Increased prognostic accuracy of TBI when a brain electrical activity biomarker is added to loss of consciousness (LOC)

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.

Review of wearable technologies and machine learning methodologies for systematic detection of mild traumatic brain injuries

Mild traumatic brain injuries (mTBIs) are the most common type of brain injury. Timely diagnosis of mTBI is crucial in making 'go/no-go' decision in order to prevent repeated injury, avoid strenuous activities which may prolong recovery, and assure capabilities of high-level performance of the subject. If undiagnosed, mTBI may lead to various short- and long-term abnormalities, which include, but are not limited to impaired cognitive function, fatigue, depression, irritability, and headaches. Existing screening and diagnostic tools to detect acute andearly-stagemTBIs have insufficient sensitivity and specificity. This results in uncertainty in clinical decision-making regarding diagnosis and returning to activity or requiring further medical treatment. Therefore, it is important to identify relevant physiological biomarkers that can be integrated into a mutually complementary set and provide a combination of data modalities for improved on-site diagnostic sensitivity of mTBI. In recent years, the processing power, signal fidelity, and the number of recording channels and modalities of wearable healthcare devices have improved tremendously and generated an enormous amount of data. During the same period, there have been incredible advances in machine learning tools and data processing methodologies. These achievements are enabling clinicians and engineers to develop and implement multiparametric high-precision diagnostic tools for mTBI. In this review, we first assess clinical challenges in the diagnosis of acute mTBI, and then consider recording modalities and hardware implementation of various sensing technologies used to assess physiological biomarkers that may be related to mTBI. Finally, we discuss the state of the art in machine learning-based detection of mTBI and consider how a more diverse list of quantitative physiological biomarker features may improve current data-driven approaches in providing mTBI patients timely diagnosis and treatment.

Comparative Effectiveness of Initial Treatment at Trauma Center vs Neurosurgery-Capable Non-Trauma Center for Severe, Isolated Head Injury

BACKGROUND

Head injury is an increasing contributor to death and disability, particularly among the elderly. Older patients are less likely to be treated at trauma centers, and head injury is the most common severe injury treated at non-trauma centers. We hypothesized that patients initially triaged to trauma centers would have lower rates of mortality and higher rates of discharge home without services than those treated at non-trauma centers.

STUDY DESIGN

We used the State Emergency Department and Inpatient Databases (2011 to 2012) for 6 states to conduct a retrospective cohort study of patients with severe, isolated head injury. Combined, these databases capture all visits to non-federal emergency departments. We compared in-hospital mortality and discharge status for all adults and for the subgroup aged 65 years or older who initially presented to either a trauma center or a neurosurgery-capable non-trauma center. To account for selection bias, we used differential distance from patients' homes to a trauma center as an instrumental variable and performed a multivariable matched analysis.

RESULTS

Of 62,198 patients who presented with severe, isolated head injury, 44.2% presented to non-trauma centers and 55.8% to trauma centers. In multivariable matched instrumental variable analysis, initial presentation to a trauma center was associated with no significant difference in overall mortality (-1.06%; 95% CI -3.36% to 1.19%), but a 5.8% higher rate of discharge home (95% CI 1.7% to 10.0%). Among patients aged 65 years or older, initial presentation to a trauma center was associated with a 3.4% reduction in mortality (95% CI 0.0% to 7.1%).

CONCLUSIONS

Patients with isolated, severe head injury have better outcomes if initially treated in designated trauma centers. As 40% of such patients were triaged to non-trauma centers, there are major opportunities for improving outcomes.