Assessment Of Injury Severity Research Articles

Neutrophil-to-lymphocyte ratio in relation to trauma severity as prognosis factors in patients with multiple injuries complicated by multiple organ dysfunction syndrome: A retrospective analysis.

This study aimed to explore potential risk factors for the occurrence of multiple organ dysfunction syndrome (MODS) in patients with multiple injuries by evaluating neutrophil-to-lymphocyte ratio (NLR)-associated trauma severity. This retrospective case-control study included 95 patients with multiple injuries, who were admitted to our hospital (between January 2018 and December 2020). Clinical data including gender, age, underlying disease, number of injury sites (NIS), injury severity score (ISS), hemoglobin level within 24 h of admission (HL-24h), neutrophil count (NC), white blood cell count, platelet count (PC), NLR, d-dimer level, activated partial thromboplastin time (APTT), complicated shock within 24 h of admission (CS-24h), length of stay, as well as prognostic outcome was systematically analyzed. According to MODS occurrence, patients were divided into a MODS group (n = 27) and a non-MODS group (n = 68). The risk factors affecting patients with multiple injuries complicated by MODS were identified using univariate and multivariate logistic regression analyses. Candidate risk factors were further analyzed using receiver operating characteristic (ROC) curves. Univariate analysis revealed a significant difference between the MODS and non-MODA groups in terms of NIS, ISS, HL-24h, PC, APTT, d-dimer level, CS-24h, NLR, NC, prognostic outcome, and other indicators (p < .05). Multivariate logistic regression analysis showed that d-dimer levels within 24 h of admission and ISS, NLR, and CS-24h were significantly associated with multiple injuries complicated by MODS. Compared with the non-MODS controls, the NLR in the MODS group showed a much higher level and tended to rise with the increase in ISS score, indicating a significant intergroup difference (p < .05). The ROC curve analysis results suggested that the NLR had good sensitivity and specificity for predicting the prognosis of patients with MODS with multiple injuries. d-dimer level, ISS, NLR, and CS-24h are important risk factors for MODS in patients with multiple injuries. Notably, NLR expression may be a good indicator of injury severity and predictor of the occurrence of MODS in patients with multiple injuries. Therefore, assessment of injury severity and coagulation function, active resuscitation, as well as prevention of infection should be emphasized during treatment of multiple injuries, to reduce and prevent the risk of MODS in patients with multiple injuries.

Early assessment of injury with optical markers in a piglet model of neonatal encephalopathy

BackgroundOpportunities for adjunct therapies with cooling in neonatal encephalopathy are imminent; however, robust biomarkers of early assessment are lacking. Using an optical platform of broadband near-infrared spectroscopy and diffuse correlation spectroscopy to directly measure mitochondrial metabolism (oxCCO), oxygenation (HbD), cerebral blood flow (CBF), we hypothesised optical indices early (1-h post insult) after hypoxia-ischaemia (HI) predicts insult severity and outcome.MethodsNineteen newborn large white piglets underwent continuous neuromonitoring as controls or following moderate or severe HI. Optical indices were expressed as mean semblance (phase difference) and coherence (spectral similarity) between signals using wavelet analysis. Outcome markers included the lactate/N-acetyl aspartate (Lac/NAA) ratio at 6 h on proton MRS and TUNEL cell count.ResultsCBF-HbD semblance (cerebrovascular dysfunction) correlated with BGT and white matter (WM) Lac/NAA (r2 = 0.46, p = 0.004, r2 = 0.45, p = 0.004, respectively), TUNEL cell count (r2 = 0.34, p = 0.02) and predicted both initial insult (r2 = 0.62, p = 0.002) and outcome group (r2 = 0.65 p = 0.003). oxCCO-HbD semblance (cerebral metabolic dysfunction) correlated with BGT and WM Lac/NAA (r2 = 0.34, p = 0.01 and r2 = 0.46, p = 0.002, respectively) and differentiated between outcome groups (r2 = 0.43, p = 0.01).ConclusionOptical markers of both cerebral metabolic and vascular dysfunction 1 h after HI predicted injury severity and subsequent outcome in a pre-clinical model.ImpactThis study highlights the possibility of using non-invasive optical biomarkers for early assessment of injury severity following neonatal encephalopathy, relating to the outcome.Continuous cot-side monitoring of these optical markers can be useful for disease stratification in the clinical population and for identifying infants who might benefit from future adjunct neuroprotective therapies beyond cooling.

0639 Delayed Presentation of Sleep Disruption and Depressive Symptoms in Males Post Mild Traumatic Brain Injury (mTBI)

Abstract Introduction The relationship between sleep disruption and depressive symptoms is well established. Mild traumatic brain injury (mTBI) is commonly associated with emotional dysregulation and decreased sleep quality. Less is known about the course of mTBI and its association with mood symptoms over time. We hypothesized that those in acute stages of mTBI (2W; 1M) would significantly differ in sleep quality and depressive symptoms compared with healthy controls. As there are often sex differences in sleep and depression, we focused here exclusively on males with mTBI. Methods This study included 56 healthy males, split into six groups. The sample included 15 healthy males (Mage=23.67, SD=5.066) and 41 males (Mage=26.88, SD=8.509) with mTBI, at differing stages post-injury: 2 weeks and 1, 3, 6, and 12 months. Participants completed the Pittsburgh Sleep Quality Index (PSQI) and the Beck Depression Index (BDI-II) to assess sleep disturbance and mood, respectively. Higher scores on both measures indicate a more severe presentation of disruptive symptoms. We conducted a one-way ANOVA to examine how the mean score for both measures varied at each time point. Results Compared to healthy controls there was no significant variance in average depression scores at 2W, 1M, 6M, and 12M (p&amp;gt;.05). However, at 3M post-mTBI males reported significantly higher depression scores than healthy controls M =9.8, SD=9.48, p=.035. Participants with mTBI reported greater sleep disruption when compared to healthy controls at 3M M =7.30, SD=3.16, p=.035 and 12M M=7.0, SD=3.39, p=.037, but not at other time points. Conclusion Contrary to our hypotheses, we found that males did not significantly vary from healthy controls for either measure in the acute stages of mTBI. These results indicate that males may not initially present depressive symptoms and sleep disruption post-mTBI, which could result in an inaccurate assessment of injury severity and an improper treatment plan moving forward in recovery. Further research conducted with a larger sample size would be useful in establishing that males suffering from mTBI experience the most severe presentation of symptoms roughly 3M after injury. Future work will need to examine whether these same trends are present among females. Support (if any) W81XWH-12-1-0386

Biomarkers of Neurological Injury for Emerging Viral Threats and Post Viral Disease

Abstract Neurotropic viral infection and the ensuant immune response are a significant cause of morbidity and mortality worldwide, which can range in severity from mild to permanent central nervous system (CNS) damage and death. Encephalitic alphaviruses include Venezuelan and eastern equine encephalitis viruses (VEEV and EEEV; Alphavirus; Togaviridae). Injury to the CNS is an important determinant of poor outcome and tools to predict this outcome are lacking. Neurons are the primary target cells of encephalitic alphaviruses where cytopathology plays a major role in CNS dysfunction. Proteins are released following cell death, such as ubiquitin carboxy-terminal hydrolase L1 (UCH-L1) and glial fibrillary acidic protein (GFAP) are neuromarkers of CNS tissue injury which could serve as biomarkers to assess injury severity, monitor disease progression, direct treatment, and as reliable endpoints to help develop novel medical countermeasures. Recent advances in the use of blood-based biomarkers for diagnosis of traumatic brain injury (TBI) which have FDA approved assays have provided a scientific foundation for expanding the biomarker technology to brain damage caused by other CNS pathologies like viral encephalitis. Here we evaluated the ability to detect these biomarkers for encephalitic alphaviruses, encephalitis of unknown origin, and hospitalized patients with severe coronavirus disease (COVID-19). Higher levels of specific biomarkers were detected in patients diagnosed with alphavirus or unknown encephalitis which may be useful in prognosis and treatment guidance of post viral disease. Collectively, our results suggest that these blood-based biomarkers may be a good indicator for brain injury resulting from viral infection.

Assessment of lung injury severity using ultrasound in critically ill COVID-19 patients in resource limited settings

BackgroundLung ultrasound is a non-invasive tool available at the bedside for the assessment of critically ill patients. The objective of this study was to evaluate the usefulness of lung ultrasound in assessing the severity of SARS-CoV-2 infection in critically-ill patients in a low-income setting.MethodsWe conducted a 12-month observational study in a university hospital intensive care unit (ICU) in Mali, on patients admitted for COVID-19 as diagnosed by a positive polymerase chain reaction for SARS-CoV-2 and/or typical lung computed tomography scan findings.ResultsThe inclusion criteria was met by 156 patients with a median age of 59 years. Almost all patients (96%) had respiratory failure at admission and many needed respiratory support (121/156, 78%). The feasibility of lung ultrasound was very good, with 1802/1872 (96%) quadrants assessed. The reproducibility was good with an intra-class correlation coefficient of elementary patterns of 0.74 (95% CI 0.65, 0.82) and a coefficient of repeatability of lung ultrasound score < 3 for an overall score of 24. Confluent B lines were the most common lesions found in patients (155/156). The overall mean ultrasound score was 23 ± 5.4, and was significantly correlated with oxygen saturation (Pearson correlation coefficient of − 0.38, p < 0.001). More than half of the patients died (86/156, 55.1%). The factors associated with mortality, as shown by multivariable analysis, were: the patients’ age; number of organ failures; therapeutic anticoagulation, and lung ultrasound score.ConclusionLung ultrasound was feasible and contributed to characterize lung injury in critically-ill COVID-19 patients in a low income setting. Lung ultrasound score was associated with oxygenation impairment and mortality.

Back to the basics: Clinical assessment yields robust mortality prediction and increased feasibility in low resource settings.

Mortality prediction aids clinical decision-making and is necessary for trauma quality improvement initiatives. Conventional injury severity scores are often not feasible in low-resource settings. We hypothesize that clinician assessment will be more feasible and have comparable discrimination of mortality compared to conventional scores in low and middle-income countries (LMICs). Between 2017 and 2019, injury data were collected from all injured patients as part of a prospective, four-hospital trauma registry in Cameroon. Clinicians used physical exam at presentation to assign a highest estimated abbreviated injury scale (HEAIS) for each patient. Discrimination of hospital mortality was evaluated using receiver operating characteristic curves. Discrimination of HEAIS was compared with conventional scores. Data missingness for each score was reported. Of 9,635 presenting with injuries, there were 206 in-hospital deaths (2.2%). Compared to 97.5% of patients with HEAIS scores, only 33.2% had sufficient data to calculate a Revised Trauma Score (RTS) and 24.8% had data to calculate a Kampala Trauma Score (KTS). Data from 2,328 patients with all scores was used to compare models. Although statistically inferior to the prediction generated by RTS (AUC 0.92-0.98) and KTS (AUC 0.93-0.99), HEAIS provided excellent overall discrimination of mortality (AUC 0.84-0.92). Among 9,269 patients with HEAIS scores was strongly predictive of mortality (AUC 0.93-0.96). Clinical assessment of injury severity using HEAIS strongly predicts hospital mortality and far exceeds conventional scores in feasibility. In contexts where traditional scoring systems are not feasible, utilization of HEAIS could facilitate improved data quality and expand access to quality improvement programming.

Brain Injury Is Prevalent and Precedes Tobacco Use among Youth and Young Adults Experiencing Homelessness.

70%+ of youth and young adults experiencing homelessness (YYEH; 14-24 years old) smoke combustible tobacco. Little is known about the prevalence of acquired brain injury (ABI) among youth and young adult smokers experiencing homelessness (YYSEH) and its impact on tobacco use progression-the aim of our study. Through an interviewer-administered survey, YYSEH were asked about timing of tobacco use; exposure to causes of ABI; including brain oxygen deprivation (BOD; strangulation; accidental; choking games) and blunt force head trauma (BFHT; intentional; shaken violently; accidental); and perpetrators of intentional assault. Participants (n = 96) were on average 22 years old and from populations who experience structural disparities; including those minoritized by race (84.4%) and gender/sexual orientation (26.0%). In total, 87% of participants reported at least one exposure to BFHT and 65% to BOD. Intentional injury was more common than accidental. Furthermore, 60.4% of participants (n = 59) were classified as having ABI using the Brain Injury Severity Assessment. A significant proportion of YYSEH living with ABI were exposed to both BFHT and BOD prior to trying (68.5%, p = 0.002) and to first regular use (82.8%, p < 0.001) of tobacco. Among YYSEH with ABI; injury exposure occurred a median of 1 and 5 years before age of first regular tobacco use, dependent on injury mechanism. ABI from intentional violence is prevalent and precedes tobacco use among YYSEH.

Time-Dependent Changes in the Biofluid Levels of Neural Injury Markers in Severe Traumatic Brain Injury Patients-Cerebrospinal Fluid and Cerebral Microdialysates: A Longitudinal Prospective Pilot Study.

Monitoring protein biomarker levels in the cerebrospinal fluid (CSF) can help assess injury severity and outcome after traumatic brain injury (TBI). Determining injury-induced changes in the proteome of brain extracellular fluid (bECF) can more closely reflect changes in the brain parenchyma, but bECF is not routinely available. The aim of this pilot study was to compare time-dependent changes of S100 calcium-binding protein B (S100B), neuron-specific enolase (NSE), total Tau, and phosphorylated Tau (p-Tau) levels in matching CSF and bECF samples collected at 1, 3, and 5 days post-injury from severe TBI patients (n = 7; GCS 3-8) using microcapillary-based western analysis. We found that time-dependent changes in CSF and bECF levels were most pronounced for S100B and NSE, but there was substantial patient-to-patient variability. Importantly, the temporal pattern of biomarker changes in CSF and bECF samples showed similar trends. We also detected two different immunoreactive forms of S100B in both CSF and bECF samples, but the contribution of the different immunoreactive forms to total immunoreactivity varied from patient to patient and time point to time point. Our study is limited, but it illustrates the value of both quantitative and qualitative analysis of protein biomarkers and the importance of serial sampling for biofluid analysis after severe TBI.

Piloting a hospital-based road traffic injury surveillance system in Nairobi County, Kenya, 2018–2019

BackgroundKenya's estimated road traffic injury (RTI) death rate is 27.8/100,000 population, which is 1.5 times the global rate. Some RTI data are collected in Kenya; however, a systematic and integrated surveillance system does not exist. Therefore, we adopted and modified the World Health Organization's injury surveillance guidelines to pilot a hospital-based RTI surveillance system in Nairobi County, Kenya. MethodsWe prospectively documented all RTI cases presenting at two public trauma hospitals in Nairobi County from October 2018–April 2019. RTI cases were defined as injuries involving ≥1 moving vehicles on public roads. Demographics, injury circumstances, and outcome information were collected using standardized case report forms. The Kampala Trauma Score (KTS) was used to assess injury severity. RTI cases were characterized with descriptive statistics. ResultsOf the 1,840 RTI cases reported during the seven-month period, 73.2% were male. The median age was 29.8 years (range 1–89 years). Forty percent (n = 740) were taken to the hospital by bystanders. Median time for hospital arrival was 77 min. Pedestrians constituted 54.1% (n = 995) of cases. Of 400 motorcyclists, 48.0% lacked helmets. Similarly, 65.7% of bicyclists (23/35) lacked helmets. Among 386 motor vehicle occupants, 59.6% were not using seat belts (19.9% unknown). Seven percent of cases (n = 129) reported alcohol use (49.0% unknown), and 8.8% (n = 161) reported mobile phone use (59.7% unknown). Eleven percent of cases (n = 199) were severely injured (KTS <11), and 220 died. ConclusionWe demonstrated feasibility of a hospital-based RTI surveillance system in Nairobi County. Integrating information from crash scenes and hospitals can guide prevention.

A data-driven on-site injury severity assessment model for car-to-electric-bicycle collisions based on positional relationship and random forest

&lt;abstract&gt; &lt;p&gt;Vulnerable road users (VRUs) are usually more susceptible to fatal injuries. Accurate and rapid assessment of VRU injury severity at the accident scene can provide timely support for decision-making in emergency response. However, evaluating VRU injury severity at the accident scene usually requires medical knowledge and medical devices. Few studies have explored the possibility of using on-site positional relationship to assess injury severity, which could provide a new perspective for on-site transportation professionals to assess accident severity. This study proposes a data-driven on-site injury severity assessment model for car-to-electric-bicycle accidents based on the relationship between the final resting positions of the car, electric bicycle and cyclist at the accident scene. Random forest is employed to learn the accident features from the at-scene positional relationship among accident participants, by which injury severity of the cyclist is assessed. Conditional permutation importance, which can account for correlation among predictor variables, is adopted to reflect the importance of predictor variables more accurately. The proposed model is demonstrated using simulated car-to-electric-bicycle collision data. The results show that the proposed model has good performance in terms of overall accuracy and is balanced in recognizing both fatal and non-fatal accidents. Model performance under partial information confirms that the position information of the electric bicycle is more important than the position information of the cyclist in assessing injury severity.&lt;/p&gt; &lt;/abstract&gt;

Studija o serumu miR-185-5p u vezi sa procenom težine stanja i prognoze kod pacijenata sa traumatskim povredama mozga

Background: This study aims to explore whether serum miR-185-5p levels are related to the injury severity and prognosis of traumatic brain injury patients. Methods: Serum miR-185-5p level was quantified in 120 TBI patients. The Glasgow Coma Scale (GCS) was used to grade the damage, and the Glasgow Outcome Scale (GOS) was used to evaluate the prognosis 3 months after TBI. Pearson correlation analysis was performed to determine the relationship between serum miR-185-5p level and injury severity and prognosis, and the value of serum miR-185-5p level to assess injury severity and prognosis was evaluated by receiver operating characteristic (ROC) curve. Results: Serum miR-185-5p level in moderate and severe TBI patients was higher than in mild TBI patients, and serum miR-185-5p was closely related to GCS score and GOS score. Serum miR-185-5p level higher than 0.36 could distinguish patients with mild to moderate TBI injury, with 72.97% sensitivity and 97.62% specificity, while that higher than 0.43 had 46.34% sensitivity and 91.89% specificity to distinguish moderate to severe TBI patients. Moreover, serum miR-185-5p levels higher than 0.36, with a sensitivity of 96.30% and a specificity of 60.24%, distinguished the poor prognosis of TBI patients. Serum miR185-5p level was an independent predictor of poor prognosis in TBI patients after 3 months and was effective in discriminating adverse outcomes at 3 months. Conclusions: Serum miR-185-5p level was significantly correlated with 3-month injury and adverse prognosis in TBI patients, suggesting that serum miR-185-5p level may be a biomarker that provides supplementary prognostic information and can be used to identify the risk of adverse prognosis in TBI patients.

Data-Driven Chronic Allograft Phenotypes: A Novel and Validated Complement for Histologic Assessment of Kidney Transplant Biopsies.

No validated system currently exists to realistically characterize the chronic pathology of kidney transplants that represents the dynamic disease process and spectrum of disease severity. We sought to develop and validate a tool to describe chronicity and severity of renal allograft disease and integrate it with the evaluation of disease activity. The training cohort included 3549 kidney transplant biopsies from an observational cohort of 937 recipients. We reweighted the chronic histologic lesions according to their time-dependent association with graft failure, and performed consensus k-means clustering analysis. Total chronicity was calculated as the sum of the weighted chronic lesion scores, scaled to the unit interval. We identified four chronic clusters associated with graft outcome, based on the proportion of ambiguous clustering. The two clusters with the worst survival outcome were determined by interstitial fibrosis and tubular atrophy (IFTA) and by transplant glomerulopathy. The chronic clusters partially overlapped with the existing Banff IFTA classification (adjusted Rand index, 0.35) and were distributed independently of the acute lesions. Total chronicity strongly associated with graft failure (hazard ratio [HR], 8.33; 95% confidence interval [CI], 5.94 to 10.88; P<0.001), independent of the total activity scores (HR, 5.01; 95% CI, 2.83 to 7.00; P<0.001). These results were validated on an external cohort of 4031 biopsies from 2054 kidney transplant recipients. The evaluation of total chronicity provides information on kidney transplant pathology that complements the estimation of disease activity from acute lesion scores. Use of the data-driven algorithm used in this study, called RejectClass, may provide a holistic and quantitative assessment of kidney transplant injury phenotypes and severity.

What is counted counts: An innovative linkage of police, hospital, and spatial data for transportation injury prevention

Introduction: Growing research indicates transportation injury surveillance using police collision reporting alone underrepresents injury to vulnerable groups, including pedestrians, cyclists, and people of color. This reflects differing reporting patterns and non-clinicians’ challenge in accurately evaluating injury severity. To our knowledge, San Francisco is the first U.S. city to link and map hospital and police injury data. Analysis of linked data injury patterns informs interventions supporting traffic fatality and injury prevention goals. Methods: Injury and fatality records 2013–2015 were collected from San Francisco Police, Emergency Medical Services (EMS), Medical Examiner, and Zuckerberg San Francisco General Hospital (ZSFG). Probabilistic linkage was conducted using LinkSolv9.0 on match variables collision/admission time, name, birthdate, sex, travel mode, and geographic collision location. Results: From 2013-2015, this study identified 17,000+ transportation-related injuries on public roadways in San Francisco. Twenty-six percent (n = 4,415) appeared in both police and ZSFG sources. Linked injury records represent 39% of police records (N = 11,403) and 43% of hospital records (N = 10,223). Among hospital records, 34% of cyclist, 38% of motor vehicle occupant, 61% of pedestrian, and 54% of motorcyclist records linked with a police record. Linkage rate varied by travel mode even after controlling for injury severity. Transportation-injured ZSFG-treated patients lacking police reports were more often cyclists, male, Hispanic or Black, and less often occupants of motor vehicles compared to those with injuries captured only in police reports. Conclusions: Incorporating hospital and EMS spatial data into injury surveillance systems historically reliant on police reports offers trifold benefits. First, linkage captures injuries absent in police data, adding data on populations empirically vulnerable to injury. Second, it improves injury severity assessment. Finally, linked data better informs and targets interventions serving injury-burdened populations and road users, advancing transportation injury prevention. Practical applications: Linkage closes data gaps, improving ability to quantify injury and develop evidence-based interventions for vulnerable groups.

Highlights mild traumatic brain injury 2021.

Mild traumatic brain injury (TBI) is one of the most common causes of morbidity worldwide. Patients at risk of unfavourable outcome may benefit from additional attention and help but identification of these patients necessitates the development of diagnostic methods to assess indices of brain injury at an early stage. The aim of this overview is to highlight studies that reflect the growing scientific attention to the early diagnosis and prognostication of mild TBI. The value of serum biomarkers for the diagnosis of TBI severity has been acknowledged in recent studies. The diagnostic and prognostic utility of several biomarkers of brain injury, such as glial fibrillary acidic protein, and of inflammation, such as interleukin (IL)-6 and IL-10, holds promise for application in daily clinical practice in a point-of-care platform. Besides head CT imaging, early advanced MRI brain imaging has been reported as a tool for assessment of injury severity and prognostication. The introduction of direct oral anticoagulants (DOACs) has raised new challenges for the treatment of intracranial traumatic haemorrhage at the Emergency Department. Promising findings of new diagnostic markers of brain injury severity highlight the potential prognostic value of serum biomarkers and early MRI imaging. The accurate assessment of patients at risk of incomplete recovery after mTBI will enhance more timely and individualized treatment.

Polytrauma scoring revisited: prognostic validity and usability in daily clinical practice

PurposeScores are widely used for the assessment of injury severity and therapy guidance in severely injured patients. They differ vastly regarding complexity, applicability, and prognostic accuracy. The objective of this study was to compare well-established with more recently developed trauma scores as well as intensive care unit (ICU) scores.MethodsRetrospective analysis of severely injured patients treated at a level I trauma centre from 2010 to 2015. Inclusion criteria: Age ≥ 18 years, Injury Severity Score ≥ 16 and ICU treatment. Primary endpoint was in-hospital mortality. Several scores (ISS, APACHE II, RTS, Marshall Score, SOFA, NISS, RISC II, EAC and PTGS) were assessed to determine their predictive quality for mortality. Statistical analysis included correlation analysis and receiver operating characteristic (ROC).Results444 patients were included. 71.8% were males, mean age was 51 ± 20.26 years. 97.4% sustained a blunt trauma. The area under the ROC curve (AUROC) revealed RISC II (0.92) as strongest predictor regarding mortality, followed by APACHE II (0.81), Marshall score (0.69), SOFA (0.70), RTS (0.66), NISS (0.62), PTGS (0.61) and EAC (0.60). ISS did not reach statistical significance.ConclusionsRISC II provided the strongest predictive capability for mortality. In comparison, more simple scores focusing on injury pattern (ISS, NISS), physiological abnormalities (RTS, EAC), or a combination of both (PTGS) only provided inferior mortality prediction. Established ICU scores like APACHE II, SOFA and Marshall score were proven to be helpful tools in severely injured trauma patients.

Injury severity assessment of rear-end crashes via approaches based on generalized estimating equations

Rear-end crashes constitute the predominant type of crashes on highways and may lead to severe injuries and high property damage. Available statistical models primarily focus on injury severity and analyze potential factors that affect it. However, rear-end crashes may also be potentially correlated to vehicle, roadway, environmental, temporal, spatial, traffic, and crash characteristics. Additionally, unobserved heterogeneity regarding the effects may be present, which may be different in different crashes. In this context, multiple generalized estimating equation (GEE)-based models, developed using different working matrices and distributions, are proposed in this study to examine factors that affect injury severity. The proposed models account for both crash-related correlations and unobserved heterogeneity, thereby outperforming traditional models in terms of prediction accuracy. Among the explanatory variables considered in this study, the passenger car, minibus, curvature ratio, rainy weather, foggy weather, early morning, Thursday, autumn, winter, and average annual daily traffic volume were identified as contributing factors. However, significant differences were observed between the elasticity effects measured by different models, especially in terms of minibus and foggy weather. Thus, this study verifies that GEE-based models account for a greater amount of unobserved heterogeneity, yield better performance in terms of precision, and exhibit more consistent explanatory power compared to traditional models.

Intimate Partner Violence-Related Brain Injury Among Colombian Women.

The aim of this study was to assess the prevalence of brain injury (BI) and its relationship to cognitive and psychological outcomes in women survivors of intimate partner violence (IPV) in Colombia, South America. Women's shelters and organizations in Barranquilla, Colombia. Seventy women from the city of Barranquilla, Colombia, who experienced any form of IPV. Cross-sectional, retrospective. Participants were administered the computerized EMBRACED neuropsychological battery to assess learning, working and long-term memory, cognitive flexibility, and processing speed. Participants also completed measures of psychological symptoms. Partner violence severity was assessed with a semistructured interview for survivors of domestic violence. Presence and severity of IPV-related BI were assessed using the Brain Injury Severity Assessment (BISA). Thirty-one percent of women sustained at least one BI during an abusive relationship, and 10% sustained repetitive BIs. Furthermore, BI was negatively associated with measures of long-term and working memory, cognitive flexibility, as well as a trending ( P = .05) positive association with depression. With the exception of the relationship between BI and cognitive flexibility, which was substantially reduced and no longer significant, all of these relationships were nearly identical in strength when controlling for abuse severity, socioeconomic status, and educational level. These data are the first to specifically examine IPV-related BI in relation to cognitive and psychological functioning in a sample of Colombian women. These data add cross-cultural knowledge to the limited work in this area that has largely focused on women in North America.

Amide Proton Transfer-Weighted Magnetic Resonance Imaging for Detecting Severity and Predicting Outcome after Traumatic Brain Injury in Rats.

After traumatic brain injury (TBI), early assessment of secondary injury severity is critically important for estimating prognosis and treatment stratification. Currently, secondary injury severity is difficult to estimate. The objective of this study was to investigate the capacity of non-invasive amide proton transfer-weighted (APTw) magnetic resonance imaging (MRI) techniques to assess TBI injury in different brain regions and predict long-term neurobehavior outcomes. Fifty-five male and female rats were subjected to a controlled cortical impact with one of three different impactor depths to produce different degrees of TBI. Multi-parameter MRI data were acquired on a 4.7-Tesla scanner at 1 h, 1 day, and 3 days. Immunofluorescence staining was used to detect activated microglia at 3 days, and neurobehavioral tests were performed to assess long-term outcomes after 28 days. The APTw signal in the injury core at 1 day correlated with deficits in sensorimotor function, the sucrose preference test (a test for anhedonia), and spatial memory function on the Barnes maze. The APTw signal in the perilesion ipsilateral cortex gradually increased after TBI, and the value at 3 days correlated with microglia density at 3 days and with spatial memory decline and anhedonia at 28 days. The correlation between APTw and activated microglia was also observed in the ipsilateral thalamus, and its correlation to memory deficit and depression was evident in other ipsilateral sites. These results suggest that APTw imaging can be used for detecting secondary injury and as a potential predictor of long-term outcomes from TBI.

Superiority of Brain and Spinal Injury Center Score for Assessing Injury Severity and Predicting Prognosis in Patients with Acute Traumatic Spinal Cord Injury.

The goal of this study was to evaluate the interrelationship between different magnetic resonance (MR) imaging measures and their validity in assessing the severity of acute traumatic spinal cord injury (tSCI) and predicting neurological outcomes. We performed apreoperative multicenter cohort study of 89patients with acute tSCI and preoperative MR imaging within 24 h after injury. We assessed several MR imaging measures of injury, including axial grade (Brain and Spinal Injury Center [BASIC] score), sagittal grade, length of injury, maximum canal compromise (MCC), and maximum spinal cord compression (MSCC). Principal component analysis (PCA) was applied to evaluate the interrelationship between different MR imaging measures. Spearman correlation and regression analyses were applied to assess injury severity and predict neurological impairment. The severity was assessed by the American Spinal Injury Association Impairment Scale (AIS) at admission, while neurological outcome was defined by AIS grade change at 6weeks, AIS grade and SCIM score at 1 year after surgery. The PCA identified 2clusters of MR imaging variables related to 1)measures of intrinsic cord signal abnormality (BASIC score, sagittal grade and length of injury) and 2)measures of extrinsic cord compression (MCC and MSCC). Neurological outcome and injury severity were best accounted for by MR imaging measures of intrinsic cord signal abnormalities, with the BASIC score representing the most accurate predictor of short-term and long-term neurological outcomes. We determined the superior significance of the BASIC score in assessing injury severity, predicting early AIS improvement, AIS grade and SCIM score at 1year compared with other MR imaging measures.

Short-Term Segment-Level Crash Risk Prediction Using Advanced Data Modeling with Proactive and Reactive Crash Data

Highway crashes, along with the property damage, personal injuries, and fatalities that they cause, continue to present one of the most significant and critical transportation problems. At the same time, provision of safe travel is one of the main goals of any transportation system. For this reason, both in transportation research and practice much attention has been given to the analysis and modeling of traffic crashes, including the development of models that can be applied to predict crash occurrence and crash severity. In general, such models assess short-term crash risks at a given highway facility, thus providing intelligence that can be used to identify and implement traffic operations strategies for crash mitigation and prevention. This paper presents several crash risk and injury severity assessment models applied at a highway segment level, considering the input data that is typically collected or readily available to most transportation agencies in real-time and at a regional network scale, which would render them readily applicable in practice. The input data included roadway geometry characteristics, traffic flow characteristics, and weather condition data. The paper develops, tests, and compares the performance of models that employ Random effects Bayesian Logistics Regression, Gaussian Naïve Bayes, K-Nearest Neighbor, Random Forest, and Gradient Boosting Machine methods. The paper applies random oversampling examples (ROSE) method to deal with the problem of data imbalance associated with the injury severity analysis. The models were trained and tested using a dataset of 10,155 crashes that occurred on two interstate highways in New Jersey over a two-year period. The paper also analyzes the potential improvement in the prediction abilities of the tested models by adding reactive data to the analysis. To that end, traffic crashes were classified in multiple classes based on the driver age and the vehicle age to assess the impact of these attributes on driver injury severity outcomes. The results of this analysis are promising, showing that the simultaneous use of reactive and proactive data can improve the prediction performance of the presented models.