Injury Metrics Research Articles

Development of a detailed human neck finite element model and injury risk curves under lateral impact

Advanced neck finite element modeling and development of neck injury criteria are important for the design of optimal neck protection systems in automotive and other environments. They are also important in virtual tests. The objectives of the present study were to develop a detailed finite element model (FEM) of the human neck and couple it to the existing head model, validate the model with kinematic data from legacy human volunteer and human cadaver impact datasets, and derive lateral impact neck injury risk curves using survival analysis from the upper and lower neck forces and moments. The detailed model represented the anatomy of a young adult mid-size male. It included all the cervical and first thoracic vertebrae, intervening discs, upper and lower spinal ligaments, bilateral facet joints, and passive musculature. Material properties were obtained from literature. Frontal, oblique, and lateral impacts to the distal end of the model was applied based on human volunteer and human cadaver experimental data. Corridor and cross-correlation methods were used for validation. The CORrelation and Analysis (CORA) score was used for objective assessments. Forces and moments were obtained at the occipital condyles (OC) and T1, and parametric survival analysis was used to derive injury risk curves to define human neck injury tolerance to lateral impact. The Brier Score Metric (BSM) was used to determine the hierarchical sequence among the injury metrics. The CORA scores for the lateral, frontal, and oblique impact loading conditions were 0.80, 0.91, and 0.87, respectively, for human volunteer data, and the mean score was 0.7 for human cadaver lateral impacts. Injury risk curves along with ±95% confidence intervals are given for all the four biomechanical metrics. The OC shear force was the optimal metric based on the BSM. A force of 1.5 kN was associated with the 50% probability level of AIS3+ neck injury. As a first step, the presented risk curves serve as human tolerance criteria under lateral impact, hitherto not available in published literatures, and they can be used in virtual testing and advancing restraint systems for improving human safety.

Evaluation of brain injury criteria based on reliability analysis

Among the proposed brain injury metrics, Brain Injury Criteria (BrIC) is a promising tool for performing safety assessment of vehicles in the future. In this paper, the available risk curves of BrIC were re-evaluated with the use of reliability analysis and new risk curves were constructed for different injury types based on literature data of tissue-level tolerances. Moreover, the comparison of different injury metrics and their corresponding risk curves were performed. Tissue-level uncertainties of the effect and resistance were considered by random variables. The variability of the tissue-level predictors was quantified by the finite element reconstruction of 100 frontal crash tests which were performed in Simulated Injury Monitor environment. The applied tests were scaled to given BrIC magnitudes and the injury probabilities were calculated by Monte Carlo simulations. New risk curves were fitted to the observed results using Weibull and Lognormal distribution functions. The available risk curves of diffuse axonal injury (DAI) could be slightly improved, and combined AIS 4+ risk curves were obtained by considering subdural hematoma and contusion as well. The performance of several injury metrics and their risk curves were evaluated based on the observed correlations with the tissue-level predictors. The cumulative strain damage measure and the BrIC provide the highest correlation (R² = 0.61) and the most reliable risk curve for the evaluation of DAI. Although the observed correlation is smaller for other injury types, the BrIC and the associated reliability analysis-based risk curves seem to provide the best available method for estimating the brain injury risk for frontal crash tests.

Development of a school bus restraint system model and its application in the evaluation of designed protective strategies on child neck injury in front-end crash

In a front-end crash of a school bus, the existing child restraint system does not provide effective protection to the neck, although it can well protect other regions of the body, including the head and chest. In this study, we developed multi-body seat models with compartmentalization (no belt), lap belt and lap/shoulder belt. We verified the models against sled tests considering dummy motion trajectory and neck injury metrics. Next, we proposed two strategies to improve school bus seats, namely, equipping an energy-absorbing block to the seatback and adding a revolute joint between the seatback and the seat frame. We evaluated the performance of each improved strategy in terms of neck protection based on the validated models. The results showed that both improvements effectively reduced neck injury. This study can provide useful guidance for the design of new school bus restraint systems.

Systemic Markers of Injury and Injury Response Are Not Associated with Impaired Cerebrovascular Reactivity in Adult Traumatic Brain Injury: A Collaborative European Neurotrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) Study

The role of extra-cranial injury burden and systemic injury response on cerebrovascular response in traumatic brain injury (TBI) is poorly documented. This study preliminarily assesses the association between admission features of extra-cranial injury burden on cerebrovascular reactivity. Using the Collaborative European Neurotrauma Effectiveness Research in TBI High-Resolution ICU (HR ICU) sub-study cohort, we evaluated those patients with both archived high-frequency digital intra-parenchymal intra-cranial pressure monitoring data of a minimum of 6 h in duration, and the presence of a digital copy of their admission computed tomography (CT) scan. Digital physiologic signals were processed for pressure reactivity index (PRx) and both the percent time above defined PRx thresholds and mean hourly dose above threshold. This was conducted for both the first 72 h and entire duration of recording. Admission extra-cranial injury characteristics and CT injury scores were obtained from the database, with quantitative contusion, edema, intraventricular hemorrhage, and extra-axial lesion volumes were obtained via semi-automated segmentation. Comparison between admission extra-cranial markers of injury and PRx metrics was conducted using Mann-Whitney U testing, and logistic regression techniques, adjusting for known CT injury metrics associated with impaired PRx. A total of 165 patients were included. Evaluating the entire ICU recording period, there was limited association between metrics of extra-cranial injury burden and impaired cerebrovascular reactivity. Using the first 72 h of recording, admission temperature (p = 0.042) and white blood cell % (WBC %; p = 0.013) were statistically associated with impaired cerebrovascular reactivity on Mann-Whitney U and univariate logistic regression. After adjustment for admission age, pupillary status, GCS motor score, pre-hospital hypoxia/hypotension, and intra-cranial CT characteristics associated with impaired reactivity, temperature (p = 0.021) and WBC % (p = 0.013) remained significantly associated with mean PRx values above +0.25 and +0.35, respectively. Markers of extra-cranial injury burden and systemic injury response do not appear to be strongly associated with impaired cerebrovascular reactivity in TBI during both the initial and entire ICU stay.

A Promising Subject-Level Classification Model for Acute Concussion Based on Cerebrovascular Reactivity Metrics

Concussion imaging research has primarily focused on neuronal disruption with lesser emphasis directed toward vascular dysfunction. However, blood flow metrics may be more sensitive than measures of neuronal integrity. Vascular dysfunction can be assessed by measuring cerebrovascular reactivity (CVR)-the change in cerebral blood flow per unit change in vasodilatory stimulus. CVR metrics, including speed and magnitude of flow responses to a standardized well-controlled vasoactive stimulus, are potentially useful for assessing individual subjects following concussion given that blood flow dysregulation is known to occur with traumatic brain injury. We assessed changes in CVR metrics to a standardized vasodilatory stimulus during the acute phase of concussion. Using a case control design, 20 concussed participants and 20 healthy controls (HCs) underwent CVR assessment measuring blood oxygen-level dependent (BOLD) magnetic resonance imaging using precise changes in end-tidal partial pressure of CO2 (PETCO2). Metrics were calculated for the whole brain, gray matter (GM), and white matter (WM) using sex-stratification. A leave-one-out receiver operating characteristic (ROC) analysis classified concussed from HCs based on CVR metrics. CVR magnitude was greater and speed of response faster in concussed participants relative to HCs, with WM showing higher classification accuracy compared with GM. ROC analysis for WM-CVR metrics revealed an area under the curve of 0.94 in males and 0.90 in females for speed and magnitude of response respectively. These greater than normal responses to a vasodilatory stimulus warrant further investigation to compare the predictive ability of CVR metrics against structural injury metrics for diagnosis and prognosis in acute concussion.

An Improved Method for Developing Injury Risk Curves Using the Brier Metric Score.

Many injury metrics are routinely proposed from measured or derived quantities from biomechanical experiments using post mortem human subjects (PMHS). The existing literature did not provide guidance on deciding between parameters collected in an experiment that would be best to use for the development of human injury probability curves (HIPC). The objective of this study was to use the Brier Metric Score (BMS) to identify the most appropriate metric from an experiment that predicts injury outcomes. The Brier Metric Score assesses how well a metric predicts the outcome for a censored data point (a lower BMS is better). Survival analysis was then conducted with the selected metric and the best distribution was selected using Akaike information criterion (AIC). Confidence intervals (CIs) and the normalized confidence interval width (NCIS) were calculated for the injury probability curve. The testing and validation of the methods described were performed using biomechanics data in the open literature. The methods for the HIPC development procedure detailed herein have been rigorously tested and used in the generation of WIAMan HIPCs and Injury Assessment Reference Curves (IARCs) for the WIAMan ATD, but can also be used in other ATD or PMHS injury risk curve development.

Machine Learning to Predict Lower Extremity Musculoskeletal Injury Risk in Student Athletes.

Injury rates in student athletes are high and often unpredictable. Injury risk factors are not agreed upon and often not validated. Here, we present a random-forest machine learning methodology for identifying the most significant injury risk factors and develop a model of lower extremity musculoskeletal injury risk in student athletes with physical performance metrics spanning joint strength measured with force transducers, postural stability measured using a force plate, and flexibility, measured with a goniometer, combined with previous injury metrics and athlete demographics. We tested our model in a population of 122 student athletes with performance metrics for the lower extremity musculoskeletal system and achieved an injury risk accuracy of 79% and identified significant injury risk factors, that could be used to increase accuracy of injury risk assessments, implement timely interventions, and decrease the number of career-ending or chronic injuries among student athletes.

A Review of On-Field Investigations into the Biomechanics of Concussion in Football and Translation to Head Injury Mitigation Strategies.

This review paper summarizes the scientific advancements in the field of concussion biomechanics in American football throughout the past five decades. The focus is on-field biomechanical data collection, and the translation of that data to injury metrics and helmet evaluation. On-field data has been collected with video analysis for laboratory reconstructions or wearable head impact sensors. Concussion biomechanics have been studied across all levels of play, from youth to professional, which has allowed for comparison of head impact exposure and injury tolerance between different age groups. In general, head impact exposure and injury tolerance increase with increasing age. Average values for concussive head impact kinematics are lower for youth players in both linear and rotational acceleration. Head impact data from concussive and non-concussive events have been used to develop injury metrics and risk functions for use in protective equipment evaluation. These risk functions have been used to evaluate helmet performance for each level of play, showing substantial differences in the ability of different helmet models to reduce concussionrisk. New advances in head impact sensor technology allow for biomechanical measurements in helmeted and non-helmeted sports for a more complete understanding of concussion tolerance in different demographics. These sensors along with advances in finite element modeling will lead to a better understanding of the mechanisms of injury and human tolerance to head impact.

Safety analysis of battery-powered ride-on toy car with seat and restraint system modifications

ABSTRACT Modified battery-powered ride-on toy cars represent novel rehabilitation tools for children with disabilities. However, safety concerns exist with the use of these battery-power toys and pose a barrier for the growth of adaptive ride-on toy programs due to the lack of evidence demonstrating that modifications made to these cars are safe. Within this context, the purpose of this study was to investigate whether modifications to ride-on toys are sufficient to prevent common injuries and determine how these modifications influence injury metrics. Specifically, we evaluated the effects of common modifications such as various seatbelt configurations and determined how increased seat back height effects neck forces. Results indicated that occupant displacement can be reduced using a lap belt, and further reductions in displacement are achieved with a 5-point harness. Although some injury metrics increased with restraints, none of the collected injury metrics even came close to approaching known tolerance thresholds, and most were well within the range that is experienced by a child in daily life. As the greatest concerns for these ride-on toys are related to displacement, findings from this study support the use of a 5-point harness system to minimize displacement-related injuries with little-to-no added risk.

Pediatric occupant human body model kinematic and kinetic response variation to changes in seating posture in simulated frontal impacts – with and without automatic emergency braking

Objective The study quantifies the kinematics of children in booster child restraint systems (CRSs) in various naturalistic seating postures exposed to frontal impacts in a full-vehicle environment, with and without the application of pre-crash automatic emergency braking. Methods The PIPER 6YO and 10YO pediatric human body models were positioned in CRSs. The 6YO was restrained on a lowback (LBB) and highback (HBB) booster, while the 10YO was positioned on an LBB and in a NoCRS condition. All simulations used the 3-point seatbelt. The child models were pre-positioned (gravity settled, seatbelt tensioned) in four naturalistic seating postures: leaning-forward, leaning-forward-inward, leaning-forward-outward, and a pre-submarining position, along with a baseline reference seating position. A 2012 Toyota Camry finite element (FE) model was used as the vehicle environment. A standard 3-point lap-shoulder belt system was modeled to restrain the child and CRS in the left-rear vehicle seat. Two vehicle impact cases were considered: with and without a pre-crash AEB. For with-AEB cases, a pre-crash phase was run to incorporate postural changes due to the application of AEB. All seating positions were ultimately subjected to a full-frontal rigid-barrier impact at 35 MPH. A total of 40 conditions were simulated in LS-DYNA. Results Injury metrics varied widely for both occupants. Shoulder belt slippage was observed for the 6YO leaning-forward-inward on HBB. No head contact was observed for any simulated cases. Forward-leaning and forward-inward-leaning postures generally had greater head excursion across all seating postures. The lap belt rode over the pelvis for pre-submarining postures. Injury metrics for cases with pre-crash AEB were lower compared to their corresponding without-AEB cases. HIC15, head acceleration, upper neck tension force, and upper neck flexion moment were similar or lower for with-AEB scenarios. Conclusions Pre-crash AEB reduces the effect of the impact despite the same collision speed as cases without-AEB. This is primarily due to the limited travel distance of the occupant, thus, starting an earlier ride-down during the crash. Moreover, different initial seating postures lead to a wide range of injury exposures. Vehicle and child restraint design should incorporate these seating postures to ensure robust protection of the occupant in a crash.

Low-Rank Representation of Head Impact Kinematics: A Data-Driven Emulator

Head motion induced by impacts has been deemed as one of the most important measures in brain injury prediction, given that the vast majority of brain injury metrics use head kinematics as input. Recently, researchers have focused on using fast approaches, such as machine learning, to approximate brain deformation in real time for early brain injury diagnosis. However, training such models requires large number of kinematic measurements, and therefore data augmentation is required given the limited on-field measured data available. In this study we present a principal component analysis-based method that emulates an empirical low-rank substitution for head impact kinematics, while requiring low computational cost. In characterizing our existing data set of 537 head impacts, each consisting of 6 degrees of freedom measurements, we found that only a few modes, e.g., 15 in the case of angular velocity, is sufficient for accurate reconstruction of the entire data set. Furthermore, these modes are predominantly low frequency since over 70% of the angular velocity response can be captured by modes that have frequencies under 40 Hz. We compared our proposed method against existing impact parametrization methods and showed significantly better performance in injury prediction using a range of kinematic-based metrics—such as head injury criterion (HIC), rotational injury criterion (RIC), and brain injury metric (BrIC)—and brain tissue deformation-based metrics—such as brain angle metric (BAM), maximum principal strain (MPS), and axonal fiber strains (FS). In all cases, our approach reproduced injury metrics similar to the ground truth measurements with no significant difference, whereas the existing methods obtained significantly different (p < 0.01) values as well as substantial differences in injury classification sensitivity and specificity. This emulator will enable us to provide the necessary data augmentation to build a head impact kinematic data set of any size. The emulator and corresponding examples are available on our website1.

Monte carlo method for estimating whole-body injury metrics from pedestrian impact simulation results

The goal of the current study was to develop a method to estimate whole-body injury metrics (WBIMs), which measure the overall impact of injuries, using stochastic injury prediction results from a computational human surrogate. First, hospitalized pedestrian data was queried to identify injuries sustained by pedestrians and their frequencies. Second, with consideration for an understanding of injury mechanisms and the capability of the computational human surrogate, the whole-body was divided into 17 body regions. Then, an injury pattern database was constructed for each body region for various maximum abbreviated injury scale (MAIS) levels. Third, a two-step Monte Carlo sampling process was employed to generate N virtual pedestrians with an assigned list of injuries in AIS codes. Then, the expected values of WBIMs such as injury severity score (ISS), probability of death, whole-body functional capacity index (WBFCI), and lost years of life (LYL), were estimated. Lastly, the proposed method was verified using injury information from the inpatient pedestrian database. Also, the proposed method was applied to pedestrian impact simulations with various impact speeds to estimate the probability of death with respect to the impact speed. The probability of death from the proposed method was compared with those from epidemiological studies.The proposed method accurately estimated WBIMs such as ISS and WBFCI using either for a given distribution of injury risk or MAIS levels. The predicted probability of death with respect to the impact speed showed a good correlation with those from the epidemiological study. These results imply that if we have a human surrogate that can predict the risk of injury accurately, we can accurately estimate WBIMs using the proposed method. The proposed method can simplify a vehicle design optimization process by transforming the multi-objective optimization problem into the single-objective one. Lastly, the proposed method can be applied to other human surrogates such as occupant models.

Mechanisms and variances of rotation-induced brain injury: a parametric investigation between head kinematics and brain strain.

There lacks a comprehensive understanding of the correlation between head kinematics and brain strain especially deep-brain strain, partially resulting the deficiency of understanding brain injury mechanisms and the difficulty of choosing appropriate brain injury metrics. Hence, we simulated 76 impacts that were focused on concussion-relevant rotational kinematics and evaluated cumulative strain damage measure (CSDM) and average strain that could represent brain strain distribution. For the whole brain, axial rotation induced the highestCSDM, while lateral bending produced the lowest CSDM. However, for the deep-brain components, lateral bending produced the highestCSDM to the corpus callosum and thalamus. We further confirmed that brain strain was mainly produced by rotational kinematics, for which the effect of rotational deceleration could not be ignored with the deceleration influencing CSDM20 up to 27%. Our data supported that peak rotational velocity correlated to brain strain with an average R2 of 0.77 across various impact directions and different shapes of loading curves. The correlation between peak rotational velocity and brain strain reached to an average R2 of 0.99 for each specific impact direction. Our results supported using direction-specific peak rotation velocity for predicting strain-related brain injury. Additionally, we highlighted the importance of investigating whole-brain and deep-brain strain, as well as considering rotational deceleration.

Injury metrics are altered in spica-casted versus non-casted child ATDs in side-impact collisions with door intrusion

Objective: There is little data defining safe transport protocols for spica-casted children. A single earlier study demonstrated the presence of a body cast alters kinematics and injury metrics during simulated side-impact crashes. Since then, the National Highway Transportation Safety Administration (NHTSA) proposed a new side-impact test protocol for evaluating child restraints. This test is more severe than the earlier tests, as it simulates an impact with a door intruding into the occupant space. As no currently available child restraint system (CRS) able to accommodate a spica-casted child has been evaluated using these updated testing criteria, the objective of this study was to evaluate current restraint options in simulated side-impact collisions using an anthropomorphic test device (ATD) modeled after a 3-year-old.Methods: Four commercially available CRSs able to accommodate a spica-casted Q3s side-impact ATD were selected for testing. Side-impact testing was performed using casted and uncasted ATDs in compliance with the NHTSA proposed side-impact test. High-speed photography and ATD instrumentation were used to measure selected injury criteria.Results: HIC15 values were highest in CRSs with less robust side wings, such as the Merritt WallenburgTM (HIC15 = 1,373), which allow for the occupant to interact with the intruding door panel. Head contact with the door panel was found to correspond with high resultant neck peak force. Pelvic acceleration magnitudes were greatest for the uncasted tests. Casted tests with a CRS that included an armrest were associated with greater torso rotation in the frontal plane with the left shoulder moving toward the door panel.Conclusions: The presence of a spica cast alters injury metrics in side-impact testing. Spica specific child safety seats are not yet optimized for side-impact with door intrusion. This is due to a lack of adequate side cushion wings, which may place both casted and uncasted occupants at increased likelihood for injury through head contact with an intruding door. Additional work is needed to improve the safety of CRSs for both casted and uncasted children in side-impact collisions.

The Roles of Vehicle Seat Cushion Stiffness and Length in Child Restraint System (CRS) Performance

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;The objective is to determine whether responses and injury risks for pediatric occupants in child restraint systems (CRS) are affected by vehicle seat cushion stiffness and fore/aft cushion length. Eighteen sled tests were conducted using the Federal Motor Vehicles Safety Standard (FMVSS) 213 frontal pulse (48 km/h). Seats from a recent model year vehicle were customized by the manufacturer with three different levels of cushion stiffness: compliant, mid-range, and stiff. Each stiffness level was quantified using ASTM D 3574-08 and all were within the realistic range of modern production seats. The usable length of each seat cushion was manipulated using foam spacers provided by the manufacturer. Two different seat lengths were examined: short (34.0 cm) and long (43.5 cm). Three different types of CRS were tested with size-appropriate anthropomorphic test devices (ATDs): rear-facing (RF) CRS with 12-month-old CRABI, forward-facing (FF) CRS with Hybrid III 3-year-old, and high-back booster with Hybrid III 6-year-old. Each CRS, vehicle seat (including cushion and frame), seat belt webbing and buckle were replaced after every test. ATD kinematic and kinetic data were compared across seat cushion lengths and cushion stiffness levels to determine which seat configurations were the most beneficial for each type of CRS. For RF CRS, short vehicle seats allowed more y-axis rotation (SAE J211) but reduced several injury metrics including HIC36. For FF CRS, long and short seats resulted in similar injury metrics across matched conditions. For boosters, short seats increased chest resultant acceleration but did not have a noticeable effect on other injury metrics. The range of cushion stiffness examined in this study did not have a consistent or relevant effect on any of the CRS or occupant responses.&lt;/div&gt;&lt;/div&gt;

Human Response and Injury Resulting from Head Impacts with Unmanned Aircraft Systems.

Unmanned aircraft systems (UAS), commonly known as drones, are part of a new and budding industry in the United States. Economic and public benefits associated with UAS use across multiple commercial sectors are driving new regulations which alter the stringent laws currently restricting UAS flights over people. As new regulations are enacted and more UAS populate the national airspace, there is a need to both understand and quantify the risk associated with UAS impacts with the uninvolved public. The purpose of this study was to investigate the biomechanical response and injury outcomes of Post Mortem Human Surrogates (PMHS) subjected to UAS head impacts. For this work, PMHS were tested with differing UAS vehicles at multiple impact angles, locations and speeds. Using a custom designed launching device, UAS vehicles were accelerated into the frontal, parietal, or vertex portions of subjects' craniums at speeds up to 22 m/s. Of the 35 UAS impacts carried out, one AIS 2+ injury was observed: a 13 cm linear skull fracture resulting from a Phantom 3 impact. Additionally, injury risk curves used in automotive testing were found to over predict the risk of injury in UAS impact scenarios. Finally, localized skull deformation was observed during severe impacts; the effect that this deformation had on measured kinematics should be further evaluated. Overall, the study found that AIS 2+ head injuries may occur as a result of UAS impacts and that automotive injury metrics may not be able to accurately predict head injury risk in UAS impact scenarios.

Head and neck injury potential during water sports falls: examining the effects of helmets

Head and neck injuries sustained during water skiing and wakeboarding occur as a result of falls in water and collisions with obstacles, equipment, or people. Though water sports helmets are designed to reduce injury likelihood from head impacts with hard objects, some believe that helmets increase head and neck injury rates for falls into water (with no impact to a solid object). The effect of water sports helmets on head kinematics and neck loads during simulated falls into water was evaluated using a custom-made pendulum system with a Hybrid-III anthropometric testing device. Two water entry configurations were evaluated: head-first and pelvis-first water impacts with a water entry speed of 8.8 ± 0.1 m/s. Head and neck injury metrics were compared to injury assessment reference values and the likelihoods of brain injury were determined from head kinematics. Water sport helmets did not increase the likelihood of mild traumatic brain injury compared to a non-helmeted condition for both water entry configurations. Though helmets did increase injury metrics (such as head acceleration, HIC, and cervical spine compression) in some test configurations, the metrics remained below injury assessment reference values and the likelihoods of injury remained below 1%. Using the effective drag coefficients, the lowest water impact speed needed to produce cervical spine injury was estimated to be 15 m/s. The testing does not support the supposition that water sports helmets increase the likelihood of head or neck injury in a typical fall into water during water sports.

Hospital Pressure Injury Metrics, an Unfulfilled Need of Paramount Importance.

Hospital Pressure Injury Metrics, an Unfulfilled Need of Paramount Importance.

64 E-cigarette and Vaporizer-related Burn Injury: Demographics and Injury Patterns

Abstract Introduction E-cigarettes and vaporizers (E-cigs) have seen a dramatic surge in popularity. The Centers for Disease Control now estimates that 1 in 4 United States (US) high school students use E-cigs. Recent recognition of E-cig-related lung injury has garnered significant attention. However, relatively little is known about E-cig-related burn injuries, which are often due to battery explosion and liquid ignition. The objective of this study was to characterize population demographics and injury patterns associated with E-cig burns. Methods A retrospective review of all patients presenting with E-cig-related cutaneous burn injury to a verified US burn center between January 2015 and August 2019 was performed using an institutional database. In addition, media reports covering the same mechanism of injury and date range were collected using databases for the National Electronic Injury Surveillance System, American Nonsmokers’ Rights Foundation, and international news outlets. Demographic data and injury metrics were recorded. Results 309 international media reports and 30 institutional cases of E-cig-related burn injuries were identified. Media-reported injuries varied with respect to geographic location: US-185, United Kingdom-35, Canada-6, all other countries-8, unclear-75. Annual incidence peaked in 2016 in media reports (2019-19, 2018–61, 2017–68, 2016–106, 2015–55) and institutional records (2019-3, 2018-5, 2017-6, 2016-16, 2015-0). Injuries predominantly involved men in both media (Male 75.7%, Female 13.9%, unknown 10.4%) and institutional (Male 93.3%, Female 6.7%) datasets. Average age was 31.0 years (media) and 30.3 years (institutional). The institutional data revealed an average TBSA of 3.5% (range 1–8.5%), an average length of stay of 3.8 days (range 0–14 days), and a median number of operations of 1 (range 0–2). 90% (n=27) of patients required inpatient admission and one patient required mechanical ventilation. On admission, 58% (n=14) of patients screened positive for cannabis use. A ranking of anatomic regions by frequency of involvement was consistent between datasets: lower extremity &amp;gt; hands &amp;gt; head/neck &amp;gt; torso &amp;gt; groin. Mortality rates were also similar: 1.62% (media) and 0% (institutional). Conclusions E-cig-related burns are non-trivial injuries, most of which require inpatient admission, operative management, and substantial resource use. They disproportionately affect young men and burns often involve sensitive areas like the hands and face. Applicability of Research to Practice Combined with rising popularity, lax regulation, proliferation of counterfeit products, and associated lung injury, E-cig-related burns represent an evolving health threat. This study highlights the importance of reporting injuries to consumer product regulatory agencies and the need for further research into the causes and consequences of E-cig explosion.

Multi-Directional Dynamic Model for Traumatic Brain Injury Detection.

Given the worldwide adverse impact of traumatic brain injury (TBI) on the human population, its diagnosis and prediction are of utmost importance. Historically, many studies have focused on associating head kinematics to brain injury risk. Recently, there has been a push toward using computationally expensive finite element (FE) models of the brain to create tissue deformation metrics of brain injury. Here, we develop a new brain injury metric, the brain angle metric (BAM), based on the dynamics of a 3 degree-of-freedom lumped parameter brain model. The brain model is built based on the measured natural frequencies of an FE brain model simulated with live human impact data. We show that it can be used to rapidly estimate peak brain strains experienced during head rotational accelerations that cause mild TBI. In our data set, the simplified model correlates with peak principal FE strain (R2 = 0.82). Further, coronal and axial brain model displacement correlated with fiber-oriented peak strain in the corpus callosum (R2 = 0.77). Our proposed injury metric BAM uses the maximum angle predicted by our brain model and is compared against a number of existing rotational and translational kinematic injury metrics on a data set of head kinematics from 27 clinically diagnosed injuries and 887 non-injuries. We found that BAM performed comparably to peak angular acceleration, translational acceleration, and angular velocity in classifying injury and non-injury events. Metrics that separated time traces into their directional components had improved model deviance compare with those that combined components into a single time trace magnitude. Our brain model can be used in future work to rapidly approximate the peak strain resulting from mild to moderate head impacts and to quickly assess brain injury risk.