Base Deficit-Based Predictive Modeling of Outcome in Trauma Patients Admitted to Intensive Care Units in Dutch Trauma Centers

Recovery to normal vital functions and acid-base status after a severe trauma in Level I versus Level II Trauma Centres

PURPOSE

In the Netherlands, approximately 70% of severely injured patients (ISS ≥ 16) are transported directly to a Level I trauma center. This study compared the time needed to return to normal vital parameters and normal acid-base status in severely injured patients and some in-hospital processes in Level I versus Level II trauma centers.

METHODS

This retrospective cohort study included all adult severely injured patients or adult trauma patients admitted to the intensive care unit between 2015 and 2020 in a Dutch trauma region. The primary endpoint was time until normal vital parameters and acid-base status. Secondary endpoints were complication rate, hospital length of stay, emergency department length of stay, and time until a computed tomography (CT) scan.

RESULTS

A total of 2345 patients were included. Patients admitted to a Level I trauma center had a significantly higher rate of normalization of vital parameters over time (HR 1.51). There was no significant difference in normalization rate of the acid-base status over time (HR 1.10). In Level I trauma centers, time spent at the emergency department and time until the CT scan was significantly shorter (respectively, β - 38 min and β - 77 min), and the complication rate was significantly lower (OR 0.35).

CONCLUSION

Severely injured patients admitted to a Level I trauma center require less time to normalize their vital functions. Level I centers are better equipped, resulting in better in-hospital processes with shorter time at the emergency department and shorter time until a CT scan.

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.

Point-of-Care Analysis of Neutrophil Phenotypes: A First Step Toward Immuno-Based Precision Medicine in the Trauma ICU

Supplemental Digital Content is available in the text.

Objectives:

The amount of tissue damage and the amplitude of the immune response after trauma are related to the development of infectious complications later on. Changes in the neutrophil compartment can be used as read out of the amplitude of the immune response after trauma. The study aim was to test whether 24/7 point-of-care analysis of neutrophil marker expression by automated flow cytometry can be achieved after trauma.

Design:

A prospective cohort study was performed. Polytrauma patients who developed infectious complications were compared with polytrauma patients who did not develop infectious complications.

Setting:

The study was performed in a level 1 trauma center.

Patients:

All trauma patients presented in the trauma bay were included.

Interventions:

An extra blood tube was drawn from all patients. Thereafter, a member of the trauma team placed the blood tube in the fully automated flow cytometer, which was located in the corner of the trauma room. Next, a modified and tailored protocol for this study was automatically performed.

Main Results:

The trauma team was able to successfully start the point-of-care automated flow cytometry analysis in 156 of 164 patients, resulting in a 95% success rate. Polytrauma patients who developed infectious complications had a significantly higher %CD16^dim/CD62L^bright neutrophils compared with polytrauma patients who did not develop infectious complications (p = 0.002). Area under the curve value for %CD16^dim/CD62L^bright neutrophils is 0.90 (0.83–0.97).

Conclusions:

This study showed the feasibility of the implementation of a fully automated point-of-care flow cytometry system for the characterization of the cellular innate immune response in trauma patients. This study supports the concept that the assessment of CD16^dim/CD62L^bright neutrophils can be used for early detection of patients at risk for infectious complications. Furthermore, this can be used as first step toward immuno-based precision medicine of polytrauma patients at the ICU.

Factors Associated with Mortality and Amputation Caused by Necrotizing Soft Tissue Infections of the Upper Extremity: A Retrospective Cohort Study

Background

It is unclear what the exact short-term outcomes of necrotizing soft tissue infections (NSTIs), also known and necrotizing fasciitis of the upper extremity, are and whether these are comparable to other anatomical regions. Therefore, the aim of this study is to assess factors associated with mortality within 30-days and amputation in patients with upper extremity NSTIs.

Methods

A retrospective study over a 20-year time period of all patients treated for NSTIs of the upper extremity was carried out. The primary outcomes were the 30-day mortality rate and the amputation rate in patients admitted to the hospital for upper extremity NSTIs.

Results

Within 20 years, 122 patients with NSTIs of the upper extremity were identified. Thirteen patients (11%) died and 17 patients (14%) underwent amputation. Independent risk factors for mortality were an American Society of Anesthesiologists (ASA) classification of 3 or higher (OR 9.26, 95% CI 1.64–52.31) and a base deficit of 3 meq/L or greater (OR 10.53, 95% CI 1.14–96.98). The independent risk factor for amputation was a NSTI of the non-dominant arm (OR 3.78, 95% CI 1.07–13.35). Length of hospital stay was 15 (IQR 9–21) days.

Conclusion

Upper extremity NSTIs have a relatively low mortality rate, but a relatively high amputation rate compared to studies assessing NSTIs of all anatomical regions. ASA classification and base deficit at admission predict the prognosis of patients with upper extremity NSTIs, while a NSTI of the non-dominant side is a risk factor for limb loss.

Machine Learning Models of Survival Prediction in Trauma Patients

BACKGROUND

We aimed to build a model using machine learning for the prediction of survival in trauma patients and compared these model predictions to those predicted by the most commonly used algorithm, the Trauma and Injury Severity Score (TRISS).

METHODS

Enrolled hospitalized trauma patients from 2009 to 2016 were divided into a training dataset (70% of the original data set) for generation of a plausible model under supervised classification, and a test dataset (30% of the original data set) to test the performance of the model. The training and test datasets comprised 13,208 (12,871 survival and 337 mortality) and 5603 (5473 survival and 130 mortality) patients, respectively. With the provision of additional information such as pre-existing comorbidity status or laboratory data, logistic regression (LR), support vector machine (SVM), and neural network (NN) (with the Stuttgart Neural Network Simulator (RSNNS)) were used to build models of survival prediction and compared to the predictive performance of TRISS. Predictive performance was evaluated by accuracy, sensitivity, and specificity, as well as by area under the curve (AUC) measures of receiver operating characteristic curves.

RESULTS

In the validation dataset, NN and the TRISS presented the highest score (82.0%) for balanced accuracy, followed by SVM (75.2%) and LR (71.8%) models. In the test dataset, NN had the highest balanced accuracy (75.1%), followed by the TRISS (70.2%), SVM (70.6%), and LR (68.9%) models. All four models (LR, SVM, NN, and TRISS) exhibited a high accuracy of more than 97.5% and a sensitivity of more than 98.6%. However, NN exhibited the highest specificity (51.5%), followed by the TRISS (41.5%), SVM (40.8%), and LR (38.5%) models.

CONCLUSIONS

These four models (LR, SVM, NN, and TRISS) exhibited a similar high accuracy and sensitivity in predicting the survival of the trauma patients. In the test dataset, the NN model had the highest balanced accuracy and predictive specificity.

Derivation and validation of different machine-learning models in mortality prediction of trauma in motorcycle riders: a cross-sectional retrospective study in southern Taiwan

OBJECTIVES

This study aimed to build and test the models of machine learning (ML) to predict the mortality of hospitalised motorcycle riders.

SETTING

The study was conducted in a level-1 trauma centre in southern Taiwan.

PARTICIPANTS

Motorcycle riders who were hospitalised between January 2009 and December 2015 were classified into a training set (n=6306) and test set (n=946). Using the demographic information, injury characteristics and laboratory data of patients, logistic regression (LR), support vector machine (SVM) and decision tree (DT) analyses were performed to determine the mortality of individual motorcycle riders, under different conditions, using all samples or reduced samples, as well as all variables or selected features in the algorithm.

PRIMARY AND SECONDARY OUTCOME MEASURES

The predictive performance of the model was evaluated based on accuracy, sensitivity, specificity and geometric mean, and an analysis of the area under the receiver operating characteristic curves of the two different models was carried out.

RESULTS

In the training set, both LR and SVM had a significantly higher area under the receiver operating characteristic curve (AUC) than DT. No significant difference was observed in the AUC of LR and SVM, regardless of whether all samples or reduced samples and whether all variables or selected features were used. In the test set, the performance of the SVM model for all samples with selected features was better than that of all other models, with an accuracy of 98.73%, sensitivity of 86.96%, specificity of 99.02%, geometric mean of 92.79% and AUC of 0.9517, in mortality prediction.

CONCLUSION

ML can provide a feasible level of accuracy in predicting the mortality of motorcycle riders. Integration of the ML model, particularly the SVM algorithm in the trauma system, may help identify high-risk patients and, therefore, guide appropriate interventions by the clinical staff.

Performance of new adjustments to the TRISS equation model in developed and developing countries

BACKGROUND

The Trauma and Injury Severity Score (TRISS) has been criticized for being based on data from the USA and Canada-high-income countries-and therefore, it may not be applicable to low-income and middle-income countries. The present study evaluated the accuracy of three adjustments to the TRISS equation model (NTRISS-like; TRISS SpO2; NTRISS-like SpO2) in a high-income and a middle-income country to compare their performance when derived and applied to different groups.

METHODS

This was a retrospective study of trauma patients admitted to two institutions: a university medical center in São Paulo, Brazil (a middle-income country), and a level 1 university trauma center in San Diego, USA (a high-income country). Patients were admitted between January 1, 2006, and December 31, 2010. The subjects were 2416 patients from Brazil and 8172 patients from the USA. All equations had adjusted coefficients for São Paulo and San Diego and for blunt and penetrating trauma. Receiver operating characteristic (ROC) curves were used to evaluate performance of the models.

RESULTS

Regardless of the population where the equation was generated, it performed better when applied to patients in the USA (AUC from 0.911 to 0.982) compared to patients in Brazil (AUC from 0.840 to 0.852). When the severity was considered and homogenized, the performance of equations were similar to both application in the USA and Brazil.

CONCLUSIONS

Survival probability models showed better performance when applied in data collected in the high-income countries (HIC) regardless the country they were derived. The severity is an important factor to consider when using non-adjusted survival probability models for the local population. Adjusted models for severely traumatized patients better predict survival probability in less severely traumatized populations. Other factors besides physiological and anatomical data may impact final outcomes and should be identified in each environment if they are to be used in the development of the trauma care performance improvement process in middle-income countries.

Base deficit is a predictor of mortality in organophosphate insecticide poisoning

OBJECTIVE

Organophosphate insecticide (OPI) self-poisoning is a major medical problem in many countries. Several studies have demonstrated that the base deficit (BD) is a prognostic tool that is correlated with the severity of injury and predicted mortality, particularly in trauma patients. Here, we aimed to investigate the prognostic significance of BD in OPI poisoning.

METHODS

This retrospective observational study was conducted between January 1, 2006, and January 31, 2015, at a single emergency department (ED). The BD values were divided into quartiles according to the number of patients: 3 mEq/L or less, 3-5.9 mEq/L, 6-9.9 mEq/L, and 10 mEq/L or greater. Survival at 30 days from ED admission was estimated using the Kaplan-Meier survival analysis.

RESULTS

Among 154 patients, 31 died, yielding a mortality of 20.1%. The highest BD quartile (≥ 10 mEq/L) and the 6-9.9 mEq/L group were associated with an increased risk of 30-day mortality. Patients with a BD of 10 mEq/L or greater had a 5.85-fold higher risk of 30-day mortality and patients with a BD of 6-9.9 mEq/L had a 5.40-fold higher risk of 30-day mortality compared to patients with a BD of 3 mEq/L or less. The area under the curves of the BD and the Acute Physiology and Chronic Health Evaluation II score for mortality were 0.748 (95% confidence interval (CI), 0.660-0.835) and 0.852 (95% CI, 0.789-0.915), respectively.

CONCLUSIONS

This study showed that the BD is a predictor of 30-day mortality in patients with OPI poisoning.

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.

Comparison of Trauma and Injury Severity Score model with alternative approach in outcome prediction in trauma using National Trauma Database in Malaysia

Objective

The coefficients for the Trauma and Injury Severity Score are derived from the Major Trauma Outcome Study (MTOS) in North America, so the applicability of the MTOS-Trauma and Injury Severity Score (TRISS) in different populations remains challenging. This study proposed an alternate TRISS model called National Trauma Database-TRISS, where the coefficients were derived from the National Trauma Database (NTrD) in Malaysia.

Methods

This study utilised data derived from the National Trauma Database in Malaysia with 70% of the cases being used to develop the NTrD-TRISS model, while model validation was conducted based on the remaining 30% of cases. Fractional polynomial method was applied to correct the non-linearity in the logit of Injury Severity Score (ISS) and New Injury Severity Score (NISS). The predictive validity of the NTrD-TRISS model was compared with the MTOS-TRISS model. The predictive validity of these models was further examined with ISS and NISS.

Results

A total of 5857 major trauma cases reported to 13 trauma centres located throughout Malaysia for patients aged 16 and above were included in the study. The outcome prediction in trauma from NTrD-TRISS model was found to be more accurate compared to the MTOS-TRISS model.

Conclusions

This study has ascertained the applicability of the MTOS-TRISS model in Malaysia. The outcome prediction in trauma from the NTrD-TRISS was found to outperform the MTOS-TRISS model. Given the complex computational nature of ISS, the MTOS-TRISS and NTrD-TRISS models with NISS were recommended for future practical usage.

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.

Validation of a base deficit-based trauma prediction model and comparison with TRISS and ASCOT

BACKGROUND

Base deficit provides a more objective indicator of physiological stress following injury as compared with vital signs constituting the revised trauma score (RTS). We have previously developed a base deficit-based trauma survival prediction model [base deficit and injury severity score model (BISS)], in which RTS was replaced by base deficit as a measurement of physiological imbalance.

PURPOSE

To externally validate BISS in a large cohort of trauma patients and to compare its performance with established trauma survival prediction models including trauma and injury severity score (TRISS) and a severity characterization of trauma (ASCOT). Moreover, we examined whether the predictive accuracy of BISS model could be improved by replacement of injury severity score (ISS) by new injury severity score (NISS) in the BISS model (BNISS).

METHODS

In this retrospective, observational study, clinical data of 3737 trauma patients (age ≥15 years) admitted consecutively from 2003 to 2007 were obtained from a prospective trauma registry to calculate BISS, TRISS, and ASCOT models. The models were evaluated in terms of discrimination [area under curve (AUC)] and calibration.

RESULTS

The in-hospital mortality rate was 8.1 %. The discriminative performance of BISS to predict survival was similar to that of TRISS and ASCOT [AUCs of 0.883, 95 % confidence interval (CI) 0.865-0.901 for BISS, 0.902, 95 % CI 0.858-0.946 for TRISS and 0.864, 95 % CI 0.816-0.913 for ASCOT]. Calibration tended to be optimistic in all three models. The updated BNISS had an AUC of 0.918 indicating that substitution of ISS with NISS improved model performance.

CONCLUSIONS

The BISS model, a base deficit-based trauma model for survival prediction, showed equivalent performance as compared with that of TRISS and ASCOT and may offer a more simplified calculation method and a more objective assessment. Calibration of BISS model was, however, less good than that of other models. Replacing ISS by NISS can considerably improve model accuracy, but further confirmation is needed.

The quest for a universal definition of polytrauma: a trauma registry-based validation study

BACKGROUND

A pilot validation recommended defining polytrauma as patients with an Abbreviated Injury Scale (AIS) score greater than 2 in at least two Injury Severity Score (ISS) body regions (2 × AIS score > 2). This study aimed to validate this definition on larger data set. We hypothesized that patients defined by the 2 × AIS score > 2 cutoff have worse outcomes and use more resources than those without 2 × AIS score > 2 and that this would therefore be a better definition of polytrauma.

METHODS

Patients injured between 2009 and 2011, with complete documentation of AIS by New South Wales Trauma Registry and 16 years and older were selected. Age and sex were obtained in addition to outcomes of ISS, hospital length of stay (LOS), intensive care unit (ICU) admission, ICU LOS, and mortality. We compared demographic characteristics and outcomes between patients with ISS greater than 15 who did and did not meet the 2 × AIS score > 2 definition. We then undertook regression analyses (logistic regression for binary outcomes [ICU admission and death] and linear regression for hospital and ICU LOS) to compare outcomes for patients with and without 2 × AIS score > 2, adjusting for sex and age categories.

RESULTS

In the adjusted analyses, patients with 2 × AIS score > 2 had twice the odds of being admitted to the ICU compared with those without 2 × AIS score > 2 (odds ratio, 2.5; 95% confidence interval [CI], 2.2-2.8) and 1.7 times the odds of dying (95% CI, 1.4-2.0; p < 0.001 for both models). Patients with 2 × AIS score > 2 also had a mean difference of 1.5 days longer stay in the hospital compared with those without 2 × AIS score > 2 (95% CI, 1.4-1.7) and 1.6 days longer ICU stay (95% CI, 1.4-1.8; p < 0.001 for all models).

CONCLUSION

Patients with 2 × AIS score > 2 had higher mortality, more frequent ICU admissions, and longer hospital and ICU stay than those without 2 × AIS score > 2 and represents a superior definition to the definitions for polytrauma currently in use.

LEVEL OF EVIDENCE

Diagnostic test/ criteria, level III.

External validation of the Emergency Trauma Score for early prediction of mortality in trauma patients

OBJECTIVES

The Emergency Trauma Score has been developed for early estimation of mortality risk in adult trauma patients with an Injury Severity Score of 16 or higher. Emergency Trauma Score combines four early predictors available at the trauma resuscitation room: age, Glasgow Coma Scale, base excess, and prothrombin time. Our goal was to validate the Emergency Trauma Score in two large external cohorts. As the Injury Severity Score is not accurately known at the time patients present at the resuscitation room, we evaluated the performance of Emergency Trauma Score in all trauma patients.

DESIGN

External validation study using data from two prospectively collected trauma registries.

SETTING

Two academic level 1 trauma centers.

PATIENTS

Adult patients admitted to the hospital after treatment at the trauma resuscitation room.

INTERVENTION

Calibration and discrimination of the original Emergency Trauma Score were assessed within each cohort separately.

MEASUREMENT AND MAIN RESULTS

A total of 4,418 consecutive patients were evaluated. Discrimination was good in both validation cohorts, with areas under the receiver-operating curve curves that were even higher (0.94 and 0.92, respectively) than that in the original cohort (0.83). Predicted mortality was systematically too high compared with actual mortality in patients with low-to-medium expected risk (< 25%). Calibration improved in the lower expected risk range after exclusion of patients with Injury Severity Score less than 16.

CONCLUSIONS

The Emergency Trauma Score model performs well in discriminating between trauma patients who will survive and who will not. If applied to all trauma patients, predicted mortality risks are too high in the low-risk category.

Base excess as a predictor for injury severity in pediatric trauma patients

BACKGROUND

Base excess is considered a predictor of mortality and severity of injury in trauma patients. Base excess had been widely examined in different settings. Only few studies have examined the role of base excess in pediatric trauma patients.

OBJECTIVE

To evaluate the value of admission base excess in pediatric trauma patients with respect to intensive care unit (ICU) admission rate and length of hospital stay.

METHODS

A retrospective study of pediatric trauma patients was conducted at a Level II trauma center. All patients aged 0-16 years for which a trauma team was activated over the years 2006-2009 were included. Study database included admission base excess, mechanism of injury, location and nature of injury, injury severity score, length of hospital stay, and ICU admission.

RESULTS

The study group consisted of 359 patients. There was a weak linear correlation between admission base excess, length of stay in the hospital, and ICU admission. Base excess seemed to show a stronger correlation for the youngest age group (0-6 years) and no correlation for the middle age group. There was a positive but weak correlation (R Spearman = 0.26) between admission base excess and Injury Severity Score (ISS). However, 40% of the children with an ISS score >25 had normal admission base excess values. The area under the curve of the receiver operating characteristic curves of base excess for predicting ICU admission was 0.66.

CONCLUSIONS

The admission base excess in pediatric trauma patients seems to be a weak prognostic factor in our facility.

Systematic review of predictive performance of injury severity scoring tools

Many injury severity scoring tools have been developed over the past few decades. These tools include the Injury Severity Score (ISS), New ISS (NISS), Trauma and Injury Severity Score (TRISS) and International Classification of Diseases (ICD)-based Injury Severity Score (ICISS). Although many studies have endeavored to determine the ability of these tools to predict the mortality of injured patients, their results have been inconsistent. We conducted a systematic review to summarize the predictive performances of these tools and explore the heterogeneity among studies. We defined a relevant article as any research article that reported the area under the Receiver Operating Characteristic curve as a measure of predictive performance. We conducted an online search using MEDLINE and Embase. We evaluated the quality of each relevant article using a quality assessment questionnaire consisting of 10 questions. The total number of positive answers was reported as the quality score of the study. Meta-analysis was not performed due to the heterogeneity among studies. We identified 64 relevant articles with 157 AUROCs of the tools. The median number of positive answers to the questionnaire was 5, ranging from 2 to 8. Less than half of the relevant studies reported the version of the Abbreviated Injury Scale (AIS) and/or ICD (37.5%). The heterogeneity among the studies could be observed in a broad distribution of crude mortality rates of study data, ranging from 1% to 38%. The NISS was mostly reported to perform better than the ISS when predicting the mortality of blunt trauma patients. The relative performance of the ICSS against the AIS-based tools was inconclusive because of the scarcity of studies. The performance of the ICISS appeared to be unstable because the performance could be altered by the type of formula and survival risk ratios used. In conclusion, high-quality studies were limited. The NISS might perform better in the mortality prediction of blunt injuries than the ISS. Additional studies are required to standardize the derivation of the ICISS and determine the relative performance of the ICISS against the AIS-based tools.

The sequential trauma score - a new instrument for the sequential mortality prediction in major trauma

BACKGROUND

There are several well established scores for the assessment of the prognosis of major trauma patients that all have in common that they can be calculated at the earliest during intensive care unit stay. We intended to develop a sequential trauma score (STS) that allows prognosis at several early stages based on the information that is available at a particular time.

STUDY DESIGN

In a retrospective, multicenter study using data derived from the Trauma Registry of the German Trauma Society (2002-2006), we identified the most relevant prognostic factors from the patients basic data (P), prehospital phase (A), early (B1), and late (B2) trauma room phase. Univariate and logistic regression models as well as score quality criteria and the explanatory power have been calculated.

RESULTS

A total of 2,354 patients with complete data were identified. From the patients basic data (P), logistic regression showed that age was a significant predictor of survival (AUC(model P), area under the curve = 0.63). Logistic regression of the prehospital data (A) showed that blood pressure, pulse rate, Glasgow coma scale (GCS), and anisocoria were significant predictors (AUC(model A) = 0.76; AU(model P + A) = 0.82). Logistic regression of the early trauma room phase (B1) showed that peripheral oxygen saturation, GCS, anisocoria, base excess, and thromboplastin time to be significant predictors of survival (AUC(model B1) = 0.78; AUC(model P + A + B1) = 0.85). Multivariate analysis of the late trauma room phase (B2) detected cardiac massage, abbreviated injury score (AIS) of the head > or = 3, the maximum AIS, the need for transfusion or massive blood transfusion, to be the most important predictors (AUC(model B2) = 0.84; AUC(final model P + A + B1 + B2) = 0.90). The explanatory power - a tool for the assessment of the relative impact of each segment to mortality - is 25% for P, 7% for A, 17% for B1 and 51% for B2. A spreadsheet for the easy calculation of the sequential trauma score is available at: www.sequential-trauma-score.com

CONCLUSIONS

This score is the first sequential, dynamic score to provide a prognosis for patients with blunt major trauma at several points in time. With every additional piece of information the precision increases. The medical team has a simple, useful tool to identify patients at high risk and to predict the prognosis of an individual patient with major trauma very early, quickly and precisely.

Influence of routine computed tomography on predicted survival from blunt thoracoabdominal trauma

Introduction

Many scoring systems have been proposed to predict the survival of trauma patients. This study was performed to evaluate the influence of routine thoracoabdominal computed tomography (CT) on the predicted survival according to the trauma injury severity score (TRISS).

Patients and methods

1,047 patients who had sustained a high-energy blunt trauma over a 3-year period were prospectively included in the study. All patients underwent physical examination, conventional radiography of the chest, thoracolumbar spine and pelvis, abdominal sonography, and routine thoracoabdominal CT. From this group with routine CT, we prospectively defined a selective CT (sub)group for cases with abnormal physical examination and/or conventional radiography and/or sonography. Type and extent of injuries were recorded for both the selective and the routine CT groups. Based on the injuries found by the two different CT algorithms, we calculated the injury severity scores (ISS) and predicted survivals according to the TRISS methodology for the routine and the selective CT algorithms.

Results

Based on injuries detected by the selective CT algorithm, the mean ISS was 14.6, resulting in a predicted mortality of 12.5%. Because additional injuries were found by the routine CT algorithm, the mean ISS increased to 16.9, resulting in a predicted mortality of 13.7%. The actual observed mortality was 5.4%.

Conclusion

Routine thoracoabdominal CT in high-energy blunt trauma patients reveals more injuries than a selective CT algorithm, resulting in a higher ISS. According to the TRISS, this results in higher predicted mortalities. Observed mortality, however, was significantly lower than predicted. The predicted survival according to MTOS seems to underestimate the actual survival when routine CT is used.

Does thoracic injury impair the predictive value of base deficit in trauma patients?

INTRODUCTION: Base deficit (BD) has been shown to be a valuable indicator to be predictive of complications and mortality after trauma. Arterial carbon dioxide (PaCO(2)) may be influenced by thoracic injuries, potentially diminishing the predictive value of BD. Therefore, the aim of this study was to assess the predictive value of admission BD for mortality and complications in trauma patients with thoracic injuries. METHODS: By a prospective database analysis of patients with an injury to the chest admitted to the University Medical Center Utrecht between 2000 and 2004 were studied. All patients with a blood gas analyses were included. Absolute BD was used for analyses. Clinical outcome parameters were recorded. RESULTS: The BD was higher in the non-surviving patients compared to the survivors (7.5 vs. 3.8, p<0.001). Mortality rate of patients with an admission BD of >/=6 was increased in thoracic trauma patients (BD<6 mortality rate 7%, BD>/=6 mortality rate 27%; p<0.001). In patients who required ICU admittance the BD was increased compared to patients without ICU admission (5.2 vs. 2.9, p<0.001). Within the subgroup of patients admitted to the ICU, the BD was higher in patients who required ventilation (3.8 vs. 5.5, p=0.025). Patients who developed chest related complications had increased BD compared with those without complications (4.9 vs. 4.0, p=0.025), the BD was particularly increased in patients who developed acute respiratory distress syndrome (ARDS) (4.1 vs. 6.4, p=0.004). Carbon dioxide (PaCO(2)) showed a predictive value for mortality (44 vs. 53, p<0.001), ICU admission (42 vs. 46, p=0.003) and hospital stay. CONCLUSION: Admission BD is a predictive factor in thoracic trauma patients for mortality and chest related complications. Furthermore it is a predictive factor for ICU admission, required ventilation and hospital stay. The use of BD in thoracic trauma patients can potentially identify patients who require additional monitoring or early aggressive therapy.

Modulation of the innate immune response after trauma visualised by a change in functional PMN phenotype

BACKGROUND

Acute Respiratory Distress Syndrome (ARDS) is a frequent and severe complication after trauma, caused by an excessive inflammatory response mediated by polymorphonuclear granulocytes (PMNs). It was previously demonstrated that patients with activated PMNs in the lungs have PMNs in the peripheral circulation with a reduced active FcgammaRII up-regulating capacity. We tested the hypothesis that a correlation exists between the severity of inflammation and the extent of decreased responsiveness of active FcgammaRII on circulating PMNs, as a sign of altered immunological capacity.

METHODS

Fifty-two patients were included and injury severity was assessed by clinical injury severity scores and base deficit. Symptoms and signs of inflammation were recorded on a daily basis and fMLP-induced active FcgammaRII on PMNs was assessed by FACS analysis within 24h after injury. Results were compared with 10, age matched healthy controls.

RESULTS

The baseline PMN membrane expression of Mac-1/CD11b and active FcgammaRII/CD32 did not correlate with injury severity. Levels of the acute phase protein Interleukin 6 (IL-6) correlated significantly with injury severity, indicating that a range in severity of the inflammatory response was present in the studied population. A statistically significant correlation between the PMN responsiveness towards the bacterial derived peptide fMLP of active FcgammaRII and injury severity was demonstrated. In addition, decreasing responsiveness of active FcgammaRII on PMNs was found in patients who developed systemic inflammatory response syndrome (SIRS) or acute lung injury (ALI)/ARDS.

CONCLUSIONS

The extent of the sustained injury and the subsequent cellular innate immune response is reflected by changes in a functional PMN phenotype of fMLP-induced active FcgammaRII in the peripheral blood.

Base excess determined within one hour of admission predicts mortality in patients with severe pelvic fractures and severe hemorrhagic shock

BACKGROUND

Unstable pelvic ring fractures with exsanguinating hemorrhages are rare but potentially lifethreatening injuries. The aim of this retrospective study was to evaluate whether early changes in acid- base parameters predict mortality of patients with severe pelvic trauma and hemorrhagic shock.

METHODS

Data for 50 patients with pelvic ring disruption and severe hemorrhage were analyzed retrospectively. In all patients, the pelvic ring was temporarily stabilized by C-clamp. Patients with ongoing bleeding underwent laparotomy with extra and/or intraperitoneal pelvic packing, as required. Base excess, lactate, and pH were measured upon admission and at 1, 2, 3, 4, 6, 8, and 12 h postadmission. Patients were categorized as early survivors (surviving the first 12 h after admission) and nonsurvivors. Statistical analysis was performed by Mann-Whitney test; significance was assumed at p < 0.05. Receiver operating characteristic curves were generated for early mortality from each acid-base variable.

RESULTS

Sixteen patients (32%) were nonsurvivors due to hemorrhagic shock (n = 13) or severe traumatic brain injury (n = 3). Thirty-four patients were early survivors. Base excess, lactate, and pH significantly discriminated between early survivors and nonsurvivors. Base excess determined 1 h after admission discriminated most strongly, with an area under the receiver operating characteristic curve of 0.915 (95% confidence interval, 0.836-0.993; p < 0.001).

CONCLUSION

Base excess, lactate, and pH discriminate early survivors from nonsurvivors suffering from severe pelvic trauma and hemorrhagic shock. Base excess measured 1 h after admission best predicted early mortality following pelvic trauma with concomitant hemorrhage.

Admission blood glucose is an independent predictive factor for hospital mortality in polytraumatised patients

PURPOSE

The goal of this study was to analyse a possible association of admission blood glucose with hospital mortality of polytraumatised patients and to develop an outcome prediction model for this patient group.

METHODS

The outcome of adult polytraumatised patients admitted to the University Hospital of Berne, Switzerland, between 2002 and 2004 with an ISS > or = 17, and more than one severely injured organ system was retrospectively analysed.

RESULTS

The inclusion criteria were met by 555 patients, of which 108 (19.5%) died. Hyperglycaemia proved to be an independent predictor for hospital mortality (P < 0.0001), following multiple regression analysis. After inclusion of admission blood glucose, the calculated mortality prediction model performed better than currently described models (P < 0.0001, AUC 0.924).

CONCLUSION

In this retrospective, single-centre study in polytraumatised patients, admission blood glucose proved to be an independent predictor of hospital mortality following regression analysis controlling for age, gender, injury severity and other laboratory parameters. A reliable admission blood glucose-based mortality prediction model for polytraumatised patients could be established. This observation may be helpful in improving the precision of future outcome prediction models for polytraumatised patients. These observations warrant further prospective evaluation.

The Utstein template for uniform reporting of data following major trauma: a joint revision by SCANTEM, TARN, DGU-TR and RITG

BACKGROUND

In 1999, an Utstein Template for Uniform Reporting of Data following Major Trauma was published. Few papers have since been published based on that template, reflecting a lack of international consensus on its feasibility and use. The aim of the present revision was to further develop the Utstein Template, particularly with a major reduction in the number of core data variables and the addition of more precise definitions of data variables. In addition, we wanted to define a set of inclusion and exclusion criteria that will facilitate uniform comparison of trauma cases.

METHODS

Over a ten-month period, selected experts from major European trauma registries and organisations carried out an Utstein consensus process based on a modified nominal group technique.

RESULTS

The expert panel concluded that a New Injury Severity Score > 15 should be used as a single inclusion criterion, and five exclusion criteria were also selected. Thirty-five precisely defined core data variables were agreed upon, with further division into core data for Predictive models, System Characteristic Descriptors and for Process Mapping.

CONCLUSION

Through a structured consensus process, the Utstein Template for Uniform Reporting of Data following Major Trauma has been revised. This revision will enhance national and international comparisons of trauma systems, and will form the basis for improved prediction models in trauma care.