Predicting in-hospital mortality of traffic victims: A comparison between AIS-and ICD-9-CM-related injury severity scales when only ICD-9-CM is reported

Background and AimInjury severity scores are important in the context of developing European and national goals on traffic safety, health-care benchmarking and improving patient communication. Various severity scores are available and are mostly based on Abbreviated Injury Scale (AIS) or International Classification of Diseases (ICD). The aim of this paper is to compare the predictive value for in-hospital mortality between the various severity scores if only International Classification of Diseases, 9th revision, Clinical Modification ICD-9-CM is reported.MethodologyTo estimate severity scores based on the AIS lexicon, ICD-9-CM codes were converted with ICD Programmes for Injury Categorization (ICDPIC) and four AIS-based severity scores were derived: Maximum AIS (MaxAIS), Injury Severity Score (ISS), New Injury Severity Score (NISS) and Exponential Injury Severity Score (EISS). Based on ICD-9-CM, six severity scores were calculated. Determined by the number of injuries taken into account and the means by which survival risk ratios (SRRs) were calculated, four different approaches were used to calculate the ICD-9-based Injury Severity Scores (ICISS). The Trauma Mortality Prediction Model (TMPM) was calculated with the ICD-9-CM-based model averaged regression coefficients (MARC) for both the single worst injury and multiple injuries. Severity scores were compared via model discrimination and calibration. Model comparisons were performed separately for the severity scores based on the single worst injury and multiple injuries.ResultsFor ICD-9-based scales, estimation of area under the receiver operating characteristic curve (AUROC) ranges between 0.94 and 0.96, while AIS-based scales range between 0.72 and 0.76, respectively. The intercept in the calibration plots is not significantly different from 0 for MaxAIS, ICISS and TMPM.DiscussionWhen only ICD-9-CM codes are reported, ICD-9-CM-based severity scores perform better than severity scores based on the conversion to AIS.     

Validation of the International Classification of Diseases 10th Edition-based Injury Severity Score (ICISS)

OBJECTIVE: To compare the predictive power of International Classification of Diseases 10th Edition (ICD-10)-based International Classification of Diseases 9th Edition-based Injury Severity Score (ICISS) with Trauma and Injury Severity Score (TRISS) and ICD-9CM-based ICISS in the injury severity measure. METHODS: ICD-10 version of survival risk ratios was derived from 47,750 trauma patients from 35 emergency centers for 1 year. The predictive power of TRISS, the ICD-9CM-based ICISS and ICD-10-based ICISS were compared in a group of 367 severely injured patients admitted to two university hospitals. The predictive power was compared by using the measures of discrimination (disparity, sensitivity, specificity, misclassification rates, and receiver operating characteristic curve analysis) and calibration (Hosmer-Lemeshow goodness-of-fit statistics), all calculated by logistic regression procedure. RESULTS: ICD-10-based ICISS showed a lower performance than TRISS and ICD-9CM-based ICISS. When age and Revised Trauma Score were incorporated into the survival probability model, however, ICD-10-based ICISS full model showed a similar predictive power compared with TRISS and ICD-9CM-based ICISS full model. ICD-10-based ICISS had some disadvantages in predicting outcomes among patients with intracranial injuries. However, such weakness was largely compensated by incorporating age and Revised Trauma Score in the model. CONCLUSION: The ICISS methodology can be extended to ICD-10 horizon as a standard injury severity measure in the place of TRISS, especially when age and Revised Trauma Score were incorporated in the model. For patients with intracranial injuries, the predictive power of ICD-10-based ICISS was relatively low because of differences in the classifying system between ICD-10 and ICD-9CM.     

The worst injury predicts mortality outcome the best: rethinking the role of multiple injuries in trauma outcome scoring

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.     

Comparing measures of injury severity for use with large databases

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.     

Comparisons of survival predictions using survival risk ratios based on International Classification of Diseases, Ninth Revision and Abbreviated Injury Scale trauma diagnosis codes

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.     

Prognostic value of trauma scores in pediatric patients with multiple injuries

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.     

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

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

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

Background:

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

Study Design:

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

Results:

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

Conclusions:

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

Predicting trauma patient mortality: ICD [or ICD-10-AM] versus AIS based approaches

Background: The International Classification of Diseases Injury Severity Score (ICISS) has been proposed as an International Classification of Diseases (ICD)‐10‐based alternative to mortality prediction tools that use Abbreviated Injury Scale (AIS) data, including the Trauma and Injury Severity Score (TRISS). To date, studies have not examined the performance of ICISS using Australian trauma registry data. This study aimed to compare the performance of ICISS with other mortality prediction tools in an Australian trauma registry. Methods: This was a retrospective review of prospectively collected data from the Victorian State Trauma Registry. A training dataset was created for model development and a validation dataset for evaluation. The multiplicative ICISS model was compared with a worst injury ICISS approach, Victorian TRISS (V‐TRISS, using local coefficients), maximum AIS severity and a multivariable model including ICD‐10‐AM codes as predictors. Models were investigated for discrimination (C‐statistic) and calibration (Hosmer–Lemeshow statistic). Results: The multivariable approach had the highest level of discrimination (C‐statistic 0.90) and calibration (H–L 7.65, P= 0.468). Worst injury ICISS, V‐TRISS and maximum AIS had similar performance. The multiplicative ICISS produced the lowest level of discrimination (C‐statistic 0.80) and poorest calibration (H–L 50.23, P < 0.001). Conclusions: The performance of ICISS may be affected by the data used to develop estimates, the ICD version employed, the methods for deriving estimates and the inclusion of covariates. In this analysis, a multivariable approach using ICD‐10‐AM codes was the best‐performing method. A multivariable ICISS approach may therefore be a useful alternative to AIS‐based methods and may have comparable predictive performance to locally derived TRISS models.     

Incorporating recent advances to make the TRISS approach universally available

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.     

Modeling probability-based injury severity scores in logistic regression models: the logit transformation should be used

BACKGROUND: The International Classification of Disease Injury Severity Score (ICISS) and the Trauma Registry Abbreviated Injury Scale Score (TRAIS) are trauma injury severity scores based on probabilities of survival. They are widely used in logistic regression models as raw probability scores to predict the logit of mortality. The aim of this study was to evaluate whether these severity indicators would offer a more accurate prediction of mortality if they were used with a logit transformation. 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 1998 and 2005. The ICISS and TRAIS were calculated using survival proportions from the National Trauma Data Bank. The performance of the ICISS and TRAIS in their widely used form, proportions varying from 0 to 1, was compared with a logit transformation of the scores in logistic regression models predicting in-hospital mortality. Calibration was assessed with the Hosmer-Lemeshow statistic. RESULTS: Neither the ICISS nor the TRAIS had a linear relation with the logit of mortality. A logit transformation of these scores led to a near-linear association and consequently improved model calibration. The Hosmer-Lemeshow statistic was 68 (35-192) and 69 (41-120) with the logit transformation compared with 272 (227-339) and 204 (166-266) with no transformation, for the ICISS and TRAIS, respectively. CONCLUSIONS: In logistic regression models predicting mortality, the ICISS and TRAIS should be used with a logit transformation. This study has direct implications for improving the validity of analyses requiring control for injury severity case mix.     

The New Injury Severity Score: a more accurate predictor of in-hospital mortality than the Injury Severity Score

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.     

A note on the disjointed nature of the injury severity score

OBJECTIVE: The Injury Severity Score (ISS) is widely used for anatomic severity assessments. The ISS is the sum of the squares of a patient's three worst Abbreviated Injury Scale (AIS) severities (1-6) from three specified body regions. The set of three AIS severities (including 0s) is called a "triplet." ISS values of 9, 17, 18, 25, 26, 27, 29, 33, 34, 41, and 50 can originate from two unique triplets, but it is not clear whether the mortalities of the triplets are equal. A related question regards the monotonicity of the ISS, that is, whether mortality increases with successive values of ISS. This study sought to compare the mortality of equivalent ISS values from different triplets and to evaluate whether ISS is a monotonic function of mortality. METHODS: The ISS, its corresponding three-digit triplet, and the ICISS (an International Classification of Diseases, Ninth Revision-based competing score) were calculated for 361,381 National Trauma Data Bank patients. Fisher's exact tests were used to test for mortality differences between triplets that yield the same ISS. Plots of mortality by score value were produced to visually assess the monotonicity of the ICISS and the ISS. RESULTS: Six of the 11 triplet pairs had mortalities that differed by greater than 20%, with the largest difference being 32% for an ISS of 25 (triplets 0, 0, 5 and 0, 3, 4). Two other values (9 and 17) have triplet pairs whose mortality differences are less but still statistically different. The ISS is markedly nonmonotonic and is characterized by large spikes in mortality for successive ISS values. Plots of the ICISS show it to be largely monotonic. CONCLUSION: The ISS is a nonmonotonic, triplet-dependent function of mortality. Those who persist in using the ISS to describe populations or make risk adjustments should do so cautiously, being sure to account for triplet type. These suspect ISS values appear in approximately 25% of cases.     

The New Injury Severity Score and the evaluation of pediatric trauma.

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.     

Quantifying injury severity for traumatic brain injury with routinely collected health data

BackgroundRoutinely collected health data (RCHD) offers many opportunities for traumatic brain injury (TBI) research, in which injury severity is an important factor.ObjectiveThe use of clinical injury severity indices in a context of RCHD is explored, as are alternative measures created for this specific purpose. To identify useful scales for full body injury severity and TBI severity this study focuses on their performance in predicting these currently used indices, while accounting for age and comorbidities.DataThis study utilized an extensive population-based RCHD dataset consisting of all patients with TBI admitted to any Belgian hospital in 2016.MethodsFull body injury severity is scored based on the (New) Injury Severity Score ((N)ISS) and the ICD-based Injury Severity Score (ICISS). For TBI specifically, the Abbreviated Injury Scale (AIS) Head, Loss of Consciousness and the ICD-based Injury Severity Score for TBI injuries (ICISS) were used in the analysis. These scales were used to predict three outcome variables strongly related to injury severity: in-hospital death, admission to intensive care and length of hospital stay. For the prediction logistic regressions of the different injury severity scales and TBI severity indices were used, and error rates and the area under the receiver operating curve were evaluated visually.ResultsIn general, the ICISS had the best predictive performance (error rate between 0.06 and 0.23; AUC between 0.82 [0.81;0.83] and 0.86 [0.85;0.86]). A clearly increasing error rate can be noticed with advancing age and accumulating comorbidity.ConclusionBoth for full body injury severity and TBI severity, the ICISS tends to outperform other scales. It is therefore the preferred scale for use in research on TBI in the context of RCHD. In their current form, the severity scales are not suitable for use in older populations.     

Defining major trauma using the 2008 Abbreviated Injury Scale

BackgroundThe Injury Severity Score (ISS) is the most ubiquitous summary score derived from Abbreviated Injury Scale (AIS) data. It is frequently used to classify patients as ‘major trauma’ using a threshold of ISS >15. However, it is not known whether this is still appropriate, given the changes which have been made to the AIS codeset since this threshold was first used. This study aimed to identify appropriate ISS and New Injury Severity Score (NISS) thresholds for use with the 2008 AIS (AIS08) which predict mortality and in-hospital resource use comparably to ISS >15 using AIS98.MethodsData from 37,760 patients in a state trauma registry were retrieved and reviewed. AIS data coded using the 1998 AIS (AIS98) were mapped to AIS08. ISS and NISS were calculated, and their effects on patient classification compared. The ability of selected ISS and NISS thresholds to predict mortality or high-level in-hospital resource use (the need for ICU or urgent surgery) was assessed.ResultsAn ISS >12 using AIS08 was similar to an ISS >15 using AIS98 in terms of both the number of patients classified major trauma, and overall major trauma mortality. A 10% mortality level was only seen for ISS 25 or greater. A NISS >15 performed similarly to both of these ISS thresholds. However, the AIS08-based ISS >12 threshold correctly classified significantly more patients than a NISS >15 threshold for all three severity measures assessed.ConclusionsWhen coding injuries using AIS08, an ISS >12 appears to function similarly to an ISS >15 in AIS98 for the purposes of identifying a population with an elevated risk of death after injury. Where mortality is a primary outcome of trauma monitoring, an ISS >12 threshold could be adopted to identify major trauma patients.Level of evidenceLevel II evidence—diagnostic tests and criteria.     

Predictive factors of operative or nonoperative management of blunt hepatic trauma

AIM: Nonoperative management (NOM) has revolutionized the care of blunt hepatic trauma patients. The aim of the present study was to identify and evaluate the predictors of NOM of these patients. METHODS: The Trauma Registry data of 55 consecutive adult patients admitted with blunt hepatic trauma over a 4-year period was reviewed. Patients were divided into immediately operated (OP-group) and selected for NOM (NOM-group). Factors analyzed were: demographics, injury mechanism, initial vital signs, liver injury grade, concomitant injuries, and total injury severity scoring systems. RESULTS: Concomitant abdominal trauma, high Injury Severity Score (ISS), low International Classification of Diseases 9(th) revision Injury Severity Score (ICISS), and low probability of survival (Ps) were predictors for operative management. Compared to NOM-patients (66%, N=36), OP-patients (34%, N=19) suffered more frequently concomitant abdominal injuries (84.2% vs 47.2%, P=0.004) and were more severely totally injured as expressed by higher ISS (25 vs 20, P=0.01), lower ICISS (0.51 vs 0.74, P=0.003), and lower Ps (0.81 vs 0.98, P=0.005). NOM resulted in lower intensive care unit admission and mortality rates (47.2% vs 78.9%, P=0.002 and 2.7% vs 15.8%, P=0.03, respectively). NOM-success rate was 92%. CONCLUSION: NOM of blunt hepatic trauma is safe and efficient. Concomitant abdominal trauma, ISS, ICISS, and Ps are predictors for operative or nonoperative management.     

Comparison of the New Injury Severity Score and the Injury Severity Score

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.     

A comparison of Abbreviated Injury Scale 1980 and 1985 versions

The 1980 and 1985 versions of the Abbreviated Injury Scale (AIS) are quantitatively and qualitatively compared based on experience gained during the recent coding of nearly 115,000 injuries from more than 33,000 seriously injured patients using both AIS versions. Quantitative comparisons are based on differences in AIS scores and Injury Severity Score (ISS) values which result under the two schemes. Qualitative comparisons concern the completeness and clinical usability of the two scales in a trauma center setting.     

Overcoming barriers to population-based injury research: development and validation of an ICD-10�Cto�CAIS algorithm

Background

Hospital administrative databases are a useful source of population-level data on injured patients; however, these databases use the International Classification of Diseases (ICD) system, which does not provide a direct means of estimating injury severity. We created and validated a crosswalk to derive Abbreviated Injury Scale (AIS) scores from injury-related diagnostic codes in the tenth revision of the ICD (ICD-10).

Methods

We assessed the validity of the crosswalk using data from the Ontario Trauma Registry Comprehensive Data Set (OTR-CDS). The AIS and Injury Severity Scores (ISS) derived using the algorithm were compared with those assigned by expert abstractors. We evaluated the ability of the algorithm to identify patients with AIS scores of 3 or greater. We used κ and intraclass correlation coefficients (ICC) as measures of concordance.

Results

In total, 10 431 patients were identified in the OTR-CDS. The algorithm accurately identified patients with at least 1 AIS score of 3 or greater (κ 0.65), as well as patients with a head AIS score of 3 or greater (κ 0.78). Mapped and abstracted ISS were similar; ICC across the entire cohort was 0.83 (95% confidence interval 0.81–0.84), indicating good agreement. When comparing mapped and abstracted ISS, the difference between scores was 10 or less in 87% of patients. Concordance between mapped and abstracted ISS was similar across strata of age, mechanism of injury and mortality.

Conclusion

Our ICD-10–to–AIS algorithm produces reliable estimates of injury severity from data available in administrative databases. This algorithm can facilitate the use of administrative data for population-based injury research in jurisdictions using ICD-10.