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

BACKGROUND: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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 Comparison of the Kampala Trauma Score (KTS) with the Revised Trauma Score (RTS), Injury Severity Score (ISS) and the TRISS Method in a Ugandan Trauma Registry

Abstract Background: The public health significance of injuries that occur in developing countries is now recognized. In 1996, as part of the injury surveillance registry in Kampala, Uganda, a new score, the Kampala Trauma Score (KTS) was instituted. The KTS, developed in light of the limited resource base of sub-Saharan Africa, is a simplified composite of the Revised Trauma Score (RTS) and the Injury Severity Score (ISS) and closely resembles the Trauma Score and Injury Severity Score (TRISS). Patients and Methods: The KTS was applied retrospectively to a cohort of prospectively accrued urban trauma patients with the RTS, ISS and TRISS calculated. Using ROC (receiver operating characteristics) analysis, logistic regression models and sensitivity and specificity cutoff analysis, the KTS was compared to these three scores. Results: Using logistic regression models and areas under the ROC curve, the RTS proved a more robust predictor of death at 2 weeks in comparison to the KTS. However, differences in screening performance were marginal (areas under the ROC curves were 87% for the RTS and 84% for the KTS) with statistical significance only reached for an improved specificity (67% vs. 47%; p < 0.001), at a fixed sensitivity of 90%. In addition, the KTS predicted hospitalization at 2 weeks more accurately. Conclusion: The KTS statistically performs comparably to the RTS and ISS alone as well as to the TRISS but has the added advantage of utility. Therefore, the KTS has potential as a triage tool in resource-poor and similar health care settings.     

Evaluating trauma center quality: does the choice of the severity-adjustment model make a difference?

CONTEXT: 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. OBJECTIVE: 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. DESIGN, SETTING AND PATIENTS: 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. RESULTS: 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. CONCLUSION: 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.     

A new method for estimating probability of survival in pediatric patients using revised TRISS methodology based on age-adjusted weights

BACKGROUND: TRISS methodology estimates probability of survival (P(s)) based on coefficients derived largely from adult data. We developed a novel pediatric age-specific method to estimate P(s). METHODS: The Pennsylvania Trauma Outcome Study database was queried for pediatric patients injured between 1993 and 1996 (n = 9730). P(s) derived from the Pediatric Age-Adjusted TRISS (PAAT) methodology was generated using our Age-Specific Pediatric Trauma Score and Injury Severity Score with corresponding weights. A test data set of 7138 pediatric patients entered in the Pennsylvania Trauma Outcome Study from 1997 to 1999 was used to compute an expected number of survivors for PAAT, TRISS, and ASCOT (A Severity Characteristic of Trauma). Observed and expected survival were compared for blunt injured patients, for head injured patients, and by age category. RESULTS: PAAT showed no significant difference between observed and expected survival. TRISS and ASCOT significantly underestimated overall survival: across age groups, for blunt injuries, for head injuries, and for patients whose P(s) was less than 91%. CONCLUSION: PAAT offers a more reliable methodology than TRISS and ASCOT for comparing pediatric trauma outcomes.     

Improving the TRISS methodology by restructuring age categories and adding comorbidities

BACKGROUND: The Trauma and Injury Severity Score (TRISS) methodology was developed to predict the probability of survival after trauma. Despite many criticisms, this methodology remains in common use. The purpose of this study was to show that improving the stratification for age and adding an adjustment for comorbidity significantly increases the predictive accuracy of the TRISS model. METHODS: The trauma registry and the hospital administrative database of a regional trauma center were used to identify all blunt trauma patients older than 14 years of age admitted with International Classification of Diseases, Ninth Revision codes 800 to 959 from April 1993 to March 2001. Each individual medical record was then reviewed to ascertain the Revised Trauma Score, the Injury Severity Score, the age of the patients, and the presence of eight comorbidities. The outcome variable was the status at discharge: alive or dead. The study population was divided into two subsamples of equal size using a random sampling method. Logistic regression was used to develop models on the first subsample; a second subsample was used for cross-validation of the models. The original TRISS and three TRISS-derived models were created using different categorizations of Revised Trauma Score, Injury Severity Score, and age. A new model labeled TRISSCOM was created that included an additional term for the presence of comorbidity. RESULTS: There were 5,672 blunt trauma patients, 2,836 in each group. For original TRISS, the Hosmer-Lemeshow statistic (HL) was 179.1 and the area under the receiver operating characteristic (AUROC) curve was 0.873. Sensitivity and specificity were 99.0% and 27.8%, respectively. For the best modified TRISS model, the HL statistic was 20.35, the AUROC curve was 0.902, the sensitivity was 99.0%, and the specificity was 27.8%. For TRISSCOM, the HL statistic was 14.95 and the AUROC curve was 0.918. Sensitivity and specificity were 99.0% and 29.7%, respectively. The difference between the two models almost reached statistical significance (p = 0.086). When TRISSCOM was applied to the cross-validation group, the HL statistic was 10.48 and the AUROC curve was 0.914. The sensitivity was 98.6% and the specificity was 34.9%. CONCLUSION: TRISSCOM can predict survival more accurately than models that do not include comorbidity. A better categorization of age and the inclusion of comorbid conditions in the logistic model significantly improves the predictive performance of TRISS.     

Performance of the modified TRISS for evaluating trauma care in subpopulations: A cohort study

IntroductionPrevious research showed that there is no agreement on a practically applicable model to use in the evaluation of trauma care. A modification of the Trauma and Injury Severity Score (modified TRISS) is used to evaluate trauma care in the Netherlands. The aim of this study was to evaluate the prognostic ability of the modified TRISS and to determine where this model needs improvement for better survival predictions.MethodsPatients were included if they were registered in the Brabant Trauma Registry from 2010 through 2015. Missing values were imputed according to multiple imputation. Subsets were created based on age, length of stay, type of injury and injury severity. Probability of survival was calculated with the modified TRISS. Discrimination was assessed with the Area Under the Receiver Operating Curve (AUROC). Calibration was studied graphically.ResultsThe AUROC was 0.84 (95% CI: 0.83, 0.85) for the total cohort (N = 69 747) but only 0.53 (95% CI: 0.51, 0.56) for elderly patients with hip fracture. Overall, calibration of the modified TRISS was adequate for the total cohort, with an overestimation for elderly patients and an underestimation for patients without brain injury.ConclusionsOutcome comparison conducted with TRISS-based predictions should be interpreted with care. If possible, future research should develop a simple prediction model that has accurate survival prediction in the aging overall trauma population (preferable with patients with hip fracture), with readily available predictors.     

Modification of the Trauma and Injury Severity Score (TRISS) Method Provides Better Survival Prediction in Asian Blunt Trauma Victims

Background  

The objective of the present study was to identify logistic regression models with better survival prediction than the Trauma and Injury Severity Score (TRISS) method in assessing blunt trauma (BT) victims in Japan and Thailand. An additional aim was to demonstrate the feasibility of probability of survival (Ps) estimation without respiratory rate (RR) on admission, which is often missing or unreliable in Asian countries.     

Exploring injury severity measures and in-hospital mortality: A multi-hospital study in Kenya

IntroductionLow- and middle-income countries (LMICs) have a disproportionately high burden of injuries. Most injury severity measures were developed in high-income settings and there have been limited studies on their application and validity in low-resource settings. In this study, we compared the performance of seven injury severity measures: estimated Injury Severity Score (eISS), Glasgow Coma Score (GCS), Mechanism, GCS, Age, Pressure score (MGAP), GCS, Age, Pressure score (GAP), Revised Trauma Score (RTS), Trauma and Injury Severity Score (TRISS) and Kampala Trauma Score (KTS), in predicting in-hospital mortality in a multi-hospital cohort of adult patients in Kenya.MethodsThis study was performed using data from trauma registries implemented in four public hospitals in Kenya. Estimated ISS, MGAP, GAP, RTS, TRISS and KTS were computed according to algorithms described in the literature. All seven measures were compared for discrimination by computing area under curve (AUC) for the receiver operating characteristics (ROC), model fit information using Akaike information criterion (AIC), and model calibration curves. Sensitivity analysis was conducted to include all trauma patients during the study period who had missing information on any of the injury severity measure(s) through multiple imputations.ResultsA total of 16,548 patients were included in the study. Complete data analysis included 14,762 (90.2%) patients for the seven injury severity measures. TRISS (complete case AUC: 0.889, 95% CI: 0.866–0.907) and KTS (complete case AUC: 0.873, 95% CI: 0.852–0.892) demonstrated similarly better discrimination measured by AUC on in-hospital deaths overall in both complete case analysis and multiple imputations. Estimated ISS had lower AUC (0.764, 95% CI: 0.736–0.787) than some injury severity measures. Calibration plots showed eISS and RTS had lower calibration than models from other injury severity measures.ConclusionsThis multi-hospital study in Kenya found statistical significant higher performance of KTS and TRISS than other injury severity measures. The KTS, is however, an easier score to compute as compared to the TRISS and has stable good performance across several hospital settings and robust to missing values. It is therefore a practical and robust option for use in low-resource settings, and is applicable to settings similar to Kenya.     

Evaluation of trauma care using TRISS method: the role of adjusted misclassification rate and adjusted w-statistic

Background  

Major trauma is a leading cause of death worldwide. Evaluation of trauma care using Trauma Injury and Injury Severity Score (TRISS) method is focused in trauma outcome (deaths and survivors). For testing TRISS method TRISS misclassification rate is used. Calculating w-statistic, as a difference between observed and TRISS expected survivors, we compare our trauma care results with the TRISS standard.     

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

BACKGROUND: 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. METHODS: 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. RESULTS: The new model accurately predicted survival for blunt- and penetrating-injured intubated patients and is applicable to 11 etiologic patient populations. CONCLUSION: 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.     

Harborview assessment for risk of mortality: an improved measure of injury severity on the basis of ICD-9-CM

BACKGROUND: There have been several attempts to develop a scoring system that can accurately reflect the severity of a trauma patient's injuries, particularly with respect to the effect of the injury on survival. Current methodologies require unreliable physiologic data for the assignment of a survival probability and fail to account for the potential synergism of different injury combinations. The purpose of this study was to develop a scoring system to better estimate probability of mortality on the basis of information that is readily available from the hospital discharge sheet and does not rely on physiologic data. METHODS: Records from the trauma registry from an urban Level I trauma center were analyzed using logistic regression. Included in the regression were Internation Classification of Diseases-9th Rev (ICD-9CM) codes for anatomic injury, mechanism, intent, and preexisting medical conditions, as well as age. Two-way interaction terms for several combinations of injuries were also included in the regression model. The resulting Harborview Assessment for Risk of Mortality (HARM) score was then applied to an independent test data set and compared with Trauma and Injury Severity Score (TRISS) probability of survival and ICD-9-CM Injury Severity Score (ICISS) for ability to predict mortality using the area under the receiver operator characteristic curve. RESULTS: The HARM score was based on analysis of 16,042 records (design set). When applied to an independent validation set of 15,957 records, the area under the receiver operator characteristic curve (AUC) for HARM was 0.9592. This represented significantly better discrimination than both TRISS probability of survival (AUC = 0.9473, p = 0.005) and ICISS (AUC = 0.9402, p = 0.001). HARM also had a better calibration (Hosmer-Lemeshow statistic [HL] = 19.74) than TRISS (HL = 55.71) and ICISS (HL = 709.19). Physiologic data were incomplete for 6,124 records (38%) of the validation set; TRISS could not be calculated at all for these records. CONCLUSION: The HARM score is an effective tool for predicting probability of in-hospital mortality for trauma patients. It outperforms both the TRISS and ICD9-CM Injury Severity Score (ICISS) methodologies with respect to both discrimination and calibration, using information that is readily available from hospital discharge coding, and without requiring emergency department physiologic data.     

An artificial neural network as a model for prediction of survival in trauma patients: validation for a regional trauma area

BACKGROUND: To develop and validate an artificial neural network (ANN) for predicting survival of trauma patients based on standard prehospital variables, emergency room admission variables, and Injury Severity Score (ISS) using data derived from a regional area trauma system, and to compare this model with known trauma scoring systems. PATIENT POPULATION: The study was composed of 10,609 patients admitted to 24 hospitals comprising a seven-county suburban/rural trauma region adjacent to a major metropolitan area. The data was generated as part of the New York State trauma registry. Study period was from January 1993 through December 1996 (1993-1994: 5,168 patients; 1995: 2,768 patients; 1996: 2,673 patients). METHODS: A standard feed-forward back-propagation neural network was developed using Glasgow Coma Scale, systolic blood pressure, heart rate, respiratory rate, temperature, hematocrit, age, sex, intubation status, ICD-9-CM Injury E-code, and ISS as input variables. The network had a single layer of hidden nodes. Initial network development of the model was performed on the 1993-1994 data. Subsequent models were generated using the 1993, 1994, and 1995 data. The model was tested first on the 1995 and then on the 1996 data. The ANN model was tested against Trauma and Injury Severity Score (TRISS) and ISS using the receiver operator characteristic (ROC) area under the curve [ROC-A(z)], Lemeshow-Hosmer C-statistic, and calibration curves. RESULTS: The ANN showed good clustering of the data, with good separation of nonsurvivors and survivors. The ROCA(z) was 0.912 for the ANN, 0.895 for TRISS, and 0.766 for ISS. The ANN exceeded TRISS with respect to calibration (Lemeshow-Hosmer C-statistic: 7.4 for ANN; 17.1 for TRISS). The prediction of survivors was good for both models. The ANN exceeded TRISS in nonsurvivor prediction. CONCLUSION: An ANN developed for trauma patients using prehospital, emergency room admission data, and ISS gave good prediction of survival. It was accurate and had excellent calibration. This study expands our previous results developed at a single Level I trauma center and shows that an ANN model for predicting trauma deaths can be applied across hospitals with good results     

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

IntroductionIn 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 methodsFrom 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 30 days 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).ResultsIn 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 (24 h) was better than late mortality (30 day).ConclusionOn-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.     

Predictors of mortality in adult trauma patients: the physiologic trauma score is equivalent to the Trauma and Injury Severity Score

Background:

Several statistical models (Trauma and Injury Severity Score [TRISS], New Injury Severity Score [NISS], and the International Classification of Disease, Ninth Revision-based Injury Severity Score [ICISS]) have been developed over the recent decades in an attempt to accurately predict outcomes in trauma patients. The anatomic portion of these models makes them difficult to use when performing a rapid initial trauma assessment. We sought to determine if a Physiologic Trauma Score, using the systemic inflammatory response syndrome (SIRS) score in combination with other commonly used indices, could accurately predict mortality in trauma.

Study Design:

Prospective data were analyzed in 9,539 trauma patients evaluated at a Level I Trauma Center over a 30-month period (January 1997 to July 1999). A SIRS score (1 to 4) was calculated on admission (1 point for each: temperature >38°C or <36°C, heart rate >90 beats per minute, respiratory rate >20 breaths per minute, neutrophil count > 12,000 or < 4,000. SIRS score, Injury Severity Score (ISS), Revised Trauma Score (RTS), TRISS, Glasgow Coma Score, age, gender, and race were used in logistic regression models to predict trauma patients’ risk of death. The area under the receiver-operating characteristic curves of sensitivity versus 1-specificity was used to assess the predictive ability of the models.

Results:

The study cohort of 9,539 trauma patients (of which 7,602 patients had complete data for trauma score calculations) had a mean ISS of 9 ± 9 (SD) and mean age of 37 ± 17 years. SIRS (SIRS score ≥ 2) was present in 2,165 of 7,602 patients (28.5%). In single-variable models, TRISS and ISS were most predictive of outcomes. A multiple-variable model, Physiologic Trauma Score combining SIRS score with Glasgow Coma Score and age (Hosmer-Lemenshow CHI-SQUARE = 4.74) was similar to TRISS and superior to ISS in predicting mortality. The addition of ISS to this model did not significantly improve its predictive ability.

Conclusions:

A new statistical model (Physiologic Trauma Score), including only physiologic variables (admission SIRS score combined with Glasgow Coma Score and age) and easily calculated at the patient bedside, accurately predicts mortality in trauma patients. The predictive ability of this model is comparable to other complex models that use both anatomic and physiologic data (TRISS, ISS, and ICISS).     

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.

     

Comparison between TRISS and ASCOT methods in controlling for injury severity.

ASCOT was developed by Champion et al. to address known limitations to TRISS. The present research attempted to validate ASCOT using an independent trauma registry. Data were collected by the Institute for Trauma and Emergency Care (ITEC), New York Medical College, between July 1, 1987 and June 30, 1989; 5685 trauma patients admitted to three level I trauma centers or five non-trauma center hospitals were included. Information was gathered by trained nurse-abstractors using all available prehospital and hospital records. ASCOT and TRISS were compared using sensitivity, disparity, misclassification rates, and the Hosmer-Lemeshow goodness-of-fit statistics. Disparity and sensitivity rates were relatively low for both indexes, particularly among blunt injury patients. Total numbers of patients misclassified by TRISS and by ASCOT were similar; most misclassifications were made by both TRISS and ASCOT and involved nonsurvivors. Each method had advantages in predicting the outcomes of particular subgroups of patients; ASCOT with regard to predicting outcomes among patients with head injuries and in correctly classifying blunt injured patients; TRISS in correctly classifying survivors. We conclude (1) the relatively small gain in predictive accuracy by ASCOT over TRISS is largely offset by its complexity and increased computer processing requirements; (2) Hosmer-Lemeshow tests indicate that neither index provides good statistical agreement between predicted and actual outcomes among either blunt or penetrating injury patients. Future models should include additional variables, stratify patients by several injury causes, and use decision rules to select variables and variable weights.     

Prediction of outcomes in trauma: anatomic or physiologic parameters?

BACKGROUND: Prediction of outcomes after injury has traditionally incorporated measures of injury severity, but recent studies suggest that including physiologic and shock measures can improve accuracy of anatomic-based models. A recent single-institution study described a mortality predictive equation [f(x) = 3.48 - .22 (GCS) - .08 (BE) + .08 (Tx) + .05 (ISS) + .04 (Age)], where GSC is Glasgow Coma Score, BE is base excess, Tx is transfusion requirement, and ISS is Injury Severity Score, which had 63% sensitivity, 94% specificity, (receiver operating characteristic [ROC] 0.96), but did not provide comparative data for other models. We have previously documented that the Physiologic Trauma Score, including only physiologic variables (systemic inflammatory response syndrome, Glasgow Coma Score, age) also accurately predicts mortality in trauma. The objective of this study was to compare the predictive abilities of these statistical models in trauma outcomes. METHODS: Area under the ROC curve of sensitivity versus 1-specificity was used to assess predictive ability and measured discrimination of the models. RESULTS: The study cohort consisted of 15,534 trauma patients (80% blunt mechanism) admitted to a Level I trauma center over a 3-year period (mean age 37 +/- 18 years; mean Injury Severity Score 10 +/- 10; mortality 4%). Sensitivity of the new predictive model was 45%, specificity was 96%, ROC was 0.91, validating this new trauma outcomes model in our institution. This was comparable with area under the ROC for Revised Trauma Score (ROC 0.88), Trauma and Injury Severity Score (ROC 0.97), and Physiologic Trauma Score (ROC 0.95), but superior compared with admission Glasgow Coma Score (ROC 0.79), Injury Severity Score (ROC 0.79), and age (ROC 0.60). CONCLUSIONS: The predictive ability of this new model is superior to anatomic-based models such as Injury Severity Score, but comparable with other physiologic-based models such as Revised Trauma Score, Physiologic Trauma Score and Trauma, and Injury Severity Score.     

The Prognostic Value of Prothrombin Time in Predicting Survival after Major Trauma: a Prospective Analysis of 1,351 Patients from the German Trauma Registry

Background: Early assessment of the individual trauma load in major trauma patients is difficult. A simple and reliable prognostic factor already available in the emergency room would help the emergency physician to make appropriate therapeutic decisions, e. g., when and how to operate on major fractures. The aim of the study was to evaluate the prognostic value of prothrombin time (PT). Patients and Methods: The German Trauma Registry is a prospective, standardized and anonymous documentation of severely injured patients. 3,814 patients were included in the registry. 1,351 patients with an Injury Severity Score (ISS) h 16 and complete data for specific variables (PT, Trauma Score + Injury Severity Score [TRISS], survival until discharge) were included in the study. The PT was measured on the patient's arrival in the emergeny room. Three different analyses were performed. 1. According to clinical judgment, three groups of patients were compared (PT S 60%, PT 40-59%, PT < 40%). A univariate analysis compared therapeutic interventions and outcome variables between the three groups. 2. A receiver-operator-characteristic (ROC) curve analysis compared the performance of PT with the prognostic standard TRISS. 3. A multivariate logistic regression was performed in order to evaluate PT as an independent prognostic variable. Results: PT values showed a good inverse correlation with the severity of injury and the level of therapeutic interventions. The ROC analysis as well as the regression revealed PT as a significant prognostic factor although it showed a slightly worse performance compared to TRISS. Conclusions: As PT, in contrast to TRISS, is readily available already in the emergency room, it can be used as a screening variable for the assessment of a patient's trauma load and thereby help in the decision-making for further operative treatment of major trauma patients.