injury severity scores

Injury Severity Scores
The document contains a thorough review of different injury severity scores employed in trauma care, their classification based on anatomic and physiologic scales and a collection of abstracts from research papers highlighting their advantages and shortcomings.

Injury severity scores are simply a way to describe and quantify the severity of traumatic injury. They aid in assessment of injury, provide a more objective measure of injury severity that results in more efficient and accurate communication with other trauma care providers, give a comparison of trauma outcomes of different institutions (quality control), and also provide some sense of the probability of survival of the victims. They are essential to quality control evaluation and trauma research efforts because, in theory, they permit valid comparison of trauma populations with differing baseline risk. There are several injury severity indices that are typically based on anatomic or physiologic parameters, or a combination of both.

ANATOMIC CLASSIFICATION
Two coding systems are available for classifying anatomic injuries: the Abbreviated Injury Scale (AIS) and the International Classification of Diseases (ICD). The AIS was developed in 1971 uniquely for the classification of injuries and includes a severity score that was developed and is updated by consensus among a group of clinical experts. The severity score varies from 1 (minor injury) to 6 (theoretically nonsurvivable injury) and is based principally on threat to life, though energy dissipation, permanent impairment, and treatment period required are also taken into consideration.1 The ICD, in contrast, is a coding system designed for use with all diagnoses, is coded systematically in hospital discharge databases, and is not accompanied by a severity score. (Moore et al) As already stated, there are two major anatomic systems for quantifying injury severity: the Abbreviated Injury Scale and the International Classification of Disease Clinical (ICD9CM) score. There are also several scoring severity systems that are derivatives or functions of these two fundamental methods, such as the Anatomic Profile (AP), the Injury Severity Scale (ISS) and the New Injury Severity Scale (NISS), all based on the AIS, and the ICISS based on ICD-9CM scores. The predictive validity and reliability of the AIS and its derivatives have been demonstrated, but their widespread use in populationbased research and evaluation is constrained because of the cost of AIS coding.6 Studies have shown that it takes, on average, between 10 and 20 minutes to review the medical record and assign AIS codes; coding time depends on the complexity of the case and the training of the coder. The study by Sacco et al. was designed to evaluate the relative performance among both AIS- and ICD-based scoring systems. Results support the integrity of the AIS and argue for its continued use in research and evaluation. The study concluded that the modified Anatomic Profile, Anatomic Profile, and New Injury Severity Score, however, should be used in preference to the Injury Severity Score as an overall measure of severity.

1. Abbreviated Injury Scale (AIS) The Abbreviated Injury Scale (AIS), developed in 1971, is a guideline of anatomical descriptors of wounds from trauma victims, which supplies for each lesion description an identifying number made up by 7 digits: the first digit identifies body region; the second represents type of anatomical structure; the third and fourth digits identify specific anatomical structure, or in cases of external surface lesions, the specific nature of the lesion; the fifth and sixth digits identify the level of lesion in a specific body region and anatomical structure; and the seventh digit to the right of decimal point is the AIS severity score identifier. This number may vary from 1 (minimal severity) to 6 (maximum severity, almost always fatal)(1). AIS is the basis for the calculation of other injury indices, including the Injury Severity Score (ISS) and the New Injury Severity Score (NISS). (See AIS 2005: A contemporary injury scale). 2. Injury Severity Score Among anatomical severity scores, the Injury Severity Score (ISS), created by Baker et al. in 1974 has been considered for over 20 years to be the gold standard to classify trauma victims, both blunt and penetrating. The ISS is based upon the Abbreviated Injury Scale AIS. ISS is obtained by summing the square value of the 3 highest AIS scores, identifying severity of patients and enabling stratification of them. A polytrauma is defined as an ISS >= 16. The greater the score value, the greater the severity of patient, and, consequently greater mortality. The ISS does have significant limitations, most notably, the ISS does not account for multiple injuries to a single body region nor for differences in severity across body regions. For instance, an AIS 5 injury is given equal weight in the ISS calculation, despite the fact that an abdomen AIS 5 injury is associated with a significantly higher probability of survival than a AIS 5 head injury. In 1997, authors of ISS changed this indicator because there was a flaw identified in its calculation, which considered a single lesion per body lesion, underestimating the severity of patients. In patients with multiple lesions located in the same body region, ISS considers only the most severe, ignoring the second most severe lesion that many times, is in the same body segment of the first. To correct these limitations the New Injury Severity Score (NISS) was created considering the three most severe lesions in the calculation, regardless of the body region(3). This change from ISS to NISS aimed at increasing predictive value of the index and simplifying its calculation. The predictive value of NISS vs. ISS has been assessed with regard to several outcomes, including survival, rate of hospital stay, admission to ICU, sepsis, multiple organ dysfunction syndrome, nosocomial infection, postoperative complications, post trauma complications, results/functional skill, transference to other hospitals and situation at discharge (whether or not resources and/or specific medical care were needed after hospital discharge), and it has generally been found that NISS outperforms or is equivalent to ISS in predicting these outcomes. 3. Anatomic Profile (AP)

The AP is an alternative index created because of the limitation of ISS. AP was proposed in 1990 and considers all body lesions of the victim for its calculation, but in spite of that, the improvement in AP performance compared to ISS was only modest, and the complexity of AP application discourage broad acceptance of this index. The study by Frankema et al. comparing ISS with AP and NISS presents conclusions that point out better performance of these two indexes compared with ISS. 4. International Classification of Diseases 9th Edition Injury Severity Score (ICISS) An ICD-9 Injury Severity Score (ICISS) is defined as the product of all survival risk ratios for an individual patient's traumatic ICD-9 codes. A survival risk ratio (SRR) is XXXXX.
The ICISS method differs from the other methods in that its building blocks are not the AIS scores for each ICD-9-CM trauma diagnosis, but rather the survival proportion for each ICD-9CM diagnosis based on some reference database. A study by Rutledge et al. (J Trauma 1998)

concluded that ICISS-derived predictions of survival, hospital charges, and hospital length of stay consistently outperformed those of ISS and TRISS. (see Osler et al. J Trauma 1996) 5. Survival Risk Ratios (SRRs) A study by Clark et al concluded the following regarding SRRs: Predictions of survival based on anatomic injury alone can be performed using ICD-9 codes, with no advantage from extra coding of AIS diagnoses. Predictions based on the single worst SRR were closer to actual outcomes than those based on multiplying SRRs. 6. Trauma Mortality Prediction Model (TMPM) A study by Osler et al. (Ann Surg 2008) concluded that trauma mortality models based on empirical estimates of individual injury severity better discriminate between survivors and nonsurvivors than does the current standard, ISS. One such model, the TMPM, has both superior discrimination and calibration when compared with the ISS. The TMPM should replace the ISS as the standard measure of overall injury severity. - TMPM-ICD9: a trauma mortality prediction model based on ICD-9-CM codes.
Glance LG, Osler TM, Mukamel DB, Meredith W, Wagner J, Dick AW. Department of Anesthesiology, University of Rochester School of Medicine, Rochester, New York 14642, USA.

Abstract OBJECTIVE: To develop and validate a new ICD-9 injury model that uses regression modeling, as opposed to a simple ratio measurement, to estimate empiric injury severities for each of the injuries in the ICD-9-CM lexicon. BACKGROUND: The American College of Surgeons now requires International Classification of diseases ninth Edition (ICD-9-CM) codes for injury coding in the National Trauma Databank. International Classification of diseases ninth Edition Injury Severity Score (ICISS) is the best-known risk-adjustment model when injuries are recorded using ICD-9-CM coding, and would

likely be used to risk-adjust outcome measures for hospital trauma report cards. ICISS, however, has been criticized for its poor calibration. METHODS: We developed and validated a new ICD-9 injury model using data on 749,374 patients admitted to 359 hospitals in the National Trauma Databank (version 7.0). Empiric measures of injury severity for each of the trauma ICD-9-CM codes were estimated using a regressionbased approach, and then used as the basis for a new Trauma Mortality Prediction Model (TMPM-ICD9). ICISS and the Single-Worst Injury (SWI) model were also reestimated. The performance of each of these models was compared using the area under the receiver operating characteristic (ROC), the Hosmer-Lemeshow statistic, and the Akaike information criterion statistic. RESULTS: TMPM-ICD9 exhibits significantly better discrimination (ROCTMPM = 0.880 [0.876-0.883]; ROCICISS = 0.850 [0.846-0.855]; ROCSWI = 0.862 [0.858-0.867]) and calibration (HLTMPM = 29.3 [12.1-44.1]; HLICISS = 231 [176-279]; HLSWI = 462 [380-548]) compared with both ICISS and the Single Worst Injury model. All models were improved with the addition of age, gender, and mechanism of injury, but TMPM-ICD9 continued to demonstrate superior model performance. CONCLUSIONS: Because TMPM-ICD9 uniformly out-performs ICISS and the SWI model, it should be used in preference to ICISS for risk-adjusting trauma outcomes when injuries are recorded using ICD9-CM codes. A Trauma Mortality Prediction Model Based on the Anatomic Injury Scale

Osler, Turner MD, MSc; Glance, Laurent MD; Buzas, Jeffery S. PhD; Mukamel, Dana PhD; Wagner, Jacob MD, PhD; Dick, Andrew PhD

Abstract Objective: To develop a statistically rigorous trauma mortality prediction model based on empiric estimates of severity for each injury in the abbreviated injury scale (AIS) and compare the performance of this new model with the injury severity score (ISS). Summary Background Data: Mortality rates at trauma centers should only be compared after adjusting for differences in injury severity, but no reliable measure of injury severity currently exists. The ISS has served as the standard measure of anatomic injury for 30 years. However, it relies on the individual injury severities assigned by experts in the AIS, is nonmonotonic with respect to mortality, and fails to perform even as well as a far simpler model based on the single worst injury a patient has sustained. Methods: This study is based on data from 702,229 injured patients in the National Trauma Data Bank (NTDB 6.1) hospitalized between 2001 and 2005. Sixty percent of the data was used to derive an empiric measure of severity of each of the 1322 injuries in the AIS lexicon by taking the weighted average of coefficients estimated using 2 separate regression models. The remaining 40% of the data was use to create 3 exploratory mortality prediction models and compare their performance with the ISS using measures of discrimination (C statistic), calibration (Hosmer Lemeshow statistic and calibration curves), and the Akaike information criterion. Results: Three new models based on empiric AIS injury severities were developed. All of these new models discriminated survivors from nonsurvivors better than the ISS, but

one, the trauma mortality prediction model (TMPM), had both better discrimination [ROCTMPM = 0.901 (0.898-0.905), ROCISS = 0.871 (0.866-0.877)] and better calibration [HLTMPM = 58 (35-91), HLISS = 296 (228-357)] than the ISS. The addition of age, gender, and mechanism of injury improved all models, but the augmented TMPM dominated ISS by every measure [ROCTMPM = 0.925(0.921-0.928), ROCISS = 0.904(0.901-0.909), HLTMPM = 18 (12-31), HLISS = 54 (30-64)]. Conclusions: Trauma mortality models based on empirical estimates of individual injury severity better discriminate between survivors and nonsurvivors than does the current standard, ISS. One such model, the TMPM, has both superior discrimination and calibration when compared with the ISS. The TMPM should replace the ISS as the standard measure of overall injury severity. 7. Trauma Registry Abbreviated Injury Scale Score (TRAIS) Worse Injury: Kilgo et al. (J Trauma 2003) showed regardless of scoring type (ICD/AIS SRRs or AIS severity), a patient's worst injury discriminates survival better, fits better, and explains more variance than currently used multiple injury scores.

PHYSIOLOGIC SEVERITY SCORES
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 study by Guzzo et al. found that the predictive ability of a novel physiologic 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. 1. Revised Trauma Score (RTS)
The Revised Trauma Score is made up of a combination of results from three categories: Glascow coma scale, systolic blood pressure, and respiratory rate.

2. Physiologic Trauma Score (PTS) A study by Kuhls et al. (J Am Coll Surg 2002) concluded that 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).

COMBINED ANATOMIC/PHYSIOLOGIC SCORES

So far, the studies suggest that predictive power of TRISS and ASCOT are roughly equal, with the advantage of TRISS being that is much easier to calculate.

1. TRISS (Trauma and Injury Severity Score) The TRISS is a composite of the ISS, Revised Trauma Score (RTS), and age. It has great predictive value but its widespread applicability is limited because it is difficult for many trauma facilities to compute because it requires 8 to 10 variables and ISS depends on the specialized Abbreviated Injury Scale (AIS) scale rather than the International Classification of Diseases scale (ICD-9). Recent advances in anatomic and physiologic scoring markedly simplify TRISS-type models at no cost to prediction. This approach uses routinely available data, requires up to seven fewer terms, and predicts at least as well as the original TRISS. These findings could increase the availability of accurate trauma scoring tools to smaller trauma facilities (Incorporating recent advances to make the TRISS approach universally available. Kilgo PD, Meredith JW, Osler TM..) 2. American College of Surgeons' Committee on Trauma (ASCOT) a predictive measure of outcome that incorporates AIS injury descriptions, age, and physiologic data into a single score. ASCOT has not been generally adopted, probably because of it provided only slightly improved predictive power over Trauma and Injury Severity Score (TRISS) at the price of substantially increased complexity of calculation. (Champion et al, J Trauma) Moreover, a recent comparison of ASCOT and TRISS scoring found no significant difference between the Receiver Operator Characteristic Curve (ROC) areas of these two approaches to outcome prediction, confirming that the predictive power of the two methods is equal. [9] List Severity Score Indices Abbreviated Injury Scale – max (AISmax), Trauma and Injury Severity Score (TRISS), A Severity Characterization of Trauma (ASCOT), Abdominal Trauma Index (ATI), Glasgow Coma Scale (GCS), and Pediatric Trauma Score (PTS), four AIS-based algorithms (Injury Severity Score [ISS], New Injury Severity Score, Anatomic Profile Score [APS], and maximum AIS [maxAIS]), their four ICD to AIS mapped counterparts, and the ICD-9– based ISS (ICISS) .

Measuring injury severity: time for a change?
J Trauma. 1998 Brenneman FD, Boulanger BR, McLellan BA, Redelmeier DA. Department of Surgery, Sunnybrook Health Science Centre, University of Toronto, Ontario, Canada.

Abstract: BACKGROUND: The Injury Severity Score (ISS) does not take into account multiple

injuries in the same body region, whereas a New ISS (NISS) may provide a more accurate measure of trauma severity by considering the patient's three greatest injuries regardless of body region. The purpose of this study was to evaluate the ISS and NISS in patients with blunt trauma. METHODS: Consecutive individuals treated from January of 1992 to September of 1996 at one institution were included if they had sustained blunt trauma and satisfied triage standards (n = 2,328). For each patient, we computed the ISS and the NISS to determine how often the two scores were identical or discrepant. Discrepant cases were then further analyzed using receiver operating characteristic curves to determine which score better predicted short-term mortality. RESULTS: The mean ISS was 25 +/- 13, and the mean NISS was 33 +/- 18. The two predictive scores were identical in 32% of patients and discrepant in 68% of patients. Patients with identical scores had a lower mortality rate than patients with discrepant scores (10% vs. 13%; p < 0.02). In patients with discrepant scores, the area under the receiver operating characteristic curves was greater for the NISS than the ISS (0.852 vs. 0.799; p < 0.001), and greater amounts of discrepancy were associated with increasing rates of mortality (p < 0.001). CONCLUSIONS: The NISS often increases the apparent severity of injury and provides a more accurate prediction of short-term mortality. The benefit associated with using the NISS rather than the ISS must be weighed against the disadvantages of changing a scoring system and the potential for still greater improvements.

A new characterization of injury severity.
J Trauma. 1990 Champion HR, Copes WS, Sacco WJ, Lawnick MM, Bain LW, Gann DS, Gennarelli T, Mackenzie E, Schwaitzberg S. Washington Hospital Center, DC 20010.

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.

NISS predicts post injury multiple organ failure better than the ISS.
J Trauma. 2000 Balogh Z, Offner PJ, Moore EE, Biffl WL. Department of Surgery, Denver Health Medical Center, University of Colorado Health Sciences Center, 80204, USA.

Abstract: BACKGROUND: The Injury Severity Score (ISS) has been observed consistently to be a robust predictor of postinjury multiple organ failure (MOF). However, the ISS fails to account for multiple injuries to the same body region. Recently, the "new" ISS (NISS) has been proposed to address this shortcoming. Preliminary studies suggest the NISS is superior to the ISS in predicting trauma mortality. Our purpose was to determine whether the NISS is a better predictor of postinjury MOF than the ISS. METHODS: A total of 558 patients admitted to our Level I trauma center with ISS > 15, age > 15 years, and survival > 48 hours were prospectively identified; 101 (18%) developed postinjury MOF. Data characterizing postinjury MOF were collected, and the NISS was calculated retrospectively. The ISS and NISS were compared as univariate predictors of MOF. Multivariate analysis was used to determine whether substitution of NISS for ISS resulted in a superior predictive model. RESULTS: In 295 patients (53%), the NISS was greater than the ISS. This subgroup of patients experienced a greater frequency of MOF (26.7% vs. 8.3%, p < 0.0001), a higher mortality (12.8% vs. 4.9%, p < 0.001), and a higher early transfusion requirement (6.7 U vs. 3.6 U, p < 0.0001) compared with the group in which NISS equaled ISS. Moreover, the NISS yielded better separation between patients with and without MOF reflected by the greater difference in median NISS scores compared with ISS scores. The multivariate predictive model, including NISS, showed a better goodness of fit compared with the same model that included ISS. CONCLUSIONS: The NISS is superior to the ISS in the prediction of postinjury MOF. This measure of tissue injury severity should replace the ISS in trauma

Comparison of alternative methods for assessing injury severity based on anatomic descriptors.
J Trauma. 1999 Sacco WJ, MacKenzie EJ, Champion HR, Davis EG, Buckman RF. ThinkSharp, Inc., Bel Air, Maryland, USA. Abstract: BACKGROUND: There is mounting confusion as to which anatomic scoring systems can be used to adequately control for trauma case mix when predicting patient survival. METHODS: Several Abbreviated Injury Scale (AIS) and International Classification of Disease Clinical (ICD-9CM)-based methods of scoring severity were compared by using

data from the Pennsylvania Trauma Outcome Study. By using a design dataset, the probability of survival was modeled as a function of each score or profile. Resulting coefficients were used to derive expected probabilities in a test dataset; expected and observed probabilities were then compared by using standard measures of discrimination and calibration. RESULTS: The modified Anatomic Profile, Anatomic Profile, and New Injury Severity Score outperformed the International Classification of Disease-based Injury Severity Score. This finding remains true when AIS values are obtained by means of a conversion from International Classification of Disease to AIS. CONCLUSION: Results support the integrity of the AIS and argue for its continued use in research and evaluation. The modified Anatomic Profile, Anatomic Profile, and New Injury Severity Score, however, should be used in preference to the Injury Severity Score as an overall measure of severity.

Consensus or data-derived anatomic injury severity scoring?
J Trauma. 2008 Moore L, Lavoie A, Le Sage N, Bergeron E, Emond M, Abdous B. Trauma and Emergency Medicine Research Unit, Department of Social and Preventative Medicne, Centre Hospitalier Affilié Universitaire Québec (Enfant-Jésus Hospital), Laval University, Quebec City, Quebec, Canada.

Abstract: BACKGROUND: Anatomic injury severity scores can be grouped into two classes; consensus-derived and data-derived. The former, including the Injury Severity Score (ISS), the New Injury Severity Score (NISS), and the Anatomic Profile Score (APS), are based on the severity score of the Abbreviated Injury Scale (AIS), assigned by clinical experts. The latter, including the International Classification of Disease Injury Severity Score (ICISS) and the Trauma Registry Abbreviated Injury Scale Score (TRAIS) are based on survival probabilities calculated in large trauma databases. We aimed to compare the predictive accuracy of consensus-derived and data-derived severity scores when considered alone and in combination with age and physiologic status. METHODS: Analyses were based on 25,111 patients from the trauma registries of the four Level I trauma centers in the province of Quebec, Canada, abstracted between April 1998 and March 2005. The predictive validity of each severity score was evaluated in logistic regression models predicting hospital mortality using measures of discrimination (Area Under the Receiver Operating Characteristics curve [AUC]) and calibration (HosmerLemeshow statistic [HL]). RESULTS: Data-derived scores had consistently better predictive accuracy than consensus-derived scores in univariate models (p < 0.0001) but very little difference between scores was observed in models including information on age and physiologic status. The difference in AUC between the least accurate severity score (ISS) and the most accurate severity score (TRAIS) was 15% in anatomic-only models but fell to 2% in models including age and physiologic status. CONCLUSIONS: Data-derived scores provide more accurate mortality prediction than consensus-derived scores do when only anatomic injury severity is considered but offer little advantage if

age and physiologic status are taken into account. This may be because of the fact that data-derived scores are not an independent measure of anatomic injury severity.

The New Injury Severity Score: a more accurate predictor of in-hospital mortality than the Injury Severity Score.
J Trauma. 2004 Lavoie A, Moore L, LeSage N, Liberman M, Sampalis JS. Centre hospitalier affilié universitaire de Québec, Enfant-Jésus Hospital, Quebec City, Quebec, Canada.

Abstract: OBJECTIVE: The purpose of this study was to determine whether the New Injury Severity Score (NISS) is a better predictor of mortality than the Injury Severity Score (ISS) in general and in subgroups according to age, penetrating trauma, and body region injured. METHODS: The study population consisted of 24,263 patients from three urban Level I trauma centers in the province of Quebec, Canada. Discrimination and calibration of NISS and ISS models were compared using receiver operator characteristic (ROC) curves and Hosmer-Lemeshow statistics. RESULTS: NISS showed better discrimination than ISS (area under the ROC curve = 0.827 vs. 0.819; p = 0.0006) and improved calibration (Hosmer-Leme-show = 62 vs. 112). The advantage of the NISS over the ISS was particularly evident among patients with head/neck injuries (area under the ROC curve = 0.819 vs. 0.784; p < 0.0001; Hosmer-Lemeshow = 59 vs. 350). CONCLUSION: The NISS is a more accurate predictor of in-hospital death than the ISS and should be chosen over the ISS for case-mix control in trauma research, especially in certain subpopulations such as head/neck-injured patients.

Prediction of outcomes in trauma: anatomic or physiologic parameters?
J Am Coll Surg. 2005 Guzzo JL, Bochicchio GV, Napolitano LM, Malone DL, Meyer W, Scalea TM. Department of Surgery, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, Baltimore, MD 21201, USA.

Abstract: 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 singleinstitution 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.

Trauma registry injury coding is superfluous: a comparison of outcome prediction based on trauma registry International Classification of DiseasesNinth Revision (ICD-9) and hospital information system ICD-9 codes.
J Trauma. 1997 Osler TM, Cohen M, Rogers FB, Camp L, Rutledge R, Shackford SR. Department of Surgery, College of Medicine, University of Vermont, Burlington 05405, USA.

BACKGROUND: Trauma registries are an essential but expensive tool for monitoring trauma system performance. The time required to catalog patients' injuries is the source of much of this expense. Typically, 15 minutes of chart review per patient are required, which in a busy trauma center may represent 25% of a full-time employee. We hypothesized that International Classification of Disease-Ninth Revision (ICD-9) codes generated by the hospital information system (HI) would be similar to those coded by a dedicated trauma registrar (TR) and would be as accurate as TR ICD-9 codes in predicting outcome. METHODS: One thousand eight hundred twelve patients admitted to a Level I trauma center during 2 years had International Classification of Disease Injury Severity Scores (ICISS) calculated based on HI and TR ICD-9 codes. The relative predictive powers of these two ICISSs were then compared for every patient using Receiver Operator Characteristic Curve Area (ROC) and Hosmer Lemeshow Statistics. RESULTS: Eighty-nine percent of patients (1,608 of 1,812) had identical HI and TR ICISSs. Eleven patients' ICISSs differed by >0.1, and only two patients' scores differed by >0.2. ICISS proved to be a powerful predictor of outcome whether derived from HI (ROC = 0.884; 95% confidence interval (CI) = 0.850-0.917) or TR (ROC = 0.872; 95% CI = 0.8370.908). Although these predictive powers were not significantly different (p = 0.076), the trend was for HI to perform better than TR. ISS calculated for the same data set using the MacKenzie dictionary proved significantly less predictive of outcome than either ICISS (ROC(MacKenzie) = 0.843; 95% CI = 0.792-0.884; p = 0.034). CONCLUSION: We conclude that in our hospital TR data on individual injuries can be replaced by HI data

without loss of predictive power. ISS based on the MacKenzie dictionary should be abandoned because it is much less predictive of outcome than ICISS.

Incorporating recent advances to make the TRISS approach universally available.
J Trauma. 2006 Kilgo PD, Meredith JW, Osler TM. Department of Biostatistics, Emory University School of Public Health, GA 30322, USA.

BACKGROUND: The Trauma and Injury Severity Score (TRISS), used to garner predictions of survival from the Injury Severity Score (ISS), the Revised Trauma Score (RTS, for physiologic reserve), and age is difficult for many trauma facilities to compute because it requires 8 to 10 variables and ISS depends on the specialized Abbreviated Injury Scale (AIS) scale rather than the International Classification of Diseases scale (ICD-9). It has been shown that metrics describing a patient's worst injury (WORSTSRR) are a powerful predictor of survival (regardless of coding type, AIS versus ICD-9) and that the Glasgow Coma Scale (GCS) motor component contains the majority of the information found in the full GCS score. This study hypothesized that the TRISS approach could be made more predictive and efficient with fewer variables by incorporating these advances. METHODS: A total of 310,958 patients with nonmissing TRISS variables were subset from the National Trauma Data Bank (NTDB). Logistic regression was used to model mortality as a function of anatomic, physiologic and age variables. A traditional TRISS model was computed (with NTDB-derived coefficients) that uses ISS, RTS, age index, and mechanism to predict survival. Four smaller three- or four-variable models employed the ICD-9 WORSTSRR, the GCS motor component, and age (both continuously and dichotomously). Two of the four models also use mechanism. These models were compared using the concordance index (c-index, a measure of model discrimination) and the pseudo-R statistic (estimates proportion of variance explained). RESULTS: Each experimental model (two models with 3 variables and two models with 4 variables) have superior discrimination and explain more variance than the traditional TRISS model that employs 8-10 variables. CONCLUSIONS: Recent advances in anatomic and physiologic scoring markedly simplify TRISS-type models at no cost to prediction. This approach uses routinely available data, requires up to seven fewer terms, and predicts at least as well as the original TRISS. These findings could increase the availability of accurate trauma scoring tools to smaller trauma facilities.

A modification of the injury severity score that both improves accuracy and simplifies scoring.
J Trauma. 1997 Osler T, Baker SP, Long W. Department of Surgery, University of Vermont, Burlington 05405, USA. turner.

OBJECTIVES: The Injury Severity Score (ISS) has served as the standard summary measure of anatomic injury for more than 20 years. Nevertheless, the ISS has an idiosyncrasy that both impairs its predictive power and complicates its calculation. We present here a simple modification of the ISS called the New Injury Severity Score (NISS), which significantly outperforms the venerable but dated ISS as a predictor of mortality. DESIGN: Retrospective calculation of NISS and comparison of NISS with prospectively calculated ISS. MATERIALS AND METHODS: The NISS is defined as the sum of the squares of the Abbreviated Injury Scale scores of each of a patient's three most severe Abbreviated Injury Scale injuries regardless of the body region in which they occur. NISS values were calculated for every patient in two large independent data sets: 3,136 patients treated during a 4-year period at the American College of Surgeons' Level I trauma center in Albuquerque, New Mexico, and 3,449 patients treated during a 4-year period at the American College of Surgeons' Level I trauma center at the Emanuel Hospital in Portland, Oregon. The power of NISS to predict mortality was then compared with previously calculated ISS values for the same patients in each of the two data sets. MEASUREMENTS AND MAIN RESULTS: We find that NISS is not only simple to calculate but more predictive of survival as well (Albuquerque: receiver operating characteristic (ROC) ISS = 0.869, ROC NISS = 0.896, p < 0.001; Portland: ROC ISS = 0.896, ROC NISS = 0.907,p < 0.004). Moreover, NISS provides a better fit throughout its entire range of prediction (Hosmer Lemeshow statistic for Albuquerque ISS = 29.12, NISS = 8.88; Hosmer Lemeshow statistic for Portland ISS = 83.48, NISS = 19.86). CONCLUSION: NISS should replace ISS as the standard summary measure of human trauma.

A comparison of the abilities of nine scoring algorithms in predicting mortality.
J Trauma. 2002 Meredith JW, Evans G, Kilgo PD, MacKenzie E, Osler T, McGwin G, Cohn S, Esposito T, Gennarelli T, Hawkins M, Lucas C, Mock C, Rotondo M, Rue L, Champion HR. Department of Surgery, Wake Forest University School of Medicine, Winston-Salem, North Carolina 27157, USA.

OBJECTIVE: The purpose of this study was to compare the abilities of nine Abbreviated Injury Scale (AIS)- and (ICD-9)-based scoring algorithms in predicting mortality. METHODS: The scores collected on 76,871 incidents consist of four AIS-based algorithms (Injury Severity Score [ISS], New Injury Severity Score, Anatomic Profile Score [APS], and maximum AIS [maxAIS]), their four ICD to AIS mapped counterparts, and the ICD-9based ISS (ICISS). A 10-fold cross-validation was performed and area under the receiver operating characteristic curve was used to determine algorithm discrimination. HosmerLemeshow statistics were computed to gauge goodness-of-fit, and model refinement measured variance of predicted probabilities. RESULTS: Overall, the ICISS has the best discrimination and model refinement, whereas the APS has the best Hosmer-Lemeshow performance. ICD-9 to AIS mapped scores have worse discrimination than their AIS-

based counterparts, but still show moderate performance. CONCLUSION: Differences in performance were relatively small. Complex scores such as the ICISS and the APS provide improvement in discrimination relative to the maxAIS and the ISS. Trauma registries should move to include the ICISS and the APS. The ISS and maxAIS perform moderately well and have bedside benefits.

Comparing measures of injury severity for use with large databases.
J Trauma. 2002 Stephenson SC, Langley JD, Civil ID. Injury Prevention Research Unit, Department of Preventive and Social Medicine, Dunedin School of Medicine, University of Otago, New Zealand.

BACKGROUND: After recent debate about the best measure of anatomic injury severity, this study aimed to compare four measures based on Abbreviated Injury Scale scores derived using ICDMAP-90-the Modified Anatomic Profile (ICD/mAP), Anatomic Profile Score (ICD/APS), Injury Severity Score (ICD/ISS), and New Injury Severity Score (ICD/NISS)-with the International Classification of Diseases-based Injury Severity Score (ICISS). METHODS: Data were selected from New Zealand public hospital discharges from 1989 to 1998. There were 349,409 patients in the dataset, of whom 3,871 had died. Models were compared in terms of their discrimination and calibration using logistic regression. Age was included as a covariate. RESULTS: The ICISS and ICD/mAP were the best performing measures. Adding age significantly improved the discrimination and calibration of almost all the models. CONCLUSION: The ICISS is a viable alternative to ICDMAP-based measures for coding anatomic injury severity on large datasets.

Predictors of mortality in adult trauma patients: the physiologic trauma score is equivalent to the Trauma and Injury Severity Score.
J Am Coll Surg. 2002 Kuhls DA, Malone DL, McCarter RJ, Napolitano LM. Department of Surgery, University of Nevada School of Medicine, Las Vegas, USA.

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 degrees C or < 36 degrees

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 > or = 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).

ICISS: an international classification of disease-9 based injury severity score.
J Trauma. 1996 Osler T, Rutledge R, Deis J, Bedrick E. Department of Surgery, University of Vermont College of Medicine, Burlington, USA.

BACKGROUND: The Injury Severity Score (ISS) has served as the standard summary measure of human trauma for 20 years. Despite its stalwart service, the ISS has two weaknesses: it relies upon the consensus derived severity estimates for each Abbreviated Injury Scale (AIS) injury and considers, at most, only three of an individual patient's injuries, three injuries that often are not even the patient's most severe injuries. Additionally, the ISS requires that all patients have their injuries described in the AIS lexicon, an expensive step that is currently taken only at hospitals with a zealous commitment to trauma care. We hypothesized that a data driven alternative to ISS that used empirically derived injury severities and considered all of an individual patient's injuries would more accurately predict survival. METHODS: Survival risk ratios were derived for every International Classification of Disease 9th Edition (ICD-9) injury category (800-959.9) using the North Carolina State Discharge Database experience with 300,000 trauma patients over 5 years. An ICD-9 Injury Severity Score (ICISS) was then defined as the product of all survival risk ratios for an individual patient's traumatic ICD9 codes. We compared the performance of ISS and ICISS in a group of 3,142 patients accrued at the University of New Mexico Trauma Center over 4 years. These patients had both AIS and ICD-9 descriptors meticulously assigned prospectively by designated trauma data base personnel. RESULTS: ICISS outperformed ISS at a level that was highly statistically significant (p < 0.0001) and may be clinically important: ISS misclassification

rate 7.67%, ISS Receiver Operator Characteristic Curve area = 0.872; ICISS misclassification rate 5.95%, ICISS Receiver Operator Characteristic Curve area = 0.921. Moreover, these improvements are largely preserved when ICISS is used in a probability of survival model that includes age, mechanism, and revised trauma score. About half of ICISS's improvement in predictive power is because of its use of an individual patient's worst three injuries regardless of body region. The remainder is because of better modeling of individual injuries and allowing all injuries to contribute to the final score. CONCLUSIONS: We conclude that ICISS is a much better predictor of survival than ISS in injured patients. The use of the ICD-9 lexicon may avoid the need for AIS coding, and thus may add an economic incentive to the statistical appeal of ICISS. It is possible that a similar data driven revision of ISS using the AIS vocabulary might perform as well or better than ICISS. Indeed, the actual lexicon used to divide up the injury "landscape" into individual injuries may be of little consequence so long as all injuries are considered and empirically derived SRRs are used to calculate the final injury measure.

Comparison of the Injury Severity Score and ICD-9 diagnosis codes as predictors of outcome in injury: analysis of 44,032 patients.
J Trauma. 1997 Rutledge R, Hoyt DB, Eastman AB, Sise MJ, Velky T, Canty T, Wachtel T, Osler TM. Department of Surgery, University of North Carolina at Chapel Hill 27599-7210, USA.

INTRODUCTION: Appropriate stratification of injury severity is a critical tool in the assessment of the treatment and the prevention of injury. Since its inception, the Injury Severity Score (ISS) has been the generally recognized "gold standard" for anatomic injury severity assessment. However, there is considerable time and expense involved in the collection of the information required to calculate an accurate ISS. In addition, the predictive power of the ISS has been shown to be limited. Previous work has demonstrated that the anatomic information about injury contained in the International Classification of Diseases Version 9 (ICD-9) can be a significant predictor of survival in trauma patients. The goal of this study was to utilize the San Diego County Trauma Registry (SDTR), one of the nation's leading trauma registries, to compare the predictive power of the ISS with the predictive power of the information contained in the injured patients' ICD-9 diagnoses codes. It was our primary hypothesis that survival risk ratios derived from patients' ICD-9 diagnoses codes would be equal or better predictors of survival than the Injury Severity Score. The implications of such a finding would have the potential for significant cost savings in the care of injured patients. METHODS: Data for the test population were obtained from the SDTR, which contains data from 1985 through 1993 from five participating hospitals. Four data sources were utilized to estimate the expected survival rate/mortality rate for each ICD-9 code in the SDTR. These were (1) the SDTR patients themselves, (2) the North Carolina State Hospital Discharge Database, (3) the North Carolina Trauma Registry Database, and (4) the Agency for Health Care Policy Research's Health Care Utilization Project Database. Each of these data sources was separately utilized to develop a survival risk ratio (SRR) for

each ICD-9 diagnoses code. The SRR was calculated by dividing the number of survivors for patients with each ICD-9 code by the total number of all patients with the particular ICD-9 diagnoses code. The four groups of SRRs derived from our four data sources were used as predictors of survival and the ability of the SRRs to predict survival was compared with the predictive power of the ISS using measures of accuracy, sensitivity, specificity, and receiver operator characteristic curves. RESULTS: During the years 1985 through 1993, complete data were available for analysis on 44,032 patients. Of these, 2,848 patients died during their hospitalization (6%). Survival risk ratios were calculated for each of the diagnoses in the data base. Logistic regression, using the SAS System for statistical analysis, was used to assess the relative predictive power of the ISS and the survival risk ratios derived from the ICD-9 diagnoses codes from each of the four data bases. The analyses demonstrated that the regression models using the SRRs were generally as good or better than ISS as predictors of survival. The predictive power of the SRRs derived from the SDTR data, the North Carolina Trauma Registry data and the Health Care Utilization Report data were the best. In a subsequent analysis, the SRR values and the ISS were added to the patient's age and the revised Trauma Scores to create new predictive models in the mode of TRISS methodology. The analyses again indicated that the models using SRRs had as good or better predictive power than the model using the ISS. CONCLUSIONS: The present study confirms previous work showing that survival risk ratios derived from injured patients' ICD-9 diagnoses codes are as good as or better than ISS as predictors of survival.

Harborview assessment for risk of mortality: an improved measure of injury severity on the basis of ICD-9-CM.
J Trauma. 2000 West TA, Rivara FP, Cummings P, Jurkovich GJ, Maier RV. Department of Surgery, University of Texas Southwestern Medical Center, Dallas 75235-9158, USA.

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.

The worst injury predicts mortality outcome the best: rethinking the role of multiple injuries in trauma outcome scoring.
J Trauma. 2003 Kilgo PD, Osler TM, Meredith W. Department of Public Health Sciences, Wake Forest University School of Medicine, WistonSalem, North Carolina 27157-1063, USA.

BACKGROUND: The prediction of outcome after injury must incorporate measures of injury severity, but there is no consensus on how many injuries should be used in calculating these measures. Initially, the single worst injury was used to predict outcome, but the introduction of the Injury Severity Score allowed up to three injuries to contribute to outcome prediction. Subsequently, other outcome prediction approaches used many (New Injury Severity Score [NISS]) or all (ICISS and Trauma Registry Abbreviated Injury Scale Score [TRAIS], which use International Classification of Diseases, Ninth Revision [ICD-9] and Abbreviated Injury Scale [AIS] survival risk ratios [SRRs], respectively) of a patient's injuries. The ability of only the most severe injury in predicting mortality has never been studied. Our objective was to determine the ability of a patient's worst injury to predict mortality. METHODS: A 10-fold cross-validation design was used to compute six scores for each of 160,208 patients from a large trauma database (the National Trauma Data Bank [NTDB]). The scores were ICISS, TRAIS, ICISS1 (only a patient's worst ICD-9 SRR), TRAIS1 (only a patient's worst AIS SRR), NISS (sum of squares of worst three AIS severity measures), and MAXAIS (worst AIS severity measure). Discrimination was assessed using the area under the receiver operating characteristic curve. Logistic regression R2 gauged the proportion of variance each score explained. The Akaike information criterion, a deviance statistic (lower is better), assessed model fit. RESULTS: The receiver operating characteristic curve, R2, and Akaike information criterion statistics (NC_ICISS and NC_ICDSRR1 represents scores derived from the original North Carolina Hospital Discharge Database SRRs) are summarized in

tabular form in the Results section. CONCLUSION: Regardless of scoring type (ICD/AIS SRRs or AIS severity), a patient's worst injury discriminates survival better, fits better, and explains more variance than currently used multiple injury scores.

Comparison of the New Injury Severity Score and the Injury Severity Score.
J Trauma. 2004 Tay SY, Sloan EP, Zun L, Zaret P. Department of Emergency Medicine, University of Illinois at Chicago, USA.

BACKGROUND: The New Injury Severity Score (NISS) was proposed in 1997 to replace the Injury Severity Score (ISS) because it is more sensitive for mortality. We aim to test whether this is true in our patients. METHODS: This study was a retrospective review of data from 6,231 consecutive patients over 3 years in the trauma registry of a Level I trauma center studying outcome, ISS, and NISS. RESULTS: Misclassification rates were 3.97% for the NISS and 4.35% for the ISS. The receiver operating characteristic curve areas were 0.936 and 0.94, respectively. Neither the ISS nor the NISS were well calibrated (Hosmer-Lemeshow statistic, 36.11 and 49.28, respectively; p < 0.001). CONCLUSION: The NISS should not replace the ISS, as they share similar accuracy and calibration.

Prediction of mortality in pediatric trauma patients: new injury severity score outperforms injury severity score in the severely injured.
J Trauma. 2003 Sullivan T, Haider A, DiRusso SM, Nealon P, Shaukat A, Slim M. Department of Surgery, New York Medical College, Valhalla, 10595, USA. BACKGROUND: The Injury Severity Score (ISS) is a widely accepted method of measuring severity of traumatic injury. A modification has been proposed--the New Injury Severity Score (NISS). This has been shown to predict mortality better in adult trauma patients, but it had no predictive benefit in pediatric patients. The aim of this study was to determine whether the NISS outperforms the ISS in a large pediatric trauma population. METHODS: Admissions in the National Pediatric Trauma Registry between April 1996 and September 1999 were included. The ISS and NISS were calculated for each patient. The study endpoints were mortality at hospital discharge, functional outcome in three domains (expression, locomotion, and feeding), and discharge disposition for the survivors. Predictive ability of each score was assessed by area under the receiver operating characteristic curve. RESULTS: The NISS and ISS performed equally well at predicting mortality in patients with lower injury severity (ISS < 25), but the NISS was significantly better at predicting mortality in the more severely injured patients. Both scores performed equally well at predicting expression and feeding ability. The NISS was superior to the ISS in predicting locomotion ability at discharge. Thirty-seven percent of patients had an NISS that was higher than their ISS. These patients had a significantly

higher mortality and suffered worse functional outcomes. CONCLUSION: The NISS performs as well as the ISS in pediatric patients with lower injury severity and outperforms the ISS in those with higher injury severity.

A comparison among the abilities of various injury severity measures to predict mortality with and without accompanying physiologic information.
J Trauma. 2005 Hannan EL, Waller CH, Farrell LS, Cayten CG. Department of Health Policy, Management, and Behavior, School of Public Health, University at Albany, State University of New York, Rensselaer, NY 12144, USA.

BACKGROUND: A few recent studies have compared the abilities of different injury severity measures to predict inpatient mortality. This study extended previous studies in that it used a registry with noncenters as well as centers, and examined the relative marginal abilities of competing severity measures to predict mortality when physiologic data also are available. METHODS: Several methods for assessing injury severity of trauma patients were compared in terms of their ability to predict mortality with and without the addition of additional demographic and physiologic information using logistic regression models. Separate determinations also were made for all patients and for three groups of patients with blunt trauma resulting from motor vehicle crashes, low falls, and other blunt injuries. Statistical models were compared using measures of discrimination and calibration. RESULTS: The International Classification of DiseaseBased Severity Score (ICISS) had the best discrimination for each of the eight models examined, and it was significantly better than all the other measures in relation to the models for all patients and for victims of motor vehicle crashes. The ICISS also had the best calibration in half of the models with and half without demographic and physiologic information. The New Injury Severity Score had the best calibration in relation to two of the remaining four models. Physiologic data add substantially to the ability to predict mortality regardless of the anatomic injury severity measure used. CONCLUSIONS: On the average, the ICISS had the best discrimination of all of the measures, as well as a slight edge with respect to calibration in predicting trauma mortality with or without the aid of demographic or physiologic measures.

Improved predictions from a severity characterization of trauma (ASCOT) over Trauma and Injury Severity Score (TRISS): results of an independent evaluation.
J Trauma. 1996 Champion HR, Copes WS, Sacco WJ, Frey CF, Holcroft JW, Hoyt DB, Weigelt JA. University of Maryland, National Study Center for Trauma and EMS, Baltimore 21201, USA.

OBJECTIVE: 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. DESIGN: This was a prospective data collection for consecutive admissions to four level I trauma centers participating in a major trauma outcome study. MATERIALS AND METHODS: 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)]. MEASUREMENTS AND MAIN RESULTS: 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 receiveroperating characteristic curves (both exceed 0.96) are comparable, and both models satisfy the H-L criterion. CONCLUSIONS: 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.

The end of the Injury Severity Score (ISS) and the Trauma and Injury Severity Score (TRISS): ICISS, an International Classification of Diseases, ninth revision-based prediction tool, outperforms both ISS and TRISS as predictors of trauma patient survival, hospital charges, and hospital length of stay.
J Trauma. 1998 Rutledge R, Osler T, Emery S, Kromhout-Schiro S. Department of Surgery, University of North Carolina at Chapel Hill, 27599-7210, USA.

INTRODUCTION: Since their inception, the Injury Severity Score (ISS) and the Trauma and Injury Severity Score (TRISS) have been suggested as measures of the quality of trauma care. In concept, they are designed to accurately assess injury severity and predict expected outcomes. ICISS, an injury severity methodology based on International Classification of Diseases, Ninth Revision, codes, has been demonstrated to be superior to ISS and TRISS. The purpose of the present study was to compare the ability of TRISS to ICISS as predictors of survival and other outcomes of injury (hospital length of stay and hospital charges). It was our hypothesis that ICISS would outperform ISS and TRISS in each of these outcome predictions. METHODS: "Training" data for creation of ICISS predictions were obtained from a state hospital discharge data base. "Test" data were obtained from a state trauma registry. ISS, TRISS, and ICISS were

compared as predictors of patient survival. They were also compared as indicators of resource utilization by assessing their ability to predict patient hospital length of stay and hospital charges. Finally, a neural network was trained on the ICISS values and applied to the test data set in an effort to further improve predictive power. The techniques were compared by comparing each patient's outcome as predicted by the model to the actual outcome. RESULTS: Seven thousand seven hundred five patients had complete data available for analysis. The ICISS was far more likely than ISS or TRISS to accurately predict every measure of outcome of injured patients tested, and the neural network further improved predictive power. CONCLUSION: In addition to predicting mortality, quality tools that can accurately predict resource utilization are necessary for effective trauma center quality-improvement programs. ICISS-derived predictions of survival, hospital charges, and hospital length of stay consistently outperformed those of ISS and TRISS. The neural network-augmented ICISS was even better. This and previous studies demonstrate that TRISS is a limited technique in predicting survival resource utilization. Because of the limitations of TRISS, it should be superseded by ICISS.

The New Injury Severity Score and the evaluation of pediatric trauma.
J Trauma. 2001 Grisoni E, Stallion A, Nance ML, Lelli JL Jr, Garcia VF, Marsh E. Department of Surgery, Rainbow Babies and Children's Hospital, University Hospitals of Cleveland, 11100 Euclid Avenue, Cleveland, OH 44106-6039.

BACKGROUND: To compare the effectiveness of the Injury Severity Score (ISS) and New Injury Severity Score (NISS) in predicting mortality in pediatric trauma patients. METHODS: NISS, the sum of the squares of a patient's three highest Abbreviated Injury Scale scores (regardless of body region), were calculated for 9,151 patients treated at four regional pediatric trauma centers and compared with previously calculated ISS values. The power of the two scoring systems to predict mortality was gauged through comparison of misclassification rates, receiver operating characteristic curves, and Hosmer-Lemeshow goodness-of-fit statistics. RESULTS: Although there were significant differences in mean NISS and ISS values for each hospital, differences in the predictive abilities of the two scoring systems were insignificant, even when analysis was restricted to the subgroup of patients with severe or penetrating injuries. CONCLUSION: The significant differences in the predictive abilities of the ISS and NISS reported in studies of adult trauma patients were not seen in this review of pediatric trauma patients.

The Injury Severity Score or the New Injury Severity Score for predicting intensive care unit admission and hospital length of stay?
Injury. 2005 Lavoie A, Moore L, LeSage N, Liberman M, Sampalis JS. Unité de Recherche en Traumatologie, Centre Hospitalier Affilié Universitaire de Québec (Enfant-Jésus Hospital), 1401, 18ème rue, Quebec City (Qc), Que., Canada G1J 1Z4.

OBJECTIVES: To compare the New Injury Severity Score (NISS) and the Injury Severity Score (ISS) as predictors of intensive care unit (ICU) admission and hospital length of stay (LOS) in an urban North American trauma population and in a subset of patients with head injuries. METHODS: The study population consisted of 23,909 patients from three urban level I trauma centres in the province of Quebec, Canada. The predictive accuracies of the NISS and the ISS were compared using Receiver Operator Characteristic (ROC) curves and Hosmer-Lemeshow (H-L) statistics for the logistic regression model of ICU admission and using r2 for the linear regression model of LOS. RESULTS: A total of 7660 (32%) patients were admitted to the ICU. Mean LOS was 8.2+/2.5 days. In the whole sample, the NISS presented equivalent discrimination (area under ROC curve: NISS = 0.839 versus ISS = 0.843, p = 0.08) but better calibration (H-L statistic: 309 versus 611) for predicting ICU admission. In the subgroup patients with moderate to serious head injuries, the NISS was a better predictor of ICU admission in terms of both discrimination (area under ROC curve: NISS = 0.771 versus ISS = 0.747, p < 0.00001) and calibration (H-L statistic: 12 versus 21). The NISS explained more variation in LOS than the ISS for the whole sample (r2 = 0.254 versus 0.249, p = 0.0008) and in the subpopulation with moderate to severe head injuries (r2 = 0.281 versus 0.263, p = 0.0002). CONCLUSIONS: The NISS is a better choice for case mix control in trauma research than the ISS for predicting ICU admission and LOS, particularly among patients with moderate to severe head injuries.

An evaluation of Ontario trauma outcomes and the development of regional norms for Trauma and Injury Severity Score (TRISS) analysis.
J Trauma. 1996 Lane PL, Doig G, Mikrogianakis A, Charyk ST, Stefanits T. Department of Trauma Services, Victoria Hospital, London, Ontario, Canada.

Outcomes analysis of patient care programs has become increasingly necessary for a variety of reasons in recent years. This has been particularly true for trauma programs. The Trauma and Injury Severity Score (TRISS) methodology was developed for this purpose in the context of the Major Trauma Outcome Study (MTOS). It provides an estimate of the probability of survival for individual patients, based on anatomic, physiologic, and etiologic factors. In addition, it allows hospitals and groups of hospitals to compare survival rates with other hospitals submitting data to the data base. However, the published coefficients for TRISS analysis have been derived from the MTOS data base. Patterns of practice, time to treatment, and other variables may be significantly different in other jurisdictions. To compare outcomes among similar hospitals within the province of Ontario, Canada, a regression analysis was performed to develop TRISS coefficients specific to the province. Data were obtained from the 12 trauma centers in the province treating the most severely injured patients (Injury Severity Score > 12). A total of 3,880 cases were eligible for TRISS analysis, over a 3-year period. Of these, 3,672 were patients with blunt trauma, and 208 were victims of

penetrating injury. Standard TRISS analysis of the patients with blunt trauma revealed z scores ranging from -10.260 to +1.849, with a mean of -6.648. Four centers had negative z scores that were significant (an absolute value of > 1.96 is considered statistically significant). Using Ontario TRISS coefficients, z scores ranged from -4.125 to +2.782, with a mean of 0.000. Four scores were significant with the Ontario coefficients, only one of which had been significant using the MTOS norms. The other three z scores were all positive, indicating more deaths than would have been predicted, but they were not significant when compared to the MTOS norms. The mean was also, of course, no longer significant. The area under the receiver operating characteristic curve analysis was strongly positive, and the Hosmer-Lemeshow Goodness-of-Fit analysis indicated good calibration. The new coefficients were subsequently validated by applying them to a subsequent year's data from patient records that did not form part of the original data set. This resulted in slightly improved z scores overall, and in most of the hospitals. This use of regional norms allows comparison with outcomes of patients cared for in hospitals within the same jurisdiction that are more similar to one another than to those in the MTOS, and helps to identify unexpected outcomes and outliers.

A trauma mortality prediction model based on the anatomic injury scale.
Ann Surg. 2008 Osler T, Glance L, Buzas JS, Mukamel D, Wagner J, Dick A. Department of Surgery, University of Vermont, Burlington, VT, USA.

OBJECTIVE: To develop a statistically rigorous trauma mortality prediction model based on empiric estimates of severity for each injury in the abbreviated injury scale (AIS) and compare the performance of this new model with the injury severity score (ISS). SUMMARY BACKGROUND DATA: Mortality rates at trauma centers should only be compared after adjusting for differences in injury severity, but no reliable measure of injury severity currently exists. The ISS has served as the standard measure of anatomic injury for 30 years. However, it relies on the individual injury severities assigned by experts in the AIS, is nonmonotonic with respect to mortality, and fails to perform even as well as a far simpler model based on the single worst injury a patient has sustained. METHODS: This study is based on data from 702,229 injured patients in the National Trauma Data Bank (NTDB 6.1) hospitalized between 2001 and 2005. Sixty percent of the data was used to derive an empiric measure of severity of each of the 1322 injuries in the AIS lexicon by taking the weighted average of coefficients estimated using 2 separate regression models. The remaining 40% of the data was use to create 3 exploratory mortality prediction models and compare their performance with the ISS using measures of discrimination (C statistic), calibration (Hosmer Lemeshow statistic and calibration curves), and the Akaike information criterion. RESULTS: Three new models based on empiric AIS injury severities were developed. All of these new models discriminated survivors from nonsurvivors better than the ISS, but one, the trauma mortality prediction model (TMPM), had both better discrimination [ROCTMPM = 0.901 (0.898-0.905), ROCISS = 0.871 (0.866-0.877)] and better calibration [HLTMPM = 58 (35-

91), HLISS = 296 (228-357)] than the ISS. The addition of age, gender, and mechanism of injury improved all models, but the augmented TMPM dominated ISS by every measure [ROCTMPM = 0.925(0.921-0.928), ROCISS = 0.904(0.901-0.909), HLTMPM = 18 (12-31), HLISS = 54 (30-64)]. CONCLUSIONS: Trauma mortality models based on empirical estimates of individual injury severity better discriminate between survivors and nonsurvivors than does the current standard, ISS. One such model, the TMPM, has both superior discrimination and calibration when compared with the ISS. The TMPM should replace the ISS as the standard measure of overall injury severity.

Comparisons of survival predictions using survival risk ratios based on International Classification of Diseases, Ninth Revision and Abbreviated Injury Scale trauma diagnosis codes.
J Trauma. 2005 Clarke JR, Ragone AV, Greenwald L. Department of Surgery, Drexel University, Philadelphia, Pennsylvania, USA.

BACKGROUND: We conducted a comparison of methods for predicting survival using survival risk ratios (SRRs), including new comparisons based on International Classification of Diseases, Ninth Revision (ICD-9) versus Abbreviated Injury Scale (AIS) six-digit codes. METHODS: From the Pennsylvania trauma center's registry, all direct trauma admissions were collected through June 22, 1999. Patients with no comorbid medical diagnoses and both ICD-9 and AIS injury codes were used for comparisons based on a single set of data. SRRs for ICD-9 and then for AIS diagnostic codes were each calculated two ways: from the survival rate of patients with each diagnosis and when each diagnosis was an isolated diagnosis. Probabilities of survival for the cohort were calculated using each set of SRRs by the multiplicative ICISS method and, where appropriate, the minimum SRR method. These prediction sets were then internally validated against actual survival by the Hosmer-Lemeshow goodness-of-fit statistic. RESULTS: The 41,364 patients had 1,224 different ICD-9 injury diagnoses in 32,261 combinations and 1,263 corresponding AIS injury diagnoses in 31,755 combinations, ranging from 1 to 27 injuries per patient. All conventional ICD-9-based combinations of SRRs and methods had better Hosmer-Lemeshow goodness-of-fit statistic fits than their AIS-based counterparts. The minimum SRR method produced better calibration than the multiplicative methods, presumably because it did not magnify inaccuracies in the SRRs that might occur with multiplication. CONCLUSION: Predictions of survival based on anatomic injury alone can be performed using ICD-9 codes, with no advantage from extra coding of AIS diagnoses. Predictions based on the single worst SRR were closer to actual outcomes than those based on multiplying SRRs.

Statistical validation of the Revised Trauma Score.
J Trauma. 2006 Moore L, Lavoie A, LeSage N, Abdous B, Bergeron E, Liberman M, Emond M.

Centre hospitalier affilié universitaire de Québec, Enfant-Jésus Hospital, Quebec City, Canada.

BACKGROUND: To validate the accuracy of the Revised Trauma Score (RTS) and its components for predicting in-hospital mortality. METHODS: Analyses were based on 22,388 patients from the trauma registries of three urban Level I trauma centers in the province of Quebec, Canada. The accuracy of RTS coded variables for the Glasgow Coma Score (GCSc), Systolic Blood Pressure (SBPc), and Respiratory Rate (RRc) for predicting mortality was evaluated in logistic regression models with measures of discrimination and model fit and compared with Fractional Polynomial (FP) transformations of each component. RESULTS: RTS coded variables were associated with sparse data distributions and did not accurately represent the relation of GCS, SBP, and RR to mortality. FP models were always associated with significantly better discrimination (all p < 0.00001) and model fit. Survival probability estimates generated by the model with FP transformations were significantly different to those generated by the model with RTS-coded variables. CONCLUSIONS: The RTS in its present form does not accurately describe the relation of GCS, SBP, and RR to mortality. FP transformation would improve the accuracy of predicted survival probabilities used for performance evaluation and may improve control of confounding caused by of physiologic severity case mix in trauma research.

Unification of the revised trauma score.
J Trauma. 2006 Moore L, Lavoie A, Abdous B, Le Sage N, Liberman M, Bergeron E, Emond M. Unité de Recherche en Traumatologie, Centre Hospitalier Affilié Universitaire de Québec, Quebec City, Canada.

BACKGROUND: The Revised Trauma Score (RTS) calculated with Major Outcome Trauma Study weights (MTOS-RTS) is currently the standard physiologic severity score in trauma research and quality control. It is often confused with the Triage-RTS (T-RTS), a version that is easier to calculate but only intended for clinical triage. OBJECTIVES: To compare the accuracy of the MTOS-RTS to the RTS calculated with weights derived from the study population (POP-RTS) and the T-RTS, for predicting mortality in a trauma population. METHODS: The study population consists of 22,388 patients, drawn from the trauma registries of three Level I trauma centers. The predictive accuracy of the MTOS-RTS, POP-RTS, and the T-RTS were compared using measures of discrimination and model fit from logistic regression models. RESULTS: The MTOS-RTS, the POP-RTS, and the T-RTS had the same discrimination (Area under the Receiver Operating Curve [AUC] = 0.841). The POP-RTS and the T-RTS had a slightly better model fit than the MTOS-RTS (AIC = 8010, 8010, and 8067, respectively). The T-RTS had equal discrimination and equal or better model fit than the MTOS-RTS in the whole sample, in each of the three trauma centers and in the population of patients with severe head trauma. The T-RTS was also equivalent to the POP-RTS in all of these population sub-groups. CONCLUSIONS: The TRTS could replace the MTOS-RTS as the standard physiologic severity score for trauma outcome prediction. The advantages of using the T-RTS over the MTOS-RTS are ease of

calculation, the need for only one measure for triage and mortality prediction purposes and universal adaptation to a broad range of trauma populations.

Two worst injuries in different body regions are associated with higher mortality than two worst injuries in the same body region.
J Trauma. 2006 Moore L, Lavoie A, Le Sage N, Liberman M, Bergeron E. From the Departments of Social and Preventative Medicine, Centre hospitalier affilié universitaire de Québec, Enfant-Jésus Hospital, Laval University, Canada.

BACKGROUND: The Injury Severity Score (ISS) accounts for only one injury in each body region. The New Injury Severity Score (NISS) considers all injuries in a body region. Despite a big difference between the two scores in patients with multiple injuries, the NISS does not offer significant improvement in mortality prediction. This paper hypothesizes that two injuries in different body regions are associated with higher mortality than two injuries in the same body region, independently of injury severity. METHODS: The population consisted of 15,200 patients with two or more injuries from the Quebec Trauma Registry, Canada, abstracted between 1998 and 2004. The two worst injuries were considered. Logistic regression analysis was used to assess the mortality odds ratio of having two worst injuries in different body regions (DR) compared with two worst injuries in the same body region (SR), while adjusting for the severity and the body region of the two worst injuries and age. RESULTS: A total of 5,869 patients (49%) had their worst injuries in DR. DR patients had 75% higher risk of mortality than SR patients (odds ratio = 1.75, 95% confidence interval = 1.42-2.15). CONCLUSION: After accounting for differing injury severity, patients with their two worst injuries in different body regions have higher mortality than those with their two worst injuries in the same region. Results suggest that the observed effect is not due to a foible in the Abbreviated Injury Scale system but rather to physiologic, clinical, or organizational elements. The results of this study should be considered in the development of future injury severity instruments and may have implications for the care of patients with multiple injuries.

Scoring of anatomic injury after trauma: AIS 98 versus AIS 90--do the changes affect overall severity assessment?
Injury. 2007 Skaga NO, Eken T, Hestnes M, Jones JM, Steen PA. Department of Anaesthesiology, Ulleval University Hospital, 0407 Oslo, Norway.

BACKGROUND: Although several changes were implemented in the 1998 update of the abbreviated injury scale (AIS 98) versus the previous AIS 90, both are still used worldwide for coding of anatomic injury in trauma. This could possibly invalidate comparisons between systems using different AIS versions. Our aim was to evaluate whether the use of different coding dictionaries affected estimation of Injury Severity

Score (ISS), New Injury Severity Score (NISS) and probability of survival (Ps) according to TRISS in a hospital-based trauma registry. MATERIALS AND METHODS: In a prospective study including 1654 patients from Ulleval University Hospital, a Norwegian trauma referral centre, patients were coded according to both AIS 98 and AIS 90. Agreement between the classifications of ISS, NISS and Ps according to TRISS methodology was estimated using intraclass correlation coefficients (ICC) with 95% CI. RESULTS: ISS changed for 378 of 1654 patients analysed (22.9%). One hundred and forty seven (8.9%) were coded differently due to different injury descriptions and 369 patients (22.3%) had a change in ISS value in one or more regions due to the different scoring algorithm for skin injuries introduced in AIS 98. This gave a minimal change in mean ISS (14.74 versus 14.54). An ICC value of 0.997 (95% CI 0.9968-0.9974) for ISS indicates excellent agreement between the scoring systems. There were no significant changes in NISS and Ps. CONCLUSIONS: There was excellent agreement for the overall population between ISS, NISS and Ps values obtained using AIS 90 and AIS 98 for injury coding. Injury descriptions for hypothermia were re-introduced in the recently published AIS 2005. We support this change as coding differences due to hypothermia were encountered in 4.3% of patients in the present study.

Usefulness of the abbreviated injury score and the injury severity score in comparison to the Glasgow Coma Scale in predicting outcome after traumatic brain injury.
J Trauma. 2007 Foreman BP, Caesar RR, Parks J, Madden C, Gentilello LM, Shafi S, Carlile MC, Harper CR, Diaz-Arrastia RR. University of Texas Southwestern Medical Center at Dallas, USA.

BACKGROUND: Assessment of injury severity is important in the management of patients with brain trauma. We aimed to analyze the usefulness of the head abbreviated injury score (AIS), the injury severity score (ISS), and the Glasgow Coma Scale (GCS) as measures of injury severity and predictors of outcome after traumatic brain injury (TBI). METHODS: Data were prospectively collected from 410 patients with TBI. AIS, ISS, and GCS were recorded at admission. Subjects' outcomes after TBI were measured using the Glasgow Outcome Scale (GOS-E) at 12 months postinjury. Uni- and multivariate analyses were performed. RESULTS: Outcome information was obtained from 270 patients (66%). ISS was the best predictor of GOS-E (rs = -0.341, p < 0.001), followed by GCS score (rs = 0.227, p < 0.001), and head AIS (rs = -0.222, p < 0.001). When considered in combination, GCS score and ISS modestly improved the correlation with GOS-E (R = 0.335, p < 0.001). The combination of GCS score and head AIS had a similar effect (R = 0.275, p < 0.001). Correlations were stronger from patients <or=48 years old. We found comparable correlations between patients who suffered severe injuries (GCS <or=8) and those who suffered mild and moderate injuries (GCS >8). CONCLUSIONS: GCS score, AIS, and ISS are weakly correlated with 12-month outcome. However, anatomic measures modestly outperform GCS as predictors of GOS-E. The combination of GCS and AIS/ISS correlate with outcome better than do any of the three measures alone. Results support

the addition of anatomic measures such as AIS and ISS in clinical studies of TBI. Additionally, most of the variance in outcome is not accounted for by currently available measures of injury severity.

Different AIS triplets: Different mortality predictions in identical ISS and NISS.
J Trauma. 2006 Aharonson-Daniel L, Giveon A, Stein M; Israel Trauma Group (ITG), Peleg K. Israel National Center for Trauma and Emergency Medicine Research, Gertner Institute for Epidemiology and Health Policy Research, Tel Hashomer, Israel.

BACKGROUND: Previous studies demonstrated different mortality predictions for identical Injury Severity Scores (ISS) from different Abbreviated Injury Scale (AIS) triplets. This study elaborates in both scope and volume producing results of a larger magnitude, applicable to specific injury subgroups of blunt or penetrating, traumatic brain injury, various age groups, and replicated on NISS. METHODS: All patients hospitalized after trauma at 10 hospitals, with ISS/NISS (new ISS) generated by two AIS triplets, excluding patients with isolated minor or moderate injuries to a single body region were studied. Patients were separated into two groups based on the different triplets. Inpatient-mortality rates were calculated for each triplet group. Odds ratios were calculated to estimate the risk of dying in one triplet group as compared with the other. The chi test determined whether the difference in mortality rate between the two groups was significantly different. Differences were further explored for various subgroups. RESULTS: There were 35,827 patients who had ISS/NISS scores generated by two different AIS triplets. Significant differences in death rates were noted between triplet groups forming identical ISS/NISS. Odds ratio for being in the second group (always containing the higher AIS score) ranged from 2.3 to 7.4. CONCLUSIONS: ISS and NISS that are formed by different AIS triplets have significantly different inpatientmortality rates. The triplet with the higher AIS score has higher inpatient-mortality rates, overall and in several sub-populations of varying vulnerability. The comparison of populations and the interpretation of ISS/NISS based outcome data should take this important information into account and the components of AIS triplets creating each ISS and NISS should be reported.

Factors associated with mortality in trauma: re-evaluation of the TRISS method using the National Trauma Data Bank.
J Trauma. 2004 Millham FH, LaMorte WW. Department of Surgery, Newton Wellesley Hospital, Newton, Massachusetts, USA.

BACKGROUND: TRISS remains a standard method for predicting survival and correcting for severity in outcome analysis. The National Trauma Data Bank (NTDB) is emerging as

a major source of trauma data that will be used for both primary research and outcome benchmarking. We used NTDB data, to determine whether TRISS is still an accurate predictor of survival coefficients and to determine whether the ability of TRISS to predict survival could be improved by updating the coefficients or by building predictive models that include information on co-morbidities. METHODS: To compare the utility of different methods of TRISS calculation we identified the records of 72,517 trauma patients (62,103 blunt trauma and 10,414 penetrating trauma) who had complete information for all of the covariates to be considered in the analysis. Multiple logistic regression was used to recalculate the TRISS coefficients in models using both the original TRISS covariates and in models which also included variables for co-morbidities that could potentially affect survival. Model discrimination was evaluated by calculating the area under the receiver operating characteristic curves (AUC), and model calibration was evaluated with the Hosmer-Lemeshow Goodness-of-Fit Statistic (H-L). RESULTS: For penetrating trauma the original TRISS equation had good discriminative ability (AUC=0.98), but was poorly calibrated (H-L=267.04). When logistic regression was used to generate revised coefficients, discrimination was unchanged, but calibration improved (H-L=38.66). The only co-morbid factor significantly associated with survival after penetrating trauma was acute alcohol consumption, which was associated with increased survival (p < 0.0001). However, its inclusion in a logistic model did not improve discrimination, but improved calibration somewhat (AUC =0.98; H-L=19.95). The original TRISS equation was a less accurate predictor of survival after blunt trauma (AUC = 0.84; H-L= 10,720.7). When logistic regression was used to generate revised coefficients for the original TRISS covariates, predictions after blunt trauma improved (AUC = 0.94; H-L=25.45). With blunt trauma, acute alcohol consumption and prior hypertension were associated with increased survival, and male gender, congestive failure, cirrhosis, and prior myocardial infarction were associated with decreased survival. However, inclusion of these covariates in a logistic model did not improve predictions of survival (AUC = 0.94; H-L= 34.83). CONCLUSIONS: In the NTDB the traditional TRISS had limited ability to predict survival after trauma. Accuracy of prediction was improved by recalculating the TRISS coefficients, but further improvements were not seen with models that included information about comorbidities.

Prognostic value of trauma scores in pediatric patients with multiple injuries.
J Trauma. 2000 Ott R, Krämer R, Martus P, Bussenius-Kammerer M, Carbon R, Rupprecht H. Department of Surgery, University of Erlangen-Nuremberg, Germany.

BACKGROUND: For the quantification of multiple injuries in children, a range of different trauma scores are available, the actual prognostic value of which has, however, not so far been investigated and compared in a group of patients. METHODS: In 261 polytraumatized children and adolescents, 11 trauma scores (Abbreviated Injury Scale [AIS], Injury Severity Score [ISS], Glasgow Coma Scale [GCS], Acute Trauma Index [ATI],

Shock Index [SI], Trauma Score [TS], Revised Trauma Score [RTS], Modified Injury Severity Score [MISS], Trauma and Injury Severity Score [TRISS]-Scan, Hannover Polytrauma Score [HPTS], and Pediatric Trauma Score [PTS]) were calculated, and their prognostic relevance in terms of survival, duration of intensive care treatment, hospital stay, and long-term outcome analyzed. RESULTS: With a specificity of 80%, physiologic scores (TS, RTS, GCS, ATI) showed a greater accuracy (79-86% vs. 73-79%) with regard to survival prediction than did the anatomic scores (AIS, HPTS, ISS, PTS); combined forms of these two types of score (TRISS-Scan, MISS) did not provide any additional information (76-80%). Overall, the TRISS-Scan was the score that showed the highest correlation with duration of treatment and long-term outcome. Trauma scores specially conceived for use with children (PTS, MISS) failed to show any superiority vis-à-vis trauma scores in general. CONCLUSION: With regard to prognostic quality and ease of use in the practical setting, TS and the TRISS-Scan are recommended for polytrauma in children and adolescents. Special pediatric scores are not necessary.

AIS 2005: a contemporary injury scale.
Gennarelli TA, Wodzin E. Injury. 2006 Department of Neurosurgery, Medical College of Wisconsin, 9200 W, Wisconsin Avenue, Milwaukee, WI 53226, United States.

To determine and to quantify outcome from injury demands that multiple factors be universally applied so that there is uniform understanding that the same outcome is understood for the same injury. It is thus important to define the variables used in any outcome assessment. Critical to defining outcomes is the need for a universal language that defines individual injuries. The abbreviated injury scale (AIS) is the only dictionary specifically designed as a system to define the severity of injuries throughout the body. In addition to a universal injury language, it provides measures of injury severity that can be used to stratify and classify injury severity in all body regions. Its revision, AIS 2005 will be discussed here.