casualties and Considerations for Prolonged Field Care

INTRODUCTION

Tranexamic acid (TXA) is a standard component of Tactical Combat Casualty Care. Recent retrospective studies have shown that TXA use is associated with a higher rate of venous thromboembolic (VTE) events in combat-injured patients. We aim to determine if selective administration should be considered in the prolonged field care environment.

MATERIALS AND METHODS

We performed a systematic review using the 2020 Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines. Clinical trials and observational studies of combat casualties published between January 1, 1960, and June 20, 2022, were included. We analyzed survival and VTE outcomes in TXA recipients and non-recipients. We discussed the findings of each paper in the context of current and future combat environments.

RESULTS

Six articles met criteria for inclusion. Only one study was powered to report mortality data, and it demonstrated a 7-fold increase in survival in severely injured TXA recipients. All studies reported an increased risk of VTE in TXA recipients, which exceeded rates in civilian literature. However, five of the six studies used overlapping data from the same registry and were limited by a high rate of missingness in pertinent variables. No VTE-related deaths were identified.

CONCLUSIONS

There may be an increased risk of VTE in combat casualties that receive TXA; however, this risk must be considered in the context of improved survival and an absence of VTE-associated deaths. To optimize combat casualty care during prolonged field care, it will be essential to ensure the timely administration of VTE chemoprophylaxis as soon as the risk of significant hemorrhage permits.

Artificial intelligence and machine learning for hemorrhagic trauma care

Artificial intelligence (AI), a branch of machine learning (ML) has been increasingly employed in the research of trauma in various aspects. Hemorrhage is the most common cause of trauma-related death. To better elucidate the current role of AI and contribute to future development of ML in trauma care, we conducted a review focused on the use of ML in the diagnosis or treatment strategy of traumatic hemorrhage. A literature search was carried out on PubMed and Google scholar. Titles and abstracts were screened and, if deemed appropriate, the full articles were reviewed. We included 89 studies in the review. These studies could be grouped into five areas: (1) prediction of outcomes; (2) risk assessment and injury severity for triage; (3) prediction of transfusions; (4) detection of hemorrhage; and (5) prediction of coagulopathy. Performance analysis of ML in comparison with current standards for trauma care showed that most studies demonstrated the benefits of ML models. However, most studies were retrospective, focused on prediction of mortality, and development of patient outcome scoring systems. Few studies performed model assessment via test datasets obtained from different sources. Prediction models for transfusions and coagulopathy have been developed, but none is in widespread use. AI-enabled ML-driven technology is becoming integral part of the whole course of trauma care. Comparison and application of ML algorithms using different datasets from initial training, testing and validation in prospective and randomized controlled trials are warranted for provision of decision support for individualized patient care as far forward as possible.

Predictors of transfusion in trauma and their utility in the prehospital environment: a scoping review

Background: Hemorrhage is a leading cause of preventable mortality from trauma, necessitating resuscitation through blood product transfusions. Early and accurate identification of patients requiring transfusions in the prehospital setting may reduce delays in time to transfusion upon arrival to hospital, reducing mortality. The purpose of this study is to characterize existing literature on predictors of transfusion and analyze their utility in the prehospital context.Objectives: The objectives of this study are to characterize the existing quantity and quality of literature regarding predictor scores for transfusion in injured patients, and to analyse the utility of predictor scores for massive transfusions in the prehospital setting and identify prehospital predictor scores for future research.Methods: A search strategy was developed in consultation with information specialists. A literature search of OVID MEDLINE from 1946 to present was conducted for primary studies evaluating the predictive ability of scoring systems or single variables in predicting transfusion in all trauma settings.Results: Of the 5824 studies were identified, 5784 studies underwent title and abstract screening, 94 studies underwent full text review, and 72 studies were included in the final review. We identified 16 single variables and 52 scoring systems for predicting transfusion. Amongst single predictor variables, fluids administered and systolic blood pressure had the highest reported sensitivity (100%) and specificity (89%) for massive transfusion protocol (MTP) activation respectively. Amongst scoring systems for transfusion, the Shock Index and Modified Shock Index had the highest reported sensitivity (96%), while the Pre-arrival Model had the highest reported specificity (95%) for MTP activation. Overall, 20 scores were identified as being applicable to the prehospital setting, 25 scores were identified as being potentially applicable, and seven scores were identified as being not applicable.Conclusions: We identified an extensive list of predictive single variables, validated scoring systems, and derived models for massive transfusion, presented their properties, and identified those with potential utility in the prehospital setting. By further validating applicable scoring tools in the prehospital setting, we may begin to administer more timely transfusions in the trauma population.