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Use of neuron-specific enolase to predict mild brain injury in motorcycle crash patients with maxillofacial fractures: A pilot study.

ABSTRACT: PURPOSE:Mild traumatic brain injury (TBI) is common but accurate diagnosis and its clinical consequences have been a problem. Maxillofacial trauma does have an association with TBI. Neuron-specific enolase (NSE) has been developed to evaluate neuronal damage. The objective of this study was to investigate the accuracy of NSE serum levels to detect mild brain injury of patients with sustained maxillofacial fractures during motor vehicle accidents. METHODS:Blood samples were drawn from 40 healthy people (control group) and 48 trauma patients who had sustained isolated maxillofacial fractures and mild brain injury in motor vehicle accidents. Brain injuries were graded by Glasgow Coma Scale. In the trauma group, correlations between the NSE serum value and different facial fracture sites were also assessed. RESULTS:The NSE serum level (mean ± SD, ng/ml) in the 48 patients with maxillofacial fractures and mild TBI was 13.12 ± 9.68, significantly higher than that measured in the healthy control group (7.72 ± 1.82, p < 0.001). The mean NSE serum level (ng/ml) in the lower part of the facial skeleton (15.44 with SD 15.34) was higher than that in the upper facial part (12.42 with SD 7.68); and the mean NSE level (ng/ml) in the middle-and lower part (11.97 with SD 5.63) was higher than in the middle part (7.88 with SD 2.64). CONCLUSION:An increase in NSE serum levels can be observed in patients sustained maxillofacial fractures and mild brain injury.

SUBMITTER: Ruslin M

PROVIDER: S-EPMC6529579 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Use of neuron-specific enolase to predict mild brain injury in motorcycle crash patients with maxillofacial fractures: A pilot study.

Ruslin Muhammad M Wolff Jan J Yusuf Harmas Yazid HY Arifin Muhammad Zaifullah MZ Boffano Paolo P Forouzanfar Tymour T

Chinese journal of traumatology = Zhonghua chuang shang za zhi 20190210 1

<h4>Purpose</h4>Mild traumatic brain injury (TBI) is common but accurate diagnosis and its clinical consequences have been a problem. Maxillofacial trauma does have an association with TBI. Neuron-specific enolase (NSE) has been developed to evaluate neuronal damage. The objective of this study was to investigate the accuracy of NSE serum levels to detect mild brain injury of patients with sustained maxillofacial fractures during motor vehicle accidents.<h4>Methods</h4>Blood samples were drawn f ...[more]

PMID: 30837107

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Project description:This study aimed to identify neurological and pathophysiological factors that predicted return of spontaneous circulation (ROSC) among patients with out-of-hospital cardiac arrest (OHCA). This prospective 1-year observational study evaluated patients with cardiogenic OHCA who were admitted to a tertiary medical center, Nippon Medical School Hospital. Physiological and neurological examinations were performed at admission for quantitative infrared pupillometry (measured with NPi-200, NeurOptics, CA, USA), arterial blood gas, and blood chemistry. Simultaneous blood samples were also collected to determine levels of neuron-specific enolase (NSE), S-100b, phosphorylated neurofilament heavy subunit, and interleukin-6. In-hospital standard advanced cardiac life support was performed for 30?minutes.The ROSC (n?=?26) and non-ROSC (n?=?26) groups were compared, which a revealed significantly higher pupillary light reflex ratio, which was defined as the percent change between maximum pupil diameter before light stimuli and minimum pupil diameter after light stimuli, in the ROSC group (median: 1.3% [interquartile range (IQR): 0.0-2.0%] vs. non-ROSC: (median: 0%), (Cut-off: 0.63%). Furthermore, NSE provided the great sensitivity and specificity for predicting ROSC, with an area under the receiver operating characteristic curve of 0.86, which was created by plotting sensitivity and 1-specificity. Multivariable logistic regression analyses revealed that the independent predictors of ROSC were maximum pupillary diameter (odds ratio: 0.25, 95% confidence interval: 0.07-0.94, P?=?0.04) and NSE at admission (odds ratio: 0.96, 95% confidence interval: 0.93-0.99, P?=?0.04). Pupillary diameter was also significantly correlated with NSE concentrations (r?=?0.31, P?=?0.027). Conclusively, the strongest predictors of ROSC among patients with OHCA were accurate pupillary diameter and a neuronal biomarker, NSE. Quantitative pupillometry may help guide the decision to terminate resuscitation in emergency departments using a neuropathological rationale. Further large-scale studies are needed.

Project description:Motorcycle crash severity is under-researched in Ghana. Thus, the probable risk factors and association between these factors and motorcycle crash severity outcomes is not known. Traditional statistical models have intrinsic assumptions and pre-defined correlations that, if flouted, can generate inaccurate results. In this study, machine learning based algorithms were employed to predict and classify motorcycle crash severity. Machine learning based techniques are non-parametric models without the presumption of relationships between endogenous and exogenous variables. The main aim of this research is to evaluate and compare different approaches to modeling motorcycle crash severity as well as investigating the effect of risk factors on the injury outcomes of motorcycle crashes. Motorcycle crash dataset between 2011 and 2015 was extracted from the National Road Traffic Crash Database at the Building and Road Research Institute (BRRI) in Ghana. The dataset was classified into four injury severity categories: fatal, hospitalized, injured, and damage-only. Three machine learning based models were developed: J48 Decision Tree Classifier, Random Forest (RF) and Instance-Based learning with parameter k (IBk) were employed to model the severity of injury in a motorcycle crash. These machine learning algorithms were validated using 10-fold cross-validation technique. The three machine learning based algorithms were compared with one another and the statistical model: multinomial logit model (MNLM). Also, the relative importance analysis of the attribute was conducted to determine the impact of these attributes on injury severity outcomes. The results of the study reveal that the predictions of machine learning algorithms are superior to the MNLM in accuracy and effectiveness, and the RF-based algorithms show the overall best agreement with the experimental data out of the three machine learning algorithms, for its global optimization and extrapolation ability. Location type, time of the crash, settlement type, collision partner, collision type, road separation, road surface type, the day of the week, and road shoulder condition were found as the critical determinants of motorcycle crash injury severity.

Project description:BackgroundRoad Traffic crash injury is one of the main public health problems resulting in premature death and disability particularly in low-income countries. However, there is limited evidence on the crash fractures in Ethiopia.ObjectiveThe study was conducted to assess the magnitude of road traffic crash fractures and visceral injuries.MethodsA hospital-based cross-sectional study was conducted on 420 fracture patients. Participants were randomly selected from Addis Ababa City hospitals. The study was carried out between November 2019 and February 2020. Data were collected using a questionnaire and record of medical findings. Multilevel logistic regression analysis was carried out. Ethical clearance was obtained from the Addis Ababa University, College of Health Sciences Institutional Review Board. Confidentiality of participants' information was maintained.ResultsThe study found out that the majority 265 (63. 1%) of fracture cases were younger in the age group of 18 to 34 years. Males were more affected-311(74.0%). The mortality rate was 59(14.1%), of those 50(85.0%) participants were males. The major road traffic victims were pedestrians-220(52.4%), mainly affected by simple fracture type -105(53.3%) and compound fracture type-92(46. 7%). Drivers mainly suffered from compound fracture type -23 (59.0%). One hundred eighty-two (43.3%) of fracture patients had a visceral injury. Homeless persons who sit or sleep on the roadside had a higher risk of thoracic visceral injury compared to traveler pedestrians (AOR = 4.600(95%CI: 1.215-17.417)); P = 0.025.ConclusionVisceral injury, simple and compound fractures were the common orthopedic injury types reported among crash victims. Males, pedestrians, and young age groups were largely affected by orthopedic fracture cases. Homeless persons who sited or slept on the roadside were significant factors for visceral injury. Therefore, preventing a harmful crash and growing fracture care should be considered to reduce the burden of crash fracture.

Dataset Information

Use of neuron-specific enolase to predict mild brain injury in motorcycle crash patients with maxillofacial fractures: A pilot study.

Publications

Use of neuron-specific enolase to predict mild brain injury in motorcycle crash patients with maxillofacial fractures: A pilot study.

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