The trauma mortality rate is higher in the elderly compared with younger patients. Ageing is associated with physiological changes in multiple systems and correlated with frailty. Frailty is a risk factor for mortality in elderly trauma patients. We aim to provide evidence-based guidelines for the management of geriatric trauma patients to improve it and reduce futile procedures.
Six working groups of expert acute care and trauma surgeons reviewed extensively the literature according to the topic and the PICO question assigned. Statements and recommendations were assessed according to the GRADE methodology and approved by a consensus of experts in the field at the 10th international congress of the WSES in 2023.
The management of elderly trauma patients requires knowledge of ageing physiology, a focused triage, including drug history, frailty assessment, nutritional status, and early activation of trauma protocol to improve outcomes. Acute trauma pain in the elderly has to be managed in a multimodal analgesic approach, to avoid side effects of opioid use. Antibiotic prophylaxis is recommended in penetrating (abdominal, thoracic) trauma, in severely burned and in open fractures elderly patients to decrease septic complications. Antibiotics are not recommended in blunt trauma in the absence of signs of sepsis and septic shock. Venous thromboembolism prophylaxis with LMWH or UFH should be administrated as soon as possible in high and moderate-risk elderly trauma patients according to the renal function, weight of the patient and bleeding risk. A palliative care team should be involved as soon as possible to discuss the end of life in a multidisciplinary approach considering the patient\'s directives, family feelings and representatives\' desires, and all decisions should be shared.
The management of elderly trauma patients requires knowledge of ageing physiology, a focused triage based on assessing frailty and early activation of trauma protocol to improve outcomes. Geriatric Intensive Care Units are needed to care for elderly and frail trauma patients in a multidisciplinary approach to decrease mortality and improve outcomes.

UNASSIGNED: The base deficit, international normalized ratio, and Glasgow Coma Scale (BIG) score was previously developed to predict the outcomes of pediatric trauma patients. We designed this study to explore and improve the prognostic value of the BIG score in adult patients with traumatic brain injury (TBI).
UNASSIGNED: Adult patients diagnosed with TBI in a public critical care database were included in this observational study. The BIG score was calculated based on the Glasgow Coma Scale (GCS), the international normalized ratio (INR), and the base deficit. Logistic regression analysis was performed to confirm the association between the BIG score and the outcome of included patients. Receiver operating characteristic (ROC) curves were drawn to evaluate the prognostic value of the BIG score and novel constructed models.
UNASSIGNED: In total, 1,034 TBI patients were included in this study with a mortality of 22.8%. Non-survivors had higher BIG scores than survivors (p < 0.001). The results of multivariable logistic regression analysis showed that age (p < 0.001), pulse oxygen saturation (SpO2) (p = 0.032), glucose (p = 0.015), hemoglobin (p = 0.047), BIG score (p < 0.001), subarachnoid hemorrhage (p = 0.013), and intracerebral hematoma (p = 0.001) were associated with in-hospital mortality of included patients. The AUC (area under the ROC curves) of the BIG score was 0.669, which was not as high as in previous pediatric trauma cohorts. However, combining the BIG score with age increased the AUC to 0.764. The prognostic model composed of significant factors including BIG had the highest AUC of 0.786.
UNASSIGNED: The age-adjusted BIG score is superior to the original BIG score in predicting mortality of adult TBI patients. The prognostic model incorporating the BIG score is beneficial for clinicians, aiding them in making early triage and treatment decisions in adult TBI patients.

UNASSIGNED: Persistent Inflammation, Immunosuppression, and Catabolism Syndrome (PIICS) is a significant contributor to adverse long-term outcomes in severe trauma patients.
UNASSIGNED: The objective of this study was to establish and validate a PIICS predictive model in severe trauma patients, providing a practical tool for early clinical prediction.
UNASSIGNED: Adult severe trauma patients with an Injury Severity Score (ISS) of ≥16, admitted between October 2020 and December 2022, were randomly divided into a training set and a validation set in a 7:3 ratio. Patients were classified into PIICS and non-PIICS groups based on diagnostic criteria. LASSO regression was used to select appropriate variables for constructing the prognostic model. A logistic regression model was developed and presented in the form of a nomogram. The performance of the model was evaluated using calibration and ROC curves.
UNASSIGNED: A total of 215 patients were included, consisting of 155 males (72.1%) and 60 females (27.9%), with a median age of 51 years (range: 38-59). NRS2002, ISS, APACHE II, and SOFA scores were selected using LASSO regression to construct the prognostic model. The AUC of the ROC analysis for the predictive model in the validation set was 0.84 (95% CI 0.72-0.95). The Hosmer-Lemeshow test in the validation set yielded a χ2 value of 14.74, with a value of p of 0.098.
UNASSIGNED: An accurate and easily implementable PIICS risk prediction model was established. It can enhance risk stratification during hospitalization for severe trauma patients, providing a novel approach for prognostic prediction.

UNASSIGNED: Despite improvements in children\'s health due to a reduction in infections, trauma continues to cause many deaths among adolescents. Strategies to mitigate morbidity and mortality from trauma include severity scores to classify and refer patients to the appropriate hospitals to provide better management; however, these strategies have not been assessed in Colombian children. This study aimed to describe the characteristics and outcomes of injured children and evaluate the performance of the Pediatric Trauma Score (PTS) in predicting survival at a major trauma centre in a Colombian city.
UNASSIGNED: This was a retrospective cohort study of children aged <18 years who were treated for injuries at a hospital in Colombia. The primary outcome was 30-day mortality. A simple logistic regression model was used with PTS as the predictor variable and vital status at discharge as the outcome variable. PTS performance was assessed by discrimination using the area under the receiver-operating characteristic (AUROC) curve and by calibration using the Hosmer-Lemeshow (HL) goodness-of-fit test.
UNASSIGNED: A total of 1047 children were admitted. The median age was 12 years (interquartile range [IQR]=5-15); 73·7% were male, and 66·1% had blunt trauma. The most frequent cause of injury was traffic accident (31·5%) followed by assaults (29%). Mortality was 5·9%; 61·3% of these deaths occurred in adolescents between 15 and 17 years of age and 71% of deaths in this age group were due to injuries from a firearm. The PTS had a median of 7 (IQR=5-9), an AUROC of 0·93, and good calibration (HL=7·97, p = 0·33).
UNASSIGNED: The highest proportion of trauma and death occurred among adolescents. Interpersonal violence was the most frequent cause of death in this age group. The PTS showed good predictive power for survival, with excellent discrimination and good calibration.
UNASSIGNED: None.

Background and Objectives: We developed a machine learning algorithm to analyze trauma-related data and predict the mortality and chronic care needs of patients with trauma. Materials and Methods: We recruited admitted patients with trauma during 2015 and 2016 and collected their clinical data. Then, we subjected this database to different machine learning techniques and chose the one with the highest accuracy by using cross-validation. The primary endpoint was mortality, and the secondary endpoint was requirement for chronic care. Results: Data of 5871 patients were collected. We then used the eXtreme Gradient Boosting (xGBT) machine learning model to create two algorithms: a complete model and a short-term model. The complete model exhibited an 86% recall for recovery, 30% for chronic care, 67% for mortality, and 80% for complications; the short-term model fitted for ED displayed an 89% recall for recovery, 25% for chronic care, and 41% for mortality. Conclusions: We developed a machine learning algorithm that displayed good recall for the healthy recovery group but unsatisfactory results for those requiring chronic care or having a risk of mortality. The prediction power of this algorithm may be improved by implementing features such as age group classification, severity selection, and score calibration of trauma-related variables.

As the older population increases, the number of elderly accessing the emergency department following a trauma increases accordingly. High-level trauma enters together with the identification of predictive parameters for poor outcome and mortality, may result in a death rate improvement of up to 30% in this group of patients. This study analyzes the epidemiology of major trauma admissions at Niguarda Trauma Center in Milan, Italy, focusing on the geriatric population and aiming to discriminate the trauma outcomes in the range of population between 65 and 75 years old (Senior Adult) and to compare it with the outcomes among people over 75 years old (Elderly). The variables analyzed included mortality, mechanism of injury, body district injured, Injury Severity Score (ISS), Trauma Injury Severity Score (TRISS), Geriatric Trauma Score (GTO), and outcome. Head trauma remains the main cause of mortality with falls and road accidents being the most common mechanism of injury. Frailty and associated use of anticoagulant and antiplatelet therapy increased the risk of death by 42%. The subdivision of the elder patients into two groups (65-75 and > 75) showed a difference in the probability of death and effective mortality rate.

OBJECTIVE: In this study, we aimed to evaluate the correlation between the trauma score of individuals wounded in the Lushan earthquake and emergency workload for treatment. We further created a trauma score-emergency workload calculation model.
METHODS: We included data from patients wounded in the Lushan earthquake and treated at West China Hospital, Sichuan University. We calculated scores per the following models separately: Revised Trauma Score (RTS), Prehospital Index (PHI), Circulation Respiration Abdominal Movement Speech (CRAMS), Therapeutic Intervention Scoring System (TISS-28), and Nursing Activities Score (NAS). We assessed the association between values for CRAMS, PHI, and RTS and those for TISS-28 and NAS. Subsequently, we built a trauma score-emergency workload calculation model to quantitative workload estimation.
RESULTS: Significant correlations were observed for all pairs of trauma scoring models with emergency workload scoring models. TISS-28 score was significantly associated with PHI score and RTS; however, no significant correlation was observed between the TISS-28 score and CRAMS score.
CONCLUSIONS: CRAMS, PHI, and RTS were consistent in evaluating the injury condition of wounded individuals; TISS-28 and NAS scores were consistent in evaluating the required treatment workload. Dynamic changes in emergency workload in unit time were closely associated with wounded patient visits.

Prospective predictors of trauma-related outcomes have been validated to guide management in low-resource settings. The primary objective of this study was to determine the optimal prospective prediction method for mortality within combat and humanitarian trauma.
Retrospective review of the Department of Defense Trauma Registry from 2008 to 2016 was performed for adult patients. Areas under receiver operating characteristic curves (AUROCs) were calculated to assess the predictability of shock index (SI), reverse SI × Glasgow Coma Scale (rSIG), SI × Glasgow Coma Scale (SIG), Revised Trauma Score, and Trauma and Injury Severity Score (TRISS) on mortality at point of injury, arrival in emergency department (ED), and the difference in vital signs between those time points.
A total of 22,218 patients were included. Overall, 97.1% were male, median age range 25-29 y, Injury Severity Score 9.4 ± 0.07, with predominantly penetrating injuries (58.1%), and mortality of 3.4%. ED vitals yielded higher predictability of mortality for all tests based on higher AUROCs. TRISS and rSIG demonstrated the highest AUROCs (0.955 and 0.923, respectively). The optimal cutoff value for rSIG was 14.1 (sensitivity 89% and specificity 87%). rSIG values <14.1 were significantly associated with mortality (P < 0.01; odds ratio = 5.901).
Initial ED vital signs represented a better predictor of early mortality compared with point of injury vital signs for all predictive tools assessed. TRISS and rSIG proved to be most predictive of mortality. However, of the prospective tools assessed, rSIG may be optimal scoring tool because of its ease of calculation and its increased ability to predict mortality.

Injury severity scores (ISS) and shock index (SI) are popular trauma scoring systems. We assessed ISS and SI in combat trauma to determine the optimal cut-off values for mortality and trauma outcomes. Retrospective analysis of the Department of Defense Trauma Registry, 2008-2016, was performed. Areas under receiver operating characteristic curves (AUROCs) were calculated for ISS and SI on mortality, massive volume transfusion (MVT), and emergent surgical procedure (ESP). Optimal cut-off values were defined using the Youden index (YI). 22,218 patients (97.1% male), median ages 25-29 years, ISS 9.4 ± 0.07, with 58.1% penetrating injury were studied. Overall mortality was 3.4%. AUROCs for ISS on mortality, MVT, and ESP were 0.882, 0.898, and 0.846, while AUROCs for SI were 0.727, 0.864, and 0.711 respectively. The optimal cut-off values for ISS on mortality, MVT, and ESP were 12.5 (YI = 0.634), 12.5 (YI = 0.666), and 12.5 (YI = 0.819), with optimal values for SI being 0.94 (YI = 0.402), 0.88 (YI = 0.608), and 0.81 (YI = 0.345) respectively. Classic values for severe ISS underrepresent combat injury while the SI values defined in this study are consistent with civilian data.

Multiple trauma scores have been developed and validated, including the Revised Trauma Score (RTS) and the Mechanism, Glasgow Coma Scale, Age, and Arterial Pressure (MGAP) score. However, these scores are complex to calculate or have low prognostic abilities for trauma mortality. Therefore, we aimed to develop and validate a trauma score that is easier to calculate and more accurate than the RTS and the MGAP score.
The study was a retrospective prognostic study. Data from patients registered in the Japan Trauma Databank (JTDB) were dichotomized into derivation and validation cohorts. Patients\' data from the Clinical Randomisation of an Antifibrinolytic in Significant Haemorrhage-2 (CRASH-2) trial were assigned to another validation cohort. We obtained age and physiological variables at baseline, created ordinal variables from continuous variables, and defined integer weighting coefficients. Score performance to predict all-cause in-hospital death was assessed using the area under the curve in receiver operating characteristics (AUROC) analyses.
Based on the JTDB derivation cohort (n = 99,867 with 12.5% mortality), the novel score ranged from 0 to 14 points, including 0-2 points for age, 0-6 points for the Glasgow Coma Scale, 0-4 points for systolic blood pressure, and 0-2 points for respiratory rate. The AUROC of the novel score was 0.932 for the JTDB validation cohort (n = 76,762 with 10.1% mortality) and 0.814 for the CRASH-2 cohort (n = 19,740 with 14.6% mortality), which was superior to RTS (0.907 and 0.808, respectively) and MGAP score (0.918 and 0.774, respectively) results.
We report an easy-to-use trauma score with better prognostication ability for in-hospital mortality compared to the RTS and MGAP score. Further studies to test clinical applicability of the novel score are warranted.