Field-Triage, Hospital-Triage and Triage-Assessment: A Literature Review of the Current Phases of Adult Trauma Triage

Using the GAP score as a complement to the NISS score in identifying severely injured patients- A registry-based cohort study of adult trauma patients in Sweden

BACKGROUND

New Injury Severity Score (NISS) and Glasgow Coma Scale, Age and Pressure (GAP) scoring systems have cutoffs to define severe injury and identify high-risk patients. This is important in trauma quality monitoring and improvement. The overall aim was to explore if GAP scoring system can be a complement or an alternative to the traditional NISS scoring system.

METHODS

Adults exposed to trauma between 2017 and 2021 were included in the study, using data from The Swedish Trauma Registry. The performance of NISS and GAP scores in predicting mortality, and ICU admissions were assessed using the area under the receiver operator characteristics (AUROC) in all patients and in subgroups (blunt, penetrating trauma and older (≥65 years) trauma patients). Patients were classified as severely injured by NISS >15 as Severely Injured NISS (SIN) or with a high-risk for mortality, by GAP 3-18 as High Risk GAP (HRG). Undertriage was calculated based on the cutoffs HRG and SIN.

RESULTS

Overall, 37,017 patients were included. The AUROC (95 % CI) for mortality using NISS was 0.84 (0.83-0.85) and for GAP 0.92 (0.91-0.93) (p-value <0.001), the AUROC (95 % CI) for ICU-admissions was 0.82 (0.82-0.83) using NISS and for GAP 0.70 (0.70-0.71) p-value <0.001, in the overall cohort. In older patients the AUROC (95 % CI) for mortality was 0.76 (0.75-0.78) using NISS and 0.79 (0.78-0.81) using GAP, p-value <0.001. Overall, 8,572 (23.2 %) and 2,908 (7.9 %) were classified as SIN and HRG, respectively, with mortality rates of 13.7 % and 34.3 %. In the HRG group low-energy falls dominated and in the SIN group most patients were exposed to MVCs. In the SIN and HRG groups the rate of Emergency Trauma Interventions according to Utstein guidelines (ETIU) and ICU admission was 14.0 vs 9.5 % and 47.0 vs 62.5 % respectively.

CONCLUSION

Our findings suggest that the GAP score and its cutoff 3-18 can be used to define severe trauma as complement to NISS >15 and can be a valuable tool in trauma quality monitoring and improvement. However, both scoring systems were less accurate in predicting mortality for the older trauma patients and should be explored further.

Markers of Futile Resuscitation in Traumatic Hemorrhage: A Review of the Evidence and a Proposal for Futility Time-Outs during Massive Transfusion

The reduction in the blood supply following the 2019 coronavirus pandemic has been exacerbated by the increased use of balanced resuscitation with blood components including whole blood in urban trauma centers. This reduction of the blood supply has diminished the ability of blood banks to maintain a constant supply to meet the demands associated with periodic surges of urban trauma resuscitation. This scarcity has highlighted the need for increased vigilance through blood product stewardship, particularly among severely bleeding trauma patients (SBTPs). This stewardship can be enhanced by the identification of reliable clinical and laboratory parameters which accurately indicate when massive transfusion is futile. Consequently, there has been a recent attempt to develop scoring systems in the prehospital and emergency department settings which include clinical, laboratory, and physiologic parameters and blood products per hour transfused as predictors of futile resuscitation. Defining futility in SBTPs, however, remains unclear, and there is only nascent literature which defines those criteria which reliably predict futility in SBTPs. The purpose of this review is to provide a focused examination of the literature in order to define reliable parameters of futility in SBTPs. The knowledge of these reliable parameters of futility may help define a foundation for drawing conclusions which will provide a clear roadmap for traumatologists when confronted with SBTPs who are candidates for the declaration of futility. Therefore, we systematically reviewed the literature regarding the definition of futile resuscitation for patients with trauma-induced hemorrhagic shock, and we propose a concise roadmap for clinicians to help them use well-defined clinical, laboratory, and viscoelastic parameters which can define futility.

In Search of the Truth: Choice of Ground Truth for Predictive Modeling of Trauma Team Activation in Pediatric Trauma

BACKGROUND

Assigning trauma team activation (TTA) levels for trauma patients is a classification task that machine learning models can help optimize. However, performance is dependent on the "ground-truth" labels used for training. Our purpose was to investigate 2 ground truths, the Cribari matrix and the Need for Trauma Intervention (NFTI), for labeling training data.

STUDY DESIGN

Data were retrospectively collected from the institutional trauma registry and electronic medical record, including all pediatric patients (age <18 years) who triggered a TTA (January 2014 to December 2021). Three ground truths were used to label training data: (1) Cribari (Injury Severity Score >15 = full activation), (2) NFTI (positive for any of 6 criteria = full activation), and (3) the union of Cribari+NFTI (either positive = full activation).

RESULTS

Of 1,366 patients triaged by trained staff, 143 (10.47%) were considered undertriaged using Cribari, 210 (15.37%) using NFTI, and 273 (19.99%) using Cribari+NFTI. NFTI and Cribari+NFTI were more sensitive to undertriage in patients with penetrating mechanisms of injury (p = 0.006), specifically stab wounds (p = 0.014), compared with Cribari, but Cribari indicated overtriage in more patients who required prehospital airway management (p < 0.001), CPR (p = 0.017), and who had mean lower Glasgow Coma Scale scores on presentation (p < 0.001). The mortality rate was higher in the Cribari overtriage group (7.14%, n = 9) compared with NFTI and Cribari+NFTI (0.00%, n = 0, p = 0.005).

CONCLUSIONS

To prioritize patient safety, Cribari+NFTI appears best for training a machine learning algorithm to predict the TTA level.

Utilization of trauma nurse screening procedure for triage of the injured patient

PURPOSE

The treatment of trauma patients requires significant hospital resources. Numerous protocols exist to triage the injured patient and determine the level of care they may require. The purpose of this work is to describe an institutional trauma nurse screening procedure and to evaluate its effectiveness in triaging injured patients.

METHODS

This retrospective study was conducted at a large, tertiary trauma center from January to June 2021. Patients were assessed by trauma nurse clinicians (TNC) utilizing a standardized screening process to determine suitability for trauma activation. If the patient did not meet activation criteria, they were sent to the main Emergency Department for evaluation and treatment. Patients could be activated later by the emergency physician. The primary variables of interest were number of activations after initial "rule out," injury severity score (ISS) for patients who were activated, mechanism of injury, and disposition.

RESULTS

A total of 1874 TNC screenings were performed. Of these, 1449 (77%) patients did not meet trauma activation criteria. Only 41 (2.8%) patients initially ruled out were later activated by the emergency physician and admitted for treatment of injuries. The average ISS of all activated patients was 9 ± 6. Thirty-six patients had an ISS ≤ 15, four between 16 and 25, and only one patient had an ISS > 25. Twenty-seven patients were admitted to the ward, five went to step-down units, and five required intensive care unit admission. Four patients required operative intervention for their injuries.

CONCLUSION

These results suggest that nursing screening protocols can be safe, effective tools for triage of trauma patients.

Prospective validation of a hospital triage predictive model to decrease undertriage: an EAST multicenter study

Background

Tiered trauma team activation (TTA) allows systems to optimally allocate resources to an injured patient. Target undertriage and overtriage rates of <5% and <35% are difficult for centers to achieve, and performance variability exists. The objective of this study was to optimize and externally validate a previously developed hospital trauma triage prediction model to predict the need for emergent intervention in 6 hours (NEI-6), an indicator of need for a full TTA.

Methods

The model was previously developed and internally validated using data from 31 US trauma centers. Data were collected prospectively at five sites using a mobile application which hosted the NEI-6 model. A weighted multiple logistic regression model was used to retrain and optimize the model using the original data set and a portion of data from one of the prospective sites. The remaining data from the five sites were designated for external validation. The area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC) were used to assess the validation cohort. Subanalyses were performed for age, race, and mechanism of injury.

Results

14 421 patients were included in the training data set and 2476 patients in the external validation data set across five sites. On validation, the model had an overall undertriage rate of 9.1% and overtriage rate of 53.7%, with an AUROC of 0.80 and an AUPRC of 0.63. Blunt injury had an undertriage rate of 8.8%, whereas penetrating injury had 31.2%. For those aged ≥65, the undertriage rate was 8.4%, and for Black or African American patients the undertriage rate was 7.7%.

Conclusion

The optimized and externally validated NEI-6 model approaches the recommended undertriage and overtriage rates while significantly reducing variability of TTA across centers for blunt trauma patients. The model performs well for populations that traditionally have high rates of undertriage.

Level of evidence

2.

Systematic Literature Review of Health-Related Quality-of-Life Measures for Caregivers of Older Adult Trauma Patients

INTRODUCTION

As the older adult population increases, hospitals treat more older adults with injuries. After leaving, these patients suffer from decreased mobility and independence, relying on care from others. Family members often assume this responsibility, mostly informally and unpaid. Caregivers of other older adult populations have increased stress and decreased caregiver-related quality of life (CRQoL). Validated CRQoL measures are essential to capture their unique experiences. Our objective was to review existing CRQoL measures and their validity in caregivers of older adult trauma patients.

METHODS

A professional librarian searched published literature from the inception of databases through August 12, 2022 in MEDLINE (via PubMed), Embase (via Elsevier), and CINAHL Complete (via EBSCO). We identified 1063 unique studies of CRQoL in caregivers for adults with injury and performed a systematic review following COnsensus-based Standards for the selection of health Measurement Instruments guidelines for CRQoL measures.

RESULTS

From the 66 studies included, we identified 54 health-related quality-of-life measures and 60 domains capturing caregiver-centered concerns. The majority (83%) of measures included six or fewer CRQoL content domains. Six measures were used in caregivers of older adults with single-system injuries. There were no validated CRQoL measures among caregivers of older adult trauma patients with multisystem injuries.

CONCLUSIONS

While many measures exist to assess healthcare-related quality of life, few, if any, adequately assess concerns among caregivers of older adult trauma patients. We found that CRQoL domains, including mental health, emotional health, social functioning, and relationships, are most commonly assessed among caregivers. Future measures should focus on reliability and validity in this specific population to guide interventions.

Cribari, does it apply in real time? An analysis of a community level 1 trauma center

BACKGROUND

Major Trauma Code 1 (TC1) activations require significant resources to provide immediate treatment to potentially unstable, critically ill, patients. The Cribari Matrix Method (CMM) and Need For Trauma Intervention (NFTI) are two ways to determine over and undertriage in trauma. We studied the overtriage rate at a community level 1 trauma center using these two methods to determine the efficacy of the triage criteria in TC1 activations.

METHOD

A retrospective review of all patients in the trauma registry of a level 1 American College of Surgeons trauma program from May to October 2021 was performed. Overtriage rates were determined using CMM and NFTI criteria.

RESULTS

The overtriage rate of 552 activations using CMM alone was 73%. CMM combined with NFTI resulted in a 56% overtriage rate.

CONCLUSION

The Cribari method can be used to determine the effectiveness of a system's trauma code 1 criteria but cannot delineate which criteria should be reviewed.

Validation of Air Medical Prehospital Triage Score in Determining Resource Utilization at Level 1 Trauma Centers

OBJECTIVE

Overtriage (ie, delivering less severely injured patients via helicopter) is costly, raises safety concerns, and reduces efficiency of the trauma system. The Air Medical Prehospital Triage (AMPT) scoring system was developed to determine which trauma patients would gain a survival benefit by air transport. The objective of this study was to evaluate the AMPT scoring system as a method of reducing trauma overtriage when helicopter emergency medical services were used.

METHODS

A retrospective study of all scene trauma transports delivered by helicopter to 1 of 2 level 1 trauma centers was evaluated for 1) hospital stay less than 1 day and 2) failure to meet 1 of the following criteria for resource utilization: intensive care unit admission, an operative procedure within the first 24 hours, the need for blood products, Injury Severity Score ≥ 16, or death during hospitalization. Helicopter emergency medical services personnel recorded specific criteria from the Centers for Disease Control and Prevention (CDC) field trauma triage guidelines and AMPT that were met by transported trauma patients.

RESULTS

There were 244 patients in the study population. Eighty-one (33.2%) patients were discharged within 24 hours; 11 (13.5%) of these patients were positive using AMPT scoring, whereas 44 (54.3%) patients met 1 of the CDC criteria. Similarly, 141 (57.8%) patients failed to meet 1 of the level 1 resource criteria; 19 (13.5%) met the AMPT criteria for air medical transport, whereas 84 (59.6%) met 1 of the CDC criteria. Undertriage was 63.5% for AMPT and 20.2% for CDC based on resource utilization criteria.

CONCLUSION

The AMPT score reduced the number of patients who were inappropriately transported to a trauma center. However, this appeared to be at the expense of undertriage. Future studies should focus on developing a refined air medical-specific triage tool that has both low overtriage rates as well as lower undertriage rates.

National analysis of over and under-triage rates in relation to trauma population risk factors and associated outcomes across various levels trauma centers

INTRODUCTION

Over and under-triage represent a misallocation of resources that can affect patient outcomes. The purpose of this study is to evaluate over and under-triage rates in relation to risk factors and associated outcomes of trauma patients nationwide.

METHODS

A retrospective cohort study using the Trauma Quality Improvement Program from 2017 to 2020. Multivariable regression models were used to assess predictors of over-triage (activation when unnecessary) and under-triage (limited activation when full activation was necessary).

RESULTS

22.2 % (32,782) of the study population were over-triaged and 20.3 % (29,996) were under-triaged. Most over-triaged patients were Black, with Medicaid, or had a penetrating injury, whereas most under-triaged patients were White, with private/commercial insurance, or had a blunt injury. With covariates adjusted for, Pacific Islander (p = 0.024) and American Indian patients (p = 0.015) were associated with higher odds of over-triage, and Hispanic patients had higher odds of under-triage (p<0.001). Patients with Medicare (p<0.001) had higher odds of over-triage, and patients with private/commercial insurance (p<0.001) had higher odds of under-triage compared to Medicaid patients. Patients in level II (p<0.001) and level III (p<0.001) trauma hospitals were associated with higher odds of over-triage.

CONCLUSION

Pacific Islander and American Indian patients, Medicare, and level II and III trauma centers are at increased risk of over-triage rates, while Hispanic and privately insured trauma patients had a higher risk for under-triage. Future studies should further investigate factors contributing to poor outcomes linked to under-triage practices and methods to improve consistency and standardization of triage tools across various levels of trauma centers.

Machine Learning Improves the Accuracy of Trauma Team Activation Level Assignments in Pediatric Patients

BACKGROUND

The assignment of trauma team activation levels can be conceptualized as a classification task. Machine learning models can be used to optimize classification predictions. Our purpose was to demonstrate proof-of-concept for a machine learning tool for predicting trauma team activation levels in pediatric patients with traumatic injuries.

METHODS

Following IRB approval, we retrospectively collected data from the institutional trauma registry and electronic medical record at our Pediatric Trauma Center for all patients (age <18 y) who triggered a trauma team activation (1/2014-12/2021), including: demographics, mechanisms of injury, comorbidities, pre-hospital interventions, numeric variables, and the six "Need for Trauma Intervention (NFTI)" criteria. Three machine learning models (Logistic Regression, Random Forest, Support Vector Machine) were tested 1000 times in separate trials using the union of the Cribari and NFTI metrics as ground-truth (Injury Severity Score >15 or positive for any of 6 NFTI criteria = full activation). Model performance was quantified and compared to emergency department (ED) staff.

RESULTS

ED staff had 75% accuracy, an area under the curve (AUC) of 0.73 ± 0.04, and an F1 score of 0.49. The best performing of all machine learning models, the support vector machine, had 80% accuracy, AUC 0.81 ± 4.1e-5, F1 Score 0.80, with less variance compared to other models and ED staff.

CONCLUSIONS

All machine learning models outperformed ED staff in all performance metrics. These results suggest that data-driven methods can optimize trauma team activations in the ED, with potential improvements in both patient safety and hospital resource utilization.

TYPE OF STUDY

Economic/Decision Analysis or Modeling Studies.

LEVEL OF EVIDENCE

II.

Assessing Effectiveness and Efficiency of Need for Trauma Intervention (NFTI) and Modified NFTI in Identifying Overtriage and Undertriage Rates and Associated Outcomes

INTRODUCTION

Limited research has assessed the effectiveness of Need for Trauma Intervention (NFTI) and Modified NFTI (MNFTI) criteria in accurately identifying triage rates in major trauma. We aim to evaluate the predictive capability of NFTI/MNFTI in determining rates of overtriage and undertriage, as well as associated outcomes.

METHODS

A literature search was conducted utilizing PubMed, Google Scholar, EMBASE, ProQuest, and Cochrane from conception to April 13th, 2023. Studies assessing the utilization of NFTI/MNFTI in identifying over and undertriage rates were included. Additional outcomes including mortality, ICU LOS, and resource allocation were evaluated. Outcomes were compared between NFTI/MNFTI and other triage metrics.

RESULTS

A total of 8 articles, including 175,650 trauma patients, were evaluated. NFTI utilization was associated with reduced overtriage rates compared to numerous tools including trauma triage matrix (TTM) and need for emergent intervention within 6 h (NEI-6) (NFTI 32.15%, TTM 44.5%, NEI-6 42.23%). Regarding undertriage, NFTI had lower rates than the secondary triage assessment tool (STAT) and TTM (NFTI 14.0%, STAT, 22.3%, TTM 14.3%) as well as Cribari Matrix Method (CMM) (NFTI .8%, CMM 7.6%, P < .0003). Additionally, the utilization of NFTI in combination with CMM yielded a significant reduction in undertriage rates compared to either tool alone (CMM/NFTI 2.7%, NFTI 4.6%, CMM 8.2%).

CONCLUSION

Implementation of NFTI/MNFTI resulted in more accurately capturing over and undertriage rates. Similar trends were identified when NFTI was used in combination with CMM. When compared to other triage tools, NFTI outperformed CMM, TTM, STAT, and NEI-6 in overtriage and/or undertriage rates.

Undertriage of Severely Injured Trauma Patients

INTRODUCTION

The American College of Surgeons (ACS) delineates trauma team activation (TTA) criteria to identify seriously injured trauma patients in the field. Patients are deemed to be severely undertriaged (SU), placing them at risk for adverse outcomes, when they do not meet TTA criteria but nonetheless sustain significant injuries (Injury Severity Score [ISS] ≥25).

OBJECTIVES

Delineate patient demographics, injuries, and outcomes after SU.

PARTICIPANTS

Trauma patients presenting to our ACS-verified Level 1 trauma center with ISS ≥25 were included (11/2015-03/2022). Transfers and private vehicle transports were excluded. Patients were dichotomized and compared by trauma arrival level: TTA (Appropriately Triaged, AT) vs routine consults (SU).

RESULTS

Study criteria were satisfied by 1653 patients: 1375 (83%) AT and 278 (17%) SU. Severely undertriaged patients were older than AT patients (47 vs 36 years, P < .001). Severely undertriaged occurred almost exclusively following blunt trauma (96% vs 71%, P < .001). Injury Severity Score was lower following SU than AT (29 vs 32, P < .001). The most common severe injuries (Abbreviated Injury Scale score [AIS] ≥3) among the SU group were in the Chest (n = 179, 64%). Severely undertriaged patients necessitated emergent intubation (n = 34, 12%), surgery (n = 59, 21%), and angioembolization (n = 22, 8%) at high rates. Severely undertriaged mortality was n = 40, 14%.

CONCLUSION

Severely undertriaged occurred among a substantial proportion of ISS ≥25 patients, predominately following blunt trauma. Severe chest injuries were most likely to evade capture. Rates of intubation, emergent intervention, and in-hospital mortality were high after SU. Efforts should be made to identify such patients in the field as they may benefit from TTA.

Explainable Prediction Model of the Need for Emergency Hemostasis Using Field Information During Physician-Staffed Helicopter Emergency Medical Service Interventions: A Single-Center, Retrospective, Observational Pilot Study

OBJECTIVE

Early recognition of hemostasis is important to prevent trauma-related deaths. We conducted a pilot study of a predictive model of hemostatic need using factors that can be collected during helicopter emergency medical service (HEMS) interventions until transport hospital selection using cases from our institution.

METHODS

This single-center, retrospective, observational pilot study included 251 trauma patients aged ≥ 18 years treated with HEMS between April 2017 and March 2022, in Nara Medical University. Cardiac arrest and pre-HEMS treatment patients were excluded. Emergency hemostatic surgery prediction models were constructed using the light gradient boosting machine cross-validation method using objective data that could be collected before hospital determination. The accuracy of this model was compared with that of the ground emergency medical service-based model, and factors influencing outcome were visualized using Shapley additive explanations.

RESULTS

The predictive accuracy of the model with HEMS intervention factors was an area under the receiver operating characteristic curve of 0.80, superior to the 0.73 accuracy area under the receiver operating characteristic curve for ground emergency medical services constructed with contact information. Clinically important factors, such as shock index, blood pressure changes, and ultrasound findings, had a significant impact on outcomes, with nonmonotonic effects observed across factors.

CONCLUSION

This pilot study suggests that predictive models of emergency hemostasis can be built using limited prehospital information. To validate this model, a larger, multicenter study is recommended.

A Disease-Prediction Protocol Integrating Triage Priority and BERT-Based Transfer Learning for Intelligent Triage

Large hospitals can be complex, with numerous discipline and subspecialty settings. Patients may have limited medical knowledge, making it difficult for them to determine which department to visit. As a result, visits to the wrong departments and unnecessary appointments are common. To address this issue, modern hospitals require a remote system capable of performing intelligent triage, enabling patients to perform self-service triage. To address the challenges outlined above, this study presents an intelligent triage system based on transfer learning, capable of processing multilabel neurological medical texts. The system predicts a diagnosis and corresponding department based on the patient’s input. It utilizes the triage priority (TP) method to label diagnostic combinations found in medical records, converting a multilabel problem into a single-label one. The system considers disease severity and reduces the “class overlapping” of the dataset. The BERT model classifies the chief complaint text, predicting a primary diagnosis corresponding to the complaint. To address data imbalance, a composite loss function based on cost-sensitive learning is added to the BERT architecture. The study results indicate that the TP method achieves a classification accuracy of 87.47% on medical record text, outperforming other problem transformation methods. By incorporating the composite loss function, the system’s accuracy rate improves to 88.38% surpassing other loss functions. Compared to traditional methods, this system does not introduce significant complexity, yet substantially improves triage accuracy, reduces patient input confusion, and enhances hospital triage capabilities, ultimately improving the patient’s medical experience. The findings could provide a reference for intelligent triage development.

Development of Imaging Criteria for Geriatric Blunt Trauma Patients

INTRODUCTION

Current decision tools to guide trauma computed tomography (CT) imaging were not validated for use in older patients. We hypothesized that specific clinical variables would be predictive of injury and could be used to guide imaging in this population to minimize risk of missed injury.

METHODS

Blunt trauma patients aged 65 y and more admitted to a Level 1 trauma center intensive care unit from January 2018 to November 2020 were reviewed for histories, physical examination findings, and demographic information known at the time of presentation. Injuries were defined using the patient's final abbreviated injury score codes, obtained from the trauma registry. Abbreviated injury score codes were categorized by corresponding CT body region: Head, Face, Chest, C-Spine, Abdomen/Pelvis, or T/L-Spine. Variable groupings strongly predictive of injury were tested to identify models with high sensitivity and a negative predictive value.

RESULTS

We included 608 patients. Median age was 77 y (interquartile range, 70-84.5) and 55% were male. Ground-level fall was the most common injury mechanism. The most commonly injured CT body regions were Head (52%) and Chest (42%). Variable groupings predictive of injury were identified in all body regions. We identified models with 97.8% sensitivity for Head and 98.8% for Face injuries. Sensitivities more than 90% were reached for all except C-Spine and Abdomen/Pelvis.

CONCLUSIONS

Decision aids to guide imaging for older trauma patients are needed to improve consistency and quality of care. We have identified groupings of clinical variables that are predictive of injury to guide CT imaging after geriatric blunt trauma. Further study is needed to refine and validate these models.

Traumatic Brain Injury Under Triage Risk: A Rural Trauma System Experience

BACKGROUND

The process of interfacility transfer may cause a delay in the necessary medical treatment, which could lead to poor outcomes and increased mortality rates. The ACS-COT considers an acceptable under triage rate of <5%. The aim of this research was to identify the likelihood of under triage among transferred-in traumatic brain injury (TBI) patients.

METHODS

This is a single-center study of Trauma Registry data, from July 1, 2016, to October 31, 2021. The inclusion criteria were based upon age (≥40 years), ICD10 diagnosis of TBI, and interfacility transfer. Under triage using the Cribari matrix method was the dependent variable. A logistic regression was performed to identify additional predictor variables on the likelihood that an adult TBI trauma patient experienced under triage.

RESULTS

878 patients were included in the analysis; 168 (19%) experienced an under triage. The logistic regression model was statistically significant (N = 837, P < .01). In addition, several significant increases in odds for under triage were identified, which included increasing injury severity score (ISS; OR 1.40, P < .01), increasing AIS head region (OR 6.19, P < .01), and personality disorders (OR 3.61, P = .02). In addition, a reduction in odds in TBI adult trauma under triage is the comorbidity of anticoagulant therapy (OR .25, P < .01).

CONCLUSIONS

The likelihood of under triage in the adult TBI trauma population is associated with increasing AIS head injuries and increasing ISS and among those with mental health comorbidities. This evidence and additional protective factors, such as patients on anticoagulant therapy, may aid in education and outreach efforts to reduce under triage among the regional referring centers.

Emergency physician and nurse discretion accurately triage high-risk trauma patients

PURPOSE

Prehospital trauma team activation (TTA) criteria allow for early identification of severely injured trauma patients. Although most TTA criteria are objective, one TTA criterion is subjective: emergency provider discretion. The study objective was to define the ability of emergency department physician and nurse discretion to accurately perform prehospital triage of high risk trauma patients.

METHODS

All highest level TTAs arriving to our American College of Surgeons (ACS)-verified Level 1 trauma center (06/2015-08/2020) were included. Exclusions were undocumented prehospital vitals or discharge disposition. At our institution, TTAs are triggered for standard ACS TTA criteria and age > 70 with traumatic mechanism other than ground level fall. Patients meeting ≥ 1 criterion apart from "Emergency Provider Discretion" were defined as Standard TTAs and patients meeting only "Emergency Provider Discretion" were defined as Discretion TTAs. Univariable/multivariable analyses compared injury data and outcomes.

RESULTS

4540 patients met inclusion/exclusion criteria: 3330 (73%) Standard TTAs and 1210 (27%) Discretion TTAs. Discretion TTAs were younger (34 vs. 37 years, p < 0.001) and more frequently injured by penetrating trauma (38% vs. 33%, p = 0.008), particularly stab wounds (64% vs. 29%). Overtriage rates were comparable after Discretion vs. Standard TTAs (33% vs. 31%, p = 0.141). Blood transfusion < 4 h (31% vs. 32%, p = 0.503) and ICU admission ≥ 3 days (25% vs. 27%, p = 0.058) were comparable between groups. Discretion TTA was independently associated with increased need for emergent surgery (OR 1.316, p = 0.005).

CONCLUSIONS

Emergency provider discretion accurately identifies major trauma, with comparable rates of overtriage as standard TTA criteria. Discretion TTAs were as likely as Standard TTAs to require early blood transfusion and prolonged ICU stay. After controlling for confounders, Discretion TTAs were significantly more likely to require emergent surgical intervention. Emergency provider discretion should be recognized as a valid method of identifying major trauma patients at high risk of need for intervention.

Accuracy of a Prehospital Triage Protocol in Predicting In-Hospital Mortality and Severe Trauma Cases among Older Adults

Background: Prehospital trauma triage tools are not tailored to identify severely injured older adults. Our trauma triage protocol based on a three-tier trauma severity grading system (A, B, and C) has never been studied in this population. The objective was to assess its accuracy in predicting in-hospital mortality among older adults (≥65 years) and to compare it to younger patients. Methods: A retrospective multicenter cohort study, from 2011 to 2021. Consecutive adult trauma patients managed by a mobile medical team were prospectively graded A, B, or C according to the initial seriousness of their injuries. Accuracy was evaluated using sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios. Results: 8888 patients were included (14.1% were ≥65 years). Overall, 10.1% were labeled Grade A (15.2% vs. 9.3% among older and younger adults, respectively), 21.9% Grade B (27.9% vs. 20.9%), and 68.0% Grade C (56.9% vs. 69.8%). In-hospital mortality was 7.1% and was significantly higher among older adults regardless of severity grade. Grade A showed lower sensitivity (50.5 (43.7; 57.2) vs. 74.6 (69.8; 79.1), p < 0.0001) for predicting mortality among older adults compared to their younger counterparts. Similarly, Grade B was associated with lower sensitivity (89.5 (84.7; 93.3) vs. 97.2 (94.8; 98.60), p = 0.0003) and specificity (69.4 (66.3; 72.4) vs. 74.6 (73.6; 75.7], p = 0.001) among older adults. Conclusions: Our prehospital trauma triage protocol offers high sensitivity for predicting in-hospital mortality including older adults.

Assessment of Trauma Team Activation Fees by US Region and Hospital Ownership

IMPORTANCE

Trauma centers must be readily equipped to handle a variety of life-threatening injuries and consequently may charge a fee for the activation of their trauma team. Regional and hospital-related variations in trauma activation fees across the US have not been formally assessed.

OBJECTIVE

To evaluate the variability of trauma activation fees from trauma centers across the US and examine whether certain hospital characteristics are associated with higher activation fees.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study used data from the American College of Surgeons website to identify all trauma centers in the US that were listed as verified from inception of the verification database through March 4, 2022 (N = 546). Five military hospitals were excluded, and trauma activation fees could not be found for 18 trauma centers; the remaining 523 hospitals were included in the analysis. Each hospital's publicly available chargemaster (a comprehensive list of a hospital's products, procedures, and services) was searched to obtain its trauma activation fees. Two levels of trauma activation fees were recorded: tier 1 (full activation) and tier 2 (partial activation). Hospital-specific data were obtained from the American Hospital Association website. All data were collected between January 2 and March 11, 2022. Linear regression analyses were performed to assess potential associations between hospital characteristics (type of control [for profit, government, church, or other nonprofit], hospital system [owner], number of staffed beds, and academic vs nonacademic status) and trauma activation fees.

MAIN OUTCOMES AND MEASURES

Median and mean trauma activation fees nationally and stratified by location, hospital system, and other hospital characteristics.

RESULTS

Of 523 trauma centers included in the analysis, most were located in the Midwest (180 centers) and West (129 centers). There were 176 adult level I trauma centers and 200 adult level II trauma centers; 69 centers had for-profit status, and 415 were academic. Overall, the median (IQR) tier 1 trauma activation fee was $9500 ($5601-$17 805), and the mean (SD) tier 1 trauma activation fee was $13 349 ($11 034); these fees ranged from $1000 to $61 734. Median (IQR) trauma activation fees were highest in the West ($18 099 [$10 741-$$27 607]), especially in California, where the median (IQR) activation fee was $24 057 ($15 979-$33 618). Trauma activation fees were also higher at for-profit hospitals, most of which were owned by the HCA Healthcare system, which had 43 trauma centers and a median (IQR) tier 1 trauma activation fee of $29 999 ($20 196-$37 589).

CONCLUSIONS AND RELEVANCE

In this study, trauma activation fees varied widely among hospitals in the US. Regional variation in these fees was substantial, with hospitals in the West charging substantially more than those in other locations. In addition, for-profit hospitals charged more than other types of hospitals. These findings suggest that some patients with serious traumatic injuries will incur disproportionately high trauma activation fees depending on the trauma center to which they are brought. Therefore, standardization of trauma activation fees is warranted.

Need for Emergent Intervention within 6 Hours: A Novel Prediction Model for Hospital Trauma Triage

Objective: A tiered trauma team activation system allocates resources proportional to patients' needs based upon injury burden. Previous trauma hospital-triage models are limited to predicting Injury Severity Score which is based on > 10% all-cause in-hospital mortality, rather than need for emergent intervention within 6 hours (NEI-6). Our aim was to develop a novel prediction model for hospital-triage that utilizes criteria available to the EMS provider to predict NEI-6 and the need for a trauma team activation.Methods: A regional trauma quality collaborative was used to identify all trauma patients ≥ 16 years from the American College of Surgeons-Committee on Trauma verified Level 1 and 2 trauma centers. Logistic regression and random forest were used to construct two predictive models for NEI-6 based on clinically relevant variables. Restricted cubic splines were used to model nonlinear predictors. The accuracy of the prediction model was assessed in terms of discrimination.Results: Using data from 12,624 patients for the training dataset (62.6% male; median age 61 years; median ISS 9) and 9,445 patients for the validation dataset (62.6% male; median age 59 years; median ISS 9), the following significant predictors were selected for the prediction models: age, gender, field GCS, vital signs, intentionality, and mechanism of injury. The final boosted tree model showed an AUC of 0.85 in the validation cohort for predicting NEI-6.Conclusions: The NEI-6 trauma triage prediction model used prehospital metrics to predict need for highest level of trauma activation. Prehospital prediction of major trauma may reduce undertriage mortality and improve resource utilization.

Sex-Based Disparities in Timeliness of Trauma Care and Discharge Disposition

Importance

Differences in time to diagnostic and therapeutic measures can contribute to disparities in outcomes. However, whether there is an association of timeliness by sex for trauma patients is unknown.

Objective

To investigate whether sex-based differences in time to definitive interventions exist for trauma patients in the US and whether these differences are associated with outcomes.

Design, Setting, and Participants

This was a retrospective cohort study conducted from July 2020 to July 2021, using the 2013 to 2016 Trauma Quality Improvement Program (TQIP) databases from level I to III trauma centers in the US. Patients 18 years or older with an Injury Severity Score (ISS) greater than 15 and who carried diagnoses of traumatic brain injury, intra-abdominal injury, pelvic fracture, femur fracture, and spinal injury as a result of their trauma were included in the study. Data were analyzed from July 2020 to July 2021.

Main Outcomes and Measures

Primary outcomes assessed timeliness to interventions, using Wilcoxon signed rank and χ2 tests. Secondary outcomes included location of discharge after injury, using propensity score-matched generalized estimating equations modeling.

Results

Of the 28 332 patients included, 20 002 (70.6%) were male patients (mean [SD] age, 43.3 [18.2] years) and 8330 (29.4%) were female patients (mean [SD] age, 48.5 [21.1] years), with significantly different distributions of ISS scores (ISS score 16-24: male patient, 10 622 [53.1%]; female patient, 4684 [56.2%]; ISS score 41-74: male patient, 2052 [10.3%]; female patient, 852 [10.2%]). Male patients more frequently had abdominal (4257 [21.3%] vs 1268 [15.2%]) and spinal cord (3989 [20.0%] vs 1274 [15.3%]) injuries, whereas female patients experienced greater proportions of femur (3670 [44.0%] vs 8422 [42.1%]) and pelvic (3970 [47.6%] vs 6963 [34.8%]) fractures. Female patients experienced significantly longer emergency department length of stay (median [IQR], 184 [92-314] minutes vs 172 [86-289] minutes; P < .001), longer time in pretriage (median [IQR], 52 [36-80] minutes vs 49 [34-77] minutes; P < .001), and increased likelihood of discharge to nursing or long-term care facilities instead of home after matching by age, ISS, mechanism, and injury type (male patient:female patient, odds ratio, 0.72; 95% CI, 0.67-0.78).

Conclusions and Relevance

Results of this cohort study suggest that female trauma patients experienced slightly longer delays in trauma care and had a higher likelihood of discharge to long-term care facilities than their male counterparts.