Identification of hematomas in mild traumatic brain injury using an index of quantitative brain electrical activity

Quantitative Electroencephalography Objectivity and Reliability in the Diagnosis and Management of Traumatic Brain Injury: A Systematic Review

Background. Persons with a history of traumatic brain injury (TBI) may exhibit short- and long-term cognitive deficits as well as psychiatric symptoms. These symptoms often reflect functional anomalies in the brain that are not detected by standard neuroimaging. In this context, quantitative electroencephalography (qEEG) is more suitable to evaluate non-normative activity in a wide range of clinical settings. Method. We searched the literature using the "Medline" and "Web of Science" online databases. The search was concluded on February 23, 2023, and revised on July 12, 2023. It returned 134 results from Medline and 4 from Web of Science. We then applied the PRISMA method, which led to the selection of 31 articles, the most recent one published in March 2023. Results. The qEEG method can detect functional anomalies in the brain occurring immediately after and even years after injury, revealing in most cases abnormal power variability and increases in slow (delta and theta) versus decreases in fast (alpha, beta, and gamma) frequency activity. Moreover, other findings show that reduced beta coherence between frontoparietal regions is associated with slower processing speed in patients with recent mild TBI (mTBI). More recently, machine learning (ML) research has developed highly reliable models and algorithms for the detection of TBI, some of which are already integrated into commercial qEEG equipment. Conclusion. Accumulating evidence indicates that the qEEG method may improve the diagnosis and management of TBI, in many cases revealing long-term functional anomalies in the brain or even neuroanatomical insults that are not revealed by standard neuroimaging. While FDA clearance has been obtained only for some of the commercially available equipment, the qEEG method allows for systematic, cost-effective, non-invasive, and reliable investigations at emergency departments. Importantly, the automated implementation of intelligent algorithms based on multimodally acquired, clinically relevant measures may play a key role in increasing diagnosis reliability.

Targeting hydrogen sulfide and nitric oxide to repair cardiovascular injury after trauma

The systemic cardiovascular effects of major trauma, especially neurotrauma, contribute to death and permanent disability in trauma patients and treatments are needed to improve outcomes. In some trauma patients, dysfunction of the autonomic nervous system produces a state of adrenergic overstimulation, causing either a sustained elevation in catecholamines (sympathetic storm) or oscillating bursts of paroxysmal sympathetic hyperactivity. Trauma can also activate innate immune responses that release cytokines and damage-associated molecular patterns into the circulation. This combination of altered autonomic nervous system function and widespread systemic inflammation produces secondary cardiovascular injury, including hypertension, damage to cardiac tissue, vascular endothelial dysfunction, coagulopathy and multiorgan failure. The gasotransmitters nitric oxide (NO) and hydrogen sulfide (H2S) are small gaseous molecules with potent effects on vascular tone regulation. Exogenous NO (inhaled) has potential therapeutic benefit in cardio-cerebrovascular diseases, but limited data suggests potential efficacy in traumatic brain injury (TBI). H2S is a modulator of NO signaling and autonomic nervous system function that has also been used as a drug for cardio-cerebrovascular diseases. The inhaled gases NO and H2S are potential treatments to restore cardio-cerebrovascular function in the post-trauma period.

ERPs predict symptomatic distress and recovery in sub-acute mild traumatic brain injury

Mild traumatic brain injury (mTBI) can affect high-level executive functioning long after somatic symptoms resolve. We tested if simple EEG responses within an oddball paradigm could capture variance relevant to this clinical problem. The P3a and P3b components reflect bottom-up and top-down processes driving engagement with exogenous stimuli. Since these features are related to primitive decision abilities, abnormal amplitudes following mTBI may account for problems in the ability to exert executive control. Sub-acute (<2 weeks) mTBI participants (N = 38) and healthy controls (N = 24) were assessed at an initial session as well as a two-month follow-up (sessions 1 and 2). We contrasted the initial assessment to a comparison group of participants with chronic symptomatology following brain injury (N = 23). There were no group differences in P3a or P3b amplitudes. Yet in the sub-acute mTBI group, higher symptomatology on the Frontal Systems Behavior scale (FrSBe), a questionnaire validated as measuring symptomatic distress related to frontal lobe injury, correlated with lower P3a in session 1. This relationship was replicated in session 2. These findings were distinct from chronic TBI participants, who instead expressed a relationship between increased FrSBe symptoms and a lower P3b component. In the sub-acute group, P3b amplitudes in the first session correlated with the degree of symptom change between sessions 1 and 2, above and beyond demographic predictors. Controls did not show any relationship between FrSBe symptoms and P3a or P3b. These findings identify symptom-specific alterations in neural systems that vary along the time course of post-concussive symptomatology.

Emergency Department Evaluation of Traumatic Brain Injury in the United States, 2009-2010

OBJECTIVE

To determine the dimensions of traumatic brain injury (TBI) evaluation in US emergency department (EDs) to inform potential application of novel diagnostic tests.

SETTING

US EDs.

PARTICIPANTS

National Hospital Ambulatory Medical Care Survey of ED visits in 2009 and 2010 where TBI was evaluated (1) and diagnosed either clinically or (2) with head computed tomographic (CT) scans.

DESIGN

Retrospective cross-sectional.

RESULTS

TBI was evaluated during 4.8 (95% confidence interval [CI], 4.2-5.4) million visits per year; and head CT scan was performed in 82% of TBI evaluations (3.9 [95% CI, 3.4-4.4] million visits per year). TBI was diagnosed in 52% of evaluations (2.5 [95% CI, 2.1-2.8] million visits per year). Among those who received head CT scans, 9% had CT evidence of traumatic abnormalities. Among patients evaluated for TBI who had a Glasgow Coma Scale score recorded, 94.5% were classified as having mild TBI, 2.1% as moderate TBI, and 3.5% as severe TBI. Among patients with International Classification of Diseases, Ninth Revision, Clinical Modification, codes permitting the calculation of head Abbreviated Injury Scale scores 9.0%, 85.0%, 2.5%, 3.2%, 0.3%, and 0% had head Abbreviated Injury Scale scores of 1, 2, 3, 4, 5, and 6, respectively. Of patients evaluated for TBI, 31% had other head/face/neck injuries, 10% had spine and back injuries, 7% had torso injuries, and 14% had extremity injuries.

CONCLUSION

The ED is the main gateway to medical care for millions of patients evaluated for TBI each year. Novel diagnostic tests are needed to improve ED diagnosis and management of TBI.

Emergency Department Triage of Traumatic Head Injury Using a Brain Electrical Activity Biomarker: A Multisite Prospective Observational Validation Trial

OBJECTIVES

A brain electrical activity biomarker for identifying traumatic brain injury (TBI) in emergency department (ED) patients presenting with high Glasgow Coma Scale (GCS) after sustaining a head injury has shown promise for objective, rapid triage. The main objective of this study was to prospectively evaluate the efficacy of an automated classification algorithm to determine the likelihood of being computed tomography (CT) positive, in high-functioning TBI patients in the acute state.

METHODS

Adult patients admitted to the ED for evaluation within 72 hours of sustaining a closed head injury with GCS 12 to 15 were candidates for study. A total of 720 patients (18-85 years) meeting inclusion/exclusion criteria were enrolled in this observational, prospective validation trial, at 11 U.S. EDs. GCS was 15 in 97%, with the first and third quartiles being 15 (interquartile range = 0) in the study population at the time of the evaluation. Standard clinical evaluations were conducted and 5 to 10 minutes of electroencephalogram (EEG) was acquired from frontal and frontal-temporal scalp locations. Using an a priori derived EEG-based classification algorithm developed on an independent population and applied to this validation population prospectively, the likelihood of each subject being CT+ was determined, and performance metrics were computed relative to adjudicated CT findings.

RESULTS

Sensitivity of the binary classifier (likely CT+ or CT-) was 92.3% (95% confidence interval [CI] = 87.8%-95.5%) for detection of any intracranial injury visible on CT (CT+), with specificity of 51.6% (95% CI = 48.1%-55.1%) and negative predictive value (NPV) of 96.0% (95% CI = 93.2%-97.9%). Using ternary classification (likely CT+, equivocal, likely CT-) demonstrated enhanced sensitivity to traumatic hematomas (≥1 mL of blood), 98.6% (95% CI = 92.6%-100.0%), and NPV of 98.2% (95% CI = 95.5%-99.5%).

CONCLUSION

Using an EEG-based biomarker high accuracy of predicting the likelihood of being CT+ was obtained, with high NPV and sensitivity to any traumatic bleeding and to hematomas. Specificity was significantly higher than standard CT decision rules. The short time to acquire results and the ease of use in the ED environment suggests that EEG-based classifier algorithms have potential to impact triage and clinical management of head-injured patients.

Clinical Evaluation of a Microwave-Based Device for Detection of Traumatic Intracranial Hemorrhage

Traumatic brain injury (TBI) is the leading cause of death and disability among young persons. A key to improve outcome for patients with TBI is to reduce the time from injury to definitive care by achieving high triage accuracy. Microwave technology (MWT) allows for a portable device to be used in the pre-hospital setting for detection of intracranial hematomas at the scene of injury, thereby enhancing early triage and allowing for more adequate early care. MWT has previously been evaluated for medical applications including the ability to differentiate between hemorrhagic and ischemic stroke. The purpose of this study was to test whether MWT in conjunction with a diagnostic mathematical algorithm could be used as a medical screening tool to differentiate patients with traumatic intracranial hematomas, chronic subdural hematomas (cSDH), from a healthy control (HC) group. Twenty patients with cSDH and 20 HC were measured with a MWT device. The accuracy of the diagnostic algorithm was assessed using a leave-one-out analysis. At 100% sensitivity, the specificity was 75%—i.e., all hematomas were detected at the cost of 25% false positives (patients who would be overtriaged). Considering the need for methods to identify patients with intracranial hematomas in the pre-hospital setting, MWT shows promise as a tool to improve triage accuracy. Further studies are under way to evaluate MWT in patients with other intracranial hemorrhages.

CT image segmentation in traumatic brain injury

Traumatic brain injury (TBI) is a major cause of disability and death. Speed and accuracy are vital in diagnosing TBI for which computer-aided imaging analysis may speedup and improve the efficiency of diagnosis and help reduce mortality, long-term complications, and the associated costs. However, developing such a system is challenging due to some factors such as the inherent noise associated with obtaining the images, artifacts and quality of the images. An automated system that can preliminary identify, localize and quantify the imaging features of TBI would be beneficial in guiding real-time clinical diagnosis as well as for quality assurance. In this paper we propose an automated system to segment the hematoma region from CT images. The proposed method first performs denoising and image enhancement and then by developing a Gaussian mixture model, segmentation is carried out. We show the performance of the system by comparing the results with ground truth generated by specialists.