Prehospital Triage Research Articles

Development and validation of prediction models for prehospital triage of military trauma patients.

The introduction of wireless sensors will enable military care providers to continuously and remotely assess/monitor vital signs. Prediction models are needed to use such data adequately and aid military care providers in their on-scene decision-making to optimise prehospital triage and improve patient outcomes. A prospective cohort comprising data from eight Emergency Medical Services and seven inclusive trauma regions was used to develop and validate prediction models that could aid military care providers in their prehospital triage decisions. Healthy (American Society of Anesthesiologists physical status classification 1 or 2) admitted adult trauma patients (aged ≥16 and ≤50 years), who suffered from a trauma mechanism that could occur to military personnel and were transported by ambulance from the scene of injury to a hospital, were included. A full model strategy was used, including prehospital predictors that are expected to be automaticly collectible by wireless sensors or to be incorporated in a personalised device that could run the models. Models were developed to predict early critical-resource use (ECRU), severe head injury (Abbreviated Injury Scale (AIS) ≥4), serious thoracic injury (AIS ≥3) and severe internal bleeding (>20% blood loss). Model performance was evaluated in terms of discrimination and calibration. Prediction models were developed with data from 4625 patients (80.0%) and validated with data from 1157 patients (20.0%). The models had good to excellent discriminative performance for the predicted outcomes in the validation cohort, with an area under the curve of 0.80 (95% CI 0.76 to 0.84) for ECRU, 0.83 (0.76 to 0.91) for severe head injury, 0.75 (0.70 to 0.80) for serious thoracic injury and 0.85 (0.78 to 0.93) for severe internal bleeding. All models showed satisfactory calibration in the validation cohort. The developed models could reliably predict outcomes in a simulated military trauma population and potentially support prehospital care providers in their triage decisions.

Travel Distances for Interhospital Transfers of Critically Ill Children: A Geospatial Analysis.

The U.S. pediatric acute care system has become more centralized, placing increasing importance on interhospital transfers. We conducted a geospatial analysis of critically ill children undergoing interfacility transfer with a specific focus on understanding travel distances between the patient's residence and the hospitals in which they receive care. Retrospective geospatial analysis using five U.S. state-level administrative databases; four states observed from 2016 to 2019 and one state from 2018 to 2019. Participants included 10,665 children who experienced 11,713 episodes of critical illness involving transfer between two hospitals. Travel distances and the incidence of "potentially suboptimal triage," in which patients were transferred to a second hospital less than five miles further from their residence than the first hospital. Patients typically present to hospitals near their residence (median distance from residence to first hospital, 4.2 miles; interquartile range [IQR], 1.8-9.6 miles). Transfer distances are relatively large (median distance between hospitals, 28.9 miles; IQR, 11.2-53.2 miles), taking patients relatively far away from their residences (median distance from residence to second hospital, 30.1 miles; IQR, 12.2-54.9 miles). Potentially suboptimal triage was frequent: 24.2 percent of patients were transferred to a hospital less than five miles further away from their residence than the first hospital. Potentially suboptimal triage was most common in children living in urban counties, and became less common with increasing medical complexity. The current pediatric critical care system is organized in a hub-and-spoke model, which requires large travel distances for some patients. Some transfers might be prevented by more efficient prehospital triage. Current transfer patterns suggest the choice of initial hospital is influenced by geography as well as by attempts to match hospital resources with perceived patient needs.

Accuracy of the Quebec Pre-Hospital Triage Scale (EQTPT) in Predicting the Need for Trauma Team Activation: A Retrospective Administrative Data Study

Background: Trauma team activation at a Level 1 trauma centre In Quebec, Canada, is primarily at the emergency department’s staff discretion. Trauma teams may be activated prehospital, based on information provided by field paramedics or in the emergency department based on the patient’s condition on arrival. In this study, we examined over and undertriage rates based on present trauma team activation criteria. We also examined if trauma team activation, for those patients solely meeting pre-hospital major trauma criteria, would result in significant overactivation of the trauma team. Methods: This is a single-centre retrospective medical record review. Primary ambulance transport reports from May 15, 2018 to December 31, 2020, were screened to identify patients aged ≥16 years who met pre-hospital trauma triage criteria to bypass community hospitals to arrive directly at the Level 1 trauma centre. We examined pre-hospital triage criteria, trauma team involvement, Injury Severity Scores (ISS) and final disposition. Patients were evaluated for over and undertriage and rates were compared to the rates assuming all patients were to have a trauma team activation. We considered patients overtriaged if they had a full trauma team activation but had an ISS <12 and were discharged from the emergency department. Undertriage was defined as any patient with an ISS ³12 and did not have a trauma team activation. Results: Of the 371 patients who met study inclusion criteria, 123 (33.3%) did not meet trauma team activation criteria, while 214 (57.7%) had a trauma team activation. Of these, 49 patients (13.2%) were undertriaged and 31 patients (8.4%) were overtriaged and 25.8% of the major trauma patients (ISS ³ 12) were undertriaged. A trauma team activation for all meeting field triage criteria increased overtriage to 25.3% and brought undertriage rates to 0%, with statistically significant differences based on the Wilcoxon signed ranks test (p<0.05). Conclusions: In this study, undertriage rates were well above 5%. Trauma team activation, based on local field trauma triage criteria adapted from the CDC-ACSCOT field triage criteria, eliminates undertriage and keeps overtriage rates below 35%. This research suggests that field triage criteria accurately predict major trauma and the need for the involvement of the trauma team and that the condition of the trauma patient in the pre-hospital setting is accurately predicting the need for advanced trauma care

A new prehospital cardiac risk assessment index derived from vital signs by first contact emergency medical service, including prehospital oxygen saturation, for accurate prehospital triage of acute m

Abstract Background: Rapid and accurate triage is essential to improve the prognosis of patients with severe acute myocardial infarction (AMI). However, prehospital triage using vital signs, including prehospital oxygen saturation (SpO2), by first medical contact has not been evaluated. Purpose: The purpose of this study was to develop a new risk stratification index for AMI using vital signs including SpO2 for rapid and accurate prehospital triage. Methods: From January 2013 to June 2022, 3006 AMI patients who were directly transported by ambulance were enrolled from the Mie ACS registry. Based on observed risk relationships in 3,006 AMI patients, we developed and evaluated a prehospital cardiac risk assessment index (P-CRAI) using age, heart rate, and systolic blood pressure (SBP) and SpO2 to predict 30-day mortality: (heart rate×[age/10]2)/SBP＋1.1(100-SpO2). We also analyzed whether P-CRAI predicted the need for mechanical circulatory support (MCS) or ventilator use. Results: In the receiver operating characteristic (ROC) curve analysis, P-CRAI 38.1 has a sensitivity of 84.4%, specificity of 72.7%, and accuracy of 84.3% for predicting 30-day mortality, and P-CRAI was a strong predictor of 30-day mortality compared with the other indices and variables (Figure 1A). When divided into two groups with P-CRAI 38, Kaplan-Meier survival curves showed significant differences in 30-day survival between the high and low groups (83.7% vs. 98.7%, P<0.001, Figure 1B). P-CRAI≥38 was the strongest independent predictor of 30-day mortality with a hazard ratio of 7.64 compared to the other factors by multivariate Cox regression analysis. The spline curve of 30-day mortality by P-CRAI shows a correlation with prognosis (Figure 2A). In this study, we then used the bootstrap method to generate a calibration curve and validate P-CRAI. The results showed an excellent fit and that P-CRAI accurately estimated patient prognosis (Figure 2B). In addition, P-CRAI was a robust predictor of the very early events of death at 24 hours and MCS or ventilator use by ROC curve analysis. (c-statistic 0.835 and 0.726, respectively, P<0.01). Conclusion: The P-CRAI was found to be a useful tool for prehospital triage of AMI patients, accurately predicting 30-day mortality and the need for MCS.Figure 1.Figure 2.

Helicopter Emergency Medical Services (HEMS) Transportation Utilization for Acute Ischemic Strokes at a Community-Based Hospital in South Florida

Background: A comprehensive stroke center (CSC) servicing remote areas in South Florida became HEMS direct-from-field capable on October 15, 2020. This analysis reviews the utilization of this service and compares it to ground EMS (GEMS). Methods: This is a retrospective cohort study from October 15, 2020, to July 31, 2021, that collected HEMS adult stroke alert data and compared it to the prior year's summary GEMS stroke alert data. Using HEMS transportation logs, presenting stroke symptoms and prehospital assessment were abstracted, in addition to initial NIHSS, downgrade rate, stroke diagnosis percentage, treatment rate, and times. Results: The analysis included a total of 52 direct transport HEMS stroke alerts. Assessment tools utilized by HEMS included the Cincinnati stroke scale (4%) and the FAST-ED (37%); most cases (59%) did not document a stroke assessment tool. The median NIHSS was 10, with 37% presenting a score <6. Cases were downgraded upon arrival in 27% and after CT imaging in 15% of cases. Significantly (p<.001), more HEMS cases were diagnosed as stroke (69%) compared to GEMS (57%). Stroke treatment rates remained similar (HEMS=23%; GEMS=24%). Median door-to-needle times were significantly faster for HEMS (15 min) than GEMS (25 min) (p<.05). Median door-to-puncture times were clinically faster for HEMS (61mins) than GEMS (66 mins). Conclusion: Prehospital triage could be improved to better detect the 77% of patients who are not eligible for acute stroke reperfusion treatments and may benefit from ground as opposed to air transportation. The current HEMS triage, although not consistently documented, appropriately selects stroke victims, but not necessarily those who need air transport to a higher care level. Patients arriving via HEMS receive similar access to stroke treatment and, if treated, significantly faster treatment times.

Prehospital Delta Shock Index Predicts Mortality and Need for Life Saving Interventions in Trauma Patients

ABSTRACT OBJECTIVES The delta shock index (ΔSI), defined as the change in shock index (SI) over time, is associated with hospital morbidity and mortality, but prehospital studies about ΔSI are limited. We investigate the association of prehospital ΔSI with mortality and resource utilization, hypothesizing that increases in SI among field trauma patients are associated with increased mortality and blood product transfusion. METHODS We performed a multicenter, retrospective, observational study from the Linking Investigators in Trauma and Emergency Services (LITES) network. We obtained data from January 2017 to June 2021. We fit logistic regression models to evaluate the association between an increase ΔSI > 0.1 and 28-day mortality and blood product transfusion within 4 hours of emergency department (ED) arrival. We used negative binomial models to evaluate the association between ΔSI > 0.1 and days in hospital, intensive care unit (ICU), and on ventilator (up to 28 days). RESULTS We identified 33,219 prehospital patients. We excluded burn patients and those without documented prehospital or ED heart rate or blood pressure, resulting in 30,511 cases for analysis. In adjusted analysis for the primary outcome of 28-day mortality, patients who had a ΔSI > 0.1 based on initial vital signs were 31% more likely to die (adjusted odds ratio (AOR) of 1.31, 95% CI 1.21-1.41) compared to those patients who had a ΔSI ≤0.1. These patients also spent 16% more days in hospital (adjusted incident rate ratio (AIRR) 1.16, 95% CI 1.14-1.19), 34% more days in ICU (AIRR 1.34, 95% CI 1.28-1.41), and 61% more days on ventilator (ARR 1.61, 95% CI 1.47-1.75). Additionally, patients with a ΔSI > 0.1 had higher odds of receiving blood products (AOR 2.00, 95% CI 1.88-2.12) within 4 hours of ED arrival. Models fit excluding hypotensive patients performed similarly. CONCLUSIONS An increase of greater than 0.1 in the ΔSI was associated with increased 28-day mortality; increased days in hospital, in ICU, and on ventilator; and increased need for blood product transfusion within 4 hours of ED arrival. This association held true for initially normotensive patients. Validation and implementation are needed to incorporate ΔSI into prehospital and ED triage.

Determinants of Timely Access to Recanalization Treatments and Outcomes in Pediatric Ischemic Stroke.

Timely revascularization in acute arterial ischemic stroke (AIS) is paramount for optimal outcomes. However, factors causing treatment delays in pediatric AIS remain understudied. We investigated determinants affecting the time from symptom onset or last-known-well to the start of recanalization treatment in pediatric AIS. We conducted an ancillary analysis of the French KID-CLOT study (The National Retrospective Study of Recanalization Treatments in Pediatric Arterial Ischemic Stroke), considering patients with pediatric AIS receiving recanalization treatments (IV thrombolysis IVT and mechanical thrombectomy) from 2015 to 2018. The study assessed prehospital triage's impact, direct versus transferred admissions, and unit type (pediatric versus adult) on treatment delay and clinical outcomes using modified Rankin Scale at 1 year. Among 68 patients (median age, 11 [IQR, 4-16]; initial PedNIHSS, 13 [IQR, 7-19]), treatment modalities were IVT (n=31), and mechanical thrombectomy (n=23), and IVT+mechanical thrombectomy (n=14). Prehospital triage significantly reduced last-known-well to treatment delay (overall, 229 versus 270 minutes; P=0.01), most notably for and mechanical thrombectomy (P<0.001). There was no substantial delay difference between direct and transferred admissions, or between unit types, although a trend favored adult units (370.3 versus 436.73 minutes; P=0.06). Prehospital triage correlated with improved outcomes, with a shift to lower modified Rankin Scale scores (P=0.021). For pediatric AIS treated with reperfusion therapy, prehospital triage emerges as a pivotal factor in reducing treatment delays and enhancing outcomes. These findings underscore the need for a dedicated prehospital stroke protocol for children. URL: https://www.clinicaltrials.gov; Unique identifier: NCT03887143.

Enhancing Performance of the National Field Triage Guidelines Using Machine Learning: Development of a Prehospital Triage Model to Predict Severe Trauma.

Prehospital trauma triage is essential to get the right patient to the right hospital. However, the national field triage guidelines proposed by the American College of Surgeons have proven to be relatively insensitive when identifying severe traumas. This study aimed to build a prehospital triage model to predict severe trauma and enhance the performance of the national field triage guidelines. This was a multisite prediction study, and the data were extracted from the National Trauma Data Bank between 2017 and 2019. All patients with injury, aged 16 years of age or older, and transported by ambulance from the injury scene to any trauma center were potentially eligible. The data were divided into training, internal, and external validation sets of 672,309; 288,134; and 508,703 patients, respectively. As the national field triage guidelines recommended, age, 7 vital signs, and 8 injury patterns at the prehospital stage were included as candidate variables for model development. Outcomes were severe trauma with an Injured Severity Score ≥16 (primary) and critical resource use within 24 hours of emergency department arrival (secondary). The triage model was developed using an extreme gradient boosting model and Shapley additive explanation analysis. The model's accuracy regarding discrimination, calibration, and clinical benefit was assessed. At a fixed specificity of 0.5, the model showed a sensitivity of 0.799 (95% CI 0.797-0.801), an undertriage rate of 0.080 (95% CI 0.079-0.081), and an overtriage rate of 0.743 (95% CI 0.742-0.743) for predicting severe trauma. The model showed a sensitivity of 0.774 (95% CI 0.772-0.776), an undertriage rate of 0.158 (95% CI 0.157-0.159), and an overtriage rate of 0.609 (95% CI 0.608-0.609) when predicting critical resource use, fixed at 0.5 specificity. The triage model's areas under the curve were 0.755 (95% CI 0.753-0.757) for severe trauma prediction and 0.736 (95% CI 0.734-0.737) for critical resource use prediction. The triage model's performance was better than those of the Glasgow Coma Score, Prehospital Index, revised trauma score, and the 2011 national field triage guidelines RED criteria. The model's performance was consistent in the 2 validation sets. The prehospital triage model is promising for predicting severe trauma and achieving an undertriage rate of <10%. Moreover, machine learning enhances the performance of field triage guidelines.

Ready to launch: a cross-sectional study of field trauma triage and air-ambulance policies across Canada.

This study aimed to understand current prehospital trauma air-ambulance policies and triage guidelines across Canada. The research question centered on understanding the guidelines used by provinces and territories and identifying potential regional variations in air-ambulance triage. We conducted a cross-sectional survey from November 2022 to May 2023, involving trauma leaders from Canada's 13 provinces and territories. Participants were identified via the Trauma Association of Canada and professional networks. The survey, developed with expert input and pilot tested for clarity, focused on prehospital trauma-triage guidelines, level of adoption of Center for Disease Control and Prevention (CDC) triage guidelines, and auto-launch air-ambulance policies. Data were collected using an 18-item electronic survey. Potential response bias was considered, and confidentiality was maintained. A cross-sectional qualitative analysis was used to evaluate the guidelines' adoption and variations, with responses compared across regions. The analysis revealed a nationwide adoption (11 of 11 respondents) of the CDC guidelines, indicating a standardized approach to patient transportation. Notably, many provinces and territories (5 of 11) endorse auto-launch protocols for air ambulances in specific scenarios. These policies offer advantages in geographically vast regions weighed against the cost of over-triage and inefficient resource allocation. Each province and territory tailors its approach based on factors such as geographic areas served, and available resources. This study provides a snapshot of the current state of prehospital trauma-triage guidelines in Canada. With some differences in nomenclature, Canadian provinces and territories widely apply the CDC guidelines to serve their populations. There is some regional variation on how transport is initiated within their borders. The findings underscore the delicate balance required for optimizing air-ambulance policies, considering factors such as timely access, resource allocation, and the local application of guidelines.

Factors affecting the accuracy of prehospital triage application and prehospital scene time in simulated mass casualty incidents

BackgroundThe contemporary management of mass casualty incidents (MCIs) relies on the effective application of predetermined, dedicated response plans based on current best evidence. Currently, there is limited evidence regarding the factors influencing the accuracy of first responders (FRs) in applying the START protocol and the associated prehospital times during the response to MCIs. The objective of this study was to investigate factors affecting FRs’ accuracy in performing prehospital triage in a series of simulated mass casualty exercises. Secondly, we assessed factors affecting triage-to-scene exit time in the same series of exercises.MethodsThis retrospective study focused on simulated casualties in a series of simulated MCIs Full Scale Exercises. START triage was the triage method of choice. For each Full-Scale Exercise (FSEx), collected data included exercise and casualty-related information, simulated casualty vital parameters, simulated casualty anatomic lesions, scenario management times, and responder experience.ResultsAmong the 1090 casualties included in the primary analysis, 912 (83.6%) were correctly triaged, 137 (12.6%) were overtriaged, and 41 (3.7%) were undertriaged. The multinomial regression model indicated that increasing heart rate (RRR = 1.012, p = 0.008), H-AIS (RRR = 1.532, p < 0.001), and thorax AIS (T-AIS) (RRR = 1.344, p = 0.007), and lower ISS (RRR = 0.957, p = 0.042) were independently associated with overtriage. Undertriage was significantly associated with increasing systolic blood pressure (RRR = 1.013, p = 0.005), AVPU class (RRR = 3.104 per class increase), and A-AIS (RRR = 1.290, p = 0.035). The model investigating the factors associated with triage-to-scene departure time showed that the assigned prehospital triage code red (TR = 0.841, p = 0.002), expert providers (TR = 0.909, p = 0.015), and higher peripheral oxygen saturation (TR = 0.998, p < 0.001) were associated with a reduction in triage-to-scene departure time. Conversely, increasing ISS was associated with a longer triage-to-scene departure time (TR = 1.004, 0.017).ConclusionsUnderstanding the predictors influencing triage and scene management decision-making by healthcare professionals responding to a mass casualty may facilitate the development of tailored training pathways regarding mass casualty triage and scene management.

The speed and accuracy of prehospital triage for simulated disaster patients: Comparing prehospital emergency triage rapid algorithm (PETRA) and simple triage and rapid treatment (START)

The speed and accuracy of prehospital triage for simulated disaster patients: Comparing prehospital emergency triage rapid algorithm (PETRA) and simple triage and rapid treatment (START)

External validation of the preHEART score and comparison with current clinical risk scores for prehospital risk assessment in patients with suspected NSTE-ACS

BackgroundEmergency Medical Services (EMS) studies have shown that prehospital risk stratification and triage decisions in patients with suspected non-ST-elevation acute coronary syndrome (NSTE-ACS) can be improved using clinical risk scores...

Survival of the fastest? A descriptive analysis of severely injured trauma patients primarily admitted or secondarily transferred to major trauma centers in a Danish inclusive trauma system

BackgroundTrauma systems are crucial for enhancing survival and quality of life for trauma patients. Understanding trauma triage and patient outcomes is essential for optimizing resource allocation and trauma care.AimsThe aim was to explore prehospital trauma triage in Region Zealand, Denmark. Specifically, characteristics for patients who were either primarily admitted or secondarily transferred to major trauma centers were described.MethodsA retrospective descriptive study of severely injured trauma patients was conducted from January 2017 to December 2021.ResultsThe study comprised 744 patients including 55.6% primary and 44.4% secondary patients. Overall, men accounted for 70.2% of patients, and 66.1% were aged 18–65 years. The secondary patients included more women—34.2% versus 26.3% and a higher proportion of Injury Severity Score of ≥ 15—59.6% versus 47.8%, compared to primary patients. 30-day survival was higher for secondary patients—92.7% versus 87%. Medical dispatchers assessed urgency as Emergency level A for 98.1% of primary patients and 86.3% for secondary patients. Physician-staffed prehospital units attended primary patients first more frequently—17.1% versus 3.5%. Response times were similar, but time at scene was longer for primary patients whereas time from injury to arrival at a major trauma center was longer for secondary patients.ConclusionsSecondary trauma patients had higher Injury Severity Scores and better survival rates. They were considered less urgent by medical dispatchers and less frequently assessed by physician-staffed units. Prospective quality data are needed for further investigation of optimal triage and continuous quality improvement in trauma care.

Improving Triage Accuracy in Prehospital Emergency Telemedicine: Scoping Review of Machine Learning-Enhanced Approaches.

Prehospital telemedicine triage systems combined with machine learning (ML) methods have the potential to improve triage accuracy and safely redirect low-acuity patients from attending the emergency department. However, research in prehospital settings is limited but needed; emergency department overcrowding and adverse patient outcomes are increasingly common. In this scoping review, we sought to characterize the existing methods for ML-enhanced telemedicine emergency triage. In order to support future research, we aimed to delineate what data sources, predictors, labels, ML models, and performance metrics were used, and in which telemedicine triage systems these methods were applied. A scoping review was conducted, querying multiple databases (MEDLINE, PubMed, Scopus, and IEEE Xplore) through February 24, 2023, to identify potential ML-enhanced methods, and for those eligible, relevant study characteristics were extracted, including prehospital triage setting, types of predictors, ground truth labeling method, ML models used, and performance metrics. Inclusion criteria were restricted to the triage of emergency telemedicine services using ML methods on an undifferentiated (disease nonspecific) population. Only primary research studies in English were considered. Furthermore, only those studies using data collected remotely (as opposed to derived from physical assessments) were included. In order to limit bias, we exclusively included articles identified through our predefined search criteria and had 3 researchers (DR, JS, and KS) independently screen the resulting studies. We conducted a narrative synthesis of findings to establish a knowledge base in this domain and identify potential gaps to be addressed in forthcoming ML-enhanced methods. A total of 165 unique records were screened for eligibility and 15 were included in the review. Most studies applied ML methods during emergency medical dispatch (7/15, 47%) or used chatbot applications (5/15, 33%). Patient demographics and health status variables were the most common predictors, with a notable absence of social variables. Frequently used ML models included support vector machines and tree-based methods. ML-enhanced models typically outperformed conventional triage algorithms, and we found a wide range of methods used to establish ground truth labels. This scoping review observed heterogeneity in dataset size, predictors, clinical setting (triage process), and reported performance metrics. Standard structured predictors, including age, sex, and comorbidities, across articles suggest the importance of these inputs; however, there was a notable absence of other potentially useful data, including medications, social variables, and health system exposure. Ground truth labeling practices should be reported in a standard fashion as the true model performance hinges on these labels. This review calls for future work to form a standardized framework, thereby supporting consistent reporting and performance comparisons across ML-enhanced prehospital triage systems.

Prehospital stroke detection scales: A head-to-head comparison of 7 scales in patients with suspected stroke.

Several prehospital scales have been designed to aid paramedics in identifying stroke patients in the ambulance setting. However, external validation and comparison of these scales are largely lacking. To compare all published prehospital stroke detection scales in a large cohort of unselected stroke code patients. We conducted a systematic literature search to identify all stroke detection scales. Scales were reconstructed with prehospital acquired data from two observational cohort studies: the Leiden Prehospital Stroke Study (LPSS) and PREhospital triage of patients with suspected STrOke (PRESTO) study. These included stroke code patients from four ambulance regions in the Netherlands, including 15 hospitals and serving four million people. For each scale, we calculated the accuracy, sensitivity and specificity for a diagnosis of stroke (ischemic, hemorrhagic or TIA). Moreover, we assessed the proportion of stroke patients who received reperfusion treatment with intravenous thrombolysis or endovascular thrombectomy that would have been missed by each scale. We identified 14 scales, of which seven (CPSS, FAST, LAPSS, MASS, MedPACS, OPSS, and sNIHSS-EMS) could be reconstructed. Of 3317 included stroke code patients, 2240 (67.5%) had a stroke (1528 ischemic, 242 hemorrhagic, 470 TIA) and 1077 (32.5%) a stroke mimic. Of ischemic stroke patients, 715 (46.8%) received reperfusion treatment. Accuracies ranged from 0.60 (LAPSS) to 0.66 (MedPACS, OPSS and sNIHSS-EMS), sensitivities from 66% (LAPSS) to 84% (MedPACS and sNIHSS-EMS), and specificities from 28% (sNIHSS-EMS) to 49% (LAPSS). MedPACS, OPSS and sNIHSS-EMS missed the fewest reperfusion-treated patients (10.3-11.2%), whereas LAPSS missed the most (25.5%). Prehospital stroke detection scales generally exhibited high sensitivity but low specificity. While LAPSS performed poorest, MedPACS, sNIHSS-EMS and OPSS demonstrated the highest accuracy and missed the fewest reperfusion-treated stroke patients. Use of the most accurate scale could reduce unnecessary stroke code activations for patients with a stroke mimic by almost a third, but at the cost of missing 16% of strokes and 10% of patients who received reperfusion treatment.

Key performance indicators and benchmarks in MCI prehospital response using technological tools: a qualitative study assessing the perception of practitioners and tool developers.

The aim of this study is to investigate the opinions and perspectives of The Novel Integrated Toolkit for Enhanced Prehospital Life Support and Triage in Challenging and Large Emergencies (NIGHTINGALE) end-users and tool developers regarding Key Performance Indicators (KPIs) and benchmarks that assess the prehospital response to Mass Casualty Incidents (MCIs) enhanced by the NIT-MR. A qualitative study employing focus group discussions was conducted to collect opinions and perspectives of end-users and tool developers regarding KPIs and benchmarks in MCI response using the NIT-MR. The criteria considered for the selection and distribution of participants within the groups was the nature of their involvement within the NIGHTINGALE project and their familiarity with the tools to be discussed. Thirty-one participants from different countries were included. Four themes emerged during data analysis which are: definition/explanation is the personal understanding of participants of the term KPI, process of KPI development and relationship with User Requirements is the decision process for assigning KPIs to user requirements, benchmarking is the mental process of associating a benchmark to a KPI or for developing a benchmark, and technical/medical gap is the gap of understanding between each sides' fields. This study emphasized the need for a structured approach to using KPIs and bridging the gap between technological and medical worlds, taking the NIGHTINGALE project, funded by the European Union, which aims to develop a technological toolkit for first responders in mass casualty incidents as an example. These insights are crucial for enhancing disaster response.

Evaluation of a new prehospital major incident triage tool: Informing current UK practice

During mass casualty incidents, the first Ambulance responders on scene face critical decisions about who receives medical treatment first. Effective clinical management of such incidents relies on triage, which prioritises patients based on injury severity, available resources, and the number of casualties. Various decision-making tools have been developed to assist emergency responders with triage decisions. However, there have been concerns raised about the efficacy of existing triage tools, which led to the development of a new pre-hospital triage tool for major incidents. The current study evaluated two versions of the new tool alongside the existing one used by UK emergency responders through a live exercise simulating two different major incidents. Participants offered valuable insights for the tools enhancement, notably in managing non-injured casualties and paediatric patients, and reducing cognitive load when making decisions. These findings shaped the final version of the new tool, deployed to UK Ambulance responders in Spring 2023. Findings demonstrate the importance of considering the visual presentation of triage tools as well as the inclusion of accurate physiological parameters to facilitate responder decision-making when categorising patients in mass casualty triage. Practical implications and suggestions for future research are discussed.

Journal Club: Prehospital Detection of Large Vessel Occlusion Stroke With Electroencephalography: Results of the ELECTRA-STROKE Study.

The ELECTRA-STROKE study investigated the potential of EEG for prehospital triage of patients with ischemic stroke due to large vessel occlusion (LVO), in which fast triage to stroke centers for endovascular treatment is crucial. The study was conducted in 4 phases, and this Journal Club article focuses on the fourth phase in the prehospital setting with suspected stroke patients. An EEG cap with dry electrodes was used to measure brain activity. The main focus was on the diagnostic accuracy of the theta/alpha ratio, which yielded an area under the receiver operator characteristic curve (AUC) of 0.80. Secondary endpoints, particularly the Brain Symmetry Index (a quantified EEG interhemispheric cortical power asymmetry index) in the delta frequency band, showed an AUC of 0.91. Despite the convenient study design and user-friendly EEG device, limitations include a single-arm design, a relatively small sample size, and exclusions due to data quality issues. The usefulness of EEG in the detection of neuronal changes based on brain ischemia was highlighted, but uncertainties remain regarding its use in certain patient groups. The improvements in the Brain Symmetry Index from phase 3 to 4 of the study indicate the potential for further refinement and investigation of combined methods to improve diagnostic accuracy. The study provides insight into the role of EEG in prehospital stroke detection, recognizing both the strengths and limitations. Overall, the study contributes to understanding the promise of EEG in optimizing LVO stroke triage and urges further refinement and exploration of complementary diagnostic approaches.

Optimizing pre-hospital triage for suspected spinal cord injuries: a neurosurgical perspective.

Optimizing pre-hospital triage for suspected spinal cord injuries: a neurosurgical perspective.

Patients suffering traumatic brain injury: patient characteristics, prehospital triage, primary referral and mortality - A population-based follow-up study

BackgroundTraumatic brain injury (TBI) is a potential high-risk condition, but appropriate care pathways, including prehospital triage and primary referral to a specialised neurosurgical centre, can improve neurological outcome and survival. The care pathway starts with layman triage, wherein the patient or bystander decides whether to contact a general practitioner (GP) or emergency services (1-1-2 call) as an entryway into the health care system. The GP or 112-health care professional then decides on the level of urgency and dispatches emergency medical services (EMS) when needed. Finally, a decision is made regarding referral of the TBI patient to a specialised neurotrauma centre or a local hospital. Recent studies have shown that injuries are generally more severe in patients entering the health care system through EMS (112-calls) than through GPs; however, no information exists on whether mortality and morbidity outcomes differ depending on the referral choice. The aim of this study was to examine triage pathways, including the method of entry into the health care system, as well as patient characteristics and place of primary referral, to determine the associated 30-day and 1-year mortality rates in TBI patients with confirmed intracranial lesions.MethodsThis retrospective observational population-based follow-up study was conducted in the Central Denmark Region from 1 February 2017 to 31 January 2019. We included all adult patients who contacted hospitals and were ascribed a predefined TBI ICD-10 diagnosis code in the Danish National Patient Register. The obtained TBI cohort was merged with prehospital data from the Prehospital Emergency Medical Services, Central Denmark Region, and vital status from the Danish Civil Registration System. Binary logistic regression analysis of mortality was conducted. In all patients with TBI (including concussions), the primary outcome was primary referral to a specialised centre based on mode of entry (‘GP/HCP’, ‘112-call’ or ‘Unreferred’) into the health care system. In the subgroup of patients with confirmed intracranial lesions, the secondary outcomes were the relative risk of death at day 30 and 1 year based on the place of primary referral.ResultsOf 5,257 first TBI hospital contacts of adult patients included in the cohort, 1,430 (27.2%) entered the health care system via 1-1-2 emergency medical calls. TBI patients triaged by 112-calls were more likely to receive the highest level of emergency response (15.6% vs. 50.3%; p < 0.001) and second-tier resources and were more frequently referred directly to a specialised centre than were patients entering through GPs or other health care personnel. In the subgroup of 1188/5257 (22.4%) patients with confirmed intracranial lesions, we found no difference in the risk ratio of 30 day (RR 1.04 (95%CI 0.65–1.63)) or 1 year (RR 0.96 (95%CI 0.72–1.25)) all-cause mortality between patients primarily referred to a regional hospital or to a specialised centre when adjusting for age, sex, comorbidities, antiplatelet/anticoagulant treatment and type of intracranial lesions.ConclusionTBI patients mainly enter the health system by contact with GPs or other health care professionals. However, patients entering through 112-calls are more frequently triaged directly to specialised centres. We were unable to demonstrate any significant difference in the adjusted 30-day and 1-year mortality based on e primary referral to a specialised centre. The inability to demonstrate an effect on mortality based on primary referral to a specialised centre may reflect a lack of clinical data in the registries used. Considerable differences may exist in nondocumented baseline characteristics (i.e., GCS, blood pressure and injury severity) between the groups and may limit conclusions about differences in mortality. Further research providing high-quality evidence on the effect of primary referral is needed to secure early neurosurgical interventions in TBI patients.