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Background: Traumatic brain injuries (TBIs) in adolescence are associated with adverse outcomes, but whether the timing of the onset of TBIs leads to greater deficits has not been determined. We evaluate the relationship between the first and most recent TBI, and current academic performance and medically treated physical injuries., Methods: Data were derived from the 2015 Ontario Student Drug Use and Health Survey (OSDUHS) administered to adolescents in grades 7 to 12 (ages 12 to 18). TBI was defined as a head injury that resulted in loss of conscious for at least five minutes or at least one overnight hospitalization., Results: One in five students reported having had a history of TBI in their lifetime and were more prevalent in males. Odds ratios were 2 times higher for males to have had their first (or only) and most recent TBI in grades 5 to 8, compared to females. Sports-related TBIs accounted for 41.1% of all TBIs. Hockey related TBIs were more frequent compared to soccer related TBIs. Reports of history of TBI was associated with lower academic performance and more physical injuries. First or only TBI occurring in grades 9-12 (occurring on average between 14 to 19 years of age) had higher significant odds of poorer academic performance than TBIs occurring in earlier grades (younger ages than 14 years old). Students who reported more visits for medical treatment of physical injuries in the past year had higher odds to report a history of TBIs in higher school grades., Conclusions: Adverse physical and academic outcomes among young TBI survivors are associated with the onset and frequency of history of lifetime TBI. Prevention efforts to minimize TBIs during youth is critical., Competing Interests: The authors have declared that no competing interests exist

Objective: This project is designed to develop and validate a predictive model that is a useful benchmarking and quality of care assessment tool based on International Classification of Diseases, Ninth Revision (ICD-9), diagnoses and procedures. This model, the ICD-9-Based Illness Severity Score (ICISS), was developed from the Agency for Health Care Policy Research's Health Care Utilization Project database and is used to predict hospital survival, hospital length of stay, and hospital charges of injured patients admitted to University of North Carolina Hospitals. The study also compared the outcome predictions of ICISS with those of the long-established diagnosis-related groups (DRG) and the 3M product APR-DRG systems., Methods: We performed a retrospective study of 9,483 trauma patients at University of North Carolina Hospitals. A model was developed to predict survival, length of stay, and hospital charges. The accuracy of the model of survival was assessed using the area under the receiver-operating characteristics curve; the adjusted R2 statistic was used to judge the proportion of variation described by the models of length of stay and hospital charges., Results: ICISS proved to be superior to both DRG and APR-DRG in predicting survival of trauma patients: the area under the receiver-operating characteristics curve for prediction of hospital survival was 0.957 for ICISS, 0.707 for DRG, and 0.808 for APR-DRG. ICISS also outperformed DRG and APR-DRG in predicting hospital length of stay and hospital charges: the adjusted R2 for the ICISS length of stay model was 0.57, compared with the DRG length of stay model with adjusted R2 of 0.31 and the APR-DRG length of stay model with adjusted R2 of 0.35. The adjusted R2 for the ICISS hospital charges model was 0.67, compared with the DRG and APR-DRG hospital charges model R2 of 0.46 and 0.51, respectively (p < 0.001 in all cases)., Conclusion: This study demonstrates that an ICD-9-based predictive model (ICISS) can markedly outperform both DRG and APR-DRG as a predictor of survival, hospital length of stay, and hospital charges.

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 = 0.837-0.908). 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.

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.

Unlabelled: The low occurrence, nonspecific signs and symptoms, and high rate of associated morbidity and mortality of pulmonary embolus (PE) create major problems in the prevention, diagnosis, and treatment of PE. The purpose of this study was to analyze the frequency and outcome of PE in an entire state's trauma population using a large, population-based, hospital discharge data base. With the inclusion of an entire population, the reported incidence, high risk groups of patients, and specific risk factors regarding PE were assessed. A multivariate, logistic regression model was created from the data to determine predictive power of selected risk factors in patients at risk., Methods: The data source was a statewide, hospital discharge data base that includes data on all hospitalized patients for all of the hospitals in North Carolina. Data were available from 1988 to 1993. Using primary discharge diagnosis and nine additional ICD-9 coded diagnoses from the discharge abstract, patients were selected by presence of diagnostic codes for traumatic injury (800-959.9) and PE (415.1). Statistical analysis was performed using univariate and multivariate analysis to determine significant risk factors and to create a candidate model for the prediction of risk in the study population., Results: Of 318,554 patients, 952 (0.30%) had a recorded diagnosis of PE. The mortality rate for patients with PE (26%) was 10 times higher than the mortality rate in patients without PE (2.6%). In evaluating specific risk factors, age was a significant predictor of the risk of PE: 0.05% for patients under age 55 and 0.7% in those 55 years and over. The rate of PE, 0.3%, was low for the entire study population, but was highest in patients with injuries of the extremities, 0.53%. Increasing Injury Severity Score and Abbreviated Injury Scale score for determined body systems were also found to correlate with an increasing risk of PE. Over the course of the study, the incidence of PE among patients discharged from non-trauma centers showed a significant decrease. There was also a decrease in the mortality in non-trauma centers for PE. This finding cannot be due to coding changes coincident with the advent of diagnosis related groups because it would be associated with more vigorous combing of charts for diagnoses? It may well be that the use of prophylactic measures in injured patients initially used at trauma centers was adopted by the physicians at non-trauma centers over this time with the resultant decline in PE and associated mortality. From the univariate linear regression models, a logistic regression model was created that confirmed age as the most significant risk factor, followed by Injury Severity Score and Abbreviated Injury Scale score for soft tissue, extremity, and chest. The calculated area under the receiver operator characteristic curve was 0.72., Conclusion: Using a large, population-based data base, we were able to determine the reported incidence of PE among trauma patients and establish specific risk factors. The reported incidence of PE in this population is low, 0.30%. The mortality among those with PE, however, is significant at 26%. In this study, age, Injury Severity Score, and injury to specific body regions (soft tissue, extremity, chest) were associated with an increased risk of PE. The investigation of prophylaxis of PE and the general management of injured patients may be influenced by the overall low reported frequency of PE and the specific high risk populations described in this study. In light of the low incidence of PE in patients without specific risk factors, prophylactic interventions cannot be routinely recommended unless their benefits clearly outweigh their risks.

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 ICD-9 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.

Unlabelled: The Injury Severity Score (ISS) has been the most frequently used tool for stratifying injured patients. The primary hypothesis of this study was that ISS fails to differentiate between severe injury and mismanagement., Methods: Data models were generated for mismanaged and ideally managed patients for isolated injuries for each body system. Flow charts of care, outcomes, and Abbreviated Injury Scale (AIS) and ISS scores were generated for each model., Results: Multiple models demonstrated that minor injuries that were mismanaged would result in AIS and ISS scores that were the same as ideally managed severe injuries. Three examples are summarized as follows: A comparison of two patients with splenic injuries demonstrates that ISS is unable to differentiate between mismanagement of a minor splenic laceration as compared to a severely lacerated spleen. In the case of the minor injury to the spleen (initial AIS = 2) that was missed by the treating physicians and allowed to bleed into shock and near arrest because of massive hemorrhage that could have been prevented by early recognition and treatment, the final AIS is coded as 4 in this mismanaged patient, the same AIS and ISS as a severely lacerated spleen (AIS = 4) managed well. Both result in a discharge ISS of 16. Similarly, the ISS at discharge is the same for a well-managed severe head injury (AIS = 4) and a mismanaged minor head injury that is unrecognized, progresses and leads to coma (AIS = 4). Finally AIS, ISS does not differentiate between a well-managed cervical fracture with complete cord injury and a mismanaged cervical spine fracture that initially does not involve a cord injury, but because of mismanagement and lack of immobilization, progresses to complete cord injury because of poor care. Both result in the same injury severity assessment (AIS = 5, ISS = 25 in both)., Conclusions: This study demonstrates a fact that should be recognized by all who rely upon the ISS for comparing quality of care: ISS fails to differentiate severe injury from mismanagement of injury. Because the ISS mixes outcome data with injury severity, ISS incorrectly assigns increased severity to the lesser injuries of mismanaged patients. These findings have important implications for use of the ISS in quality of care assessments.

Trauma is the leading killer of Americans under the age of 40 years and the fourth most common cause of death for all Americans. Its impact has not been studied adequately or understood completely. Rational decisions in the care and treatment of trauma patients, the prevention of injury, and the reduction of trauma's annual $177 billion costs can only be made with adequate information on the effectiveness of prevention and treatment measures. This information includes current and accurate data on who is at risk, what types of injuries are sustained, how they are treated, the severity of their consequences, and their outcomes. The availability of this information enables local and national agencies and hospitals to establish priorities, characterize high-risk groups, target prevention and treatment measures within each community, and evaluate the effectiveness of injury-control interventions. The value of hospital trauma registries as a major research tool is recognized increasingly because of their role in improving the care of the trauma patient and bringing about better resource utilization.

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

Unlabelled: If a trauma system is to deliver "the right patient to the right hospital at the right time," a simple triage tool that can be used in the field to rapidly identify patients with a significant risk of morbidity and mortality is vital., Objective: The purpose of this study was to evaluate the effectiveness of the Glasgow Coma Scale's Motor Response Component (GMR) in identifying such patients., Methods: From patient records in a well established state trauma registry, the predictive power of the Glasgow Motor Score was compared with the predictive power of the Injury Severity Score (ISS), Trauma Score, and whole Glasgow Coma Scale Score (GCS), using discriminant analysis and logistic regression., Results: Complete data were available for analysis for 29,573 patients. Nine percent died. The analyses demonstrated that the GMR was almost as good as the TS and better than the other scores in identifying patients at risk of dying. The highest discriminant accuracy was obtained by predicting that patients with a GMR of 6 would live and that all others (GMR 1-5) were at risk of dying. This means separating patients according to whether or not they follow simple commands., Conclusions: The study demonstrated the GMR to be a good predictor of mortality in injured patients. Patients at risk of dying can best be identified by separating patients into those who cannot follow simple commands (GMR 1-5) from all others. This rapid and simple assessment could be useful as a prehospital tool to identify patients at risk of dying. First responders and nonmedical personnel not skilled in the use of the Trauma Score can easily determine if victims are able to follow commands and potentially identify the patients who are likely to require urgent trauma center care.

Quality assurance/quality improvement (QA-QI) is a priority for maintaining the highest standards of care in trauma systems. To be an effective tool for system review, the QA-QI indicators should identify patients with higher rates of morbidity and mortality from injury. While the American College of Surgeons (ACS) and the Joint Commission on Accreditation of Health Care Operations have identified certain audit filters within the trauma system, there are few data to substantiate the value of these audit filters for trauma care. The purpose of this study was to analyze the ability of the ACS trauma indicators to predict adverse patient outcome following injury requiring review. The study population consisted of 44,019 patients from the North Carolina State Trauma Registry from 1987 to 1992. Of the 22 audit filters nine were available for analysis. Mortality rate, length of stay, and total charges were used as measures of outcome. The hypotheses tested were that patients who met the indicator criteria would have higher mortality rates and worse outcomes than the non-indicator group. Student's t test and Chi-square analysis were used to test the differences between the group which met the criteria for the indicator and those without. Of the nine audit filters tested, only three were found to have significantly worse outcomes than their non-indicator comparison group: gunshot wound to the abdomen with non-surgical management, femur fracture without fixation, and complications from pulmonary embolism-deep vein thrombosis-decubitus ulcer (p < 0.05). Contrary to expectations, four of the audit filters, coma without intubation, laparotomy > 2 hours, transfer > 6 hours, and admission to non-surgical service, actually had significantly better outcomes than their non-indicator counterpart. Scene time > 20 minutes, laparotomy > 2 hours after arrival, and craniotomy > 4 hours after arrival may be indicators of patients at risk for morbidity. This study demonstrates that several ACS clinical indicators, as currently written, are not useful in identifying patients at higher risk for poor outcome. The indicators need further definition to be of value in the quality review process. Specifically, the study suggests that audit filters should be data driven and based upon analyses of large populations of injured patients and their outcomes to be valid QA-QI tools.

Objective: To determine the association between measures of medical manpower available to treat trauma patients and county trauma death rates in the United States. The primary hypothesis was that greater availability of medical manpower to treat trauma injury would be associated with lower trauma death rates., Summary Background Data: When viewed from the standpoint of the number of productive years of life lost, trauma has a greater effect on health care and lost productivity in the United States than any disease. Allocation of health care manpower to treat injuries seems logical, but studies have not been done to determine its efficacy. The effect of medical manpower and hospital resource allocation on the outcome of injury in the United States has not been fully explored or adequately evaluated., Methods: Data on trauma deaths in the United States were obtained from the National Center for Health Statistics. Data on the number of surgeons and emergency medicine physicians were obtained from the American Hospital Association and the American Medical Association. Data on physicians who have participated in the American College of Surgeons (ACS) Advanced Trauma Life Support Course (ATLS) were obtained from the ACS. Membership information for the American Association for Surgery of Trauma (AAST) was obtained from that organization. Demographic data were obtained from the United States Census Bureau. Multivariate stepwise linear regression and cluster analysis were used to model the county trauma death rates in the United States. The Statistical Analysis System (Cary, NC) for statistical analysis was used., Results: Bivariate and multivariate analyses showed that a variety of medical manpower measures and demographic factors were associated with county trauma death rates in the United States. As in other studies, measures of low population density and high levels of poverty were found to be strongly associated with increased trauma death rates. After accounting for these variables, using multivariate analysis and cluster analysis, an increase in the following medical manpower measures were associated with decreased county trauma death rates: number of board-certified general surgeons, number of board-certified emergency medicine physicians, number of AAST members, and number of ATLS-trained physicians., Conclusions: This study confirms previous work that showed a strong relation among measures of poverty, rural setting, and increased county trauma death rates. It also found that counties with more board-certified surgeons per capita and with more surgeons with an increased interest (AAST membership) or increased training (ATLS) in trauma care have lower per-capita trauma death rates.(ABSTRACT TRUNCATED AT 400 WORDS)

Objective: This study analyzed the association between demographic and medical system factors and the pediatric trauma death rate in North Carolina., Summary Background Data: Trauma is the leading cause of death in children. Various medical system factors have been suggested to reduce pediatric morbidity and mortality rates, but the association with these rates has not been tested., Methods: Data were obtained from the North Carolina medical examiner's database. The dependent variable was the county per capita pediatric trauma death rate. Twenty-one demographic and medical system measures were selected as independent variables., Results: Nine hundred forty-one pediatric trauma deaths from 1986 to 1989 were included in our sample. Multivariate analysis identified the variables most highly associated with the dependent variables. The presence of advanced life support (ALS) training was the only medical system factor associated significantly with pediatric trauma death rates. Trauma centers, emergency (911) telephone access, and other medical resource variables had no significant association., Conclusions: The study confirms other reports showing that demographic factors have an important predictive association with the trauma death rate in children. Advanced life support was the only medical system resource associated significantly with pediatric trauma death rates. This study underlines the significance of pre-hospital care in the treatment of pediatric trauma.

Objective: This study used population-based data bases to assess the association of trauma centers with per capita county hospitalization and trauma death rates in the State of North Carolina., Summary Background Data: The current study extended previous work using two North Carolina data bases to assess the association of the presence of a trauma center with per capita county trauma death rates., Methods: Data on per capita county trauma hospitalizations and deaths were obtained from the state hospital discharge data base and the North Carolina Medical Examiner's data base. Bivariate and multivariate analysis techniques were used. The dependent variables of interest were prehospital, hospital, and total trauma death rates and hospitalization rates for injury., Results: Bivariate analysis identified a number of factors associated with per capita county hospitalizations and trauma death rates. These included the per cent unemployment, racial distribution, county alcohol tax receipts, and advanced life support certified emergency medical services providers. The per capita trauma death rates were significantly lower in counties with trauma centers compared with those without trauma centers (4.0 +/- 0.5 and 5.0 +/- 1.1 deaths per 10,000 population, p = 0.0001, respectively). The per capita hospitalizations for trauma were also lower in counties with trauma centers. Multivariate modeling showed that the presence of a trauma center and advanced life support providers were the best predictors of decreased per capita county trauma death rates., Conclusions: The study showed that the presence of a trauma center and advanced life support training were the two medical system factors that were the best predictors of the per capita county prehospital and total trauma death rates. These findings are consistent with the hypothesis that trauma centers are associated with a decrease in trauma death rates.

Trauma Score (TS), APACHE II score, and Injury Severity Score (ISS) have been utilized to quantitate severity of illness in various groups of patients. The purpose of this study was to compare the relationship of the APACHE II score, TS, and "computer-derived" ISS with outcome in critically injured trauma patients. Data were recorded prospectively in a computer database for 428 consecutive trauma admissions. Stepwise discriminate analysis was utilized to determine the best predictor of both intensive care unit (ICU) and hospital outcome. Forty-two patients died in the ICU (10%), and another 18 patients died after leaving the ICU (4%), for a total mortality rate of 14%. The mean p value and partial R2 value obtained from stepwise discriminant analysis of the relationships between APACHE II score, TS, and ISS to ICU and hospital survival are shown. APACHE II score was the best predictor of both ICU and hospital outcome in these critically ill trauma patients. However, when combining all three measures (APACHE II score, TS, and ISS), only a portion of the variance in outcome is explained by the scores (R2 < 0.05). We conclude that scoring systems for outcome prediction should be utilized only as an adjunct to clinical assessment in the evaluation of the severity of illness and mortality risk in critically ill trauma patients.

Data from the North Carolina Trauma Registry were analyzed to determine the effect of seat belt usage on outcome in motor vehicle accidents. Of 6237 persons involved in motor vehicle accidents, data on seat belt usage were available for 3396. Of these, 1916 were not and 1480 were wearing seat belts. The mean hospital charge in belted patients was $10,500 +/- $18,200; and in unbelted patients, $15,250 +/- $26,300 (p < 0.001). The total hospital charges were $23 million for the 1508 patients not wearing seat belts. If the unbelted patients had outcomes similar to belted patients, the charges resulting from caring for the 1508 patients would have been $15.8 million, a potential savings of $7.2 million. There were 135 deaths among the unbelted patients (7.0%) and 47 deaths among the belted patients (3.2%) (p < 0.001). A similar projection of belted outcome for unbelted patients suggests that seat belt usage could have reduced the unbelted mortality rate by over one half. Patients wearing seat belts also had significantly shorter hospital stays, fewer days in the intensive care unit, and fewer days on the ventilator (p < 0.001). Seat belt usage is associated with a significant decrease in mortality rate, hospital charges, length of stay, intensive care unit stay, and ventilator requirements. Seat belts could have saved at least 74 lives and 7.2 million dollars during the period from October 1, 1987 to July 1, 1989 in patients seen in the seven trauma centers in North Carolina.

This study identified a number of significant predictors of per capita county trauma mortality rates: rurality, percentage nonwhite population, percentage unemployment, and Advanced Life Support (ALS) versus Basic Life Support (BLS) status. Of these, ALS versus BLS status is not only the most significant independent predictor, it is the only predictor readily amenable to change. The aspects of ALS clearly associated with decreased trauma death rates should be identified and, if possible, undergo widespread implementation.

This study documents the strong association of alcohol in trauma-related deaths. In a previous study alcohol was present in 62.8% of homicide victims, 48.6% of unintentional injury fatality victims, 35.3% of persons who committed suicide, and 14.4% of persons who died of natural causes. The present study uses the legal limit of 100 mg/100 mL to include patients as having an alcohol-associated trauma death. In addition the study includes patients who die up to 20 hours after injury. These features of our study result in the lower reported frequency rates. Our study confirms that alcohol is strongly associated with trauma deaths resulting from motor vehicle crashes. It also demonstrates a strong association between alcohol use and victims of all types of trauma mortality; specifically those victims of gunshot wounds, burns, stabbings, and falls all are frequently using alcohol. This information is of importance for those who treat such injured patients, since such tests as neurologic examination frequently will be compromised by the use of alcohol in the victims of major trauma. Perhaps most importantly this information can be of help in designing appropriate strategies in attacking this problem the best possible way--by prevention.

Study Objective: Decreased response times for injury should decrease the morbidity and mortality of trauma. The 911 telephone access is designed to improve the response time for emergencies. The purpose of this study was to analyze the association between county 911 access and per-capita county trauma death rates., Methods: Data on all trauma deaths from 1986 through 1988 were obtained from the North Carolina Medical Examiner's data base. Counties were divided into those that had 911 access during the entire study period (15), those that never had 911 access (62), and those that installed 911 during 1987 (ten). Counties obtaining 911 access in 1986 or 1988 were excluded (13)., Results: The per-capita trauma death rate in counties that had 911 access throughout the study was 4.3 +/- 0.8 versus 5.0 +/- 1.1 per 10,000 population in counties that never had 911 access (P less than .01). Compared with counties with 911 access, counties without 911 were more rural, were less likely to have a trauma center, and were less likely to have advanced life support certification (P less than .03 for all). Controlling for these other factors, multivariate analysis demonstrated that 911 access had no significant independent association with per-capita county trauma death rates. In the ten counties that implemented 911 access in 1987, no significant change occurred in per-capita county trauma death rates after implementation of 911., Conclusion: Although counties with 911 access had lower trauma death rates by t-test, multivariate analysis showed no significant independent association of 911 access with per-capita county trauma death rates. In the ten counties that implemented 911 access in 1987, no significant changes in trauma death rates occurred after implementation. Although other factors may explain these findings, this study showed no significant independent impact of 911 access on per-capita county trauma death rates.

Unlabelled: The purpose of this study was to utilize a large population-based data base to determine the association of trauma centers with per capita county trauma death rates., Methods: Per capita county trauma death rate, the dependent variable in the model, was obtained from a well-validated state Medical Examiner's data base. Over 200 county demographic, prehospital, and hospital trauma care resource variables were obtained from a variety of sources for multivariate modeling. Bivariate analysis identified candidate variables for multivariate modeling, excluding highly correlated independent variables to avoid problems of collinearity. Multivariate linear regression, logistic regression, and stepwise discriminant analysis were used to determine the relative association of the candidate variables with per capita county trauma death rates., Results: Bivariate analysis identified multiple factors associated with per capita county trauma death rates. These included, among others: county rurality, percentage of unemployment, percentage nonwhite, 911 access, and ALS certified EMS. Per capita trauma death rates were significantly lower in counties with trauma centers compared with counties without trauma centers (4.0 +/- 0.5 and 5.0 +/- 1.1 deaths per 10,000 population, p = 0.0001, respectively). Multivariate analysis demonstrated that the presence of a trauma center in the county and ALS were the best medical system factors predicting decreased per capita county trauma death rates., Conclusions: This study is unique in utilizing a regional population-based data base of all trauma deaths in a large state to analyze the association of trauma centers and trauma death rates. Multivariate modeling controlling for other county variables demonstrated that the presence of a trauma center and Advanced Life Support training were the best predictors of per capita county trauma death rates. These findings are consistent with the hypothesis that trauma centers decrease trauma death rates.

The demographics, etiology, and outcome of 1148 vascular injuries suffered by 978 patients reported from eight trauma centers in a largely rural state to a trauma registry (NCTR) data base containing 26,617 patients entered over a 39-month time interval were analyzed. Vascular injury patients were more frequently transferred by helicopter (18%), referred from other hospitals (45%), transfused more blood (8 units mean/24 hours), had higher mean ISS values (14 vs. 9), had lower systolic blood pressures on admission (113 vs. 128 mm Hg), had higher emergency department mortality (3.3%), and required immediate surgery more often (79%) when compared with nonvascular injury NCTR patients (p = 0.0001). Vascular injury patients had significantly longer hospital stays (13 vs. 10 days), longer ICU stays (5 vs. 4 days), and greater hospital costs ($22,500 vs. $12,300) while incurring more serious AIS values for the regions of the chest, abdomen, and extremities. One hundred twenty-nine (13.1%) died, 97 after admission compared with a 6.2% mortality for NCTR nonvascular injury victims. Forty-seven percent of vascular injuries were extremity lesions; the amputation rate was 1.3%; and management was most often by simple repair (41.9%) or patching (22.2%). Rural vascular injury patients had a high incidence of blunt trauma (43.4%) and were older (average, 51 years); they were transported by helicopter more often (30.3%) and were frequently referred from another hospital (77.8%); they had longer ICU, ventilator, and hospital stays and greater hospital charges; and they had higher mortality (14.2%) compared with urban vascular trauma victims. The data suggest a need for the trauma care system to focus on earlier recognition, stabilization, and rapid transportation of this most seriously injured group of patients.

The Pediatric Trauma Score (PTS) has been identified as the only accurate and adequate means of predicting outcome in pediatric trauma. In answer to the increasing number of trauma patients arriving at local hospitals, the ability of the adult Trauma Score (TS) to predict pediatric trauma outcome was tested. Of the total 2,604 pediatric trauma cases in the North Carolina State Trauma Registry, 441 had both a PTS and TS available for analysis. The primary measures of outcome were emergency department and hospital dispositions. Logistic regression demonstrated that TS (R2 = 0.50) was a stronger predictor of pediatric outcome and PTS (R2 = 0.35) for emergency department disposition and TS (R2 = 0.63) with PTS (R2 = 0.51) for hospital disposition. The correlation between TS and PTS was high (R = 0.8). Stepwise discriminant analysis demonstrated that TS was the stronger predictor of outcome and the PTS added only 9% (partial R2 = 0.09) more accuracy to TS for emergency department disposition and only 6% (partial R2 = 0.06) for hospital disposition. The results of this research demonstrate that TS is a useful method of predicting outcome in pediatric trauma. The use of both scores for each patient does not increase the predictive value of the scores.