The Trauma and Injury Severity Score (TRISS) revised

Incidence, Risk Factors, and Outcomes of Central Line-Associated Bloodstream Infections in Trauma Patients

Introduction: Central line-associated blood stream infection (CLABSI) is a hospital-acquired infection (HAI) associated with increased morbidity and mortality among the general patient population. However, few studies have evaluated the incidence, outcomes, and risk factors for CLABSI in trauma patients. This study aimed to identify the rate of positive (+)CLABSI in trauma patients and risk factors associated with (+)CLABSI. Methods: The 2017-2021 Trauma Quality Improvement Program database was queried for trauma patients aged ≥18 years undergoing central-line placement. We compared patients with (+)CLABSI vs. (-)CLABSI patients. Bivariate and multivariable logistic regression analyses were performed. Results: From 175,538 patients undergoing central-line placement, 469 (<0.1%) developed CLABSI. The (+)CLABSI patients had higher rates of cirrhosis (3.9% vs. 2.0%, p = 0.003) and chronic kidney disease (CKD) (4.3% vs. 2.6%, p = 0.02). The (+)CLABSI group had increased injury severity score (median: 25 vs. 13, p < 0.001), length of stay (LOS) (median 33.5 vs. 8 days, p < 0.001), intensive care unit LOS (median 21 vs. 6 days, p < 0.001), and mortality (23.7% vs. 19.6%, p = 0.03). Independent associated risk factors for (+)CLABSI included catheter-associated urinary tract infection (CAUTI) (odds ratio [OR] = 5.52, confidence interval [CI] = 3.81-8.01), ventilator-associated pneumonia (VAP) (OR = 4.43, CI = 3.42-5.75), surgical site infection (SSI) (OR = 3.66, CI = 2.55-5.25), small intestine injury (OR = 1.91, CI = 1.29-2.84), CKD (OR = 2.08, CI = 1.25-3.47), and cirrhosis (OR = 1.81, CI = 1.08-3.02) (all p < 0.05). Conclusion: Although CLABSI occurs in <0.1% of trauma patients with central-lines, it significantly impacts LOS and morbidity/mortality. The strongest associated risk factors for (+)CLABSI included HAIs (CAUTI/VAP/SSI), specific injuries (small intestine), and comorbidities. Providers should be aware of these risk factors with efforts made to prevent CLABSI in these patients.

Assessing optimal methods for transferring machine learning models to low-volume and imbalanced clinical datasets: experiences from predicting outcomes of Danish trauma patients

Introduction

Accurately predicting patient outcomes is crucial for improving healthcare delivery, but large-scale risk prediction models are often developed and tested on specific datasets where clinical parameters and outcomes may not fully reflect local clinical settings. Where this is the case, whether to opt for de-novo training of prediction models on local datasets, direct porting of externally trained models, or a transfer learning approach is not well studied, and constitutes the focus of this study. Using the clinical challenge of predicting mortality and hospital length of stay on a Danish trauma dataset, we hypothesized that a transfer learning approach of models trained on large external datasets would provide optimal prediction results compared to de-novo training on sparse but local datasets or directly porting externally trained models.

Methods

Using an external dataset of trauma patients from the US Trauma Quality Improvement Program (TQIP) and a local dataset aggregated from the Danish Trauma Database (DTD) enriched with Electronic Health Record data, we tested a range of model-level approaches focused on predicting trauma mortality and hospital length of stay on DTD data. Modeling approaches included de-novo training of models on DTD data, direct porting of models trained on TQIP data to the DTD, and a transfer learning approach by training a model on TQIP data with subsequent transfer and retraining on DTD data. Furthermore, data-level approaches, including mixed dataset training and methods countering imbalanced outcomes (e.g., low mortality rates), were also tested.

Results

Using a neural network trained on a mixed dataset consisting of a subset of TQIP and DTD, with class weighting and transfer learning (retraining on DTD), we achieved excellent results in predicting mortality, with a ROC-AUC of 0.988 and an F2-score of 0.866. The best-performing models for predicting long-term hospitalization were trained only on local data, achieving an ROC-AUC of 0.890 and an F1-score of 0.897, although only marginally better than alternative approaches.

Conclusion

Our results suggest that when assessing the optimal modeling approach, it is important to have domain knowledge of how incidence rates and workflows compare between hospital systems and datasets where models are trained. Including data from other health-care systems is particularly beneficial when outcomes are suffering from class imbalance and low incidence. Scenarios where outcomes are not directly comparable are best addressed through either de-novo local training or a transfer learning approach.

Diagnostic Peritoneal Aspiration or Lavage in Stratified Groups of Hypotensive Blunt Trauma Patients

BACKGROUND

Some reports suggest Diagnostic peritoneal aspiration (DPA) or lavage (DPL) may better select which hypotensive blunt trauma patients (BTPs) require operation, compared to ultrasonography. However, whether both moderately hypotensive (systolic blood pressure [SBP] < 90 mmHg) and severely hypotensive (SBP < 70 mmHg) patients benefit from DPA/DPL is unclear. We hypothesized DPA/DPL used within the first hour increases risk of death for severely vs moderately hypotensive BTPs.

METHODS

The 2017-2019 Trauma Quality Improvement Program database was queried for BTPs ≥ 18 years old with hypotension upon arrival. We compared moderately and severely hypotensive groups. A multivariable logistic regression analysis was performed controlling for age, comorbidities, emergent operation, blood transfusions, and injury profile.

RESULTS

From 134 hypotensive patients undergoing DPA/DPL, 66 (49.3%) had severe hypotension. Patients in both groups underwent an emergent operation (43.9% vs 58.8%, P = .09) in a similar amount of time (median, 42-min vs 54-min, P = .11). Compared to the moderately hypotensive group, severely hypotensive patients had a higher rate and associated risk of death (84.8% vs 50.0%, P < .001) (OR 5.40, CI 2.07-14.11, P < .001). The strongest independent risk factor for death was age ≥ 65 (OR 24.81, CI 4.06-151.62, P < .001).

DISCUSSION

Among all BTPs undergoing DPA/DPL within the first hour of arrival, an over 5-fold increased risk of death for patients with severe hypotension was demonstrated. As such, DPA/DPL within this group should be used with caution, particularly for older patients, as they may be better served by immediate surgeries. Future prospective research is needed to confirm these findings and elucidate the ideal DPA/DPL population in the modern era of ultrasonography.

Development and Validation of Two Prediction Models for 72-Hour Mortality in High-Risk Trauma Patients Using a Benchmark Dataset: A Comparative Study of Logistic Regression and Neural Networks Models

Background Many studies have been conducted to develop scoring systems for trauma patients, with the majority using logistic regression (LR) models. Neural networks (NN), which is a machine learning algorithm, has a potential to increase the performance of these models. Objectives The aim of this study was to develop and validate two separate prediction models for 72-hour mortality of high-risk trauma patients using LR and NN and to compare the performances of these models in detail. We also aimed to share the SPSS calculators for our models. Materials and methods This is a retrospective, single-center study conducted using a benchmark dataset where the patients were retrospectively gathered from a level 1 trauma center. Patients older than 18 years of age, who had multiple injuries, and were treated at the University Hospital Zurich between January 1, 1996, and January 1, 2013, were included. Patients with a condition that may have an impact on the musculoskeletal system, with Injury Severity Score<16, and with missing outcome data were excluded. Results A total of 3,075 patients were included in the analysis. The area under the curve values of the LR and NN models for predicting 72-hour mortality in patients with high-risk trauma in the hold-out cohort were 0.859 (95% CI=0.836 to 0.883) and 0.856 (95% CI=0.831 to 0.880), respectively. There was no statistically significant difference in the performance of the models (p = 0.554, DeLong's test). Conclusion Both of the models showed good discrimination. Our study suggests that the NN and LR models we developed hold promise as screening tools for predicting 72-hour mortality in high-risk trauma patients. These models were made available to clinicians as clinical prediction tools via SPSS calculators. However, further external validation studies in diverse populations are necessary to substantiate their clinical utility. Moreover, in subsequent studies, it would be beneficial to derive NN models with substantial events per predictor variable to attain more robust and greater predictive accuracy. If the dataset is relatively limited, using LR seems to be a viable alternative.

Traumatic injury mortality prediction (TRIMP-ICDX): A new comprehensive evaluation model according to the ICD-10-CM codes

Various assessment methods based on the International Classification of Diseases, Tenth Edition, Clinical Modification (ICD-10-CM), such as ICD-10-CM Injury Severity Score (ICISS), trauma mortality prediction model (TMPM-ICD10), and injury mortality prediction (IMP-ICDX), are purely anatomic trauma assessment, which need to be further improved. Traumatic injury mortality prediction (TRIMP-ICDX) is a comprehensive assessment method based on anatomic injuries and incorporating available information to determine whether it is superior to Trauma and Injury Severity Score (TRISS) and IMP-ICDX in predicting trauma outcomes. This retrospective cohort study was based on data from 704,287 trauma patients admitted to 710 trauma centers in the National Trauma Data Bank of the United States in 2016. The TRIMP-ICDX was established using anatomical injury, physiological reserves, and physiological response indicators. Its performance was compared with the IMP-ICDX and TRISS by examining the area under the receiver operating characteristic curve (AUC), calibration (Hosmer-Lemeshow goodness-of-fit test, HL), and the Akaike information criterion (AIC). The TRIMP-ICDX showed significantly better discrimination (AUCTRIMP-ICDX 0.968; 95% confidence interval (CI), 0.966-0.970, AUCTRISS 0.922; 95% CI, 0.918-0.925, and AUCIMP-ICDX 0.894; 95% CI, 0.890-0.899), better calibration (HLTRIMP-ICDX 5.6; 95% CI, 3.0-8.0, HLTRISS 72.7; 95% CI, 38.4-104.5, and HLIMP-ICDX 53.1; 95% CI, 26.6-77.8), and a lower AIC (AICTRIMP-ICDX 24,774, AICTRISS 30,753, and AICIMP-ICDX 32,780) compared with TRISS and IMP-ICDX. Similar results were found in statistical comparisons among different body regions. As a comprehensive evaluation method based on the ICD-10-CM lexicon TRIMP-ICDX is significantly better than IMP-ICDX and TRISS with respect to both discriminative power and calibration. The TRIMP-ICDX should become a research method for the comprehensive evaluation of trauma severity.

Blunt Trauma Mortality: Does Trauma Center Level Matter?

INTRODUCTION

Trauma centers have improved outcomes compared to nontrauma centers when caring for injured patients. A multicenter report found blunt trauma patients treated at American College of Surgeons' Level I trauma centers have improved survival compared to Level II centers. In a subsequent multicenter study, Level II centers had improved survival in all trauma patients. We sought to provide a more granular analysis by stratifying blunt mechanisms-to determine if there was a difference in mortality between Level I and Level II centers.

METHODS

The Trauma Quality Improvement Program (2010-2016) was queried for patients presenting to an American College of Surgeons' Level I or II trauma center after blunt trauma. A multivariable logistic regression analysis was performed controlling for comorbidities and Trauma and Injury Severity Score.

RESULTS

From 734,473 patients with blunt trauma, 507,715 (69.1%) were treated at a Level I center and 226,758 (30.9%) at a Level II center. The Level I cohort was younger (median age, 53 versus 58, P = 0.01), with a higher median injury severity score (13 versus 10, P < 0.001) and with more patients presenting after a motor vehicle accident (MVA) (27.9% versus 22.4%, P < 0.001) and lower rates of falls (46.6% versus 54.5%, P < 0.001). After adjusting for covariates, there was no difference in mortality between Level I and Level II centers (P > 0.05). When stratifying by mechanisms, Level I centers had a decreased associated mortality for MVA (odds ratio = 0.94, CI: 0.88-0.99, P = 0.04) and bicycle accidents (odds ratio = 0.77, CI: 0.74-0.03, P = 0.01) but no difference in falls or pedestrians struck (P > 0.05).

CONCLUSIONS

Overall, blunt trauma patients presenting to a Level I center have no difference in mortality compared to a Level II center. However, when stratified by mechanism, those involved in MVA or bicycle accidents have a decreased associated risk of mortality. Future prospective studies examining variations in practice to account for these differences are warranted.

The derivation of an International Classification of Diseases, Tenth Revision-based trauma-related mortality model using machine learning

BACKGROUND

Existing mortality prediction models have attempted to quantify injury burden following trauma-related admissions with the most notable being the Injury Severity Score (ISS). Although easy to calculate, it requires additional administrative coding. International Classification of Diseases (ICD)-based models such as the Trauma Mortality Prediction Model (TMPM-ICD10) circumvent these limitations, but they use linear modeling, which may not adequately capture the intricate relationships of injuries on mortality. Using ICD-10 coding and machine learning (ML) algorithms, the present study used the National Trauma Data Bank to develop mortality prediction models whose performance was compared with logistic regression, ISS, and TMPM-ICD10.

METHODS

The 2015 to 2017 National Trauma Data Bank was used to identify adults following trauma-related admissions. Of 8,021 ICD-10 codes, injuries were categorized into 1,495 unique variables. The primary outcome was in-hospital mortality. eXtreme Gradient Boosting (XGBoost), a ML technique that uses iterations of decision trees, was used to develop mortality models. Model discrimination was compared with logistic regression, ISS, and TMPM-ICD10 using receiver operating characteristic curve and probabilistic accuracy with calibration curves.

RESULTS

Of 1,611,063 patients, 54,870 (3.41%) experienced in-hospital mortality. Compared with those who survived, those who died more frequently suffered from penetrating trauma and had a greater number of injuries. The XGBoost model exhibited superior receiver operating characteristic curve (0.863 [95% confidence interval (CI), 0.862-0.864]) compared with logistic regression (0.845 [95% CI, 0.844-0.846]), ISS (0.828 [95% CI, 0.827-0.829]), and TMPM-ICD10 (0.861 [95% CI, 0.860-0.862]) (all p < 0.001). Importantly, the ML model also had significantly improved calibration compared with other methodologies (XGBoost, coefficient of determination (R2) = 0.993; logistic regression, R2 = 0.981; ISS, R2 = 0.649; TMPM-ICD10, R2 = 0.830).

CONCLUSION

Machine learning models using XGBoost demonstrated superior performance and calibration compared with logistic regression, ISS, and TMPM-ICD10. Such approaches in quantifying injury severity may improve its utility in mortality prognostication, quality improvement, and trauma research.

LEVEL OF EVIDENCE

Prognostic and Epidemiologic; level III.

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.

A traumatic injury mortality prediction (TRIMP) based on a comprehensive assessment of abbreviated injury scale 2005 predot codes

Abbreviated Injury Scale (AIS)-based systems such as injury severity score (ISS), exponential injury severity score (EISS), trauma mortality prediction model (TMPM), and injury mortality prediction (IMP), classify anatomical injuries with limited accuracy. The widely accepted alternative, trauma and injury severity score (TRISS), improves the prediction rate by combining an anatomical index of ISS, physiological index (the Revised Trauma Score, RTS), and the age of patients. The study introduced the traumatic injury mortality prediction (TRIMP) with the inclusion of extra clinical information and aimed to compare the ability against the TRISS as predictors of survival. The hypothesis was that TRIMP would outperform TRISS in prediction power by incorporating clinically available data. This was a retrospective cohort study where a total of 1,198,885 injured patients hospitalized between 2012 and 2014 were subset from the National Trauma Data Bank (NTDB) in the United States. A TRIMP model was computed that uses AIS 2005 (AIS_05), physiological reserve and physiological response indicators. The results were analysed by examining the area under the receiver operating characteristic curve (AUC), the Hosmer-Lemeshow (HL) statistic, and the Akaike information criterion. TRIMP gave both significantly better discrimination (AUCTRIMP, 0.964; 95% confidence interval (CI), 0.962 to 0.966 and AUCTRISS, 0.923; 95% CI, 0.919 to 0.926) and calibration (HLTRIMP, 14.0; 95% CI, 7.7 to 18.8 and HLTRISS, 411; 95% CI, 332 to 492) than TRISS. Similar results were found in statistical comparisons among different body regions. TRIMP was superior to TRISS in terms of accurate of mortality prediction, TRIMP is a new and feasible scoring method in trauma research and should replace the TRISS.

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.

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.

The Relationship between Hospital Volume and In-Hospital Mortality of Severely Injured Patients in Dutch Level-1 Trauma Centers

Centralization of trauma centers leads to a higher hospital volume of severely injured patients (Injury Severity Score (ISS) > 15), but the effect of volume on outcome remains unclear. The aim of this study was to determine the association between hospital volume of severely injured patients and in-hospital mortality in Dutch Level-1 trauma centers. A retrospective observational cohort study was performed using the Dutch trauma registry. All severely injured adults (ISS > 15) admitted to a Level-1 trauma center between 2015 and 2018 were included. The effect of hospital volume on in-hospital mortality was analyzed with random effects logistic regression models with a random intercept for Level-1 trauma center, adjusted for important demographic and injury characteristics. A total of 11,917 severely injured patients from 13 Dutch Level-1 trauma centers was included in this study. Hospital volume varied from 120 to 410 severely injured patients per year. Observed mortality rates varied between 12% and 24% per center. After case-mix correction, no statistically significant differences between low- and high-volume centers were demonstrated (adjusted odds ratio 0.97 per 50 extra patients per year, 95% Confidence Interval 0.90–1.04, p = 0.44). The variation in hospital volume of the included Level-1 trauma centers was not associated with the outcome of severely injured patients. Our results suggest that well-organized trauma centers with a similar organization of care could potentially achieve comparable outcomes.

Structured and Systematic Team and Procedure Training in Severe Trauma: Going from 'Zero to Hero' for a Time-Critical, Low-Volume Emergency Procedure Over Three Time Periods

Background

Resuscitative emergency thoracotomy is a potential life-saving procedure but is rarely performed outside of busy trauma centers. Yet the intervention cannot be deferred nor centralized for critically injured patients presenting in extremis. Low-volume experience may be mitigated by structured training. The aim of this study was to describe concurrent development of training and simulation in a trauma system and associated effect on one time-critical emergency procedure on patient outcome.

Methods

An observational cohort study split into 3 arbitrary time-phases of trauma system development referred to as ‘early’, ‘developing’ and ‘mature’ time-periods. Core characteristics of the system is described for each phase and concurrent outcomes for all consecutive emergency thoracotomies described with focus on patient characteristics and outcome analyzed for trends in time.

Results

Over the study period, a total of 36 emergency thoracotomies were performed, of which 5 survived (13.9%). The “early” phase had no survivors (0/10), with 2 of 13 (15%) and 3 of 13 (23%) surviving in the development and mature phase, respectively. A decline in ‘elderly’ (>55 years) patients who had emergency thoracotomy occurred with each time period (from 50%, 31% to 7.7%, respectively). The gender distribution and the injury severity scores on admission remained unchanged, while the rate of patients with signs on life (SOL) increased over time.

Conclusion

The improvement over time in survival for one time-critical emergency procedure may be attributed to structured implementation of team and procedure training. The findings may be transferred to other low-volume regions for improved trauma care.

Supplementary Information

The online version contains supplementary material available at (doi:10.1007/s00268-021-05980-1).

Development and internal validation of China mortality prediction model in trauma based on ICD-10-CM lexicon: CMPMIT-ICD10

Background:

Models to predict mortality in trauma play an important role in outcome prediction and severity adjustment, which informs trauma quality assessment and research. Hospitals in China typically use the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) to describe injury. However, there is no suitable prediction model for China. This study attempts to develop a new mortality prediction model based on the ICD-10-CM lexicon and a Chinese database.

Methods:

This retrospective study extracted the data of all trauma patients admitted to the Beijing Red Cross Emergency Center, from January 2012 to July 2018 (n = 40,205). We used relevant predictive variables to establish a prediction model following logistic regression analysis. The performance of the model was assessed based on discrimination and calibration. The bootstrapping method was used for internal validation and adjustment of model performance.

Results:

Sex, age, new region-severity codes, comorbidities, traumatic shock, and coma were finally included in the new model as key predictors of mortality. Among them, coma and traumatic shock had the highest scores in the model. The discrimination and calibration of this model were significant, and the internal validation performance was good. The values of the area under the curve and Brier score for the new model were 0.9640 and 0.0177, respectively; after adjustment of the bootstrapping method, they were 0.9630 and 0.0178, respectively.

Conclusions:

The new model (China Mortality Prediction Model in Trauma based on the ICD-10-CM lexicon) showed great discrimination and calibration, and performed well in internal validation; it should be further verified externally.

The effect of socioeconomic status on severe traumatic injury: a statistical analysis

PURPOSE

The amount of studies performed regarding a link between socioeconomic status (SES) and fatal outcome after traumatic injury is limited. Most research is focused on work-related injuries without taking other important characteristics into account. The aim of this study is to examine the association between SES and outcome after traumatic injury.

METHODS

The study involved polytrauma patients [Injury Severity Score (ISS) ≥ 16] admitted to the Amsterdam University Medical Center (location VUmc) and Northwest Clinics Alkmaar (level 1 trauma centers). The SES of every patient was based on their postal code and represented with a "status score". Univariate and multivariable analyses were performed to estimate the association between SES and mortality, length of stay at the hospital and length of stay at the Intensive Care Unit (ICU). Z-statistics were used to determine the difference between the expected and actual survival, based on Trauma Revised Injury Severity Score (TRISS) and PSNL15 (probability of survival based on the Dutch population).

RESULTS

A total of 967 patients were included in this study. The lowest SES group was significantly associated with more penetrating injuries and a younger age (45 years versus 55 years). Additionally, severely injured patients with lower SES were noted to have a prolonged stay at the ICU. Furthermore, differences were found in the expected and observed survival, especially for the lower SES groups.

CONCLUSION

Polytrauma patients with lower SES have more often penetrating injuries, are younger and have a longer stay at the ICU. No association was found between SES and length of hospital stay and neither between SES and mortality.

Traumatic brain injury probability of survival assessment in adults using iterative random comparison classification

Trauma brain injury (TBI) is the most common cause of death and disability in young adults. A method to determine the probability of survival (Ps) in trauma called iterative random comparison classification (IRCC) was developed and its performance was evaluated in TBI. IRCC operates by iteratively comparing the test case with randomly chosen subgroups of cases from a database of known outcomes (survivors and not survivors) and determines the overall percentage match. The performance of IRCC to determine Ps in TBI was compared with two existing methods. One was Ps14 that uses regression and the other was predictive statistical diagnosis (PSD) that is based on Bayesian statistic. The TBI database contained 4124 adult cases (mean age 67.9 years, standard deviation 21.6) of which 3553 (86.2%) were survivors and 571 (13.8%) were not survivors. IRCC determined Ps for the survivors and not survivors with an accuracy of 79.0 and 71.4%, respectively, while the corresponding values for Ps14 were 97.4% (survivors) and 40.2% (not survivors) and for PSD were 90.8% (survivors) and 50% (not survivors). IRCC could be valuable for determining Ps in TBI and with a suitable database in other traumas.

Prognosis of the Trauma Patients According to the Trauma and Injury Severity Score (TRISS); A Diagnostic Accuracy Study

Objective

To investigate the prognosis and survival rates of a group of Iranian patients with traumatic injuries using the trauma and injury severity score (TRISS) model.

Methods

In this prospective cohort study, all the patients with multi-trauma referring to the Yasuj Shahid Beheshti hospital during 2018 were included. The patients' demographic information, trauma and history of previous illness were recorded. Vital symptoms including respiratory rate, heart rate, hypertension, pulse rate and Glasgow coma scale (GCS) score were assessed. The injury severity score (ISS) was calculated based on the type and location of the injuries and according to the abbreviated injury scale (AIS) classification. The survival probability of the patients was assessed according to the TRISS model.

Results

Overall, 252 trauma patients were evaluated out of whom, 195 (77.4%) were men and 57 (22.6%) women. If we consider the TRISS score probability above 0.5 as the chance of being alive, the mortality rate was 6.75%, that was lower than our series (7.1%). The ISS score and GCS had a positive significant relationship with other variables except respiratory rate, body temperature and hospitalization. Revised trauma score (RTS) was significantly associated with other variables including age, GCS, hemoglobin, systolic blood pressure and respiratory rate. TRISS had an area under curve (AUC) of 0.988 indicating a high prognostic accuracy.

Conclusion

The mortality rate was lower than that of being predicted by TRISS. This might be due to treatment effectiveness and care for traumatic patients leading to decreased mortality. TRISS had high prognostic accuracy in trauma patients. We also reported an association between hemoglobin and survival rate. Therefore, it seems that considering the laboratory parameters can be useful in patients with trauma.

A Retrospective Study of Transfusion Requirements in Trauma Patients Receiving Tranexamic Acid

The Military Application of Tranexamic Acid in Trauma Emergency Resuscitation Study (MATTERs) and Clinical Randomisation of an Antifibrinolytic in Significant Haemorrhage-2 (CRASH-2) studies demonstrate that tranexamic acid (TXA) reduces mortality in patients with traumatic hemorrhage. However, their results, conducted in foreign countries and U.S. military soldiers, provoke concerns over generalizability to civilian trauma patients in the United States. We report the evaluation of patient outcomes and transfusion requirements following treatment with TXA by a civilian air medical program. We conducted a retrospective chart review of trauma patients transported by air service to a Level 1 trauma center. For the purposes of intervention evaluation, patients meeting this criterion for the 2 years (2012-2014) prior to therapy implementation were compared with patients treated during the 2-year study period (2014-2016). Goals were to evaluate morbidity, mortality, transfusion requirements, and length of stay. During the review, 52 control (non-TXA) and 43 study (TXA) patients were identified as meeting inclusion criteria. Patients in the control group were found to be less acute, which correlated with shorter hospitals stays. There was reduced mortality for patients receiving TXA in spite of their increased acuity and decreased likelihood of survival. Trauma patients from this cohort study receiving TXA demonstrate decreased mortality in spite of increased acuity. This increased acuity is associated with increased transfusion requirements. Future research should evaluate patient selection with concern for fibrinolysis and provider bias. Randomized controlled trial is needed to evaluate the role of TXA administration in the United States.

The volume-outcome relationship among severely injured patients admitted to English major trauma centres: a registry study

BACKGROUND

Many countries have centralized and dedicated trauma centres with high volumes of trauma patients. However, the volume-outcome relationship in severely injured patients (Injury Severity Score (ISS) > 15) remains unclear. The aim of this study was to determine the association between hospital volume and outcomes in Major Trauma Centres (MTCs).

METHODS

A retrospective observational cohort study was conducted using the Trauma Audit and Research Network (TARN) consisting of all English Major Trauma Centres (MTCs). Severely injured patients (ISS > 15) admitted to a MTC between 2013 and 2016 were included. The effect of hospital volume on outcome was analysed with random effects logistic regression models with a random intercept for centre and was tested for nonlinearity. Primary outcome was in-hospital mortality.

RESULTS

A total of 47,157 severely injured patients from 28 MTCs were included in this study. Hospital volume varied from 69 to 781 severely injured patients per year. There were small between-centre differences in mortality after adjusting for important demographic and injury severity characteristics (adjusted 95% odds ratio range: 0.99-1.01). Hospital volume was found to be linear and not associated with in-hospital mortality (adjusted odds ratio (aOR) 1.02 per 10 patients, 95% confidence interval (CI) 0.68-1.54, p = 0.92).

CONCLUSIONS

Despite the large variation in volume of the included MTCs, no relationship between hospital volume and outcome of severely injured patients was found. These results suggest that centres with similar structure and processes of care can achieve comparable outcomes in severely injured patients despite the number of severely injured patients they treat.

Are Pre-hospital Trauma Deaths Preventable? A Systematic Literature Review

BACKGROUND

The first and largest peak of trauma mortality is encountered on the trauma site. The aim of this study was to determine whether these trauma-related deaths are preventable. We performed a systematic literature review with a focus on pre-hospital preventable deaths in severely injured patients and their causes.

METHODS

Studies published in a peer-reviewed journal between January 1, 1990 and January 10, 2018 were included. Parameters of interest: country of publication, number of patients included, preventable death rate (PP = potentially preventable and DP = definitely preventable), inclusion criteria within studies (pre-hospital only, pre-hospital and hospital deaths), definition of preventability used in each study, type of trauma (blunt versus penetrating), study design (prospective versus retrospective) and causes for preventability mentioned within the study.

RESULTS

After a systematic literature search, 19 papers (total 7235 death) were included in this literature review. The majority (63.1%) of studies used autopsies combined with an expert panel to assess the preventability of death in the patients. Pre-hospital death rates range from 14.6 to 47.6%, in which 4.9-11.3% were definitely preventable and 25.8-42.7% were potentially preventable. The most common (27-58%) reason was a delayed treatment of the trauma victims, followed by management (40-60%) and treatment errors (50-76.6%).

CONCLUSION

According to our systematic review, a relevant amount of the observed mortality was described as preventable due to delays in treatment and management/treatment errors. Standards in the pre-hospital trauma system and management should be discussed in order to find strategies to reduce mortality.

Association of Initial Low Serum Selenium Level with Infectious Complications and 30-Day Mortality in Multiple Trauma Patients

Low serum selenium levels are commonly observed in critically injured multiple trauma patients. This study aimed to identify the association between initial serum selenium levels and in-hospital infectious complications in multiple trauma patients. We retrospectively reviewed multiple trauma patients admitted between January 2015 and November 2017. We selected 135 patients whose serum selenium levels were checked within 48 h of admission. Selenium deficiency was defined as a serum selenium level <70 ng/mL. Survival analyses of selenium deficiency and 30-day mortality were performed. Multivariate logistic regression analysis was performed to identify the association between initial serum selenium level and in-hospital infectious complications. Thirty-day mortality (8.3% vs. 0.0%; p = 0.018) and incidence rates of pneumonia (66.7% vs. 28.3%; p < 0.001) and infectious complications (83.3% vs. 46.5%; p < 0.001) were higher in patients with selenium deficiency than in patients without selenium deficiency. Kaplan–Meier survival cures also showed similar results (log rank test, p = 0.021). Of 135 patients, 76 (56.3%) experienced at least one infectious complication during admission. High injury severity score (ISS, odds ratio (OR) 1.065, 95% confidence interval (CI) 1.024–1.108; p = 0.002) and selenium deficiency (OR 3.995, 95% CI 1.430–11.156; p = 0.008) increased the risk of in-hospital infectious complications in multiple trauma patients. Patients with selenium deficiency showed higher 30-day mortality and higher risks of pneumonia and infectious complications.

A study of mortality risk factors among trauma referrals to trauma center, Shiraz, Iran, 2017

PURPOSE

Trauma is well known as one of the main causes of death and disability throughout the world. Identifying the risk factors for mortality in trauma patients can significantly improve the quality of care and patient outcomes, as well as reducing mortality rates.

METHODS

In this retrospective cohort study, systematic randomization was used to select 849 patients referred to the main trauma center of south of Iran during a period of six months (February 2017-July 2017); the patients' case files were evaluated in terms of demographic information, pre- and post-accident conditions, clinical conditions at the time of admission and finally, accident outcomes. A logistic regression model was used to analyze the role of factors affecting mortality among subjects.

RESULTS

Among subjects, 60.4% were in the age-group of 15-39 years. There was a 10.4% mortality rate among patients and motor-vehicle accidents were the most common mechanism of injury (66.7%). Aging led to increased risk of fatality in this study. For each unit increase in Glasgow coma scale (GCS), risk of death decreased by about 40% (odds ratio (OR) = 0.63, 95% confidence interval (CI): 0.59-0.67). For each unit increase in injury severe score (ISS), risk of death increased by 10% (OR = 1.11%, 95% CI: 1.08-1.14) and for each unit increase in trauma revised injury severity score (TRISS), there was 18% decrease in the risk of fatality (OR = 0.82, 95% CI: 0.71-0.88).

CONCLUSION

The most common cause of trauma and the most common cause of death from trauma was traffic accidents. It was also found that an increase in the ISS index increases the risk of death in trauma patients, but the increase in GCS, revised trauma score (RTS) and TRISS indices reduces the risk of death in trauma patients. The TRISS indicator is better predictor of traumatic death than other indicators.

Effects of Trauma Center Establishment on the Clinical Characteristics and Outcomes of Patients with Traumatic Brain Injury : A Retrospective Analysis from a Single Trauma Center in Korea

Objective

To investigate the effects of trauma center establishment on the clinical characteristics and outcomes of trauma patients with traumatic brain injury (TBI).

Methods

We enrolled 322 patients with severe trauma and TBI from January 2015 to December 2016. Clinical factors, indexes, and outcomes were compared before and after trauma center establishment (September 2015). The outcome was the Glasgow outcome scale classification at 3 months post-trauma.

Results

Of the 322 patients, 120 (37.3%) and 202 (62.7%) were admitted before and after trauma center establishment, respectively. The two groups were significantly different in age (p=0.038), the trauma location within the city (p=0.010), the proportion of intensive care unit (ICU) admissions (p=0.001), and the emergency room stay time (p<0.001). Mortality occurred in 37 patients (11.5%). Although the preventable death rate decreased from before to after center establishment (23.1% vs. 12.5%), the difference was not significant. None of the clinical factors, indexes, or outcomes were different from before to after center establishment for patients with severe TBI (Glasgow coma scale score ≤8). However, the proportion of inter-hospital transfers increased and the time to emergency room arrival was longer in both the entire cohort and patients with severe TBI after versus before trauma center establishment.

Conclusion

We confirmed that for patients with severe trauma and TBI, establishing a trauma center increased the proportion of ICU admissions and decreased the emergency room stay time and preventable death rate. However, management strategies for handling the high proportion of inter-hospital transfers and long times to emergency room arrival will be necessary.

Quantifying the burden of pre-existing conditions in older trauma patients: A novel metric based on mortality risk

INTRODUCTION

Pre-existing medical conditions (PEC) represent a unique domain of risk among older trauma patients. The study objective was to develop a metric to quantify PEC burden for trauma patients.

METHODS

A cohort of 4526 non-severe blunt-injured trauma patients aged 55 years and older admitted to a Level I trauma center between January 2006 and December 2012 were divided into development (80%) and test (20%) sets. Cox regression was used to develop the model based on in-hospital and 90-day mortality. Regression coefficients were converted into a point-based PEC Risk Score. Performance of the PEC Risk Score was compared in the test set with two other PEC-based metrics and three injury-based metrics. An external cohort of 2284 trauma patients admitted in 2013 was used to evaluate combined metric performance.

RESULTS

Total mortality was 9.4% and 9.1% in the development and test set, respectively. The final model included 12 PEC. In the test set, the PEC Risk Score (c-statistic: 79.7) was superior for predicting in-hospital and 90-day mortality compared with all other metrics. For in-hospital mortality alone, the PEC Risk Score similarly outperformed all other metrics. Combination of the PEC Risk Score and any injury-based metric significantly improved prediction compared with any injury-based metric alone.

CONCLUSION

Our 12-item PEC Risk Score performed well compared with other metrics, suggesting that the classification of trauma-related mortality risk may be improved through its use. Among non-severely injured older trauma patients, the utility of prognostic metrics may be enhanced through the incorporation of comorbidities.

A data-driven artificial intelligence model for remote triage in the prehospital environment

In a mass casualty incident, the factors that determine the survival rate of injured patients are diverse, but one of the key factors is the time for triage. Additionally, the main factor that determines the time of triage is the number of medical personnel. However, when relying on a small number of medical personnel, the ability to increase survivability is limited. Therefore, developing a classification model for survival prediction that can quickly and precisely triage via wearable devices without medical personnel is important. In this study, we designed a consciousness index to substitute the factor by manpower and improved the classification accuracy by applying a machine learning algorithm. First, logistic regression analysis using vital signs and a consciousness index capable of remote monitoring through wearable devices confirmed the high efficiency of the consciousness index. We then developed a classification model with high accuracy which corresponds to existing injury severity scoring systems through the machine learning algorithms. We extracted 460,865 cases which met our criteria for developing the survival prediction from the national sample project in the national trauma databank which contains 408,316 cases of blunt injury and 52,549 cases of penetrating injury. Among the dataset, 17,918 (3.9%) cases died while the other survived. The AUCs with 95% confidence intervals (CIs) for the different models with the proposed simplified consciousness score as follows: RTS (as baseline), 0.78 (95% CI = 0.775 to 0.785); logistic regression, 0.87 (95% CI = 0.862 to 0.870); random forest, 0.87 (95% CI = 0.862 to 0.872); deep neural network, 0.89 (95% CI = 0.882 to 0.890). As a result, we confirmed the possibility of remote triage using a wearable device. It is expected that the time required for triage can be effectively reduced by using the developed classification model of survival prediction.

Trauma and Injury Severity Score modification for predicting survival of trauma in one regional emergency medical center in Korea: Construction of Trauma and Injury Severity Score coefficient model

Background:

The problem that is central to trauma research is the prediction of survival rate after trauma. Trauma and Injury Severity Score is being used for predicting survival rate after trauma. Many countries have conducted a study on the classification, characteristics of variables, and the validity of the Trauma and Injury Severity Score model. However, few investigations have been made on the characteristics of coefficients or variables related to Trauma and Injury Severity Score in Korea. Objectives: There is a need for coefficient analysis of Trauma and Injury Severity Score which was created based on the United States database to be optimized for the situation in Korea.

Methods:

This study examined how the currently used Trauma and Injury Severity Score coefficients were developed and created for trauma patients visiting the emergency department in a hospital in Korea using the analytical method. A total of 34,340 trauma patients who were hospitalized into an emergency center from January 2012 to December 2014 for 3 years were analyzed with trauma registry established on August 2006.

Results:

Trauma and Injury Severity Score coefficients were transformed with the methods that were used to make the existing Trauma and Injury Severity Score coefficients using the trauma patients’ data. Regression coefficients (B) were drawn by building up a logistic regression analysis model that used variables such as Injury Severity Score, Revised Trauma Score, and age depending on survival with Trauma and Injury Severity Score.

Conclusion:

With regard to Trauma and Injury Severity Score established in the United States differing from Korea in injury types, it seems possible to realize significant survival rate by deriving coefficients with data in Korea and reanalyzing them.

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

Background

The objective of this study is to propose three new adjustments to the Trauma and Injury Severity Score (TRISS) equation and compare their performances with the original TRISS as well as this index with coefficients adjusted for the study population.

Methods

This multicenter, retrospective study evaluated trauma victims admitted to two hospitals in São Paulo-Brazil and San Diego-EUA between January 1st, 2006, and December 31st, 2010. The proposed models included a New Trauma and Injury Severity Score (NTRISS)-like model that included Best Motor Response (BMR), systolic blood pressure (SBP), New Injury Severity Score (NISS), and age variables; a TRISS peripheral oxygen saturation (SpO₂) model that included Glasgow Coma Scale (GCS), SBP, SpO₂, Injury Severity Score, and age variables; and a NTRISS-like SpO₂ model that included BMR, SBP, SpO₂, NISS, and age variables. All equations were adjusted for blunt and penetrating trauma coefficients. The model coefficients were established by logistic regression analysis. Receiver operating characteristic (ROC) curve analysis was used to evaluate the performance of the models.

Results

The original TRISS (area under the curve (AUC) = 0.90), TRISS with adjusted coefficients (AUC = 0.89), and the new proposals (NTRISS-like, TRISS SpO₂, and NTRISS-like SpO₂) showed no difference in performance (AUC = 0.89, 0.89, and 0.90, respectively).

Conclusions

The new models demonstrated good accuracy and similar performance to the original TRISS and TRISS adjusted for coefficients in the study population; therefore, the new proposals may be useful for the assessments of quality of care in trauma patients using variables that are routinely measured and recorded.

Data Improvement Through Simplification: Implications for Low-Resource Settings

BACKGROUND

The focus of many data collection efforts centers on creation of more granular data. The assumption is that more complex data are better able to predict outcomes. We hypothesized that data are often needlessly complex. We sought to demonstrate this concept by examination of the American Society of Anesthesiologists (ASA) scoring system.

METHODS

First, we created every possible consecutive two, three and four category combinations of the current five category ASA score. This resulted in 14 combinations of simplified ASA. We compared the predictive ability of these simplified scores for postoperative outcomes for 2.3 million patients in the NSQIP database. Individual model performance was assessed by comparing receiver operator characteristic (ROC) curves for each model with the standard ASA.

RESULTS

Two of our 4-category models and one of our 3-category models had ability to predict all outcomes equivalent to standard ASA. These results held for all outcomes and on all subgroups tested. The performance of the three best performing simplified ASA scores were also equivalent to the standard ASA score in the univariate analysis and when included in a multivariate model.

CONCLUSIONS

It is assumed that the most granular data and use of the largest number of variables for risk-adjusted predictions will increase accuracy. This complexity is often at the expense of utility. Using the single best predictor in surgical outcomes research, we have shown this is not the case. In this example, we demonstrate that one can simplify ASA into a 3-category variable without losing any ability to predict outcomes.

Epidemiological and Clinical Profile of Fatality in Vulnerable Road Users at a High Volume Trauma Center

Background:

Motorized two-wheelers, pedestrians, and cyclists are termed vulnerable road users (VRUs).Globally up to 50% of road deaths involve VRU and up to 80% in developing and rapidly motorizing economies.

Objective:

The objective of this study is to study the prehospital and clinical profile of fatally injured VRU. This would help in informed decision-making regarding prevention and trauma care infrastructure.

Materials and Methods:

A hospital-based study was performed at a Trauma Centre in Puducherry from January 2013 to June 2014 (18 months). Puducherry is a union territory of India in the state of Tamil Nadu. A total of 193 deaths due to Road traffic accident were included in this study. The demographics, prehospital findings, and the clinical progress of fatally injured VRU are described.

Results:

More than 80% of road traffic collision/crash deaths involved VRU of which the elderly comprised a significant proportion. Alcohol was a serious issue even in the elderly pedestrian. This study revealed specific injury patterns and severity. Head injury was the most common cause of death. Early deaths, that is within 24 h of injury was common at 50%.

Conclusion:

The majority of deaths were in the early phase of trauma hence mandating a strong call for prevention, along with strengthening of trauma care.

Evaluation of the Revised Trauma Score in Predicting Outcomes of Trauma Patients

Introduction

The revised trauma score (RTS) was developed more than 20 years ago. Few studies investigated its usefulness in predicting trauma outcomes. This is especially true for the weighted version of RTS (RTS-w). The aim of this study was to test the predicting power of RTS-w for the trauma outcomes including mortality, admission to intensive care unit (ICU), hospital length of stay and ICU length of stay through a comparison with Injury Severity Score (ISS).

Methods

Descriptive data, variables related to the trauma scores and outcomes were collected. The statistical performance of RTS-w and ISS in predicting the trauma outcomes using receiver operating characteristics (ROC) curves and the area under the curve (AUC) with 95% confidence interval and p value were calculated. The Hosmer-Lemeshow chi-squared statistic was performed to measure its calibration.

Results

A total of 3323 patients were enrolled in the study. RTS-w was significantly better than ISS in predicting mortality of trauma patients (AUC: 0.934 vs.0.880, p<0.0001). However, for the other three outcomes, i.e. admission to ICU, hospital length of stay and intensive care unit length of stay, the performance of RTS-w was inferior to ISS.

Conclusions

The RTS-w is a better predictor of mortality than ISS. But its ability to predict other trauma outcomes is not as good as ISS. More studies are needed to identify the predictive ability of RTS-w for the outcomes other than mortality. Besides, updating the coefficients of the formula may make RTS-w more accurate.

Analysis of Shahid Rajaee hospital administrative data on injuries resulting from car accidents in Shiraz, Iran: 2011-2014 data

PURPOSE

The administrative data from trauma centers could serve as potential sources of invaluable information while studying epidemiologic features of car accidents. In this cross-sectional analysis of Shahid Rajaee hospital administrative data, we aimed to evaluate patients injured in car accidents in terms of age, gender, injury severity, injured body regions and hospitalization outcome in the recent four years (2011-2014).

METHODS

The hospital registry was accessed at Shiraz Trauma Research Center (Shiraz, Iran) and the admission's unit data were merged with the information gathered upon discharge. A total number of 27,222 car accident patients aged over 15 years with International Classification of Diseases 10th revision (ICD-10) external causes of injury codes (V40.9-V49.9) were analyzed. Injury severity score and injured body regions were determined based on converting ICD-10 injury codes to Abbreviated Injury Scale (AIS-98) severity codes using a domestically developed electronic algorithm. A binary logistic regression model was applied to the data to examine the contribution of all independent variables to in-hospital mortality.

RESULTS

Men accounted for 68.9% of the injuries and the male to female ratio was 2.2:1. The age of the studied population was (34 ± 15) years, with more than 77.2% of the population located in the 15-45 years old age group. Head and neck was the most commonly injured body region (39.0%) followed by extremities (27.2%). Injury severity score (ISS) was calculated for 13,152 (48.3%) patients, of whom, 80.9% had severity scores less than 9. There were 332 patients (1.2%) admitted to the intensive care units and 422 in-hospital fatalities (1.5%) were recorded during the study period. Age above 65 years [OR = 7.4, 95% CI (5.0-10.9)], ISS above 16 [OR = 9.1, 95% CI (5.5-14.9)], sustaining a thoracic injury [OR = 7.4, 95% CI (4.6-11.9)] and head injury [OR = 4.9, 95% CI (3.1-7.6)] were the most important independent predictors of death following car accidents.

CONCLUSION

Hospital administrative databases of this hospital could be used as reliable sources of information in providing epidemiologic reports of car accidents in terms of severity and outcomes. Improving the quality of recordings at hospital databases is an important initial step towards more comprehensive injury surveillance in Fars, Iran.

Mortality prediction models in the general trauma population: A systematic review

BACKGROUND

Trauma is the leading cause of death in individuals younger than 40 years. There are many different models for predicting patient outcome following trauma. To our knowledge, no comprehensive review has been performed on prognostic models for the general trauma population. Therefore, this review aimed to describe (1) existing mortality prediction models for the general trauma population, (2) the methodological quality and (3) which variables are most relevant for the model prediction of mortality in the general trauma population.

METHODS

An online search was conducted in June 2015 using Embase, Medline, Web of Science, Cinahl, Cochrane, Google Scholar and PubMed. Relevant English peer-reviewed articles that developed, validated or updated mortality prediction models in a general trauma population were included.

RESULTS

A total of 90 articles were included. The cohort sizes ranged from 100 to 1,115,389 patients, with overall mortality rates that ranged from 0.6% to 35%. The Trauma and Injury Severity Score (TRISS) was the most commonly used model. A total of 258 models were described in the articles, of which only 103 models (40%) were externally validated. Cases with missing values were often excluded and discrimination of the different prediction models ranged widely (AUROC between 0.59 and 0.98). The predictors were often included as dichotomized or categorical variables, while continuous variables showed better performance.

CONCLUSION

Researchers are still searching for a better mortality prediction model in the general trauma population. Models should 1) be developed and/or validated using an adequate sample size with sufficient events per predictor variable, 2) use multiple imputation models to address missing values, 3) use the continuous variant of the predictor if available and 4) incorporate all different types of readily available predictors (i.e., physiological variables, anatomical variables, injury cause/mechanism, and demographic variables). Furthermore, while mortality rates are decreasing, it is important to develop models that predict physical, cognitive status, or quality of life to measure quality of care.

Validation of trauma scales: ISS, NISS, RTS and TRISS for predicting mortality in a Colombian population

BACKGROUND

Our purpose was to validate the performance of the ISS, NISS, RTS and TRISS scales as predictors of mortality in a population of trauma patients in a Latin American setting.

MATERIALS AND METHODS

Subjects older than 15 years with diagnosis of trauma, lesions in two or more body areas according to the AIS and whose initial attention was at the hospital in the first 24 h were included. The main outcome was inpatient mortality. Secondary outcomes were admission to the intensive care unit, requirement of mechanical ventilation and length of stay. A logistic regression model for hospital mortality was fitted with each of the scales as an independent variable, and its predictive accuracy was evaluated through discrimination and calibration statistics.

RESULTS

Between January 2007 and July 2015, 4085 subjects were enrolled in the study. 84.2% (n = 3442) were male, the mean age was 36 years (SD = 16), and the most common trauma mechanism was blunt type (80.1%; n = 3273). The medians of ISS, NISS, TRISS and RTS were: 14 (IQR = 10-21), 17 (IQR = 11-27), 4.21 (IQR = 2.95-5.05) and 7.84 (IQR = 6.90-7.84), respectively. Mortality was 9.3%, and the discrimination for ISS, NISS, TRISS and RTS was: AUC 0.85, 0.89, 0.86 and 0.92, respectively. No one scale had appropriate calibration.

CONCLUSION

Determining the severity of trauma is an essential tool to guide treatment and establish the necessary resources for attention. In a Colombian population from a capital city, trauma scales have adequate performance for the prediction of mortality in patients with trauma.

Validation of international trauma scoring systems in urban trauma centres in India

INTRODUCTION

In the Lower-Middle Income Country setting, we validate trauma severity scoring systems, namely Injury Severity Score (ISS), New Injury Severity Scale (NISS) score, the Kampala Trauma Score (KTS), Revised Trauma Score (RTS) score and the TRauma Injury Severity Score (TRISS) using Indian trauma patients.

PATIENTS AND METHODS

From 1 September 2013 to 28 February 2015, we conducted a prospective multi-centre observational cohort study of trauma patients in four Indian university hospitals, in three megacities, Kolkata, Mumbai and Delhi. All adult patients presenting to the casualty department with a history of injury and who were admitted to inpatient care were included. The primary outcome was in-hospital mortality within 30-days of admission. The sensitivity and specificity of each score to predict inpatient mortality within 30days was assessed by the areas under the receiver operating characteristic curve (AUC). Model fit for the performance of individual scoring systems was accomplished by using the Akaike Information criterion (AIC).

RESULTS

In a registry of 8791 adult trauma patients, we had a cohort of 7197 patients eligible for the study. 4091 (56.8%)patients had all five scores available and was the sample for a complete case analysis. Over a 30-day period, the scores (AUC) was TRISS (0.82), RTS (0.81), KTS (0.74), NISS (0.65) and ISS (0.62). RTS was the most parsimonious model with the lowest AIC score. Considering overall mortality, both physiologic scores (RTS, KTS) had better discrimination and goodness-of-fit than ISS or NISS. The ability of all Injury scores to predict early mortality (24h) was better than late mortality (30day).

CONCLUSION

On-admission physiological scores outperformed the more expensive anatomy-based ISS and NISS. The retrospective nature of ISS and TRISS score calculations and incomplete imaging in LMICs precludes its use in the casualty department of LMICs. They will remain useful for outcome comparison across trauma centres. Physiological scores like the RTS and KTS will be the practical score to use in casualty departments in the urban Indian setting, to predict early trauma mortality and improve triage.

Ten year maturation period in a level-I trauma center, a cohort comparison study

Purpose

Many changes have been made to improve trauma care. Improved trauma team response and usage of a hybrid resuscitation room are examples of how this trauma center has developed. The aim was to assess how the outcome of the trauma population was influenced by the maturation.

Methods

A cohort comparison, between June 2004–July 2005 and 2014, was performed. All adult trauma patients with an Injury Severity Score (ISS) >15 were included. Variables collected were: patient demographics, mechanism of trauma, total prehospital time, pre- and inhospital trauma scores, vital signs, blood values and interventions, and physician staffed helicopter emergency medical services (P-HEMS) involvement and outcome.

Results

From June 2004 to July 2005 219, patients were admitted, and for the year 2014, this was 282 patients. The 2014 cohort was significantly older (mean age of 53.6 ± 23.8 vs 45.6 ± 22.7 years). The mean RTS did not differ. P-HEMS assists increased to 116 (13.5 %). The number of CT scans, blood transfusion, and acute trauma surgical interventions decreased. Mean LOS, ICU admission, and ICU LOS did not differ. The mortality rate, however, decreased by 7.0 %, observed and predicted survival was significantly different in favour of the 2014 cohort, with a Z-score of 4.25.

Conclusion

An increase in age is seen, though trauma scores remain comparable. The number of blood products transfused and acute trauma surgical interventions performed declines. Mortality significantly decreased and a significant difference in observed and predicted survival is seen. Showing improved trauma care in our hospital, in favour of the second period.

Factors associated with adverse events after emergency laparotomy in Cape Town, South Africa: identifying opportunities for quality improvement

BACKGROUND

Surgical outcomes research is limited in areas of the world with the greatest unmet surgical need and likely greatest variation in outcomes. Measurement alone may improve outcomes-the so-called Hawthorne effect. The purpose of this multicenter cohort study was to identify factors that are both feasible to collect and are associated with a major adverse event following a targeted procedure in Cape Town, South Africa.

METHODS

A collaborative of four acute care surgical units was formed to develop a data set with minimal data burden describing outcomes after an emergency exploratory laparotomy during a 3-mo period (February-April 2015). Controlling for patient, problem, provider, procedure and process predictors, multivariate models were built to identify risk factors for a major adverse event and higher resource use after surgery in our collaborative.

RESULTS

The outcomes of 450 exploratory laparotomies from the four participating hospitals were audited, 319 (70.9%) were for non-trauma and 131 (29.1%) were for trauma. The major adverse event rate was 15.7% (95% CI 12.6-19.4). In the multivariate analysis, factors associated with the primary outcome included age, American Society of Anesthesia score of greater than 2, bowel resection, preoperative CT scan, and a nontherapeutic laparotomy. A major adverse event was associated with all three outcomes assessing increased resource utilization.

CONCLUSIONS

This study supports the comparative outcome assessment of a high-volume or high-risk procedure as a proxy for measuring the quality of care provided in a surgical collaborative. Such an exercise can identify opportunities for quality improvement.

The Applicability of Trauma and Injury Severity Score for a Blunt Trauma Population in Korea and a Proposal of New Models Using Score Predictors

PURPOSE

The purpose of this study was to verify the utility of existing Trauma and Injury Severity Score (TRISS) coefficients and to propose a new prediction model with a new set of TRISS coefficients or predictors.

MATERIALS AND METHODS

Of the blunt adult trauma patients who were admitted to our hospital in 2014, those eligible for Korea Trauma Data Bank entry were selected to collect the TRISS predictors. The study data were input into the TRISS formula to obtain "probability of survival" values, which were examined for consistency with actual patient survival status. For TRISS coefficients, Major Trauma Outcome Study-derived values revised in 1995 and National Trauma Data Bank-derived and National Sample Project-derived coefficients revised in 2009 were used. Additionally, using a logistic regression method, a new set of coefficients was derived from our medical center's database. Areas under the receiver operating characteristic (ROC) curve (AUC) for each prediction ability were obtained, and a pairwise comparison of ROC curves was performed.

RESULTS

In the statistical analysis, the AUCs (0.879-0.899) for predicting outcomes were lower than those of other countries. However, by adjusting the TRISS score using a continuous variable rather than a code for age, we were able to achieve higher AUCs [0.913 (95% confidence interval, 0.899 to 0.926)].

CONCLUSION

These results support further studies that will allow a more accurate prediction of prognosis for trauma patients. Furthermore, Korean TRISS coefficients or a new prediction model suited for Korea needs to be developed using a sufficiently sized sample.

Does Mode of Transport Confer a Mortality Benefit in Trauma Patients? Characteristics and Outcomes at an Ontario Lead Trauma Hospital

Objectives

Evidence-based guidelines regarding the optimal mode of transport for trauma patients from scene to trauma centre are lacking. The purpose of this study was to investigate the relationship between trauma patient outcomes and mode of transport at a single Ontario Level I Trauma Centre, and specifically to investigate if the mode of transport confers a mortality benefit.

Methods

A historical, observational cohort study was undertaken to compare rotor-wing and ground transported patients. Captured data included demographics, injury severity, temporal and mortality variables. TRISS-L analysis was performed to examine mortality outcomes.

Results

387 rotor-wing transport and 2,759 ground transport patients were analyzed over an 18-year period. Rotor-wing patients were younger, had a higher Injury Severity Score, and had longer prehospital transport times. Mechanism of injury was similarly distributed between groups. After controlling for heterogeneity with TRISS-L analysis, the mortality of rotor-wing patients was found to be lower than predicted mortality, whereas the converse was found with ground patients.

Conclusion

Rotor-wing and ground transported trauma patients represent heterogeneous populations. Accounting for these differences, rotor-wing patients were found to outperform their predicted mortality, whereas ground patients underperformed predictions.

Do male and female trauma patients receive the same prehospital care?: an observational follow-up study

BACKGROUND

Trauma-related mortality can be lowered by efficient prehospital care. Less is known about whether gender influences the prehospital trauma care provided. The aim of this study was to explore gender-related differences in prehospital trauma care of severely injured trauma patients, with a special focus on triage, transportation, and interventions.

METHODS

We performed a retrospective observational study based on local trauma registries and hospital and ambulance records in Stockholm County, Sweden. A total of 383 trauma patients (279 males and 104 females) > 15 years of age with an Injury Severity Score (ISS) of > 15 transported to emergency care hospitals in the Stockholm area were included.

RESULTS

Male patients had a 2.75 higher odds ratio (95 % CI, 1.2-6.2) for receiving the highest prehospital priority compared to females on controlling for injury mechanism and vital signs on scene. No significant difference between genders was detected regarding other aspects of the prehospital care provided.

CONCLUSIONS

This study indicated that prehospital prioritization among severely injured late adolescent and adult trauma patients differs between genders. Knowledge of a more diffuse presentation of symptoms in female trauma patients despite severe injury may help to adapt and improve prehospital trauma care for this group.

Desempenho dos ajustes do Trauma and Injury Severity Score (TRISS): revisão integrativa

RESUMO Objetivo identificar estudos que realizaram ajustes na equação do Trauma and InjurySeverity Score (TRISS) e compararam a capacidade discriminatória da equação modificada com a original. Método Revisão integrativa de pesquisas publicadas entre 1990 e 2014 nas bases de dados LILACS, MEDLINE, PubMed e SciELO utilizando-se a palavra TRISS. Resultados foram incluídos 32 estudos na revisão. Dos 67 ajustes de equações do TRISS identificados, 35 (52,2%) resultaram em melhora na acurácia do índice para predizer a probabilidade de sobrevida de vítimas de trauma. Ajustes dos coeficientes do TRISS à população de estudo foram frequentes, mas nem sempre melhoraram a capacidade preditiva dos modelos analisados. A substituição de variáveis fisiológicas do Revised Trauma Score (RTS) e modificações do Injury Severity Score (ISS) na equação original tiveram desempenho variado. A mudança na forma de inclusão da idade na equação, assim como a inserção do gênero, comorbidades e mecanismo do trauma apresentaram tendência de melhora do desempenho do TRISS. Conclusão Diferentes propostas de ajustes no TRISS foram identificadas nesta revisão e indicaram, principalmente, fragilidades do RTS no modelo original e necessidade de alteração da forma de inclusão da idade na equação para melhora da capacidade preditiva do índice.

Predicting in-hospital death among patients injured in traffic crashes in Saudi Arabia

INTRODUCTION

Traffic-related injuries are a major cause of premature death in developing countries. Saudi Arabia has struggled with high rates of traffic-related deaths for decades, yet little is known about health outcomes of motor vehicle victims seeking medical care. This study aims to develop and validate a model to predict in-hospital death among patients admitted to a large-urban trauma centre in Saudi Arabia for treatment following traffic-related crashes.

METHODS

The analysis used data from King Abdulaziz Medical City (KAMC) in Riyadh, Saudi Arabia. During the study period 2001-2010, 5325 patients met the inclusion criteria of being injured in traffic crashes and seen in the Emergency Department (ED) and/or admitted to the hospital. Backward stepwise logistic regression, with in-hospital death as the outcome, was performed. Variables with p<0.05 were included in the final model. The Bayesian Information Criterion (BIC) was employed to identify the most parsimonious model. Model discrimination was evaluated by the C-statistic and calibration by the Hosmer-Lemeshow Goodness of Fit statistic. Bootstrapping was used to assess overestimation of model performance and obtain a corrected C-statistic.

RESULTS

457 (8.5%) patients died at some time during their treatment in the ED or hospital. Older age, the Triage-Revised Trauma Scale (T-RTS), and Injury Severity Score were independent risk factors for in-hospital death: T-RTS was best modelled with linear and quadratic terms to capture a flattening of the relationship to death in the more severe range. The model showed excellent discrimination (C-statistic=0.96) and calibration (H-L statistic 4.29 [p>0.05]). Internal bootstrap validation gave similar results (C-statistic=0.96).

CONCLUSIONS

The proposed model can predict in-hospital death accurately. It can facilitate the triage process among injured patients, and identify unexpected deaths in order to address potential pitfalls in the care process. Conversely, by identifying high-risk patients, strategies can be developed to improve trauma care for these patients and reduce case-fatality. This is the first study to develop and validate a model to predict traffic-related mortality in a developing country. Future studies from developing countries can use this study as a reference for case fatality achievable for different risk profiles at a well-equipped trauma centre.

Update of the trauma risk adjustment model of the TraumaRegister DGU™: the Revised Injury Severity Classification, version II

INTRODUCTION

The TraumaRegister DGU™ (TR-DGU) has used the Revised Injury Severity Classification (RISC) score for outcome adjustment since 2003. In recent years, however, the observed mortality rate has fallen to about 2% below the prognosis, and it was felt that further prognostic factors, like pupil size and reaction, should be included as well. Finally, an increasing number of cases did not receive a RISC prognosis due to the missing values. Therefore, there was a need for an updated model for risk of death prediction in severely injured patients to be developed and validated using the most recent data.

METHODS

The TR-DGU has been collecting data from severely injured patients since 1993. All injuries are coded according to the Abbreviated Injury Scale (AIS, version 2008). Severely injured patients from Europe (ISS ≥ 4) documented between 2010 and 2011 were selected for developing the new score (n = 30,866), and 21,918 patients from 2012 were used for validation. Age and injury codes were required, and transferred patients were excluded. Logistic regression analysis was applied with hospital mortality as the dependent variable. Results were evaluated in terms of discrimination (area under the receiver operating characteristic curve, AUC), precision (observed versus predicted mortality), and calibration (Hosmer-Lemeshow goodness-of-fit statistic).

RESULTS

The mean age of the development population was 47.3 years; 71.6% were males, and the average ISS was 19.3 points. Hospital mortality rate was 11.5% in this group. The new RISC II model consists of the following predictors: worst and second-worst injury (AIS severity level), head injury, age, sex, pupil reactivity and size, pre-injury health status, blood pressure, acidosis (base deficit), coagulation, haemoglobin, and cardiopulmonary resuscitation. Missing values are included as a separate category for every variable. In the development and the validation dataset, the new RISC II outperformed the original RISC score, for example AUC in the development dataset 0.953 versus 0.939.

CONCLUSIONS

The updated RISC II prognostic score has several advantages over the previous RISC model. Discrimination, precision and calibration are improved, and patients with partial missing values could now be included. Results were confirmed in a validation dataset.

In-hospital mortality among patients injured in motor vehicle crashes in a Saudi Arabian hospital relative to large U.S. trauma centers

Background

Traffic-related fatalities are a leading cause of premature death worldwide. According to the 2012 report the Global Burden of Disease 2010, traffic injuries ranked 8th as a cause of death in 2010, compared to 10th in 1990. Saudi Arabia is estimated to have an overall traffic fatality rate more than double that of the U.S., but it is unknown whether mortality differences also exist for injured patients seeking medical care. We aim to compare in-hospital mortality between Saudi Arabia and the United States, adjusting for severity and demographic variables.

Methods

The analysis included 485,611 patients from the U.S. National Trauma Data Bank (NTDB) and 5,290 patients from a trauma registry at King Abdulaziz Medical City (KAMC) in Riyadh, Saudi Arabia. For comparability, we restricted our sample to NTDB data from level-I public trauma centers (≥400 beds) in the U.S. Multiple logistic regression analyses were performed to evaluate the effect of setting (KAMC vs. NTDB) on in-hospital mortality after adjusting for age, sex, Triage-Revised Scale (T-RTS), Injury Severity Score (ISS), mechanism of injury, hypotension, surgery and head injuries. Interactions between setting and ISS, and predictors were also evaluated.

Results

Injured patients in the Saudi registry were more likely to be males, and younger than those from the NTDB. Patients at the Saudi hospital were at higher risk of in-hospital death than their U.S. counterparts. In the highest severity group (ISSs, 25–75), the odds ratio of in-hospital death in KAMC versus NTDB was 5.0 (95% CI 4.3-5.8). There were no differences in mortality between KAMC and NTDB among patients from lower ISS groups (ISSs, 1–8, 9–15, and 16–24).

Conclusions

Patients who are severely injured following traffic crash injuries in Saudi Arabia are significantly more likely to die in the hospital than comparable patients admitted to large U.S. trauma centers. Further research is needed to identify reasons for this disparity and strategies for improving the care of patients severely injured in traffic crashes in Saudi Arabia.

Electronic supplementary material

The online version of this article (doi:10.1186/s40621-014-0021-4) contains supplementary material, which is available to authorized users.

Epidemiology of trauma in an acute care hospital in Singapore

Background:

Trauma injury is the leading cause of mortality and hospitalization worldwide and the leading cause of potential years of productive life lost. Patients with multiple injuries are prevalent, increasing the complexity of trauma care and treatment. Better understanding of the nature of trauma risk and outcome could lead to more effective prevention and treatment strategies.

Materials and Methods:

A retrospective review of 1178 trauma patients with Injury Severity Score (ISS) ≥ 9, who were admitted to the Acute and Emergency Care of an acute care hospital between January 2011 and December 2012. The statistical analysis included calculation of percentages and proportions and application of test of significance using Pearson's chi-square test or Fisher's exact test where appropriate.

Results:

Over the study period, 1178 patients were evaluated, 815 (69.2%) males and 363 (30.8%) females. The mean age of patients was 52.08 ± 21.83 (range 5-100) years. Falls (604; 51.3%) and road traffic accidents (465; 39.5%) were the two most common mechanisms of injury. Based on the three most common mechanisms of injury, i.e. fall on the same level, fall from height, and road traffic accident, the head region (484; 45.40%) was the most commonly injured in the body, followed by lower limbs (377; 35.37%) and thorax (299; 28.05%).

Conclusion:

Fall was the leading cause of injury among the elderly population with road traffic injuries being the leading cause among the younger group. There is a need to address the issues of injury control and prevention in these areas.