New Trauma and Injury Severity Score (TRISS) adjustments for survival prediction

Settlement Is at the End-Common Trauma Scores Require a Critical Reassessment Due to the Possible Dynamics of Traumatic Brain Injuries in Patients' Clinical Course

Background: Scientific studies on severely injured patients commonly utilize the Abbreviated Injury Scale (AIS) and the Injury Severity Score (ISS) for injury assessment and to characterize trauma cohorts. However, due to potential deterioration (e.g., in the case of an increasing hemorrhage) during the clinical course, the assessment of injury severity in traumatic brain injury (TBI) can be challenging. Therefore, the aim of this study was to investigate whether and to what extent the worsening of TBI affects the AIS and ISS. Methods: We retrospectively evaluated 80 polytrauma patients admitted to the trauma room of our level I trauma center with computed-tomography-confirmed TBI. The initial AIS, ISS, and Trauma and Injury Severity Score (TRISS) values were reevaluated after follow-up imaging. Results: A total of 37.5% of the patients showed a significant increase in AIShead (3.7 vs. 4.1; p = 0.002) and the ISS (22.9 vs. 26.7, p = 0.0497). These changes resulted in an eight percent reduction in their TRISS-predicted survival probability (74.82% vs. 66.25%, p = 0.1835). Conclusions: The dynamic nature of intracranial hemorrhage complicates accurate injury severity assessment using the AIS and ISS, necessitating consideration in clinical studies and registries to prevent systematic bias in patient selection and subsequent data analysis.

Survival benefits of interventional radiology and surgical teams collaboration during primary trauma surveys: a single-centre retrospective cohort study

BACKGROUND

A team approach is essential for effective trauma management. Close collaboration between interventional radiologists and surgeons during the initial management of trauma patients is important for prompt and accurate trauma care. This study aimed to determine whether trauma patients benefit from close collaboration between interventional radiology (IR) and surgical teams during the primary trauma survey.

METHODS

A retrospective observational study was conducted between 2014 and 2021 at a single institution. Patients were assigned to an embolization group (EG), a surgery group (SG), or a combination group (CG) according to their treatment. The primary and secondary outcomes were survival at hospital discharge compared with the probability of survival (Ps) and the time course of treatment.

RESULTS

The analysis included 197 patients, consisting of 135 men and 62 women, with a median age of 56 [IQR, 38-72] years and an injury severity score of 20 [10-29]. The EG, SG, and CG included 114, 48, and 35 patients, respectively. Differences in organ injury patterns were observed between the three groups. In-hospital survival rates in all three groups were higher than the Ps. In particular, the survival rate in the CG was 15.5% higher than the Ps (95% CI: 7.5-23.6%; p < 0.001). In the CG, the median time for starting the initial procedure was 53 [37-79] min and the procedure times for IR and surgery were 48 [29-72] min and 63 [35-94] min, respectively. Those times were significantly shorter among three groups.

CONCLUSION

Close collaboration between IR and surgical teams, including the primary survey, improves the survival of severe trauma patients who require both IR procedures and surgeries by improving appropriate treatment selection and reducing the time process.

Comparison of nine trauma scoring systems in prediction of inhospital outcomes of pediatric trauma patients: a multicenter study

Hereby, we aimed to comprehensively compare different scoring systems for pediatric trauma and their ability to predict in-hospital mortality and intensive care unit (ICU) admission. The current registry-based multicenter study encompassed a comprehensive dataset of 6709 pediatric trauma patients aged ≤ 18 years from July 2016 to September 2023. To ascertain the predictive efficacy of the scoring systems, the area under the receiver operating characteristic curve (AUC) was calculated. A total of 720 individuals (10.7%) required admission to the ICU. The mortality rate was 1.1% (n = 72). The most predictive scoring system for in-hospital mortality was the adjusted trauma and injury severity score (aTRISS) (AUC = 0.982), followed by trauma and injury severity score (TRISS) (AUC = 0.980), new trauma and injury severity score (NTRISS) (AUC = 0.972), Glasgow coma scale (GCS) (AUC = 0.9546), revised trauma score (RTS) (AUC = 0.944), pre-hospital index (PHI) (AUC = 0.936), injury severity score (ISS) (AUC = 0.901), new injury severity score (NISS) (AUC = 0.900), and abbreviated injury scale (AIS) (AUC = 0.734). Given the predictive performance of the scoring systems for ICU admission, NTRISS had the highest predictive performance (AUC = 0.837), followed by aTRISS (AUC = 0.836), TRISS (AUC = 0.823), ISS (AUC = 0.807), NISS (AUC = 0.805), GCS (AUC = 0.735), RTS (AUC = 0.698), PHI (AUC = 0.662), and AIS (AUC = 0.651). In the present study, we concluded the superiority of the TRISS and its two derived counterparts, aTRISS and NTRISS, compared to other scoring systems, to efficiently discerning individuals who possess a heightened susceptibility to unfavorable consequences. The significance of these findings underscores the necessity of incorporating these metrics into the realm of clinical practice.

Dysregulation of iron transport-related biomarkers in blood leukocytes is associated with poor prognosis of early trauma

Objective

The early targeted and effective diagnosis and treatment of severe trauma are crucial for patients' outcomes. Blood leukocytes act as significant effectors during the initial inflammation and activation of innate immunity in trauma. This study aims to identify hub genes related to patients' prognosis in blood leukocytes at the early stages of trauma.

Methods

The expression profiles of Gene Expression Omnibus (GEO) Series (GSE) 36809 and GSE11375 were downloaded from the GEO database. R software, GraphPad Prism 9.3.1 software, STRING database, and Cytoscape software were used to process the data and identify hub genes in blood leukocytes of early trauma.

Results

Gene Ontology (GO) analysis showed that the differentially expressed genes (DEGs) of blood leukocytes at the early stages of trauma (0-4 h, 4-8 h, and 8-12 h) were mainly involved in neutrophil activation and neutrophil degranulation, neutrophil activation involved in immune response, neutrophil mediated immunity, lymphocyte differentiation, and cell killing. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the DEGs were mainly involved in Osteoclast differentiation and Hematopoietic cell lineage. Sixty-six down-regulated DEGs and 148 up-regulated DEGs were identified and 37 hub genes were confirmed by Molecular Complex Detection (MCODE) of Cytoscape. Among the hub genes, Lipocalin 2 (LCN2), Lactotransferrin (LTF), Olfactomedin 4 (OLFM4), Resistin (RETN), and Transcobalamin 1 (TCN1) were related to prognosis and connected with iron transport closely. LCN2 and LTF were involved in iron transport and had a moderate predictive value for the poor prognosis of trauma patients, and the AUC of LCN2 and LTF was 0.7777 and 0.7843, respectively.

Conclusion

As iron transport-related hub genes in blood leukocytes, LCN2 and LTF can be used for prognostic prediction of early trauma.

Performance of severity indices for admission and mortality of trauma patients in the intensive care unit: a retrospective cohort study

BACKGROUND

Little is known about the performance of severity indices for indicating intensive care and predicting mortality in the Intensive Care Unit (ICU) of trauma patients. This study aimed to compare the performance of severity indices to predict trauma patients' ICU admission and mortality.

METHODS

A retrospective cohort study which analyzed the electronic medical records of trauma patients aged ≥ 18 years, treated at a hospital in Brazil, between 2014 and 2017. Physiological [Revised Trauma Score (RTS), New Trauma Score (NTS) and modified Rapid Emergency Medicine Score (mREMS)], anatomical [Injury Severity Score (ISS) and New Injury Severity Score (NISS)] and mixed indices [Trauma and Injury Severity Score (TRISS), New Trauma and Injury Severity Score (NTRISS), Base-deficit Injury Severity Score (BISS) and Base-deficit and New Injury Severity Score (BNISS)] were compared in analyzing the outcomes (ICU admission and mortality) using the Area Under the Receiver Operating Characteristics Curves (AUC-ROC).

RESULTS

From the 747 trauma patients analyzed (52.5% female; mean age 51.5 years; 36.1% falls), 106 (14.2%) were admitted to the ICU and 6 (0.8%) died in the unit. The ISS (AUC 0.919) and NISS (AUC 0.916) had better predictive capacity for ICU admission of trauma patients. The NISS (AUC 0.949), TRISS (AUC 0.909), NTRISS (AUC 0.967), BISS (AUC 0.902) and BNISS (AUC 0.976) showed excellent performance in predicting ICU mortality.

CONCLUSIONS

Anatomical indices showed excellent predictive ability for admission of trauma patients to the ICU. The NISS and the mixed indices had the best performances regarding mortality in the ICU.

Predicting the Future in Trauma: Trauma and Injury Severity Score Loses Accuracy and Validity for Late Deaths After Injury

BACKGROUND

The Trauma and Injury Severity Score (TRISS) is widely used to predict mortality in trauma patients, but its performance metrics have not been analyzed for early vs later deaths. Therefore, we aimed to investigate the impact of time to death on the accuracy of TRISS.

METHODS

Patients from 2013 to 2018 American College of Surgeons Trauma Quality Improvement Program database were included. We compared predicted survival by TRISS using the areas under receiver operating characteristic curves (AUCs) and calibration curves between different cut-off times and subgroups. We further compared early (≤72 hr) and late (>72 hr) deaths based on mechanisms and severity.

RESULTS

Among the 1,180,745 patients, the total mortality rate was 6.4%, with 59% early deaths and 41% late deaths. The AUC of TRISS for all patients was .919 (95% CI: .918-.921) for ≤72 hr survival and .845 (95% CI: .843-.848) for >72 hr survival. Significant discrepancies in AUCs between the early and late death groups existed in all cohorts based on blunt/penetrating mechanisms and severity. TRISS predicted well in early survival of penetrating injury but was less reliable in late survival of penetrating injury and all blunt injury. TRISS tended to underestimate survival, particularly for patients with lower probability of survival, with increased discrepancies seen for predicting late deaths.

CONCLUSIONS

The predictive ability of TRISS varies significantly based on the timing of deaths and key injury factors. TRISS may be best utilized in predicting early survival in penetrating injury, but its reliability and accuracy diminish when predicting late deaths for all kinds of injury.

Comparison of Preventable Trauma Death Rates in Patients With Traumatic Brain Injury Before and After the Establishment of Regional Trauma Center: A Single Center Experience

Objective

To compare preventable trauma death rates (PTDRs) in patients with traumatic brain injury before and after the establishment of a regional trauma center (RTC) at a single center.

Methods

Our institution established an RTC in 2014. A total of 709 patients were enrolled from January 2011 to December 2013 (before RTC) and 672 from January 2019 to December 2021 (after RTC). The revised trauma score, injury severity score, and trauma and injury severity score (TRISS) were evaluated. Definitive preventable (DP), possibly preventable (PP), and non-preventable deaths were defined as TRISS >0.5, TRISS 0.25-0.5, and TRISS <0.25, respectively. PTDR was the proportion of deaths from DP+PP out of all deaths, and the preventable major trauma death rate (PMTDR) was the proportion of deaths from DP+PP out of all DP+PP.

Results

The overall mortality rates before and after the establishment of RTC were 20.3 and 13.1%, respectively. PTDR was lower after the establishment of RTC than before (90.3% vs. 79.5%). The PMTDR was also lower after the establishment of RTC than before (18.8% vs. 9.7%). The ratio of direct hospital visits was higher in patients before the establishment of RTC than in those after (74.9% vs. 61.3%, p<0.001).

Conclusion

Establishing the RTC reduced PTDRs. Additional studies on factors associated with PTDR reduction are required.

Use of the Trauma and Injury Severity Score (TRISS) as a Predictor of Patient Outcome in Cases of Trauma Presenting in the Trauma and Emergency Department of a Tertiary Care Institute

BACKGROUND

In this study, we used the anatomic scoring system Abbreviated Injury Scale (AIS) to calculate the Injury Severity Score (ISS) and the physiological scoring system for the Revised Trauma Score (RTS) on the arrival of patients. Both scores were used to calculate the Trauma and Injury Severity Score (TRISS) for predicting the patient outcome in a case of trauma.

METHODS

This prospective, cross-sectional, observational study was carried out at the trauma centre of a tertiary care institute and included patients of either sex, age ≥18 years, and ISS ≥15. A total of 2084 cases of trauma over a period of 18 months were assessed, and 96 cases of blunt trauma meeting the inclusion criteria were studied.

RESULTS

Patients injured in road traffic accidents constituted the maximum caseload. Out of a sample size of 96 patients with ISS ≥15, 77 died during the treatment and 19 survived. The ISS ranged from 15 to 66, with a mean ± SD score of 27.48 ± 8.79. Non-survivors had a statistically higher significant ISS than survivors (p<0.001). The RTS ranged from <1 to 7.84, with a mean ± SD score of 4.52 ± 2.08. Non-survivors had low RTS (RTS <5, n=52) compared to survivors, and the difference was statistically significant (p<0.001). The mean ± SD TRISS (Ps) score was 0.69 ± 2.288. In the non-survivor (NS) group, 15 patients had TRISS (Ps) between 0.26-0.50, followed by 0.51-0.75 (n=18), 0.76-0.90 (n=12), and 0.90-0.95 (n=11). While 16 survivors had TRISS (Ps) between 0.96 and 1, a statistically significant association was found between TRISS and patient outcome (p-value <0.001). On the receiver operating characteristic (ROC) curve analysis, the sensitivity of TRISS (94.7%) and RTS was found to be comparable (94.7%), whereas ISS was less sensitive (36.8%) in predicting the patient outcome. RTS (79.2%) and TRISS (76.6%) scores were more specific than ISS (5.2%) for outcome analysis.

CONCLUSION

The TRISS score is useful in the management of trauma patients as it can satisfactorily predict mortality in a case of trauma. The trauma scores are of immense help in determining the nature of injury in medicolegal cases.

Prediction of Mortality among Patients with Isolated Traumatic Brain Injury Using Machine Learning Models in Asian Countries: An International Multi-Center Cohort Study

Traumatic brain injury (TBI) is a significant healthcare concern in several countries, accounting for a major burden of morbidity, mortality, disability, and socioeconomic losses. Although conventional prognostic models for patients with TBI have been validated, their performance has been limited. Therefore, we aimed to construct machine learning (ML) models to predict the clinical outcomes in adult patients with isolated TBI in Asian countries. The Pan-Asian Trauma Outcome Study registry was used in this study, and the data were prospectively collected from January 1, 2015, to December 31, 2020. Among a total of 6540 patients (≥ 15 years) with isolated moderate and severe TBI, 3276 (50.1%) patients were randomly included with stratification by outcomes and subgrouping variables for model evaluation, and 3264 (49.9%) patients were included for model training and validation. Logistic regression was considered as a baseline, and ML models were constructed and evaluated using the area under the precision-recall curve (AUPRC) as the primary outcome metric, area under the receiver operating characteristic curve (AUROC), and precision at fixed levels of recall. The contribution of the variables to the model prediction was measured using the SHapley Additive exPlanations (SHAP) method. The ML models outperformed logistic regression in predicting the in-hospital mortality. Among the tested models, the gradient-boosted decision tree showed the best performance (AUPRC, 0.746 [0.700-0.789]; AUROC, 0.940 [0.929-0.952]). The most powerful contributors to model prediction were the Glasgow Coma Scale, O₂ saturation, transfusion, systolic and diastolic blood pressure, body temperature, and age. Our study suggests that ML techniques might perform better than conventional multi-variate models in predicting the outcomes among adult patients with isolated moderate and severe TBI.

THE INITIAL ION SHIFT INDEX AS A PROGNOSTIC INDICATOR TO PREDICT PATIENT SURVIVAL IN TRAUMATIC DAMAGE CONTROL LAPAROTOMY PATIENTS

ABSTRACT

Objective: The ion shift index (ISI), which considers extracellular fluid ions such as phosphate, calcium, and magnesium, represents the ion shift following ischemia; concentrations of these ions are maintained within narrow normal ranges by adenosine triphosphate-dependent homeostasis. The ISI is defined as follows: {potassium (mmol/L-1) + phosphate (mmol/L-1) + Mg (mmol/L-1)}/calcium (mmol/L-1). This study investigated the possibility of predicting the 30-day survival rate of patients who underwent traumatic damage control laparotomy by comparing ISI and other laboratory findings, as well as the initial Trauma and Injury Severity Score (TRISS) and shock indices. Methods: Among the 134 patients who underwent damage control surgery between November 2012 and December 2021, 115 patients were enrolled in this study. Data regarding injury mechanism, age, sex, laboratory findings, vital signs, Glasgow Coma Scale score, Injury Severity Score, Abbreviated Injury Scale score, blood component transfusion, type of surgery, postoperative laboratory outcomes, morbidity, mortality rates, fluids administered, and volume of transfusions were collected and analyzed. Results: In univariate analysis, the odds ratio of the initial ISI was 2.875 (95% confidence interval, 1.52-5.43; P = 0.04), which showed a higher correlation with mortality compared with other indices. The receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) were derived from different multivariable logistic regression models. The initial ISI had high sensitivity and specificity in predicting patient mortality (AUC, 0.7378). In addition, in the model combining the initial ISI, crystalloids, and TRISS, the AUC showed a high value (AUC, 0.8227). Conclusion: The ISI evaluated using electrolytes immediately after admission in patients undergoing traumatic damage control surgery may be a predictor of patient mortality.

Evaluation of the SEVERITY of FACIAL and GENERAL TRAUMA in child and adolescent victims of traffic accidents

•Evaluation of the FISS and TRISS SpO2 to obtain greater rigor in a standardized epidemiological profile of injury severity.•More accurate determination of trauma severity in child and adolescent victims of traffic accidents for improved urgency and emergency care.•Relation between the scales allows for better decisions on patient hospitalization, adequate treatment and the prevention of irreversible injuries.

Civilian vascular trauma, treatment and outcome at a level 1-trauma centre

BACKGROUND

Outcomes after vascular injuries in wartime are well documented, but studies on vascular injuries in a civilian European populations are scarce.

METHODS

A retrospective study on all adults admitted to a North-European level 1-trauma centre 2009-2018 with The Abbreviated Injury Scale-codes for non-iatrogenic vascular trauma (VT). Data were extracted from both national and regional trauma-registries, as well as patient charts. Patient demographics, mechanism, and location of vascular injury were registered as well as its treatment. Incidence and injury scores (ISS, NISS and TRISS) were calculated and overall survival (Kaplan-Meier) estimated.

RESULTS

Of 4042 trauma-patients, 68 (1.7%) (median age 44 years, 76% males) sustained 81 vascular injuries (69 arterial; 12 venous); 46 blunt and 22 (32%) penetrating injuries. The total incidence of vascular injuries was 1.45/100,000 inhabitants and did not change over the study-period (95% confidence interval 1.13-1.82). The injuries were located in thorax (n = 17), neck (n = 16) and abdominal region (n = 15); most of the blunt injuries followed traffic (n = 31) or falling accidents (n = 10), and with 17 of the 22 penetrating injuries due to stabbing. The median ISS and NISS-scores were 22 and 33, with 50 (74%) and 55 (81%) patients having scores > 15, respectively. Forty-three (63%) patients had open surgical repair and 8 (12%) received endovascular treatment. Twenty-one patients died within 30-days (31%), 33% and 27% after blunt and penetrating injuries, respectively. Half of the patients that died within 24 h sustained aortic injury.

CONCLUSIONS

Traumatic vascular injuries are rare in civilian settings and are less than 2% of major trauma admissions. These patients are often seriously injured and their treatment can be challenging with high 30-day mortality.

TRIAL REGISTRATION

Retrospectively registered.

Long-Term Outcomes in Severe Traumatic Brain Injury and Associated Factors: A Prospective Cohort Study

OBJECTIVE

The presence of focal lesion (FL) after a severe traumatic brain injury is an important factor in determining morbidity and mortality. Despite this relevance, few studies show the pattern of recovery of patients with severe traumatic brain injury (TBI) with FL within one year. The objective of this study was to identify the pattern of recovery, independence to perform activities of daily living (ADL), and factors associated with mortality and unfavorable outcome at six and twelve months after severe TBI with FL.

METHODOLOGY

This is a prospective cohort, with data collected at admission, hospital discharge, three, six, and twelve months after TBI.

RESULTS

The study included 131 adults with a mean age of 34.08 years. At twelve months, 39% of the participants died, 80% were functionally independent by the Glasgow Outcome Scale Extended, 79% by the Disability Rating Scale, 79% were independent for performing ADLs by the Katz Index, and 53.9% by the Lawton Scale. Report of alcohol intake, sedation time, length of stay in intensive care (ICU LOS), Glasgow Coma Scale, trauma severity indices, hyperglycemia, blood glucose, and infection were associated with death. At six and twelve months, tachypnea, age, ICU LOS, trauma severity indices, respiratory rate, multiple radiographic injuries, and cardiac rate were associated with dependence.

CONCLUSIONS

Patients have satisfactory functional recovery up to twelve months after trauma, with an accentuated improvement in the first three months. Clinical and sociodemographic variables were associated with post-trauma outcomes. Almost all victims of severe TBI with focal lesions evolved to death or independence.

A Novel Strategy for Predicting 72-h Mortality After Admission in Patients With Polytrauma: A Study on the Development and Validation of a Web-Based Calculator

Background

Early and accessible screening of patients with polytrauma at a high risk of hospital death is essential. The purpose of this research was to seek an accurate and convenient solution to predict deaths occurring within 72 h after admission of these patients.

Methods

A secondary analysis was conducted on 3,075 patients with polytrauma from the Dryad database. We imputed missing values in eligible individuals with the k-nearest neighbor algorithm and then randomly stratified them into the training group (n = 2,461) and the validation group (n = 614) based on a proportion of 8:2. The restricted cubic spline, univariate, backward stepwise, and multivariate logistic regression methods were employed to determine the suitable predictors. Calibration and receiver operating characteristic (ROC) curves were applied to assess the calibration and discrimination of the obtained model. The decision curve analysis was then chosen as the measure to examine the clinical usage.

Results

Age, the Glasgow Coma Scale score, the Injury Severity Score, base excess, and the initial lactate level were inferred as independent prognostic factors related to mortality. These factors were then integrated and applied to construct a model. The performance of calibration plots, ROC curves, and decision curve analysis indicated that the model had satisfactory predictive power for 72-h mortality after admission of patients with polytrauma. Moreover, we developed a nomogram for visualization and a web-based calculator for convenient application (https://songandwen.shinyapps.io/DynNomapp/).

Conclusions

A convenient web-based calculator was constructed to robustly estimate the risk of death in patients with polytrauma within 72 h after admission, which may aid in further rationalization of clinical decision-making and accurate individual treatment.

New Injury Severity Score and Trauma Injury Severity Score are superior in predicting trauma mortality

BACKGROUND

Trauma scores are used to give clinicians appropriate quantitative context in making decisions. Studies show that anatomical trauma scores predicted intensive care unit admission better, while physiological trauma scores predicted mortality better. We hypothesize that trauma scores have a hierarchy of efficacies at predicting mortality and operative decision making.

METHODS

We performed a retrospective analysis of our trauma patient database at a level 1 trauma center from 2016 to 2020 and calculated the following trauma scores: Glasgow Coma Scale, Revised Trauma Score, Trauma Injury Severity Score, Injury Severity Score, Shock Index, and New Trauma Injury Severity Score (NISS). Receiver operating characteristic curves were used to evaluate the sensitivity and specificity of trauma scores for predicting mortality.

RESULTS

A total of 738 patients were included (mean ± SD age, 35.7 ± 15.6 years). Area under the curve (AUC) results from the DeLong test showed that NISS predicted mortality the best compared with other trauma scores. New Trauma Injury Severity Score was superior in predicting mortality for penetrating trauma (AUC, 0.86 ± 0.02; p < 0.001) compared with blunt trauma (AUC, 0.73 ± 0.04; p < 0.001). Trauma Injury Severity Score was the best predictor of mortality for patients with gunshot wounds (AUC, 0.83; 95% confidence interval [CI], 0.73-0.92; p < 0.001), motor vehicle accidents (AUC, 0.80; 95% CI, 0.61-1.00; p = 0.01), and falls (AUC, 0.73; 95% CI, 0.61-0.85; p = 0.007).

CONCLUSION

New Trauma Injury Severity Score was the best scoring index for predicting mortality in trauma patients, especially for penetrating trauma. Clinicians should consider incorporating other trauma scores, especially NISS and Trauma Injury Severity Score, in determining injury severity and the likelihood of mortality. These scores can help physicians determine the best course of action in patient management.

LEVEL OF EVIDENCE

Prognostic and Epidemiologic; level IV.

Validation of the Trauma and Injury Severity Score for Prediction of Mortality in a Greek Trauma Population

BACKGROUND

Although the Trauma and Injury Severity Score (TRISS) has been extensively used for mortality risk adjustment in trauma, its applicability in contemporary trauma populations is increasingly questioned.

OBJECTIVE

The study aimed to evaluate the predictive performance of the TRISS in its original and revised version and compare these with a recalibrated version, including current data from a Greek trauma population.

METHODS

This is a retrospective cohort study of admitted trauma patients conducted in two tertiary Greek hospitals from January 2016 to December 2018. The model algorithm was calculated based on the Major Trauma Outcome Study coefficients (TRISSMTOS), the National Trauma Data Bank coefficients (TRISSNTDB), and reweighted coefficients of logistic regression obtained from a Greek trauma dataset (TRISSGrTD). The primary endpoint was inhospital mortality. Models' prediction was performed using discrimination and calibration statistics.

RESULTS

A total of 8,988 trauma patients were included, of whom 854 died (9.5%). The TRISSMTOS displayed excellent discrimination with an area under the curve (AUC) of 0.912 (95% CI 0.902-0.923) and comparable with TRISSNTDB (AUC = 0.908, 95% CI 0.897-0.919, p = .1195). Calibration of both models was poor (Hosmer-Lemeshow test p < .001), tending to underestimate the probability of mortality across almost all risk groups. The TRISSGrTD resulted in statistically significant improvement in discrimination (AUC = 0.927, 95% CI 0.918-0.936, p < .0001) and acceptable calibration (Hosmer-Lemeshow test p = .113).

CONCLUSION

In this cohort of Greek trauma patients, the performance of the original TRISS was suboptimal, and there was no evidence that it has benefited from its latest revision. By contrast, a strong case exists for supporting a locally recalibrated version to render the TRISS applicable for mortality prediction and performance benchmarking.

Association of Risk of Mortality in Pediatric Patients Transferred From Scene by Helicopter With Major But Not Minor Injuries

OBJECTIVES

Helicopter emergency medical services (HEMS) are used for 16% of pediatric trauma. National HEMS guidelines advised that triage criteria be standardized for pediatric patients. A national report found pediatric HEMS associated with decreased mortality compared with ground emergency medical services (GEMS) but did not control for transport time. We hypothesized that the rate of HEMS has decreased nationally and the mortality risk for HEMS to be similar when adjusting for transport time compared with GEMS.

METHODS

The Pediatric Trauma Quality Improvement Program (2014-2016) was queried for patients younger than 16 years transported by HEMS or GEMS. A multivariable logistic regression was used.

RESULTS

From 25,647 patients, 4527 (17.7%) underwent HEMS. The rate of HEMS from scene decreased from 21.2% in 2014 to 18.2% in 2016. The rate of HEMS for minor trauma (Injury Severity Score <15) decreased from 14.9% in 2014 to 13.5% in 2016 and major trauma (Injury Severity Score > 15) from 38.4% in 2014 to 35.9% in 2016. After controlling for predictors of mortality and transport time, HEMS was associated with decreased risk of mortality for only those with major injuries transferred from scene (odds ratio, 0.48; 95% confidence interval, 0.26-0.88; P = 0.01) compared with GEMS.

CONCLUSIONS

The rate of HEMS in pediatric trauma has decreased. However, there is room for improvement as 14% of those with minor trauma are transported by HEMS. Given the similar risk of mortality compared with GEMS, further development of guidelines that avoid the unnecessary use of HEMS appears warranted. However, utilization of HEMS for transport of pediatric major trauma should continue.

The effect of participation of interventional radiology team in a primary trauma survey on patient outcome

PURPOSE

The purpose of this study was to examine the survival benefits of a workflow in which an interventional radiology (IR) team participates in a primary trauma survey on patients with hemodynamically unstable trauma.

MATERIALS AND METHODS

A retrospective observational study was conducted between 2012 and 2019 at a single institution. Patients who underwent an IR procedure as the initial hemostasis were assigned to the hemodynamically stable group (HSG) or hemodynamically unstable group (HUG). The primary and secondary outcomes were survival at hospital discharge compared with the probability of survival (Ps) and the time course.

RESULTS

A total of 160 patients (100 men, 60 women; median age, 57.5 years [interquartile range (IQR): 31.5-72 years]) with an injury severity score of 24 (IQR: 13.75-34) were included. A total of 125 patients were included in the HSG group and 35 patients in the HUG group. The observational survival rate was significantly greater than the Ps rate by 4.9% (95% confidence interval [CI]: 1.6-8.4%; P = 0.005) in HSG and by 24.6% in HUG (95% CI: 16.9-32.3%; P < 0.001). The observational survival rate was significantly greater than Ps in HUG than in HSG (P < 0.001). The median time to initiate IR procedures and the median procedure time in HUG were 54 min [IQR: 45-66 min] and 48 min [IQR: 30-85 min], respectively; both were significantly shorter than those in the HSG.

CONCLUSION

A trauma workflow utilizing an IR team in a primary survey is associated with improved survival of patients with hemodynamically unstable trauma when compared with Ps with a shorter time course.

Beyond Mortality: Does Trauma-related Injury Severity Score Predict Complications or Lengths of Stay Using a Large Administrative Dataset

Introduction:

Despite its shortcomings, trauma-related injury severity score (TRISS) correlates well with mortality in large trauma datasets. The aim of this study was to determine if TRISS correlates with morbidity and hospital lengths of stay using data from an institutional registry at a Level I Trauma Center. We hypothesized that higher TRISS correlates with increased complications and longer hospital stays.

Methods:

A retrospective review of our institutional registry was performed, examining all trauma admissions between January 1999 and June 30, 2015. Out of a total of 32,026 patient records, TRISS data were available in 23,205 cases. Abstracted data included patient age, gender, ISS, TRISS, presence of complication, Glasgow Coma Scale (GCS), hospital length of stay, intensive care unit LOS, step-down unit LOS, functional independence measure, and 30-day mortality.

Results:

TRISS was highly predictive of mortality, with the AUC value of 0.95 (95% confidence interval 0.936–0.954, P < 0.01) compared to ISS (AUC 0.794), GCS (AUC 0.827), and age (AUC 0.650). TRISS also performed better than the other variables in terms of the ability to predict morbidity events (AUC 0.813). TRISS was comparable to ISS in terms of prediction of ICU admission (AUC 0.801 versus 0.811, respectively). After correcting for patient age and gender, higher TRISS significantly correlated with longer hospital stays .

Conclusions:

Despite previous criticisms, we found that TRISS is superior to ISS for mortality and morbidity prediction. TRISS correlated significantly with a hospital, step down, and ICU lengths of stay using a large administrative dataset.

Severe penetrating trauma in Switzerland: first analysis of the Swiss Trauma Registry (STR)

PURPOSE

The purpose of this study was to examine the epidemiology, demographics, injury characteristics and outcomes of patients who presented to Swiss trauma centers following severe penetrating trauma.

METHODS

Swiss Trauma Registry (STR)-cohort analysis including patients with severe (ISS ≥ 16 or AIS head ≥ 3) penetrating trauma between 2017 and 2019. Primary outcome was mortality. Secondary outcomes were hospital and intensive care unit (ICU) length of stay (LOS), and prehospital times.

RESULTS

During the 3-year study period, 134 (1.6% of entire STR) patients with severe penetrating trauma were identified [64 (48%) gunshot wounds (GSW), 70 (52%) stab wounds (SW)]. Median age was 40.5 (IQR 29.0-59.0) and 82.8% were male. Mortality rate was 50% for GSW; 9% for SW. Overall, prehospital time [incident to arrival emergency department (ED)] was 65 (IQR 45-94) minutes. The median number of patients admitted for a severe GSW/SW per center and year was 2 (range 0-14). Of 64 patients who sustained a GSW, 42 (65.6%) were self-inflicted. Mortality in self-inflicted GSW reached 66.7%, with the head being severely injured in 78.6%. The 67 patients with severe isolated torso GSW/SW had an ISS of 20 (IQR 16-26) and a mortality of 15%. Multivariable analysis identified severe chest trauma, ED Glasgow Coma Scale ≤ 8, age, self-infliction, massive blood transfusion and ISS as independent predictors for mortality.

CONCLUSION

Severe penetrating trauma is very rare in Switzerland. Mortality ranges from 9% in SW to 67% in self-inflicted GSW. Particularly in the setting of GSW/SW to the torso, reduction in prehospital time may further improve patient outcomes.

A Data Augmentation Method for War Trauma Using the War Trauma Severity Score and Deep Neural Networks

The demand for large-scale analysis and research of data on trauma from modern warfare is increasing day by day, but the amount of existing data is not sufficient to meet such demand. In this study, an integrated modeling approach incorporating a war trauma severity scoring algorithm (WTSS) and deep neural networks (DNN) is proposed. First, the proposed WTSS, which uses multiple non-linear regression based on the characteristics of war trauma data and the medical evaluation by an expert panel, performed a standardized assessment of an injury and predicts its trauma consequences. Second, to generate virtual injury, based on the probability of occurrence, the injured parts, injury types, and complications were randomly sampled and combined, and then WTSS was used to assess the consequences of the virtual injury. Third, to evaluate the accuracy of the predicted injury consequences, we built a DNN classifier and then trained it with the generated data and tested it with real data. Finally, we used the Delphi method to filter out unreasonable injuries and improve data rationality. The experimental results verified that the proposed approach surpassed the traditional artificial generation methods, achieved a prediction accuracy of 84.43%, and realized large-scale and credible war trauma data augmentation.

The pros and cons of motor, memory, and emotion-related behavioral tests in the mouse traumatic brain injury model

Traumatic brain injury (TBI) is a medical emergency with high morbidity and mortality. Motor, memory, and emotion-related deficits are common symptoms following TBI, yet treatment is very limited. To develop new drugs and find new therapeutic avenues, a wide variety of TBI models have been established to mimic the heterogeneity of TBI. In this regard, along with histologic measures, behavioral functional outcomes provide valuable insight into the underlying neuropathology and guide neurorehabilitation efforts for neuropsychiatric impairment after TBI. Development, characterization, and application of behavioral tests that can assess functional neurologic deficits are essential to the development of translational therapies. This comprehensive review aims to summarize 19 common behavioral tests from three aspects (motor, memory, and emotion-related) that are associated with TBI pathology. Discussion covers the apparatus, the test steps, the evaluation indexes, data collection and analysis, animal performance and applications, advantages and disadvantages as well as precautions to eliminate bias wherever possible. We discussed recent studies on TBI-related preconditioning, biomarkers, and optimized behavioral protocols. The neuropsychologic tests employed in clinics were correlated with those used in mouse TBI models. In summary, this review provides a comprehensive, up-to-date reference for TBI researchers to choose the right neurobehavioral protocol according to the research objectives of their translational investigation.

Prediction of mortality in severely injured patients with facial bone fractures

PURPOSE

Identify the most common concomitant injuries associated with facial trauma, and compare the efficacy of various scoring systems in estimation of mortality risks in this category of patients.

METHODS

The study evaluated patients with facial and concomitant injuries, who received the multidisciplinary treatment in a specialized trauma hospital. Values of New Injury Severity Score, Glasgow Coma Scale, Facial Injury Severity Scale, age, and length of hospital stay were statistically analysed to determine presence of relationships between these indicators and define factors that significantly associated with lethal outcome.

RESULTS

During 6-year observation period, 719 patients were treated with multiple or combined maxillofacial trauma, brain injuries and polytrauma. Mainly with isolated midface bones (49.7%), pan-facial (34.6%), mandible (12.9%), and frontal bone and walls (2.8%) fractures. Mortality was (2.2%). The mortality rates in patients with severe pan-facial fractures were higher (p = 0.008) than in single anatomical area (6% vs 1.5%). Age, GCS, and NISS were the most reliable indicator of lethal outcome.

CONCLUSION

Age, Glasgow Coma Scale and New Injury Severity Score main factors, that predicts lethal outcome with high accuracy. New Injury Severity Score value ≥ 41 is a critical level for survival prognosis and should be considered in treatment planning and management of this category of patients.

Validation of a Visual-Based Analytics Tool for Outcome Prediction in Polytrauma Patients (WATSON Trauma Pathway Explorer) and Comparison with the Predictive Values of TRISS

Introduction: Big data-based artificial intelligence (AI) has become increasingly important in medicine and may be helpful in the future to predict diseases and outcomes. For severely injured patients, a new analytics tool has recently been developed (WATSON Trauma Pathway Explorer) to assess individual risk profiles early after trauma. We performed a validation of this tool and a comparison with the Trauma and Injury Severity Score (TRISS), an established trauma survival estimation score. Methods: Prospective data collection, level I trauma centre, 1 January 2018–31 December 2019. Inclusion criteria: Primary admission for trauma, injury severity score (ISS) ≥ 16, age ≥ 16. Parameters: Age, ISS, temperature, presence of head injury by the Glasgow Coma Scale (GCS). Outcomes: SIRS and sepsis within 21 days and early death within 72 h after hospitalisation. Statistics: Area under the receiver operating characteristic (ROC) curve for predictive quality, calibration plots for graphical goodness of fit, Brier score for overall performance of WATSON and TRISS. Results: Between 2018 and 2019, 107 patients were included (33 female, 74 male; mean age 48.3 ± 19.7; mean temperature 35.9 ± 1.3; median ISS 30, IQR 23–36). The area under the curve (AUC) is 0.77 (95% CI 0.68–0.85) for SIRS and 0.71 (95% CI 0.58–0.83) for sepsis. WATSON and TRISS showed similar AUCs to predict early death (AUC 0.90, 95% CI 0.79–0.99 vs. AUC 0.88, 95% CI 0.77–0.97; p = 0.75). The goodness of fit of WATSON (X² = 8.19, Hosmer–Lemeshow p = 0.42) was superior to that of TRISS (X² = 31.93, Hosmer–Lemeshow p < 0.05), as was the overall performance based on Brier score (0.06 vs. 0.11 points). Discussion: The validation supports previous reports in terms of feasibility of the WATSON Trauma Pathway Explorer and emphasises its relevance to predict SIRS, sepsis, and early death when compared with the TRISS method.

Developing a systematic approach for Population-based Injury Severity Assessment (PISA): a million-person survey in rural Bangladesh

INTRODUCTION

There is currently no defined method for assessing injury severity using population-based data, which limits our understanding of the burden of non-fatal injuries and community-based approaches for primary prevention of injuries. This study describes a systematic approach, Population-based Injury Severity Assessment (PISA) index, for assessing injury severity at the population level.

METHODS

Based on the WHO International Classification of Functionality conceptual model on health and disability, eight indicators for assessing injury severity were defined. The eight indicators assessed anatomical, physiological, postinjury immobility, hospitalisation, surgical treatment, disability, duration of assisted living and days lost from work or school. Using a large population-based survey conducted in 2013 including 1.16 million individuals from seven subdistricts of rural Bangladesh, information on the eight indicators were derived for all non-fatal injury events, and these were summarised into a single injury severity index using a principal component analysis (PCA). Principal component loadings derived from the PCA were used to predict the severity (low, moderate, high) of non-fatal injuries, and were applied to the fatal injury data to assess the criterion validity of the index. The determinants of non-fatal injury severity were determined using ordered logistic regression.

RESULTS

There were 119 703 non-fatal injuries and 14% were classified as high severity based on the PISA index. The PISA index accurately predicted 82% of all fatal injuries as highly severe. Non-fatal injuries of high severity were frequent with unintentional poisoning (57%) and violence (35%). Injuries of high severity were commoner among males (OR 1.16, 95% CI 1.12 to 1.21), adults 65 years and older (OR 1.30, 95% CI 1.23 to 1.36), lower socioeconomic status and intentional injuries. Education was associated with reduced odds of high severe injuries.

CONCLUSION

The PISA index provides a valid and systematic approach for assessing injury severity at the population level, and is relevant for improving the characterisation of the burden and epidemiology of injuries in non-health facility-based settings. Additional testing of the PISA index is needed to further establish its validity and reliability.

RISC II is superior to TRISS in predicting 30-day mortality in blunt major trauma patients in Hong Kong

PURPOSE

Hong Kong (HK) trauma registries have been using the Trauma and Injury Severity Score (TRISS) for audit and benchmarking since their introduction in 2000. We compare the mortality prediction model using TRISS and Revised Injury Severity Classification, version II (RISC II) for trauma centre patients in HK.

METHODS

This was a retrospective cohort study with all five trauma centres in HK. Adult trauma patients with Injury Severity Score (ISS) > 15 suffering from blunt injuries from January 2013 to December 2015 were included. TRISS models using the US and local coefficients were compared with the RISC II model. The primary outcome was 30-day mortality and the area under the receiver operating characteristic curve (AUC) for tested models.

RESULTS

1840 patients were included, of whom 1236/1840 (67%) were male. Median age was 59 years and median ISS was 25. Low falls were the most common mechanism of injury. The 30-day mortality was 23%. RISC II yielded a superior AUC of 0.896, compared with the TRISS models (MTOS: 0.848; PATOS: 0.839; HK: 0.858). Prespecified subgroup analyses showed that all the models performed worse for age ≥ 70, ASA ≥ III, and low falls. RISC II had a higher AUC compared with the TRISS models in all subgroups, although not statistically significant.

CONCLUSION

RISC II was superior to TRISS in predicting the 30-day mortality for Hong Kong adult blunt major trauma patients. RISC II may be useful when performing future audit or benchmarking exercises for trauma in Hong Kong.

ISS is not an appropriate tool to estimate overtriage

PURPOSE

The aim of this work is to study a cohort of patients of ISS < 15 admitted to a TC, and to determine the number of patients that ultimately benefited from the skills and resources specific of a level 1 trauma center.

METHODS

Retrospective study from a prospective cohort of patients admitted to TC (Beaujon Hospital, APHP) for suspected severe trauma from January 2011 to December 2017. The main outcome criterion was the use of surgery or interventional radiology within the first 24 h after admission of patients with ISS < 15. The secondary outcomes were stratified into severe (mortality, resuscitation care, length of stay in intensive care units) and non-severe criteria (mild head injury, hospital discharge or transfer within 24 h).

RESULTS

Of 3035 patients admitted during the study period, 1409 with an ISS < 15 were included, corresponding to a theoretical overtriage rate of 46.4%. Among these, 611 patients (43.4%) underwent emergency intervention within the first 24 h (586 surgical interventions, 19 direct transfers to the operating theater and 6 acts of interventional radiology), 238 (16.9%) of patients presented with severe and 531 (38%) with non-severe outcome criteria.

CONCLUSION

This work demonstrates that in a cohort of patients classified as ISS < 15 admitted to a TC, a considerable amount of TC-specific resources are required, and patients present with severe outcome criteria despite being classified as overtriaged. These results suggest that triage of trauma patients should be based on resource use and clinical outcome rather than anatomic criteria.

Impact of Obesity on Mortality in Adult Trauma Patients

Introduction

Trauma is a major cause of morbidity and mortality amongst all populations in the United States. With the widespread increase of obesity in the United States, studies have been conducted to compare different body mass index (BMI) groups and their clinical outcomes for traumatic injuries. The goal of this study was to retrospectively compare mortality between adult trauma patients with a high BMI to those with a lower BMI as well as investigate whether the mechanism of trauma had an effect on the outcome.

Methods

This study was a retrospective review of all adult trauma patients presented to the emergency department at Arrowhead Regional Medical Center (ARMC) between January 2014 and October 2019. The outcome was all-cause mortality. Patients were grouped according to BMI and mechanisms of injury, including blunt trauma, low velocity penetrating trauma, and high velocity penetrating trauma. Patients were also stratified by injury severity scores (ISS).

Results

Among the 9642 patients assessed in this study, majority (88%) of patients sustained blunt trauma. The number of patients among the three different BMI groups was appropriately equal with 34.4% of normal BMI, 34.6% overweight, and 31.1% obese. The overall mortality of all patients studied was 2.6% (n=248). There was no statistically significant difference in mortality among the three different BMI groups for blunt trauma, penetrating trauma, and subgroup analyses stratified by ISS score (ISS<16 or ISS ≥ 16).

Conclusion

Our study found no statistically significant differences in mortality among the three BMI groups in regard to mortality, even when stratified by ISS, or mechanism of injury, and traumatic velocities.

Prediction of in-hospital mortality in patients on mechanical ventilation post traumatic brain injury: machine learning approach

Background

The study aimed to introduce a machine learning model that predicts in-hospital mortality in patients on mechanical ventilation (MV) following moderate to severe traumatic brain injury (TBI).

Methods

A retrospective analysis was conducted for all adult patients who sustained TBI and were hospitalized at the trauma center from January 2014 to February 2019 with an abbreviated injury severity score for head region (HAIS) ≥ 3. We used the demographic characteristics, injuries and CT findings as predictors. Logistic regression (LR) and Artificial neural networks (ANN) were used to predict the in-hospital mortality. Accuracy, area under the receiver operating characteristics curve (AUROC), precision, negative predictive value (NPV), sensitivity, specificity and F-score were used to compare the models` performance.

Results

Across the study duration; 785 patients met the inclusion criteria (581 survived and 204 deceased). The two models (LR and ANN) achieved good performance with an accuracy over 80% and AUROC over 87%. However, when taking the other performance measures into account, LR achieved higher overall performance than the ANN with an accuracy and AUROC of 87% and 90.5%, respectively compared to 80.9% and 87.5%, respectively. Venous thromboembolism prophylaxis, severity of TBI as measured by abbreviated injury score, TBI diagnosis, the need for blood transfusion, heart rate upon admission to the emergency room and patient age were found to be the significant predictors of in-hospital mortality for TBI patients on MV.

Conclusions

Machine learning based LR achieved good predictive performance for the prognosis in mechanically ventilated TBI patients. This study presents an opportunity to integrate machine learning methods in the trauma registry to provide instant clinical decision-making support.

Prediction of in-hospital mortality in patients with post traumatic brain injury using National Trauma Registry and Machine Learning Approach

BACKGROUND

The use of machine learning techniques to predict diseases outcomes has grown significantly in the last decade. Several studies prove that the machine learning predictive techniques outperform the classical multivariate techniques. We aimed to build a machine learning predictive model to predict the in-hospital mortality for patients who sustained Traumatic Brain Injury (TBI).

METHODS

Adult patients with TBI who were hospitalized in the level 1 trauma center in the period from January 2014 to February 2019 were included in this study. Patients' demographics, injury characteristics and CT findings were used as predictors. The predictive performance of Artificial Neural Networks (ANN) and Support Vector Machines (SVM) was evaluated in terms of accuracy, Area Under the Curve (AUC), sensitivity, precision, Negative Predictive Value (NPV), specificity and F-score.

RESULTS

A total of 1620 eligible patients were included in the study (1417 survival and 203 non-survivals). Both models achieved accuracy over 91% and AUC over 93%. SVM achieved the optimal performance with accuracy 95.6% and AUC 96%.

CONCLUSIONS

for prediction of mortality in patients with TBI, SVM outperformed the well-known classical models that utilized the conventional multivariate analytical techniques.

How to detect a polytrauma patient at risk of complications: A validation and database analysis of four published scales

Introduction

Early accurate assessment of the clinical status of severely injured patients is crucial for guiding the surgical treatment strategy. Several scales are available to differentiate between risk categories. They vary between expert recommendations and scores developed on the basis of patient data (level II). We compared four established scoring systems in regard to their predictive abilities for early (e.g., hemorrhage-induced mortality) versus late (Multiple Organ Failure (MOF), sepsis, late death) in-hospital complications.

Methods

A database from a level I trauma center was used. The inclusion criteria implied an injury severity score (ISS) of ≥16 points, primary admission, and a complete data set from admission to hospital-day 21. The following four scales were tested: the clinical grading scale (CGS; covers acidosis, shock, coagulation, and soft tissue injuries), the modified clinical grading scale (mCGS; covers CGS with modifications), the polytrauma grading score (PTGS; covers shock, coagulation, and ISS), and the early appropriate care protocol (EAC; covers acid–base changes). Admission values were selected from each scale and the following endpoints were compared: mortality, pneumonia, sepsis, death from hemorrhagic shock, and multiple organ failure.

Statistics

Shapiro-Wilk test for normal distribution, Pearson Chi square, odds ratios (OR) for all endpoints, 95% confidence intervals. Fitted, generalized linear models were used for prediction analysis. Krippendorff was used for comparison of CGS and mCGS. Alpha set at 0.05.

Results

In total, 3668 severely injured patients were included (mean age, 45.8±20 years; mean ISS, 28.2±15.1 points; incidence of pneumonia, 19.0%; incidence of sepsis, 14.9%; death from hem. shock, 4.1%; death from multiple organ failure (MOF), 1.9%; mortality rate, 26.8%). Our data show distinct differences in the prediction of complications, including mortality, for these scores (OR ranging from 0.5 to 9.1). The PTGS demonstrated the highest predictive value for any late complication (OR = 2.0), sepsis (OR = 2.6, p = 0.05), or pneumonia (OR = 2.0, p = 0.2). The EAC demonstrated good prediction for hemorrhage-induced early mortality (OR = 7.1, p<0.0001), but did not predict late complications (sepsis, OR = 0.8 and p = 0.52; pneumonia, OR = 1.1 and p = 0.7) CGS and mCGS are not comparable and should not be used interchangeably (Krippendorff α = 0.045).

Conclusion

Our data show that prediction of complications is more precise after using values that covers different physiological systems (coagulation, hemorrhage, acid–base changes, and soft tissue damage) when compared with using values of only one physiological system (e.g., acidosis). When acid–base changes alone were tested in terms of complications, they were predictive of complications within 72 hours but failed to predict late complications. These findings should be considered when performing early assessment of trauma patients or for the development of new scores.