A Severity Characterization Of Trauma Research Articles

Prediction of Trauma Mortality Incorporating Pre-injury Comorbidities Into Existing Mortality Scoring Indices.

The purpose of the study is to develop a comprehensive risk score of mortality in trauma victims that can predict the in-hospital mortality better than trauma injury severity score (TRISS) and A Severity Characterization of Trauma (ASCOT) score. All hospitalized trauma patients, between the ages of 16 and 89years old were included in the study. The National Trauma Quality Improvement Program (TQIP) database of the calendar year 2011-2016 was accessed for the development of a traum mortality scoring system (TMS). The prediction of mortality was tested by creating a receiver operating characteristics (ROC) curve and an area under the curve (AUC). ROCs and AUCs of TMS were compared with TRISS and ASCOT score. The AUC of TMS (0.892, 95% CI: 0.888-0.896) was better than TRISS (0.864, 95% CI: 0.859-0.869, P <0.0001) and ASCOT (0.841, 95% CI: 0.835-0.846, P <0.0001), respectively, in blunt injury. Similarly, TMS prediction (AUC: 0.949, 95% CI: 0.940-0.958) was better in penetrating injury when compared with TRISS (0.942, 95% CI: 0.934-0.951, P = 0.030) and ASCOT (0.924, 95% CI: 0.912-0.936, P <0.0001), respectively. TMS can predict the in-hospital mortality better than TRISS and ASCOT.

A comparative study on the ability of trauma severity-assessing methods in determining the prognosis of patients having accident and referred to Imam Khomeini Hospital of Urmia in 2016

Introduction: Trauma is the leading cause of death and disability in developing countries. According to the World Health Organization projections by 2020, accidents alone are the second leading cause of missing year around the world. Therefore, the aim of this study was to compare the ability of trauma severity-assessing methods in determining the prognosis of accident patients referred to Imam Khomeini Hospital of Urmia in 2016. Methodology: In this study, the severity of trauma in traffic accident patients referring to Imam Khomeini Hospital of Urmia was determined using the Injury Severity Score (ISS), Revised Trauma Score (RTS), Trauma and Injury Severity Score (TRISS), and A Severity Characterization of Trauma (ASCOT), in a 1-year period, via the census method on 2015 hospitalized accident victims, and the ability of these methods to predict mortality and morbidity, length of stay in hospital, and hospital length charges were investigated by the receiver operating characteristic curve of regression methods. To this end, the hospital records of accident victims who were hospitalized for at least 1 day or whose deaths were examined along with their autopsy reports were the main components of this study. Results: The mean age of the patients was 33.63 ± 18.53 years, and their age range was 1–96 years. The ratio of males to females was 2.73. In addition, in terms of job status (46.8%), they were free. The majority of women were homemakers (70.8%). In the two gender groups, most accidents occurred within the city, The mean severity of lesions based on ISS system was 16.44 ± 16.28 and 16.8% of ISS injuries were above 25. The mean RTS of the patients was 7.69 ± 0.54, which ranged from 0 to 7.84, and the mean TRISS of the patients was 92.24 ± 15.87. The trauma means via the ASCOT method was 5.35 ± 1.85. The difference in the severity of trauma (calculated by each of the methods of ISS, RTS, TRISS, and ASCOT) was significant between those who survived and those who died. Conclusion: In this study, most of the accidents happened in males, married, undergraduate, and homemaker, and those on the street as well, and the RTS and TRISS methods showed the highest ability in predicting mortality. Given the usefulness and special applications of these methods, their use in designing a national care system in the trauma area is recommended.

A novel fuzzy-logic inference system for predicting trauma-related mortality: emphasis on the impact of response to resuscitation.

Trauma scoring aims for quantification and uniform reporting of trauma-related outcomes. Despite significant advances in trauma scoring, the exact time period at which relevant calculations should be made is not clear. Considering the importance of response to resuscitation, calculation of trauma scores after a period of resuscitation can allow better discrimination of patients who will survive. A fuzzy-logic inference system, which is completely based on expert opinion and uses Glasgow Coma Scale (GCS) and systolic blood pressure at arrival to emergency room (ER) and their response to resuscitation as inputs, was developed. Records of the last 150 trauma patients admitted to our surgical intensive care unit (ICU) were used for calculations related to Injury Severity Score (ISS), Revised Trauma Score (RTS), Trauma and Injury Severity Score, and A Severity Characterization of Trauma (ASCOT) systems. Calculation of trauma severity and predicted mortality was performed at different time intervals during resuscitation [at arrival to emergency room (ER), after 1h of resuscitation, and at ICU admission]. The performance of conventional systems and fuzzy-logic system was compared. Mean ISS was 32.31±14.01. All systems included showed acceptable discriminative power. Among the conventional systems calculated at emergency room admission, ISS was the best performing [receiver operating characteristics (ROC), 0.9033] and RTS was the worst (ROC, 0.8106). Their performances were improved by up to 13% by use of post-resuscitation physiologic variables. Fuzzy-logic inference system performed slightly better (ROC, 0.9247) then the conventional systems calculated at arrival to ER. Response to resuscitation has significant impact on trauma mortality and must be considered in trauma scoring and mortality prediction. Fuzzy logic provides important opportunities for design of better predictive systems.

Predictors of mortality, length of stay and discharge destination in blunt trauma

The present study explored a range of variables to identify predictors of mortality and morbidity and to develop prediction models based on these variables. Tools for predicting mortality, hospital length of stay and a patient's destination post-hospital discharge were developed using logistic regression in one dataset (design) and evaluated for prediction performance in a separate dataset (validation). The performance of the mortality model was compared to the trauma and injury severity score (TRISS) and a severity characterization of trauma (ASCOT). The profile of variables contributing to the final prediction models developed from the design dataset varied across the different outcomes of interest although age, injury severity score, development of complications and triage category were common predictors of all three outcomes. The performance of the new mortality prediction model was superior to both TRISS and ASCOT in the validation dataset. Overall, the new models did not meet the prespecified performance criteria. The present study identified key predictors of mortality and morbidity (length of hospital stay and discharge destination). The newly developed mortality model out-performed published trauma scoring methods. However, further development and trial of the new prediction models is required before implementation as definitive audit and benchmarking tools could be recommended.

Evaluating Trauma Center Quality: Does the Choice of the Severity-Adjustment Model Make a Difference?

The Major Trauma Outcome Study (MTOS) database was created by the American College of Surgeons over 20 years ago to establish national norms for trauma care. The primary trauma outcome prediction models used for evaluating the quality of trauma care, TRISS and ASCOT (A Severity Characterization of Trauma), were developed using the MTOS database. First, to determine whether TRISS and ASCOT agree on hospital quality. Second, to determine whether TRISS and ASCOT accurately reflect contemporary outcomes in trauma care. A retrospective cohort study based on 91,112 patients admitted to 69 hospitals between 2000 and 2001 in the National Trauma Databank. Using TRISS and ASCOT, the ratio of the observed to expected mortality rate (O/E ratio) was calculated for each hospital. Hospitals whose O/E ratio was statistically different from 1 were identified as quality outliers. Kappa analysis was used to assess the degree to which TRISS and ASCOT agreed on the identity of hospital quality outliers. TRISS and ASCOT disagreed on the outlier status of 35 of the 69 hospitals. Kappa analysis revealed only fair agreement (kappa = 0.23; p = 0.0015) between TRISS and ASCOT in identifying quality outliers. Thirty-eight hospitals were identified by the TRISS method as high-performance hospitals. First, TRISS and ASCOT exhibit substantial disagreement on the identity of quality outliers within the NTDB. Second, an unrealistically high number of hospitals were identified as high-performance outliers using either TRISS or ASCOT. These findings have important implications for the use of TRISS and ASCOT for benchmarking performance and quality improvement.

Comparison of Current Injury Scales for Survival Chance Estimation: An Evaluation Comparing the Predictive Performance of the ISS, NISS, and AP Scores in a Dutch Local Trauma Registration

Prediction of survival chances for trauma patients is a basic requirement for evaluation of trauma care. The current methods are the Trauma and Injury Severity Score (TRISS) and A Severity Characterization of Trauma (ASCOT). Scales for scoring injury severity are part of these methods. This study compared three injury scales, the Injury Severity Score (ISS), the New ISS (NISS), and the Anatomic Profile (AP), in three otherwise identical predictive models. Records of the Rotterdam Trauma Center were analyzed using logistic regression. The variables used in the models were age (as a linear variable), the corrected Revised Trauma Score (RTS), a denominator for blunt or penetrating trauma, and one of the three injury scales. The original TRISS and ASCOT models also were evaluated. The resulting models were compared in terms of their discriminative power, as indicated by the receiver-operator characteristic (ROC), and calibration (Hosmer-Lemeshow [HL]) for the entire range of injury severity. For this study, 1,102 patients, with an average ISS of 15, met the inclusion criteria. The TRISS and ASCOT models, using original coefficients, showed excellent discriminative power (ROC, 0.94 and 0.96, respectively), but insufficient fits (HL, p = 0.001 and p = 0.03, respectively). The three fitted models also had excellent discriminative abilities (ROC, 0.95, 0.97, and 0.96, respectively). The custom ISS model was unable to fit the entire range of survival chances sufficiently (p = 0.01). Models using the NISS and AP scales provided adequate fits to the actual survival chances of the population (HL, 0.32 and 0.12, respectively). The AP and NISS scores particularly both managed to outperform the ISS score in correctly predicting survival chances among a Dutch trauma population. Trauma registries stratifying injuries by the ISS score should evaluate the use of the NISS and AP scores.

A new "TRISS-like" probability of survival model for intubated trauma patients.

Prehospital trauma patient field intubations and paralyzations, using neuromuscular blocking agents before emergency department respiratory and neurologic assessments are made, bias assessments and outcome evaluations using probability-of-survival models, such as TRISS and A Severity Characterization of Trauma (ASCOT). We present a newly developed "TRISS-like" probability-of-survival model for intubated blunt- and penetrating-injured patient assessment. From a population of 51397 consecutively admitted trauma patients, this study used all 5740 (11.2% of the total injured population) intubated patients with complete data from a statewide trauma registry from October 1, 1993, to September 30, 1996. Model performance was evaluated using standard calibration and discrimination measures and z and W statistics of significance. The new model accurately predicted survival for blunt- and penetrating-injured intubated patients and is applicable to 11 etiologic patient populations. Study findings suggest that the new TRISS-like model should be used to assess both blunt- and penetrating-injured intubated patients. Use of this new model provides an analytical method for addressing a significant limitation of both the standard TRISS and ASCOT models, which are not applicable to intubated injured patient assessment. In addition, use of this model will complement TRISS/ASCOT assessments of nonintubated trauma patients and thus permit appropriate assessments for both intubated and nonintubated injured patient study populations.

Preinjury warfarin does not impact outcome in trauma patients.

The objective of this study was to determine whether the preinjury condition of anticoagulation had an adverse impact on patients sustaining injury. A retrospective analysis was performed for prospectively collected registry data from 1995-2000 from all accredited trauma centers in Pennsylvania. The registry was queried for all trauma patients who had anticoagulation therapy as a preinjury condition (PIC). This group served as our experimental cohort. A control cohort (not having warfarin therapy as a PIC) was developed using case-matching techniques for age, sex, Glasgow Coma Scale (GCS), Injury Severity Score (ISS), A Severity Characterization of Trauma (ASCOT) score, and in the head injured patients, International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnoses. Head and non-head injured patients were evaluated separately. The cohorts were examined for 28-day mortality, intensive care unit length of stay (ICU-LOS), hospital length of stay (HOS-LOS), PICs, occurrences, discharge destinations, and functional status at discharge. Chi2 and Student's t test were used to evaluate the data; p values < 0.05 were considered significant. Two thousand nine hundred forty-two patients were available for analysis. The prevalence of PICs was significantly greater in the warfarin group for both the head and non-head injured populations (p < 0.003 and p < 0.0001, respectively). The incidence of occurrences in the non-head injured population was statistically higher for the warfarin patients (p < 0.001), but showed no difference in the head injured group regardless of warfarin use (p = 0.15). Functional status at discharge demonstrated no clinically significant difference between the warfarin and non-warfarin groups in both head and non-head injured populations. There was no difference in discharge destination in the head injured population; however, in the non-head injured population a greater percentage of non-warfarin patients was discharged to home when compared with the warfarin patients. Our data suggest that the PIC of anticoagulation with warfarin does not adversely impact mortality or LOS outcomes in both head and non-head injured patients. In non-head injured patients, however, the occurrence rates and discharge destination were different. More research needs to be done to determine whether this is related to anticoagulation or other reasons (i.e., number of PICs). These data should be used when weighing risk/benefit ratios of prescribing chronic anticoagulation.

Adult trauma scoring models: a critical review

Since the development of the TRISS (Revised Trauma Score and Injury Severity Score) trauma scoring model several alternative models have been developed. Trauma scoring systems can be broadly categorized into anatomically based injury severity models, e.g. NISS (New Injury Severity Score) and ASCOT (A Severity Characterization of Trauma) or data driven models, e.g. ICISS (Insternational Classification of Diseases Injury Severity Score). Trauma scoring models using death/survival as the outcome measure can either be developed using logistic regression or a neural network approach. Assessment of the worth of a model is most commonly performed using receiver operating curve analysis or the Hosmer-Lemeshow statistic. Both of these statistical methods have their inherent weaknesses when applied to trauma scoring model development. This article aims to review four trauma scoring models and to discuss the limitations of the statistical methods used to assess the worth of these models.

Intestinal permeability correlates with severity of injury in trauma patients.

Increased intestinal permeability (IP) and the release of toxic intraluminal materials have been implicated in the systemic inflammatory response syndrome (SIRS) and multiple organ failure (MOF) observed in patients after severe trauma. Previous studies of intestinal permeability have failed to demonstrate a correlation between early measurements of IP and indicators of injury severity. This study examines the relationship between standard measures of injury severity and the early (day 1) and delayed (day 4) changes in IP. Associations between IP and the development of SIRS, MOF, and infectious complications were also studied. The metabolically inactive markers lactulose (L) and mannitol (M) were used to measure IP in 29 consecutive patients who sustained injuries that required admission to the surgical intensive care unit and in 10 healthy control subjects. Measurements were made within 24 hours of admission and on hospital day 4. Severity of injury was assessed by A Severity Characterization of Trauma (ASCOT), Trauma and Injury Severity Score (TRISS), Injury Severity Score (ISS), Revised Trauma Score (RTS), and Acute Physiology and Chronic Health Evaluation (APACHE) II score. Postinjury infections and parameters of SIRS and MOF were recorded. The IP of healthy volunteers (L/M, 0.025 +/- 0.008) was within the normal range (L/M < or = 0.03), whereas the average IP in injured patients was increased both within 24 hours (L/M, 0.139 +/- 0.172) and on the fourth hospital day (L/M, 0.346 +/- 0.699). No significant correlation between severity of injury and increased IP was seen within 24 hours of injury. A significant correlation was seen on hospital day 4, however, with all severity indices measured (ASCOT: r = 0.93, R2 = 0.87, p < 0.001; TRISS: r = 0.93, R2 = 0.87, p < 0.001; ISS: r = 0.84, R2 = 0.70, p < 0.001; RTS: r = 0.68, R2 = 0.47, p = 0.002; APACHE II score: r = 0.51, R2 = 0.26, p = 0.04). Patients with markedly increased IP (L/M > or = 0.100) experienced a significant increase in the development of SIRS (83 vs. 44%; p = 0.03) and subsequent infectious complications (58 vs. 13%; p = 0.01) and showed close correlation with the multiple organ dysfunction scores (r = 0.87, R2 = 0.76, p < 0.001). These observations demonstrate that the increased IP observed after trauma correlates with severity of injury only after 72 to 96 hours and not within the initial 24 hours of injury. A large increase in IP is associated with the development of SIRS, multiple organ dysfunction, and an increased incidence of infectious complications.

The major trauma management study: an analysis of the efficacy of current trauma care.

An audit of the management and outcome of major trauma patients was carried out to determine ways in which the system of care may be improved. The Major Trauma Management Study (MTMS) collected data prospectively on all consecutively admitted major trauma patients at eight major hospitals during a 12-month period. Outcome was studied using trauma and injury severity score (TRISS) and a severity characterization of trauma (ASCOT) analyses, as well as a preventable outcome analysis, which looked at survivors with complications or with a Glasgow Coma Score < 15 on discharge from hospital, as well as studying deaths. The group of 859 patients was more severely injured than most described previously, with a mortality of 14.8% and a mean injury severity score of 19.8. Formal ASCOT analysis indicated 2.25% fewer survivors than would be predicted by Major Trauma Outcome Study norms. Extrapolating the TRISS and ASCOT process to include those patients with missing data, and then comparing groups of matched severity with the norms, gave no statistically different outcome in the MTMS group of patients. Preventable outcome analysis revealed rates of preventable and potentially preventable (P/PP) outcomes of 32% among deaths and 8% among survivors. The types of management deficiencies responsible for P/PP outcomes are identified. The points of deficiency in a system of care have been identified, and the development of an integrated trauma system in Victoria, based upon these facts, is recommended. Children, the elderly, patients with head injuries and patients being transferred between hospitals would benefit from improvements to the system of care. The calculation of efficacy rate (0.95 for the MTMS patients) is recommended to accurately assess the system of care. Preventable Outcome Analysis is more relevant to auditing a system of trauma care in detail, than is ASCOT or TRISS. The MTMS has refined and defined the process so that it is reproducible in further comparative studies.

Predicting survival of victims of motor vehicle crashes in New York State

This study assesses the relative ability of three different models to predict in-hospital mortality for victims of motor vehicle crashes. The first two models, the trauma and injury severity score (TRISS) and a severity characterization of trauma (ASCOT), are models that have been used in many earlier studies and have been quoted extensively in the literature. The third model, which is developed in this study, uses essentially the same risk factors as the other two studies, but employs them in a different manner. In order to provide a fair comparison, new (logistic regression) model coefficients are fit to the first two models using the study data. The models are compared with respect to typical criteria for assessing the fit of logistic regression models as well as their ability to predict mortality for various subsets of seriously injured patients. The study concludes that the new model provides a substantially more accurate prediction of mortality, and that it may be wise for regions attempting to assess relative outcomes in their subregions to develop statistical models that are tailored to their own patients.

Improved Prediction From a Severity Characterization of Trauma (ASCOT) Over Trauma and Injury Severity Score (TRISS)

Improved Prediction From a Severity Characterization of Trauma (ASCOT) Over Trauma and Injury Severity Score (TRISS)

Improved Predictions from A Severity Characterization of Trauma (ASCOT) over Trauma and Injury Severity Score (TRISS)

In 1986, data from 25,000 major trauma outcome study patients were used to relate Trauma and Injury Severity Score (TRISS) values to survival probability. The resulting norms have been widely used. Motivated by TRISS limitations, A Severity Characterization of Trauma (ASCOT) was introduced in 1990. The objective of this study was to evaluate and compare TRISS and ASCOT probability predictions using carefully collected and independently reviewed data not used in the development of those norms. This was a prospective data collection for consecutive admissions to four level I trauma centers participating in a major trauma outcome study. Data from 14,296 patients admitted to the four study sites between October 1987 through 1989 were used. The indices were evaluated using measures of discrimination (disparity, sensitivity, specificity, misclassification rate, and area under the receiver-operating characteristic curve) and calibration [Hosmer-Lemeshow goodness-of-fit statistic (H-L)]. For blunt-injured adults, ASCOT has higher sensitivity than TRISS (69.3 vs. 64.3) and meets the criterion for model calibration (H-L statistic < 15.5) needed for accurate z and W scores. The TRISS does not meet the calibration criterion (H-L = 30.7). For adults with penetrating injury, ASCOT has a substantially lower H-L value than TRISS (20.3 vs. 138.4), but neither meets the criterion. Areas under TRISS and ASCOT ROC curves are not significantly different and exceed 0.91 for blunt-injured adults and 0.95 for adults with penetrating injury. For pediatric patients, TRISS and ASCOT sensitivities (near 77%) and areas under receiver-operating characteristic curves (both exceed 0.96) are comparable, and both models satisfy the H-L criterion. In this age of health care decisions influenced by outcome evaluations, ASCOT's more precise description of anatomic injury and its improved calibration with actual outcomes argue for its adoption as the standard method for outcome prediction.

Injury Severity and Probability of Survival Assessment in Trauma Patients Using a Predictive Hierarchical Network Model Derived from ICD-9 Codes

Accurate assessment of injury severity is critical for decision making related to the prevention, triage, and treatment of injured patients. Presently, the standard method of controlling for variations of injury severity between groups has been based upon the Injury Severity Score (ISS) and the Trauma Score and the Trauma and Injury Severity Score (TRISS) methodology. The purpose of this study was to attempt to build upon previous work using International Classification of Diseases, ninth revision (ICD-9) coded diagnosis, and procedure information available from standard hospital discharge abstracts (UB-82 Billing format) to create a hierarchical network to provide a tool for predicting injury severity and probability of survival. Data were obtained for this analysis from the North Carolina Medical Database. Data were available on all trauma patients admitted to hospitals in North Carolina from January 1, 1988 until June 30, 1992. The dependent variable of interest was the patient's survival after injury, coded as live or die. The independent variables used in the study included the ISS derived using the technique described by MacKenzie Abbreviated Injury Score (AIS) and body system maximum AIS scores, mortality risk ratios derived from the ICD-9-DM primary, secondary, and tertiary diagnoses, primary and secondary procedures as described in previous work, age and gender. Network generation used a commercial software package, AIM (Abtech Corp., Charlottesville, Va.), which is a numeric modeling tool that automatically "learns" knowledge from a data base of examples. In the test data set an ISS and a prediction of survival based upon the derived network were calculated for each and every patient. The relative predictive power of these two scores were compared by calculating the overall accuracy, sensitivity, and specificity and the false positive and false negative rates. The receiver operator characteristic curves demonstrate that the network is a more effective tool in predicting the outcome of trauma patients. All the measures of predictive power show that the network was the better predictor of outcome than the ISS. Given the recognized limitations of the ISS, the widespread availability of the ICD-9 coded diagnoses and procedures, and the availability of many state and regional data bases that have no ISS or Trauma Score, the purpose of this study was to assess the ability of a network derived from limited but widely available hospital discharge data to predict the outcome of injured patients. The study confirms previous work showing that the ICD-9 codes were strongly associated with outcome. The study demonstrated that the network created from these data was a better predictor of outcome than the derived ISS. When the results of the network were compared with other published series, the network, created without access to physiologic information, was almost as accurate, sensitive, and specific as reported values for TRISS and A Severity Characterization of Trauma (ASCOT). Because the present study is the first of its type, further investigations are needed to validate these findings. If other studies corroborate this study, a network model based upon ICD-9 codes could become the principal method for grading injury severity. This would provide superior predictive power of injury severity with important cost savings and universal application.

American College of Surgeons audit filters: associations with patient outcome and resource utilization.

To determine if trauma patients qualifying by a 1990 American College of Surgeons (ACS) audit filter have the same outcomes and resource utilizations as similar (matching) patients not qualifying by the filter. Retrospective, case control study. Data for 21,175 patients submitted during 1992 to the Pennsylvania Trauma Outcome Study (PTOS) were analyzed. Patients qualifying by each 1990 ACS audit filter were identified, except filters 13 and 22 that were not accommodated by the PTOS form. In addition, qualifiers by filter 21 (trauma deaths) were not analyzed. For each qualifier by a filter, matching patients who were not qualifiers by the filter were identified. Matching patients had the same cause of injury, A Severity Characterization of Trauma (ASCOT) age category, distribution of serious (Abbreviated Injury Score of > 2) injuries, intubation status, and coded Revised Trauma Score values on Emergency Department arrival. Qualifiers and matching patients were compared for their survival (z and W statistics), discharge disability (PTOS-Functional Independence Measure), and lengths of stay in the hospital (H-LOS) and in the Intensive Care Unit (ICU-LOS). More than 57% of the study sample qualified by one or more filters. Filters 10 and 12 did not have sufficient qualifiers for evaluation. No filter's qualifiers were associated with significantly more disability at discharge than matching patients. The most frequently occurring filters (4, 2, and 5, respec-tively) deal with documentation deficiencies, but were not associated with significant results. Qualifiers by the nine filters below were associated with significantly greater mortality or H-LOS or ICU-LOS. [table: see text] Additional studies of the efficacy and efficiency of trauma quality assurance filters are needed. Objective criteria should be established for the definition, evaluation, modification, and adoption of trauma audit filters.

Validation of TRISS and ASCOT using a non-MTOS trauma registry.

To validate the Trauma and Injury Severity Score (TRISS) and A Severity Characterization of Trauma (ASCOT) models for patients with blunt injuries using an independent trauma registry, and to develop new TRISS and ASCOT models for types of patients with blunt injuries and examine their fit. Retrospective analysis of clinical data from the Institute for Trauma and Emergency Care (ITEC). Statistical models were developed using TRISS and ASCOT variables applied to ITEC data for patients with blunt injuries. These models were compared to Major Trauma Outcome Study (MTOS) models with regard to the resulting coefficients and hospital quality assessments. Also, separate models were developed for different groups of blunt injuries, and these models were compared with one another and tested for adequacy of fit. ASCOT performed acceptably well when new coefficients were derived using ITEC data, but TRISS did not. Although the models developed from MTOS and from ITEC coefficients generally yielded similar hospital quality assessments, there were some notable exceptions. Some TRISS and ASCOT variables were not significantly related to survival for some subgroups of blunt injuries, and neither the TRISS nor the ASCOT model was an adequate predictor of survival for patients suffering from low falls. New TRISS and ASCOT coefficients should be derived if survival for patients with blunt injuries is to be predicted accurately in independent trauma registries. Also, it may be wise to consider developing separate models for subgroups of patients, particularly if hospitals in the registry have different mixes of patient types.

A New Characterization of Injury Severity

ASCOT (A Severity Characterization of Trauma) is a physiologic and anatomic characterization of injury severity which combines emergency department admission values of Glasgow Coma Scale, systolic blood pressure, respiratory rate, patient age, and AIS-85 anatomic injury scores in a way that obviates ISS shortcomings. ASCOT values are related to survival probability using the logistic function and regression weights reaffirm the importance of head injury and coma to the prediction of patient outcome. The ability of TRISS and ASCOT to discriminate survivors from non-survivors and the reliability of their predictions, as measured by the Hosmer-Lemeshow statistic, were compared using Major Trauma Outcome Study (MTOS) patient data. ASCOT performance matched or exceeded TRISS's for blunt-injured patients and for penetrating-injured patients. ASCOT performance gains were modest for blunt-injured patients. The Hosmer-Lemeshow statistics suggest that ASCOT reliably predicts patient outcome for penetrating-injured patients and nearly so for blunt-injured patients. Statistically reliable predictions were not achieved by TRISS for either set. ASCOT provides a more precise description of patient physiologic status and injury number, location, and severity than TRISS. The ASCOT patient description may be useful in relating to other important outcomes not highly correlated with TRISS or the Injury Severity Score (ISS) such as disability, length of stay, and resources required for treatment.