Prehospital continuous vital signs predict need for resuscitative endovascular balloon occlusion of the aorta and resuscitative thoracotomy prehospital continuous vital signs predict resuscitative endovascular balloon occlusion of the aorta

A machine learning-based Coagulation Risk Index predicts acute traumatic coagulopathy in bleeding trauma patients

BACKGROUND

Acute traumatic coagulopathy (ATC) is a well-described phenomenon known to begin shortly after injury. This has profound implications for resuscitation from hemorrhagic shock, as ATC is associated with increased risk for massive transfusion (MT) and mortality. We describe a large-data machine learning-based Coagulation Risk Index (CRI) to test the early prediction of ATC in bleeding trauma patients.

METHODS

Coagulation Risk Index was developed using continuous vital signs (VSs) available during the first 15 minutes after admission at a single trauma center over 4 years. Data to compute the CRI were derived from continuous features of photoplethymographic and electrocardiographic waveforms, oximetry values, and blood pressure trends. Two groups of patients at risk for ATC were evaluated: critical administration threshold and patients who received an MT. Acute traumatic coagulopathy was evaluated in separate models and defined as an international normalized ratio (INR) >1.2 and >1.5 upon arrival. The CRI was developed using 2 years of cases for training and 2 years for testing. The accuracy of the models is described by area under the receiver operator curve with 95% confidence intervals.

RESULTS

A total of 17,567 patients were available for analysis with continuous VS data, 52.8% sustained blunt injury, 30.2% were female, and the mean age was 44.6 years. The ability of CRI to predict ATC in critical administration threshold patients was excellent. The true positive and true negative rates were 95.6% and 88.3%, and 94.9% and 89.2% for INR >1.2 and INR >1.5, respectively. The CRI also demonstrated excellent accuracy in patients receiving MT; true positive and true negative rates were 92.8% and 91.3%, and 100% and 88.1% for INR >1.2 and INR >1.5, respectively.

CONCLUSION

Using continuous VSs and large-data machine learning capabilities, the CRI accurately predicts early ATC in bleeding patients. Clinical application may guide early hemostatic resuscitation. Extension of this technology into the prehospital setting could provide earlier treatment of ATC.

LEVEL OF EVIDENCE

Diagnostic Tests/Criteria; Level III.

PROGRESS OF RESUSCITATIVE ENDOVASCULAR BALLOON OCCLUSION OF THE AORTA IN PREHOSPITAL EMERGENCY TREATMENT FOR PELVIC FRACTURE

ABSTRACT

Pelvic fractures are severe traumatic injuries often accompanied by potentially fatal massive bleeding. Rapid control of hemorrhages in prehospital emergency settings is critical for improving outcomes in traumatic bleeding. Resuscitative endovascular balloon occlusion of the aorta (REBOA) is a promising technique for controlling active bleeding from pelvic fractures. By inserting a balloon catheter into the aorta, REBOA helps maintain blood flow to vital organs such as the brain and heart. This paper provides a comprehensive overview of the initial management of noncompressive trunk hemorrhage caused by pelvic fractures, introduces the technical principles and developments of REBOA, and explores its extensive application in prehospital emergency care. It delves into the operational details and outlines strategies for effectively managing potential complications. We aim to offer a theoretical framework for the future utilization of REBOA in managing uncontrollable hemorrhage associated with pelvic fractures in prehospital emergencies.

Damage Control Resuscitation in Traumatic Hemorrhage: It Is More Than Fixing the Holes and Filling the Tank

Damage control resuscitation is the foundation of hemorrhagic shock management and includes early administration of plasma, tranexamic acid, and limited crystalloid-containing products.

Applications of Artificial Intelligence in Helicopter Emergency Medical Services: A Scoping Review

OBJECTIVE

Recent systematic reviews of acute care medicine applications of artificial intelligence (AI) have focused on hospital and general prehospital uses. The purpose of this scoping review was to identify and describe the literature on AI use with a focus on applications in helicopter emergency medical services (HEMS).

METHODS

A literature search was performed with specific inclusion and exclusion criteria. Articles were grouped by characteristics such as publication year and general subject matter with categoric and temporal trend analyses.

RESULTS

We identified 21 records focused on the use of AI in HEMS. These applications included both clinical and triage uses and nonclinical uses. The earliest study appeared in 2006, but over one third of the identified studies have been published in 2021 or later. The passage of time has seen an increased likelihood of HEMS AI studies focusing on nonclinical issues; for each year, the likelihood of a nonclinical focus had an odds ratio of 1.3.

CONCLUSION

This scoping review provides overview and hypothesis-generating information regarding AI applications specific to HEMS. HEMS AI may be ultimately deployed in nonclinical arenas as much as or more than for clinical decision support. Future studies will inform future decisions as to how AI may improve HEMS systems design, asset deployment, and clinical care.

A Novel Continuous Real-Time Vital Signs Viewer for Intensive Care Units: Design and Evaluation Study

BACKGROUND

Clinicians working in intensive care units (ICUs) are immersed in a cacophony of alarms and a relentless onslaught of data. Within this frenetic environment, clinicians make high-stakes decisions using many data sources and are often oversaturated with information of varying quality. Traditional bedside monitors only depict static vital signs data, and these data are not easily viewable remotely. Clinicians must rely on separate nursing charts-handwritten or electric-to review physiological patterns, including signs of potential clinical deterioration. An automated physiological data viewer has been developed to provide at-a-glance summaries and to assist with prioritizing care for multiple patients who are critically ill.

OBJECTIVE

This study aims to evaluate a novel vital signs viewer system in a level 1 trauma center by subjectively assessing the viewer's utility in a high-volume ICU setting.

METHODS

ICU attendings were surveyed during morning rounds. Physicians were asked to conduct rounds normally, using data reported from nurse charts and briefs from fellows to inform their clinical decisions. After the physician finished their assessment and plan for the patient, they were asked to complete a questionnaire. Following completion of the questionnaire, the viewer was presented to ICU physicians on a tablet personal computer that displayed the patient's physiologic data (ie, shock index, blood pressure, heart rate, temperature, respiratory rate, and pulse oximetry), summarized for up to 72 hours. After examining the viewer, ICU physicians completed a postview questionnaire. In both questionnaires, the physicians were asked questions regarding the patient's stability, status, and need for a higher or lower level of care. A hierarchical clustering analysis was used to group participating ICU physicians and assess their general reception of the viewer.

RESULTS

A total of 908 anonymous surveys were collected from 28 ICU physicians from February 2015 to June 2017. Regarding physicians' perception of whether the viewer enhanced the ability to assess multiple patients in the ICU, 5% (45/908) strongly agreed, 56.6% (514/908) agreed, 35.3% (321/908) were neutral, 2.9% (26/908) disagreed, and 0.2% (2/908) strongly disagreed.

CONCLUSIONS

Morning rounds in a trauma center ICU are conducted in a busy environment with many data sources. This study demonstrates that organized physiologic data and visual assessment can improve situation awareness, assist clinicians with recognizing changes in patient status, and prioritize care.

Outcomes and potential for improvement in the prehospital treatment of penetrating chest injuries in a European metropolitan area: A retrospective analysis of 2009 - 2017

BACKGROUND

Trauma is the leading cause of death in patients <45 years living in high-resource settings. However, penetrating chest injuries are still relatively rare in Europe - with an upwards trend. These cases are of particular interest to emergency medical services (EMS) due to available invasive treatment options like chest tube placement or resuscitative thoracotomy. To date, there is no sufficient data from Austria regarding penetrating chest trauma in a metropolitan area, and no reliable source to base decisions regarding further skill proficiency training on.

METHODS

For this retrospective observational study, we screened all trauma emergency responses of the Viennese EMS between 01/2009 and 12/2017 and included all those with a National Advisory Committee for Aeronautics (NACA) score ≥ IV (= potentially life-threatening). Data were derived from EMS mission documentations and hospital files, and for those cases with the injuries leading to cardiopulmonary resuscitation (CPR), we assessed the EMS cardiac arrest registry and consulted a forensic physician.

RESULTS

We included 480 cases of penetrating chest injuries of NACA IV-VII (83% male, 64% > 30 years old, 74% stab wounds, 16% cuts, 8% gunshot wounds, 56% inflicted by another party, 26% self-inflicted, 18% unknown). In the study period, the incidence rose from 1.4/100,000 to 3.5/100,000 capita, and overall, about one case was treated per week. In the cases with especially severe injury patterns (= NACA V-VII, 43% of total), (tension-)pneumothorax was the most common injury (29%). The highest mortality was seen in injuries to pulmonary vessels (100%) or the heart (94%). Fifty-eight patients (12% of total) deceased, whereas in 15 cases, the forensic physician stated survival could theoretically have been possible. However, only five of these CPR patients received at least unilateral thoracostomy. Regarding all penetrating chest injuries, thoracostomy had only been performed in eight patients.

CONCLUSIONS

Severe cases of penetrating chest trauma are rare in Vienna and happened about once a week between 2009 and 2017. Both incidence and case load increased over the years, and potentially life-saving invasive procedures were only reluctantly applied. Therefore, a structured educational and skill retention approach aimed at both paramedics and emergency physicians should be implemented.

TRIAL REGISTRATION

Retrospective analysis without intervention.

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.

Rapid prediction of secondary neurologic decline after traumatic brain injury: a data analytic approach

Secondary neurologic decline (ND) after traumatic brain injury (TBI) is independently associated with outcome, but robust predictors of ND are lacking. In this retrospective analysis of consecutive isolated TBI admissions to the R. Adams Cowley Shock Trauma Center between November 2015 and June 2018, we aimed to develop a triage decision support tool to quantify risk for early ND. Three machine learning models based on clinical, physiologic, or combined characteristics from the first hour of hospital resuscitation were created. Among 905 TBI cases, 165 (18%) experienced one or more ND events (130 clinical, 51 neurosurgical, and 54 radiographic) within 48 h of presentation. In the prediction of ND, the clinical plus physiologic data model performed similarly to the physiologic only model, with concordance indices of 0.85 (0.824-0.877) and 0.84 (0.812-0.868), respectively. Both outperformed the clinical only model, which had a concordance index of 0.72 (0.688-0.759). This preliminary work suggests that a data-driven approach utilizing physiologic and basic clinical data from the first hour of resuscitation after TBI has the potential to serve as a decision support tool for clinicians seeking to identify patients at high or low risk for ND.

Advanced interventions in the pre-hospital resuscitation of patients with non-compressible haemorrhage after penetrating injuries

Abstract

Early haemorrhage control and minimizing the time to definitive care have long been the cornerstones of therapy for patients exsanguinating from non-compressible haemorrhage (NCH) after penetrating injuries, as only basic treatment could be provided on scene. However, more recently, advanced on-scene treatments such as the transfusion of blood products, resuscitative thoracotomy (RT) and resuscitative endovascular balloon occlusion of the aorta (REBOA) have become available in a small number of pre-hospital critical care teams. Although these advanced techniques are included in the current traumatic cardiac arrest algorithm of the European Resuscitation Council (ERC), published in 2021, clear guidance on the practical application of these techniques in the pre-hospital setting is scarce. This paper provides a scoping review on how these advanced techniques can be incorporated into practice for the resuscitation of patients exsanguinating from NCH after penetrating injuries, based on available literature and the collective experience of several helicopter emergency medical services (HEMS) across Europe who have introduced these advanced resuscitation interventions into routine practice.

Graphical Abstract