Congruency of Crash- and Hospital- Reported Injuries Among Child Passengers.

INTRODUCTION: Prior work has found incongruencies in injury information reported by crash and hospital records. However, no work has focused on child passengers. The objective of this study was to compare crash scene and hospital-reported injury information for crash-involved child passengers. This study also explored injury location and severity by child age and restraint type. METHODS: Utilizing linked New Jersey data from 2017 through 2019, the authors identified crash-involved child passengers <13 years old and their injuries in crash and hospital reports. Then, they characterized the congruency of injury frequency, severity, and location, as well as the frequency of injuries by child age and restraint type. Analyses were conducted from December 2023 through February 2024. RESULTS: Of 84,060 crash-involved child passengers, crash reports documented 7,858 (9%) children with at least "possible" injuries, while 2,577 (3%) had at least one injury in hospital events. Crash report and hospital data were incongruent for both body region of injury and injury severity. The proportion of children injured increased as children's ages increased and as restraint type progressed. CONCLUSIONS: Crash reports overestimated the number of injured child passengers and misrepresented injury severity and locations. Child restraint systems mitigated a child's injury risk. Importantly, injury information documented on crash reports currently informs the allocation of traffic safety resources. These results highlight the importance of improving these reports' accuracy and underscore calls to link administrative datasets for public health efforts.

The American Journal of Emergency Medicine's policy on large language model usage in manuscript preparation: Balancing innovation and responsibility.

Large Language Models (LLMs) represent a transformative advancement in the preparation of medical scientific manuscripts, offering significant benefits such as reducing drafting time, enhancing linguistic precision, and aiding non-native English speakers. These models, which generate text by learning from extensive datasets, can streamline the publication process and maintain consistency across collaborative projects. However, their limitations, including the risk of generating plausible yet incorrect text and the potential for biases, necessitate careful oversight. Ethical concerns about accuracy, authorship, and transparency need to be carefully considered. The American Journal of Emergency Medicine has adopted a policy permitting LLM use with full disclosure and author responsibility, emphasizing the need for ongoing policy evolution in response to technological advancements.

Comparison of Deep Learning Approaches for Conversion of International Classification of Diseases Codes to the Abbreviated Injury Scale.

The injury severity classifications generated from the Abbreviated Injury Scale (AIS) provide information that allows for standardized comparisons in the field of trauma injury research. However, the majority of injuries are coded in International Classification of Diseases (ICD) and lack this severity information. A system to predict injury severity classifications from ICD codes would be beneficial as manually coding in AIS can be time-intensive or even impossible for some retrospective cases. It has been previously shown that the encoder-decoder-based neural machine translation (NMT) model is more accurate than a one-to-one mapping of ICD codes to AIS. The objective of this study is to compare the accuracy of two architectures, feedforward neural networks (FFNN) and NMT, in predicting Injury Severity Score (ISS) and ISS ≥16 classification. Both architectures were tested in direct conversion from ICD codes to ISS score and indirect conversion through AIS for a total of four models. Trauma cases from the U.S. National Trauma Data Bank were used to develop and test the four models as the injuries were coded in both ICD and AIS. 2,031,793 trauma cases from 2017-2018 were used to train and validate the models while 1,091,792 cases from 2019 were used to test and compare them. The results showed that indirect conversion through AIS using an NMT was the most accurate in predicting the exact ISS score, followed by direct conversion with FFNN, direct conversion with NMT, and lastly indirect conversion with FFNN, with statistically significant differences in performance on all pairwise comparisons. The rankings were similar when comparing the accuracy of predicting ISS ≥16 classification, however the differences were smaller. The NMT architecture continues to demonstrate notable accuracy in predicting exact ISS scores, but a simpler FFNN approach may be preferred in specific situations, such as if only ISS ≥16 classification is needed or large-scale computational resources are unavailable.

Seat Belt Use in the US by Pregnant Motor Vehicle Occupants.

This cross-sectional study assesses patterns of seat belt use among pregnant, nonpregnant, and male occupants.

Comparison of mortality in emergency department patients with immediate versus delayed hypotension.

BACKGROUND: Hypotension in the emergency department (ED) is known to be associated with increased mortality, however, the relationship between timing of hypotension and mortality has not been investigated. The objective of the study was to compare the mortality rate of patients presenting with hypotension with those who develop hypotension while in the ED. METHODS: This was a retrospective cohort study in a large academic medical center collected from January 2018-December 2021. Patients were included if they were ≥ 18 years old and had at least one recorded systolic blood pressure (SBP) ≤ 90 in the ED. Patients were separated into medical and trauma presentations by chief compliant. The primary outcome was in-hospital mortality, which included any deaths between ED arrival and hospital discharge. Further analysis examined the association of time to the first hypotensive SBP measurement with mortality. RESULTS: There were 212,085 adult patients who presented to the ED during the study period, with 4053 (1.9%) patients having at least one hypotensive blood pressure measurement. The mortality rate was 0.8% for all patients and 10.0% for patients with hypotension. There were 676 unique chief complaints, of which 86 (12.7%) were determined to be trauma related. This grouping resulted in 176,947(83.4%) patients classified as medical and 35,138(16.6%) patients as trauma. For patients presenting with medical complaints, there was not a significant difference in mortality for patients who were hypotensive on arrival and those who developed hypotension during their ED stay (RR 1.19 [95% CI:0.97-1.39]). Similarly, there was no difference for patients with trauma (RR 0.6 [95% CI: 0.31-1.24]). However, for all patients, there was a significant trend toward decreased mortality for every hour after arrival until the development of hypotension, and increased mortality with increasing number of hypotensive measurements recorded. CONCLUSION: This study demonstrated hypotension in the ED was associated with a very significantly increased risk of in-hospital mortality. However, there was no significant increase in mortality between those patients with hypotension on arrival those who develop hypotension while in the ED. These finding underscore the importance of careful hemodynamic monitoring for patients in the ED throughout their stay.

Evaluation of Neural Machine translation for conversion of International Classification of disease codes to the Abbreviated injury Scale.

The Abbreviated Injury Scale (AIS) is an essential tool for injury research since it allows for comparisons of injury severity among patients, however, the International Classification of Diseases (ICD) is more widely used to capture medical information. The problem of conversion between these two medical coding systems has similarities to the challenges encountered in language translation. We therefore hypothesize that neural machine translation (NMT), a deep learning technique which is commonly used for human language translation, could be used to convert ICD codes to AIS. The objective of this study was to compare the accuracy of a NMT model for determining injury severity compared to two established methods of conversion. The injury severity classifications used for this study were Injury Severity Score (ISS) ≥ 16, Maximum AIS severity (MAIS) ≥ 3, and MAIS ≥ 2. Data from a US national trauma registry, which has patient injuries coded in both AIS and ICD, was used to train a NMT model. Testing data from a separate year was used to determine the accuracy of the NMT model predictions against the actual ISS recorded in the registry. The prediction accuracy of the NMT model was compared to that of the official Association for the Advancement of Automotive Medicine (AAAM) ICD-AIS map and the R package 'ICD Program for Injury Categorization in R' (ICDPIC-R). The results show that the NMT model was the most accurate across all injury severity classifications, followed by the ICD-AIS map and then ICDPIC-R package. The NMT model also showed the highest correlation between the predicted and observe ISS scores. Overall, NMT appears to be a promising method for predicting injury severity from ICD codes, however, validation in external databases is needed.

Spinal injury rates and specific causation in motor vehicle collisions.

Motor vehicle collisions (MVCs) are a leading cause of acute spinal injuries. Chronic spinal pathologies are common in the population. Thus, determining the incidence of different types of spinal injuries due to MVCs and understanding biomechanical mechanism of these injuries is important for distinguishing acute injuries from chronic degenerative disease. This paper describes methods for determining causation of spinal pathologies from MVCs based on rates of injury and analysis of the biomechanics require to produce these injuries. Rates of spinal injuries in MVCs were determined using two distinct methodologies and interpreted using a focused review of salient biomechanical literature. One methodology used incidence data from the Nationwide Emergency Department Sample and exposure data from the Crash Report Sample System supplemented with a telephone survey to estimate total national exposure to MVC. The other used incidence and exposure data from the Crash Investigation Sampling System. Linking the clinical and biomechanical findings yielded several conclusions. First, spinal injuries caused by an MVC are relatively rare (511 injured occupants per 10,000 exposed to an MVC), which is consistent with the biomechanical forces required to generate injury. Second, spinal injury rates increase as impact severity increases, and fractures are more common in higher-severity exposures. Third, the rate of sprain/strain in the cervical spine is greater than in the lumbar spine. Fourth, spinal disc injuries are extremely rare in MVCs (0.01 occupants per 10,000 exposed) and typically occur with concomitant trauma, which is consistent with the biomechanical findings 1) that disc herniations are fatigue injuries caused by cyclic loading, 2) the disc is almost never the first structure to be injured in impact loading unless it is highly flexed and compressed, and 3) that most crashes involve predominantly tensile loading in the spine, which does not cause isolated disc herniations. These biomechanical findings illustrate that determining causation when an MVC occupant presents with disc pathology must be based on the specifics of that presentation and the crash circumstances and, more broadly, that any causation determination must be informed by competent biomechanical analysis.

Breadth of use of The Abbreviated Injury Scale in The National Trauma Data Bank bank.

OBJECTIVE: The Abbreviated Injury Scale (AIS) is an anatomic-based injury coding system that strives to provide sufficient detail to differentiate unique injuries for the purposes of research and quality assurance, while limiting the total number of codes to facilitate efficient use. It has been shown that a substantial portion of codes are unused in automotive-trauma specific databases. The goal of this study was to determine the percentage of codes utilized in a nationwide trauma registry that includes multiple mechanisms of injury. Secondary objectives were to examine unused codes and determine the number of codes that were most frequently utilized. METHODS: Data were obtained from the National Trauma Data Bank (NTDB) years 2016 and 2017. All injury data were recorded using AIS version 2005 update 2008 (AIS08), which contains 1,999 distinct injury codes. The percentage of the total number of AIS08 codes used in NTDB were determined for each year individually and the combination of both years. The unused codes were then examined manually to identify common characteristics. Finally, the number of codes that provided 95% coverage of all recorded injuries was calculated. RESULTS: There were 6,661,110 injuries recorded for 1,953,775 patients in NTDB over the two-year period. A small percentage of codes had an incorrect severity level (0.07%) or an incorrect injury code (0.0002%). There were 1,987 (99.4% of the entire AIS dictionary) unique AIS08 codes utilized in each year, with the unused codes varying between years. The unused codes tended to involve specific nerves, dural sinuses, or severe, bilateral injuries. During the combined two-year period, 1,996 (99.8% of the entire dictionary) unique AIS08 codes were used. Although almost every code was used at least once, 95% of the injuries in NTDB used only the 631 (31.6%) most frequent AIS08 codes. CONCLUSIONS: In contrast to automotive specific databases, nearly all the AIS08 codes are used each year in the NTDB. Over a two-year period, only three AIS08 injuries were unused. However, less than a third of AIS08 codes encompass 95% of the injuries. Further research is necessary to determine if common codes should be separated into multiple distinct codes to enhance discriminatory ability of AIS.

Body mass index influence on lap belt position and abdominal injury in frontal motor vehicle crashes.

OBJECTIVE: As obesity rates climb, it is important to study its effects on motor vehicle safety due to differences in restraint interaction and biomechanics. Previous studies have shown that an abdominal seatbelt sign (referred hereafter as seatbelt sign) sustained from motor vehicle crashes (MVCs) is associated with abdominal trauma when located above the anterior superior iliac spine (ASIS). This study investigates whether placement of the lap belt causing a seatbelt sign is associated with abdominal organ injury in occupants with increased body mass index (BMI). We hypothesized that higher BMI would be associated with a higher incidence of superior placement of the lap belt to the ASIS level, and a higher incidence of abdominal organ injury. METHODS: A retrospective data analysis was performed using 230 cases that met inclusion criteria (belted occupant in a frontal collision that sustained at least one abdominal injury) from the Crash Injury Research and Engineering Network (CIREN) database. Computed tomography (CT) scans were rendered to visualize fat stranding to determine the presence of a seatbelt sign. 146 positive seatbelt signs were visualized. ASIS level was measured by adjusting the transverse slice of the CT to the visualized ASIS level, which was used to determine seatbelt sign location as superior, on, or inferior to the ASIS. RESULTS: Obese occupants had a significantly higher incidence of superior belt placement (52%) vs on-ASIS placement (24%) compared to their normal (27% vs 67%) BMI counterparts (p < 0.001). Notable trends included obese occupants with superior placement having less abdominal organ injury incidence than those with on-ASIS belt placement (42% superior placement vs 55% on-ASIS). In non-obese occupants, there was a higher incidence of abdominal organ injury with superior lap belt placement compared to on-ASIS placement counterparts (Normal BMI: 62% vs 41%, Overweight: 57% vs 43%). CONCLUSIONS: In CIREN occupants with abdominal injury, those with obesity are more prone to positioning the lap belt superior to the ASIS, though the impact on abdominal injury incidence remains a key point for continued exploration into how occupant BMI affects crash safety and belt design.

Proximity to fatal accidents predicts police citation rates on urban and rural roads.

Objectives: Police enforcement can effectively deter risky driving behaviors and reduce traffic fatalities, including speed-related fatalities. Unlike other areas of data-driven policing, spatial methods to improve road safety are not well-described. The objectives of this study were as follows: (1) determine if proximity to a prior roadway fatality increases the risk of a traffic citation being issued after adjusting for relevant roadway variables; (2) determine if this effect varies between rural and urban roads.Methods: The study region included a rural county and adjacent small city (City of Charlottesville, Albemarle County, Virginia). Fatality locations were obtained from the Fatality Analysis Reporting System (FARS) from 2008 to 2018. Police citation data were obtained from the State of Virginia for 2020. Data on fatalities and roadway features were used to create a model to predict traffic citation density. Traffic stop locations were analyzed as a point pattern on a linear network, assuming a Poisson process with varying intensity. The model adjusted for average traffic volume, distance to the nearest fatal crash along the road network, rural vs urban roadway, posted speed limit, and interstate vs non-interstate road. To account for over-dispersion, quasi-Poisson model was used.Results: There were 138 fatalities and 651 traffic citations during the time periods examined. After adjusting for other covariates, the expected number of citations/km was higher with increasing proximity to prior fatal crashes, RR = 1.34 (95% CI: 1.04, 1.72) per km. The effect of proximity did not vary significantly between urban and rural roads (p = 0.2707). However, citation intensity was significantly higher on urban roads vs. rural roads, RR = 2.65 (1.09, 6.45). Predicted citation intensity reflected anticipated enforcement clusters inside the city limits and on major county roads, suggesting satisfactory model fit.Conclusions: This study demonstrated a novel approach to quantify the impact of road fatalities on police activity, measured by traffic citations. Proximity to fatal crashes was found to affect police citation rates, and this effect is consistent between urban and rural areas. Future work will aim to identify areas of under enforcement based on proximity to fatal crashes and other roadway variables.

Evaluation of mechanism of injury criteria for field triage of occupants involved in motor vehicle collisions.

OBJECTIVE: The mechanism of injury (MOI) criteria assist in determining which patients are at high risk of severe injury and would benefit from direct transport to a trauma center. The goal of this study was to determine whether the prognostic performance of the Centers for Disease Control's (CDC) MOI criteria for motor vehicle collisions (MVCs) has changed during the decade since the guidelines were approved. Secondary objectives were to evaluate the performance of these criteria for different age groups and evaluate potential criteria that are not currently in the guidelines. METHODS: Data were obtained from NASS and Crash Investigation Sampling System (CISS) for 2000-2009 and 2010-2019. Cases missing injury severity were excluded, and all other missing data were imputed. The outcome of interest was Injury Severity Score (ISS) ≥16. The area under the receiver operator characteristic (AUROC) and 95% confidence intervals (CIs) were obtained from 1,000 bootstrapped samples using national case weights. The AUROC for the existing CDC MOI criteria were compared between the 2 decades. The performance of the criteria was also assessed for different age groups based on accuracy, sensitivity, and specificity. Potential new criteria were then evaluated when added to the current CDC MOI criteria. RESULTS: There were 150,683 (weighted 73,423,189) cases identified for analysis. There was a small but statistically significant improvement in the AUROC of the MOI criteria in the later decade (2010-2019; AUROC = 0.77, 95% CI [0.76-0.78]) compared to the earlier decade (2000-2009; AUROC = 0.75, 95% CI [0.74-0.76]). The accuracy and specificity did not vary with age, but the sensitivity dropped significantly for older adults (0-18 years: 0.62, 19-54 years: 0.59, ≥55 years: 0.37, and ≥65 years: 0.36). The addition of entrapment improved the sensitivity of the existing criteria and was the only potential new criterion to maintain a sensitivity above 0.95. CONCLUSIONS: The MOI criteria for MVCs in the current CDC guidelines still perform well even as vehicle design has changed. However, the sensitivity of these criteria for older adults is much lower than for younger occupants. The addition of entrapment improved sensitivity while maintaining high specificity and could be considered as a potential modification to current MOI criteria.

Effectiveness of barrier devices, high-volume evacuators, and extraoral suction devices on reducing dental aerosols for the dental operator: A pilot study.

BACKGROUND: The COVID-19 pandemic has increased the importance of minimizing exposure to aerosols generated during dental procedures. The authors' objective was to measure the aerosolized particles in the breathing zone of operators using several facial protection and filtration methods. METHODS: Twenty-one dentists performed maxillary anterior incisor veneer preparations using a microscope and drape and loupes with or without a face shield. In each test condition, the following 3 levels of filtration were tested: no filtration, a high-volume evacuator [HVE], and an HVE with an extraoral suction device. Measurements were made using a mass monitor attached to the operator's chest with inlet within 10 inches of the operator's face. RESULTS: The authors found that the microscope and drape provided the lowest levels of aerosolized particles compared with loupes with or without a face shield (P < .001). There was no detectable difference in the concentration of particles between operators wearing a face shield and wearing loupes alone (P = .47). The particles in each test condition were lowered when an HVE was used (P < .001) and further lowered with an extraoral suction device. CONCLUSIONS: The findings of this study suggest that the use of a surgical microscope and bag barrier drape, HVE, and extraoral suction device result in the lowest concentration of aerosolized particles. The face shield did not appear to offer any protection from aerosolized particles. HVE and extraoral suction were effective in decreasing aerosols regardless of the type of facial protection used. PRACTICAL IMPLICATIONS: Dentists can reduce exposure to aerosols with a drape, HVE, and extraoral suction.

The relationship of body mass index, belt placement, and abdominopelvic injuries in motor vehicle crashes: A Crash Injury Research and Engineering Network (CIREN) study.

OBJECTIVE: Obesity has important implications for motor vehicle safety due to altered crash injury responses from increased mass and improper seatbelt placement. Abdominal seatbelt signs (ASBS) above the anterior superior iliac spine (ASIS) in motor vehicle crashes (MVCs) often correlate with abdominopelvic trauma. We investigated the relationship of body mass index (BMI), lap belt placement, and the incidence of abdominopelvic injury using computed tomography (CT) evaluation for subcutaneous ASBS mark and its location relative to the ASIS. METHODS: A retrospective analysis of 235 Crash Injury Research and Engineering Network (CIREN) cases and their associated abdominal injuries was conducted. CT Scans were analyzed to visualize fat stranding. 150 positive ASBS were found and their ASBS mark location was classified as superior, on, or inferior to the ASIS. RESULTS: Obese occupants had a higher incidence rate of belt placement superior to the ASIS, and occupants with normal BMI had a higher incidence of proper belt placement (p < 0.05). Trends of interest developed, notably that non-obese occupants with superior belt placement had increased incidence of internal abdominopelvic organ injury compared to those with proper belt placement (Normal BMI: 53.3% superior vs 39.4% On-ASIS, Overweight: 47.8% superior vs 34.7% On-ASIS). CONCLUSIONS: Utilizing CT scans to confirm ASBS and lap belt placement relative to the ASIS, superior belt placement above the ASIS was associated with elevated BMI and a trend of increasing incidence of internal abdominopelvic organ injury.

Development of a concise injury severity prediction model for pediatric patients involved in a motor vehicle collision.

OBJECTIVE: Transporting severely injured pediatric patients to a trauma center has been shown to decrease mortality. A decision support tool to assist emergency medical services (EMS) providers with trauma triage would be both as parsimonious as possible and highly accurate. The objective of this study was to determine the minimum set of predictors required to accurately predict severe injury in pediatric patients. METHODS: Crash data and patient injuries were obtained from the NASS and CISS databases. A baseline multivariable logistic model was developed to predict severe injury in pediatric patients using the following predictors: age, sex, seat row, restraint use, ejection, entrapment, posted speed limit, any airbag deployment, principal direction of force (PDOF), change in velocity (delta-V), single vs. multiple collisions, and non-rollover vs. rollover. The outcomes of interest were injury severity score (ISS) ≥16 and the Target Injury List (TIL). Accuracy was measured by the cross-validation mean of the receiver operator curve (ROC) area under the curve (AUC). We used Bayesian Model Averaging (BMA) based on all subsets regression to determine the importance of each variable separately for each outcome. The AUC of the highest performing model for each number of variables was compared to the baseline model to assess for a statistically significant difference (p < 0.05). A reduced variable set model was derived using this information. RESULTS: The baseline models performed well (ISS ≥ 16: AUC 0.91 [95% CI: 0.86-0.95], TIL: AUC 0.90 [95% CI: 0.86-0.94]). Using BMA, the rank of the importance of the predictors was identical for both ISS ≥ 16 and TIL. There was no statistically significant decrease in accuracy until the models were reduced to fewer than five and six variables for predicting ISS ≥ 16 and TIL, respectively. A reduced variable set model developed using the top five variables (delta-V, entrapment, ejection, restraint use, and near-side collision) to predict ISS ≥ 16 had an AUC 0.90 [95% CI: 0.84-0.96]. Among the models that did not include delta-V, the highest AUC was 0.82 [95% CI: 0.77-0.87]. CONCLUSIONS: A succinct logistic regression model can accurately predict severely injured pediatric patients, which could be used for prehospital trauma triage. However, there remains a critical need to obtain delta-V in real-time.

Dynamic data in the ED predict requirement for ICU transfer following acute care admission.

Misidentification of illness severity may lead to patients being admitted to a ward bed then unexpectedly transferring to an ICU as their condition deteriorates. Our objective was to develop a predictive analytic tool to identify emergency department (ED) patients that required upgrade to an intensive or intermediate care unit (ICU or IMU) within 24 h after being admitted to an acute care floor. We conducted a single-center retrospective cohort study to identify ED patients that were admitted to an acute care unit and identified cases where the patient was upgraded to ICU or IMU within 24 h. We used data available at the time of admission to build a logistic regression model that predicts early ICU transfer. We found 42,332 patients admitted between January 2012 and December 2016. There were 496 cases (1.2%) of early ICU transfer. Case patients had 18.0-fold higher mortality (11.1% vs. 0.6%, p < 0.001) and 3.4 days longer hospital stays (5.9 vs. 2.5, p < 0.001) than those without an early transfer. Our predictive analytic model had a cross-validated area under the receiver operating characteristic of 0.70 (95% CI 0.67-0.72) and identified 10% of early ICU transfers with an alert rate of 1.6 per week (162.2 acute care admits per week, 1.9 early ICU transfers). Predictive analytic monitoring based on data available in the emergency department can identify patients that will require upgrade to ICU or IMU if admitted to acute care. Incorporating this tool into ED practice may draw attention to high-risk patients before acute care admit and allow early intervention.

Factors associated with EMS transport decisions for pediatric patients after motor vehicle collisions.

OBJECTIVE: Prehospital non-transport events occur when emergency medicine service (EMS) providers respond to a scene, but the patient is ultimately not transported to a hospital for evaluation. The objective of this study was to determine the rate of non-transport of pediatric patients who were involved in a motor vehicle collision (MVC) and the factors associated with non-transport decisions. METHODS: We searched the National Emergency Medical Services Information System (NEMSIS) database using ICD-10 mechanism of injury codes to identify cases in which EMS responded to a pediatric occupant (age < 18 years) who had been involved in an MVC. We excluded interfacility transports, scene assists, deaths at the scene, and collisions that occurred outside the US. The outcome of interest was if pediatric patients were not transported to a hospital for evaluation. We performed univariate and multivariate analysis to identify which risk factors were associated with non-transport. We also analyzed regional variation and the reasons recorded for not transporting patients. RESULTS: We identified 92,254 pediatric patients who were evaluated by EMS after an MVC, of which 31,404 (34.0%) were not transported to a hospital for evaluation. In our adjusted analysis, the factors associated with non-transport were age <1 year or >16 years, male sex, normal Glasgow Coma Scale (GCS = 15), level of training of EMS providers, response time later than 6 a.m., and region of the country. GCS was the most important factor, with only 3.0% (108/3,616) of patients not transported who had abnormal GCS (< 15). In cases of non-transport, 32.7% (10257) were due to patient or caregiver refusal, and 33.3% (10,442) were due to patients being discharged against medical advice. Only 11.5% (3,627) pediatric patients who were not transported were discharged based on an established protocol. CONCLUSIONS: Pediatric patients were not transported after EMS responded to an MVC in approximately one-third of cases, and there was considerable variation in the rate of non-transports based on geographic region, provider level, and time of day. The majority of non-transports occurred because patients were discharged against medical advice or the patient/caregiver refused transport, which may indicate conflicting priorities between EMS providers and patients.

Accuracy of algorithms to predict injury severity in older adults for trauma triage.

Objective: Older adults make up a rapidly increasing proportion of motor vehicle occupants and previous studies have demonstrated that this population is more susceptible to traumatic injuries. The CDC recommends that patients anticipated to have severe injuries (Injury Severity Score [ISS] ≥ 16) be transported to a trauma center. The recommended target rate for undertriage is ≤ 5% and for overtriage is ≤ 50%. Several regression-based algorithms for injury prediction have been developed in order to predict severe injury in occupants involved in a motor vehicle collision (MVC). The objective of this study to was to determine if the accuracy of regression-based injury severity prediction algorithms decreases for older adults.Methods: Data were obtained from the National Automotive Sampling System - Crashworthiness Data System (NASS-CDS) from the years 2000-2015. Adult occupants involved in non-rollover MVCs were included. Regression-based injury risk models to predict severe injury (ISS ≥ 16) were developed using random split-samples with the following variables: age, delta-V, direction of impact, belt status, and number of impacts. Separate models were trained using data from the following age groups: (1) all adults, (2) 15-54 years, (3) ≥45 years, (4) ≥55 years, and (5) ≥65 years. The models were compared using the mean receiver operating characteristic area under curve (ROC-AUC) after 1,000 iterations of training and testing. The predicted rates of overtriage were then determined for each group in order to achieve an undertriage rate of 5%.Results: There were 24,577 occupants (6,863,306 weighted) included in this analysis. The injury prediction model trained using data from all adults did not perform as well when tested on older adults (ROC-AUC: 15-54 years: 0.874 [95% CI: [0.851-0.895]; 45+ years: 0.837 [95% CI: 0.802-869]; 55+ years: 0.821 [95% CI: 0.775-0.864]; and 65+ years: 0.813 [95% CI: 0.754-0.866]). The accuracy of this model decreased in each decade of life. The performance did not change significantly when age-specific data were used to train the prediction models (ROC-AUC: 18-54 years: 0.874 [95% CI: 0.851-0.896]; 45+ years: 0.836 [95% CI: 0.798-0.871]; 55+ years: 0.822 [95% CI: 0.779-0.864]; and 65+ years: 0.808 [95% CI: 0.748-0.868]). In order to achieve an undertriage rate of 5%, the predicted overtriage rate by these models were 50% for occupants 15-54 years, 61% for occupants ≥ 55 years, 70% for occupants ≥ 55 years, and 71% for occupants ≥ 65 years.Conclusion: The results of this study indicate that it is more difficult to accurately predict severe injury in older adults involved in MVCs, which has the potential to result in significant overtriage. This decreased accuracy is likely due to variations in fragility in older adults. These findings indicate that special care should be taken when using regression-based prediction models to determine the appropriate hospital destination for older occupants.

Development of injury risk models to guide CT evaluation in the emergency department after motor vehicle collisions.

OBJECTIVE: The clinical evaluation of motor vehicle collision (MVC) victims is challenging and commonly relies on computed tomography (CT) to detect internal injuries. CT scans are financially expensive and each scan exposes the patient to additional ionizing radiation with an associated, albeit low, risk of cancer. Injury risk prediction based on regression modeling has been to be shown to be successful in estimating Injury Severity Scores (ISSs). The objective of this study was to (1) create risk models for internal injuries of occupants involved in MVCs based on CT body regions (head, neck, chest, abdomen/pelvis, cervical spine, thoracic spine, and lumbar spine) and (2) evaluate the performance of these risk prediction models to predict internal injury. METHODS: All Abbreviated Injury Scale (AIS) 2008 injury codes were classified based on which CT body region would be necessary to scan in order to make the diagnosis. Cases were identified from the NASS-CDS. The NASS-CDS data set was queried for cases of adult occupants who sought medical care and for which key crash characteristics were all present. Forward stepwise logistic regression was performed on data from 2010-2014 to create models predicting risk of internal injury for each CT body region. Injury risk for each region was grouped into 5 levels: very low (<2%), low (2-5%), medium (5-10%), high (10-20%), and very high (20%). The models were then tested using weighted data from 2015 in order to determine whether injury rates fell within the predicted risk level. RESULTS: The inclusion and exclusion criteria identified 5,477 cases in the NASS-CDS database. Cases from 2010-2014 were used for risk modeling (n = 4,826). Seven internal injury risk models were created based on the CT body regions using data from 2010-2014. These models were tested against data from 2015 (n = 651). In all CT body regions, the majority of occupants fell in the very low or low predicted injury rate groups, except for the head. On average, 57% of patients were classified as very low risk and 15% as low risk for each body region. In most cases the actual rate of injury was within the predicted injury risk range. The 95% confidence interval overlapped with predicting injury risk range in all cases. CONCLUSION: This study successfully demonstrated the ability for internal injury risk models to accurately identify occupants at low risk for internal injury in individual body regions. This represents a step towards incorporating telemetry data into a clinical tool to guide physicians in the use of CT for the evaluation of MVC victims.

Does obesity affect the position of seat belt loading in occupants involved in real-world motor vehicle collisions?

OBJECTIVE: Previous work has shown that the lap belt moves superior and forward compared to the bony pelvis as body mass index (BMI) increases. The goal of this project was to determine whether the location of lap belt loading is related to BMI for occupants who sustained real-world motor vehicle collisions (MVCs). METHODS: A national MVC database was queried for vehicle occupants over a 10-year period (2003-2012) who were at least 16 years old, restrained by a 3-point seat belt, sitting in the front row, and involved in a front-end collision with a change in velocity of at least 56 km/h. Cases were excluded if there was not an available computed tomography (CT) scan of the abdomen. CT scans were then analyzed using adipose enhancement of 3-dimensional reconstructions. Scans were assessed for the presence a radiographic seat belt sign (rSBS), or subcutaneous fat stranding due to seat belt loading. In scans in which the rSBS was present, anterior and superior displacement of rSBS from the anterior-superior iliac spine (ASIS) was measured bilaterally. This displacement was correlated with BMI and injury severity. RESULTS: The inclusion and exclusion criteria yielded 151 cases for analysis. An rSBS could definitively be identified in 55 cases. Cases in which occupants were older and had higher BMI were more likely to display an rSBS. There was a correlation between increasing BMI and anterior rSBS displacement (P <.01 and P <.01, right and left, respectively). There was no significant correlation between BMI and superior displacement of the rSBS (P =.46 and P =.33, right and left, respectively). When the data were examined in terms of relating increasing superior displacement of the lap belt with Injury Severity Scale (P =.34) and maximum Abbreviated Injury Score (AIS) injury severity (P =.63), there was also no significant correlation. CONCLUSION: The results from this study demonstrated that anterior displacement of the radiographic seat belt sign but not superior displacement increased with higher BMI. These results suggest that obesity may worsen horizontal position but not the vertical position of the lap belt loading during real-world frontal MVCs.