Accuracy of a Prehospital Triage Protocol in Predicting In-Hospital Mortality and Severe Trauma Cases among Older Adults.
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SUBMITTER: Benhamed A
PROVIDER: S-EPMC9916137 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
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Accuracy of a Prehospital Triage Protocol in Predicting In-Hospital Mortality and Severe Trauma Cases among Older Adults.
Benhamed Axel A Emond Marcel M Mercier Eric E Heidet Matthieu M Gauss Tobias T Saint-Supery Pierre P Yadav Krishan K David Jean-Stéphane JS Claustre Clement C Tazarourte Karim K
International journal of environmental research and public health 20230120 3
<i>Background</i>: 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. <i>Methods</i>: A retrospective multicenter cohort study, from 2011 to 2021. Consecutive adult trauma pat ...[more]
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Project description:ImportanceAdequate prehospital triage is pivotal to enable optimal care in inclusive trauma systems and reduce avoidable mortality, lifelong disabilities, and costs. A model has been developed to improve the prehospital allocation of patients with traumatic injuries and was incorporated in an application (app) to be implemented in prehospital practice.ObjectiveTo evaluate the association between the implementation of a trauma triage (TT) intervention with an app and prehospital mistriage among adult trauma patients.Design, setting, and participantsThis population-based, prospective quality improvement study was conducted in 3 of the 11 Dutch trauma regions (27.3%), with full coverage of the corresponding emergency medical services (EMS) regions participating in this study. Participants included adult patients (age ≥16 years) with traumatic injuries who were transported by ambulance between February 1, 2015, and October 31, 2019, from the scene of injury to any emergency department in the participating trauma regions. Data were analyzed between July 2020 and June 2021.ExposuresImplementation of the TT app and the awareness of need for adequate triage created by its implementation (ie, the TT intervention).Main outcomes and measuresThe primary outcome was prehospital mistriage, evaluated in terms of undertriage and overtriage. Undertriage was defined as the proportion of patients with an Injury Severity Score (ISS) of 16 or greater who were initially transported to a lower-level trauma center (designated to treat patients who are mildly and moderately injured) and overtriage as the proportion of patients with an ISS of less than 16 who were initially transported to a higher-level trauma center (designated to treat patients who are severely injured).ResultsA total of 80 738 patients were included (40 427 [50.1%] before and 40 311 [49.9%] after implementation of the intervention), with a median (IQR) age of 63.2 (40.0-79.7) years and 40 132 (49.7%) male patients. Undertriage decreased from 370 of 1163 patients (31.8%) to 267 of 995 patients (26.8%), while overtriage rates did not increase (8202 of 39 264 patients [20.9%] vs 8039 of 39 316 patients [20.4%]). The implementation of the intervention was associated with a statistically significantly reduced risk for undertriage (crude risk ratio [RR], 0.95; 95% CI, 0.92 to 0.99, P = .01; adjusted RR, 0.85; 95% CI, 0.76-0.95; P = .004), but the risk for overtriage was unchanged (crude RR, 1.00; 95% CI, 0.99-1.00; P = .13; adjusted RR, 1.01; 95% CI, 0.98-1.03; P = .49).Conclusions and relevanceIn this quality improvement study, implementation of the TT intervention was associated with improvements in rates of undertriage. Further research is needed to assess whether these findings are generalizable to other trauma systems.
| S-EPMC10074221 | biostudies-literature
Project description:BackgroundIt is argued that many older trauma patients are under-triaged in prehospital care which may adversely affect their outcomes. This systematic review aimed to assess prehospital under-triage rates for older trauma patients, the accuracy of the triage criteria, and the impact of prehospital triage decisions on outcomes.MethodsA computerised literature search using MEDLINE, Scopus, and CINHAL databases was conducted for studies published between 1966 and 2021 using a list of predetermined index terms and their associated alternatives. Studies which met the inclusion criteria were included and critiqued using the Critical Appraisal Skills Programme tool. Due to the heterogeneity of the included studies, narrative synthesis was used in this systematic review.ResultsOf the 280 identified studies, 23 met the inclusion criteria. Current trauma triage guidelines have poor sensitivity to identify major trauma and the need for TC care for older adults. Although modified triage tools for this population have improved sensitivity, they showed significantly decreased specificity or were not applied to all older people. The issue of low rates of TC transport for positively triaged older patients is not well understood. Furthermore, the benefits of TC treatment for older patients remain uncertain.ConclusionsThis systematic review showed that under-triage is an ongoing issue for older trauma patients in prehospital care and its impact on their outcomes is still uncertain. Further high-quality prospective research is needed to assess the accuracy of prehospital triage criteria, the factors other than the triage criteria that affect transport decisions, and the impact of under-triage on outcomes.
| S-EPMC8463357 | biostudies-literature
Project description:BackgroundInjury severity scales have traditionally been used to assess the performance of prehospital trauma triage protocols, but they correlate weakly with the urgent needs of specialized trauma care interventions. This study aimed to develop a list of in-hospital urgent and specialized trauma care interventions that require direct transport to the highest-level trauma centre within the catchment area.MethodsBased on a list of potential participants we obtained using data on training, experience, geographic location, affiliations and role within key trauma organizations, we recruited multidisciplinary trauma experts (including prehospital, emergency, surgery and intensive care clinicians, epidemiologists and clinician/decision-makers) from across Canada to complete a 3-round modified Delphi survey. We conducted a literature review of the criteria used to define urgent and specialized trauma care, and included all diagnostic and therapeutic interventions presented in previously published studies in the list of interventions to present to the panellists. The final list was determined by our advisory committee, 5 clinicians with experience in trauma care. Participants were asked to rate their level of agreement for potentially including the 38 items as urgent and specialized trauma care interventions on a 9-point Likert scale. Interventions were retained if more than 67% of participants moderately or strongly agreed (7-9 on the Likert scale). Interventions that did not reach consensus were presented again in the subsequent round.ResultsTwenty-three panellists were recruited. The response rate was 91%, 96% and 83% for the 3 rounds. After the Delphi process, 30 of the 38 interventions, including endotracheal intubation, blood product administration and angioembolization, and abdominal, thoracic, neurosurgical, spinal and/or orthopedic operations (excluding hip or limb surgery, and toe or finger amputation), were selected. Hospital admission to the intensive care unit and/or for observation of brain, spinal, thoracic or abdominal injuries were also retained.ConclusionWe developed a Canadian consensus-based list of urgent and specialized in-hospital trauma care interventions requiring direct transportation to a major trauma centre. This list should help standardize assessments of current protocols and derive new triage tools.
| S-EPMC10069413 | biostudies-literature
Project description:OBJECTIVE:To prevent misjudgment of the severity of patients in the emergency department who initially seem non-severe but are in a critical state, methods that differ from the conventional viewpoint are needed. We aimed to determine whether vital sign changes between prehospital and in-hospital could predict in-hospital mortality among non-trauma patients. METHODS:This observational cohort study was conducted in two tertiary care hospitals. Patients were included if they were transported by ambulance for non-trauma-related conditions but were excluded if they experienced prehospital cardiopulmonary arrest, were pregnant, were aged <15 years, had undergone inter-hospital transfer, or had complete missing data regarding prehospital or in-hospital vital signs. The main outcome was in-hospital mortality, and the study variables were changes in vital signs, pulse pressure, and/or shock index between the prehospital and in-hospital assessments. Logistic regression analyses were performed to obtain adjusted odds ratios for each variable. Receiver operating characteristic curve analyses were performed to identify cut-off values that produced a positive likelihood ratio of ≥2. RESULTS:Among the 2,586 eligible patients, 170 died in the two hospitals. Significantly elevated risks of in-hospital mortality were associated with changes in the Glasgow Coma Scale (cut-off ≤-3), respiratory rate (no clinically significant cut-off), systolic blood pressure (cut-off ≥47 mmHg), pulse pressure (cut-off ≥55 mmHg), and shock index (cut-off ≥0.3). CONCLUSIONS:Non-trauma patients who exhibit changes in some vital signs between prehospital and in-hospital have an increased risk of in-hospital mortality. Therefore, it is useful to incorporate these changes in vital signs to improve triaging and predict the occurrence of in-hospital mortality.
| S-EPMC6355016 | biostudies-literature
Project description:BackgroundPrehospital 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.ObjectiveThis study aimed to build a prehospital triage model to predict severe trauma and enhance the performance of the national field triage guidelines.MethodsThis 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.ResultsAt 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.ConclusionsThe 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.
| S-EPMC11474124 | biostudies-literature
Project description:Objective: Older adults make up a rapidly increasing proportion of motor vehicle occupants and previous studies have demonstrated that this population is more susceptible to traumatic injuries. The CDC recommends that patients anticipated to have severe injuries (Injury Severity Score [ISS] ≥ 16) be transported to a trauma center. The recommended target rate for undertriage is ≤ 5% and for overtriage is ≤ 50%. Several regression-based algorithms for injury prediction have been developed in order to predict severe injury in occupants involved in a motor vehicle collision (MVC). The objective of this study to was to determine if the accuracy of regression-based injury severity prediction algorithms decreases for older adults.Methods: Data were obtained from the National Automotive Sampling System - Crashworthiness Data System (NASS-CDS) from the years 2000-2015. Adult occupants involved in non-rollover MVCs were included. Regression-based injury risk models to predict severe injury (ISS ≥ 16) were developed using random split-samples with the following variables: age, delta-V, direction of impact, belt status, and number of impacts. Separate models were trained using data from the following age groups: (1) all adults, (2) 15-54 years, (3) ≥45 years, (4) ≥55 years, and (5) ≥65 years. The models were compared using the mean receiver operating characteristic area under curve (ROC-AUC) after 1,000 iterations of training and testing. The predicted rates of overtriage were then determined for each group in order to achieve an undertriage rate of 5%.Results: There were 24,577 occupants (6,863,306 weighted) included in this analysis. The injury prediction model trained using data from all adults did not perform as well when tested on older adults (ROC-AUC: 15-54 years: 0.874 [95% CI: [0.851-0.895]; 45+ years: 0.837 [95% CI: 0.802-869]; 55+ years: 0.821 [95% CI: 0.775-0.864]; and 65+ years: 0.813 [95% CI: 0.754-0.866]). The accuracy of this model decreased in each decade of life. The performance did not change significantly when age-specific data were used to train the prediction models (ROC-AUC: 18-54 years: 0.874 [95% CI: 0.851-0.896]; 45+ years: 0.836 [95% CI: 0.798-0.871]; 55+ years: 0.822 [95% CI: 0.779-0.864]; and 65+ years: 0.808 [95% CI: 0.748-0.868]). In order to achieve an undertriage rate of 5%, the predicted overtriage rate by these models were 50% for occupants 15-54 years, 61% for occupants ≥ 55 years, 70% for occupants ≥ 55 years, and 71% for occupants ≥ 65 years.Conclusion: The results of this study indicate that it is more difficult to accurately predict severe injury in older adults involved in MVCs, which has the potential to result in significant overtriage. This decreased accuracy is likely due to variations in fragility in older adults. These findings indicate that special care should be taken when using regression-based prediction models to determine the appropriate hospital destination for older occupants.
| S-EPMC7035169 | biostudies-literature
Project description:IntroductionSeveral prehospital trauma triage tools have been recently developed, but no standardized tools currently exist to identify trauma patients at risk of requiring resuscitative interventions (RIs) within the first 24 h post-injury and to prioritize their transport to high-level trauma facilities.MethodsThis prognostic study employed a retrospective cohort design to evaluate the predictive performance of the Triage Revised Score (T-RTS), Glasgow Coma Scale, Age, and Systolic Blood Pressure Score (GAP), Mechanism, Glasgow Coma Scale, Age, and Systolic Blood Pressure Score (MGAP), National Early Warning Score 2 (NEWS-2), Shock Index (SI), and Reverse Shock Index multiplied by Glasgow Coma Scale (rSIG) in predicting the need for RIs within 24 h. Data was retrieved from the electronic medical records of Ramathibodi Hospital, and the study included patients aged ≥ 15 years who were categorized as high-risk or life-threatening and subsequently transported to the emergency department. We used Area Under the Receiver Operating Characteristic (AUROC) curve and calibration plots to assess the performance of prehospital trauma triage tools.ResultsThere were 440 traumatic injury patients enrolled in the study, with 44 (10%) receiving RIs. T-RTS, GAP, MGAP, and NEWS-2 demonstrate good discriminative and predictive performance for RIs within 24 h after an injury (AUROC of 0.969, 0.949, 0.971, and 0.929, respectively, with the O:E ratio of 1). With the predefined standard cut-off values, the GAP score of less than 19 results in the highest accuracy for ruling out patients who do not need RIs (Specificity = 94.4% and NPV = 94.1%, p-value < 0.001).ConclusionsSeveral commonly used prehospital trauma triage tools demonstrate good predictive abilities for identifying the need for RIs. Among these, the GAP score with a threshold value of 19 serves as an optimal tool for identifying patients who require transfer to high-level trauma facilities.
| S-EPMC11841352 | biostudies-literature
Project description:PurposeFor optimal prehospital trauma care, it is essential to adequately recognize potential life-threatening injuries in order to correctly triage patients and to initiate life-saving measures. The aim of the present study was to determine the accuracy of prehospital diagnoses suspected by helicopter emergency medical services (HEMS).MethodsThis retrospective multicenter study included patients from the Swiss Trauma Registry with ISS ≥ 16 or AIS head ≥ 3 transported by Switzerland's largest HEMS and subsequently admitted to one of twelve Swiss trauma centers from 01/2020 to 12/2020. The primary outcome was the comparison of injuries suspected prehospital with the final diagnoses obtained at the hospital using the abbreviated injury scale (AIS) per body region. As secondary outcomes, prehospital interventions were compared to corresponding relevant diagnoses.ResultsRelevant head trauma was the most commonly injured body region and was identified in 96.3% (95% CI: 92.1%; 98.6%) of the cases prehospital. Relevant injuries to the chest, abdomen, and pelvis were also common but less often identified prehospital [62.7% (95% CI: 54.2%; 70.6%), 45.5% (95% CI: 30.4%; 61.2%), and 61.5% (95% CI: 44.6%; 76.6%)]. Overall, 7 of 95 (7.4%) patients with pneumothorax received a chest decompression and in 22 of 39 (56.4%) patients with an instable pelvic fracture a pelvic binder was applied prehospital.ConclusionApproximately half of severe chest, abdominal, and pelvic diagnoses made in hospital went undetected in the challenging prehospital environment. This underlines the difficult circumstances faced by the rescue teams. Potentially life-saving interventions such as prehospital chest decompression and increased use of a pelvic binder were identified as potential improvements to prehospital care.
| S-EPMC11458725 | biostudies-literature
Project description: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.
| S-EPMC11426006 | biostudies-literature
Project description:Importance:Prehospital trauma triage protocols are used worldwide to get the right patient to the right hospital and thereby improve the chance of survival and avert lifelong disabilities. The American College of Surgeons Committee on Trauma set target levels for undertriage rates of less than 5%. None of the existing triage protocols has been able to achieve this target in isolation. Objective:To develop and validate a new prehospital trauma triage protocol to improve current triage rates. Design, Setting, and Participants:In this multicenter cohort study, all patients with trauma who were 16 years and older and transported to a trauma center in 2 different regions of the Netherlands were included in the analysis. Data were collected from January 1, 2012, through June 30, 2014, in the Central Netherlands region for the design data cohort and from January 1 through December 31, 2015, in the Brabant region for the validation cohort. Data were analyzed from May 3, 2017, through July 19, 2018. Main Outcomes and Measures:A new prediction model was developed in the Central Netherlands region based on prehospital predictors associated with severe injury. Severe injury was defined as an Injury Severity Score greater than 15. A full-model strategy with penalized maximum likelihood estimation was used to construct a model with 8 predictors that were chosen based on clinical reasoning. Accuracy of the developed prediction model was assessed in terms of discrimination and calibration. The model was externally validated in the Brabant region. Results:Using data from 4950 patients with trauma from the Central Netherlands region for the design data set (58.3% male; mean [SD] age, 47 [21] years) and 6859 patients for the validation Brabant region (52.2% male; mean [SD] age, 51 [22] years), the following 8 significant predictors were selected for the prediction model: age; systolic blood pressure; Glasgow Coma Scale score; mechanism criteria; penetrating injury to the head, thorax, or abdomen; signs and/or symptoms of head or neck injury; expected injury in the Abbreviated Injury Scale thorax region; and expected injury in 2 or more Abbreviated Injury Scale regions. The prediction model showed a C statistic of 0.823 (95% CI, 0.813-0.832) and good calibration. The cutoff point with a minimum specificity of 50.0% (95% CI, 49.3%-50.7%) led to a sensitivity of 88.8% (95% CI, 87.5%-90.0%). External validation showed a C statistic of 0.831 (95% CI, 0.814-0.848) and adequate calibration. Conclusions and Relevance:The new prehospital trauma triage prediction model may lower undertriage rates to approximately 10% with an overtriage rate of 50%. The next step should be to implement this prediction model with the use of a mobile app for emergency medical services professionals.
| S-EPMC6537785 | biostudies-literature