The Age of Undertriage: Current Trauma Triage Criteria Underestimate The Role of Age and Comorbidities in Early Mortality

Benjamin, Elizabeth; Khor, Desmond; Cho, Jayun; Biswas, Subarna; Inaba, Kenji; Δημητριάδης, Δημήτριος

doi:10.1016/j.jemermed.2018.02.001

Cited by 36 publications

(47 citation statements)

References 15 publications

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“…Age has been shown to be associated with increased overall mortality after trauma in numerous studies 8 27–29. Due to reduced physiologic reserve, comorbid conditions, polypharmacy including anticoagulant medication, and increased risk of malnutrition the elderly injured patients are at increased risk of complications and death compared with younger patients 5–7 9 30–35.…”

Section: Discussionmentioning

confidence: 99%

“…The Eastern Association for the Surgery of Trauma’s practice management guideline from 2012 recommended lowering the threshold for trauma team activation in patients aged 65 or older. Numerous reports have pointed out that the use of standard triage criteria is followed by undertriage of patients over the age of 60 1 11 12 14 28 36. Several studies have tried to demonstrate an effect on undertriage and outcome by redefining trauma criteria for the elderly 13 37 38.…”

Section: Discussionmentioning

confidence: 99%

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Improvement in geriatric trauma outcomes in an evolving trauma system

Ringen

Gaski

Rustad

et al. 2019

Trauma Surg Acute Care Open

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BackgroundThe elderly trauma patient has increased mortality compared with younger patients. During the last 15 years, initial treatment of severely injured patients at Oslo University Hospital Ulleval (OUHU) has changed resulting in overall improved outcomes. Whether this holds true for the elderly trauma population needs exploration and was the aim of the present study.MethodsWe performed a retrospective study of 2628 trauma patients 61 years or older admitted to OUHU during the 12-year period, 2002–2013. The population was stratified based on age (61–70 years, 71–80 years, 81 years and older) and divided into time periods: 2002–2009 (P1) and 2010–2013 (P2). Multiple logistic regression models were constructed to identify clinically relevant core variables correlated with mortality and trauma team activation rate.ResultsCrude mortality decreased from 19% in P1 to 13% in P2 (p<0.01) with an OR of 0.77 (95 %CI 0.65 to 0.91) when admitted in P2. Trauma team activation rates increased from 53% in P1 to 72% in P2 (p<0.01) with an OR of 2.16 (95% CI 1.93 to 2.41) for being met by a trauma team in P2. Mortality increased from 10% in the age group 61–70 years to 26% in the group above 80 years. Trauma team activation rates decreased from 71% in the age group 61–70 years to 50% in the age group older than 80 years. Median ISS were 17 in all three age groups and in both time periods.DiscussionDevelopment of a multidisciplinary dedicated trauma service is associated with increased trauma team activation rate as well as survival in geriatric trauma patients. As expected, mortality increased with age, although inversely related to the likelihood of being met by a trauma team. Trauma team activation should be considered for all trauma patients older than 70 years.Level of evidenceLevel IV.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Improvement in geriatric trauma outcomes in an evolving trauma system

Ringen

Gaski

Rustad

et al. 2019

Trauma Surg Acute Care Open

View full text Add to dashboard Cite

show abstract

“…Paradoxically, road users considered as more vulnerable, either by lack of physical protection such as pedestrians or cyclists, or due to age-related frailty, were more likely to be secondarily transferred. The challenge is actually the clinical recognition of traumatic brain injury, especially in elderly patients and/or after low energy transfer mechanisms (pedestrian or bicycle) [32,39]. Indeed, high-force injuries such as high energy transfer motor vehicle accidents are usually the prerogative of younger patients and also result in clinically more obvious injuries easier to triage, as exemplified with facial trauma, clinically obvious pelvic fracture or combined severe injuries which where protective factors of secondary transfer.…”

Section: Discussionmentioning

confidence: 99%

Direct transport vs secondary transfer to level I trauma centers in a French exclusive trauma system: Impact on mortality and determinants of triage on road-traffic victims

et al. 2019

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BackgroundTransporting a severely injured patient directly to a trauma center (TC) is consensually considered optimal. Nevertheless, disagreement persists regarding the association between secondary transfer status and outcome. The aim of the study was to compare adjusted mortality between road traffic trauma patients directly or secondarily transported to a level 1 trauma center (TC) in an exclusive French trauma system with a physician staffed prehospital emergency medical system (EMS).MethodsA retrospective cohort study was performed using 2015–2017 data from a regional trauma registry (Traumabase®), an administrative database on road-traffic accidents and prehospital-EMS records.Multivariate logistic regression models were computed to determine the role of the modality of admission on mortality and to identify factors associated with secondary transfer. The primary outcome was day-30 mortality.Results: During the study period, 121.955 victims of road-traffic accident were recorded among which 4412 trauma patients were admitted in the level 1 regional TCs, 4031 directly and 381 secondarily transferred from lower levels facilities. No significant association between all-cause 30-day mortality and the type of transport was observed (Odds ratio 0.80, 95% confidence interval (CI) [0.3–1.9]) when adjusted for potential confounders. Patients secondarily transferred were older, with low-energy mechanism and presented higher head and abdominal injury scores. Among all 947 death, 43 (4.5%) occurred in lower-level facilities. The population-based undertriage leading to death was 0.15%, 95%CI [0.12–0.19].ConclusionIn an exclusive trauma system with physician staffed prehospital care, road-traffic victims secondarily transferred to a TC do not have an increased mortality when compared to directly transported patients.

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“…Given that the TRISS model estimates survival probability using linear statistical models and only a small set of variables were selected for analysis, it is questionable as to whether these models are capable of analyzing complex biological systems such as traumatized humans. Furthermore, it is important to know whether the provision of additional information, including the presence of pre-existing comorbidities or laboratory data, would improve the predictive performance of TRISS [17]. Nonetheless, limitations of overfitting and multicollinearity of regression analysis may preclude the analysis of many explanatory variables.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Models of Survival Prediction in Trauma Patients

Rau

Chuang

et al. 2019

JCM

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Background: We aimed to build a model using machine learning for the prediction of survival in trauma patients and compared these model predictions to those predicted by the most commonly used algorithm, the Trauma and Injury Severity Score (TRISS). Methods: Enrolled hospitalized trauma patients from 2009 to 2016 were divided into a training dataset (70% of the original data set) for generation of a plausible model under supervised classification, and a test dataset (30% of the original data set) to test the performance of the model. The training and test datasets comprised 13,208 (12,871 survival and 337 mortality) and 5603 (5473 survival and 130 mortality) patients, respectively. With the provision of additional information such as pre-existing comorbidity status or laboratory data, logistic regression (LR), support vector machine (SVM), and neural network (NN) (with the Stuttgart Neural Network Simulator (RSNNS)) were used to build models of survival prediction and compared to the predictive performance of TRISS. Predictive performance was evaluated by accuracy, sensitivity, and specificity, as well as by area under the curve (AUC) measures of receiver operating characteristic curves. Results: In the validation dataset, NN and the TRISS presented the highest score (82.0%) for balanced accuracy, followed by SVM (75.2%) and LR (71.8%) models. In the test dataset, NN had the highest balanced accuracy (75.1%), followed by the TRISS (70.2%), SVM (70.6%), and LR (68.9%) models. All four models (LR, SVM, NN, and TRISS) exhibited a high accuracy of more than 97.5% and a sensitivity of more than 98.6%. However, NN exhibited the highest specificity (51.5%), followed by the TRISS (41.5%), SVM (40.8%), and LR (38.5%) models. Conclusions: These four models (LR, SVM, NN, and TRISS) exhibited a similar high accuracy and sensitivity in predicting the survival of the trauma patients. In the test dataset, the NN model had the highest balanced accuracy and predictive specificity.

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The Age of Undertriage: Current Trauma Triage Criteria Underestimate The Role of Age and Comorbidities in Early Mortality

Cited by 36 publications

References 15 publications

Improvement in geriatric trauma outcomes in an evolving trauma system

Improvement in geriatric trauma outcomes in an evolving trauma system

Direct transport vs secondary transfer to level I trauma centers in a French exclusive trauma system: Impact on mortality and determinants of triage on road-traffic victims

Machine Learning Models of Survival Prediction in Trauma Patients

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