Faculty Bibliography

BACKGROUND: Extremely high accuracy for predicting CT+ traumatic brain injury (TBI) using a quantitative EEG (QEEG) based multivariate classification algorithm was demonstrated in an independent validation trial, in Emergency Department (ED) patients, using an easy to use handheld device. This study compares the predictive power using that algorithm (which includes LOC and amnesia), to the predictive power of LOC alone or LOC plus traumatic amnesia. PARTICIPANTS: ED patients 18-85years presenting within 72h of closed head injury, with GSC 12-15, were study candidates. 680 patients with known absence or presence of LOC were enrolled (145 CT+ and 535 CT- patients). METHODS: 5-10min of eyes closed EEG was acquired using the Ahead 300 handheld device, from frontal and frontotemporal regions. The same classification algorithm methodology was used for both the EEG based and the LOC based algorithms. Predictive power was evaluated using area under the ROC curve (AUC) and odds ratios. RESULTS: The QEEG based classification algorithm demonstrated significant improvement in predictive power compared with LOC alone, both in improved AUC (83% improvement) and odds ratio (increase from 4.65 to 16.22). Adding RGA and/or PTA to LOC was not improved over LOC alone. CONCLUSIONS: Rapid triage of TBI relies on strong initial predictors. Addition of an electrophysiological based marker was shown to outperform report of LOC alone or LOC plus amnesia, in determining risk of an intracranial bleed. In addition, ease of use at point-of-care, non-invasive, and rapid result using such technology suggests significant value added to standard clinical prediction.

OBJECTIVES: A brain electrical activity biomarker for identifying traumatic brain injury (TBI) in Emergency Department (ED) patients presenting with high 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 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 12-15were candidates for study. 720 patients (18-85 years) meeting inclusion/exclusion criteria were enrolled in this observational, prospective validation trial, at 11 US Emergency Departments. Glasgow Coma Scale was 15 in 97%, with the first and third quartile being 15 (IQR=0) in the study population at the time of the evaluation. Standard clinical evaluations were conducted and 5-10 minutes of 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 (CT+ or CT-) was 92.3% (87.8%, 95.5%) for detection of any intracranial injury visible on CT (CT+), with specificity of 51.6% (48.1%, 55.1%) and negative predictive value of 96.0% (93.2%, 97.9%). Using ternary classification (CT+, Equivocal, CT-) demonstrated enhanced sensitivity to traumatic hematomas (>/=1cc of blood), 98.6% (92.6%, 100.0%) and negative predictive value of 98.2% (95.5%, 99.5%). CONCLUSIONS: 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

The historical evolution of QEEG will be explored with emphasis on significant steps its development. The following will be highlighted: 1) Early steps in quantification that pave the way, including normative equations (John et al., 1980), source localization (Pascual-Marqui, Esslen, Kochi, & Lehman, 2002), and Default Mode Network (Buckner, Andrews- Hanna, & Schacter, 2008); 2) QEEG treatment predictive biomarkers, including cognitive decline (Jelic et al., 2000; Prichep et al., 2006) and OCD (Dohrmann, Stengler, Jahn, & Olbrich, 2017); 3) QEEG as a surrogate for advanced neuroimaging, including TBI (Hanley et al., 2017) and chronic pain (Prichep et al., 2017). Impact of the 'perfect storm' represented by advances over the last decade in technology, signal processing, and machine learning classification methodologies will be discussed in this context

OBJECTIVE: The aim of our study is to examine quantitative Electroencephalogram (QEEG) differences between ADHD patients that are responders and non-responders to long-term treatment with Atomoxetine at baseline and after 6 and 12months of treatment. Patients with attention deficit hyperactivity disorder (ADHD) received atomoxetine titrated, over 7days, from 0.5 to 1.2mg/kg/day. QEEG and Swanson, Nolan, and Pelham-IV Questionnaire (SNAP-IV) scores were recorded before treatment and after therapy. METHODS: Twenty minutes of eyes closed resting EEG was recorded from 19 electrodes referenced to linked earlobes. Full frequency and narrow band spectra of two minutes of artifact-free EEG were computed as well as source localization using Variable Resolution Electrical Tomography (VARETA). Abnormalities were identified using Z-spectra relative to normative values. RESULTS: Patients were classified as responders, non-responders and partial responders based upon the SNAP-IV findings. At baseline, the responders showed increased absolute power in alpha and delta in frontal and temporal regions, whereas, non-responders showed increased absolute power in all frequency bands that was widely distributed. With treatment responders' absolute power values moved toward normal values, whereas, non-responders remained at baseline values. CONCLUSIONS: Patients with increased power in the alpha band with no evidence of alterations in the beta or theta range, might be responders to treatment with atomoxetine. Increased power in the beta band coupled with increased alpha seems to be related to non-responders and one should consider atomoxetine withdrawal, especially if there is persistence of increased alpha and beta accompanied by an increase of theta.

STUDY OBJECTIVE: We compared the performance of a handheld quantitative electroencephalogram (QEEG) acquisition device to New Orleans Criteria (NOC), Canadian CT Head Rule (CCHR), and National Emergency X-Radiography Utilization Study II (NEXUS II) Rule in predicting intracranial lesions on head computed tomography (CT) in acute mild traumatic brain injury in the emergency department (ED). METHODS: Patients between 18 and 80 years of age who presented to the ED with acute blunt head trauma were enrolled in this prospective observational study at 2 urban academic EDs in Detroit, MI. Data were collected for 10 minutes from frontal leads to determine a QEEG discriminant score that could maximally classify intracranial lesions on head CT. RESULTS: One hundred fifty-two patients were enrolled from July 2012 to February 2013. A total 17.1% had acute traumatic intracranial lesions on head CT. Quantitative electroencephalogram discriminant score of greater than or equal to 31 was found to be a good cutoff (area under receiver operating characteristic curve = 0.84; 95% confidence interval [CI], 0.76-0.93) to classify patients with positive head CT. The sensitivity of QEEG discriminant score was 92.3 (95% CI, 73.4-98.6), whereas the specificity was 57.1 (95% CI, 48.0-65.8). The sensitivity and specificity of the decision rules were as follows: NOC 96.1 (95% CI, 78.4-99.7) and 15.8 (95% CI, 10.1-23.6); CCHR 46.1 (95% CI, 27.1-66.2) and 86.5 (95% CI, 78.9-91.7); NEXUS II 96.1 (95% CI, 78.4-99.7) and 31.7 (95% CI, 23.9-40.7). CONCLUSION: At a sensitivity of greater than 90%, QEEG discriminant score had better specificity than NOC and NEXUS II. Only CCHR had better specificity than QEEG discriminant score but at the cost of low (<50%) sensitivity.

Abstract Rapid identification of traumatic intracranial hematomas following closed head injury represents a significant health care need because of the potentially life-threatening risk they present. This study demonstrates the clinical utility of an index of brain electrical activity used to identify intracranial hematomas in traumatic brain injury (TBI) presenting to the emergency department (ED). Brain electrical activity was recorded from a limited montage located on the forehead of 394 closed head injured patients who were referred for CT scans as part of their standard ED assessment. A total of 116 of these patients were found to be CT positive (CT+), of which 46 patients with traumatic intracranial hematomas (CT+) were identified for study. A total of 278 patients were found to be CT negative (CT-) and were used as controls. CT scans were subjected to quanitative measurements of volume of blood and distance of bleed from recording electrodes by blinded independent experts, implementing a validated method for hematoma measurement. Using an algorithm based on brain electrical activity developed on a large independent cohort of TBI patients and controls (TBI-Index), patients were classified as either positive or negative for structural brain injury. Sensitivity to hematomas was found to be 95.7% (95% CI=85.2, 99.5), specificity was 43.9% (95% CI=38.0, 49.9). There was no significant relationship between the TBI-Index and distance of the bleed from recording sites (F=0.044, p=0.833), or volume of blood measured F=0.179, p=0.674). Results of this study are a validation and extension of previously published retrospective findings in an independent population, and provide evidence that a TBI-Index for structural brain injury is a highly sensitive measure for the detection of potentially life-threatening traumatic intracranial hematomas, and could contribute to the rapid, quantitative evaluation and treatment of such patients.