The prevalence of stroke worldwide and the paucity of effective therapies have triggered interest in the use of transcranial ultrasound as an adjuvant to thrombolytic therapy. Previous studies have shown that 120-kHz ultrasound enhanced thrombolysis and allowed efficient penetration through the temporal bone. The objective of our study was to develop an accurate finite-difference model of acoustic propagation through the skull based on computed tomography (CT) images. The computational approach, which neglected shear waves, was compared with a simple analytical model including shear waves. Acoustic pressure fields from a two-element annular array (120 kHz and 60 kHz) were acquired in vitro in four human skulls. Simulations were performed using registered CT scans and a source term determined by acoustic holography. Mean errors below 14% were found between simulated pressure fields and corresponding measurements. Intracranial peak pressures were systematically underestimated and reflections from the contralateral bone were overestimated. Determination of the acoustic impedance of the bone from the CT images was the likely source of error. High correlation between predictions and measurements (R2=0.93 and R2=0.88 for transmitted and reflected waves amplitude, respectively) demonstrated that this model is suitable for quantitative estimation of acoustic fields generated during 40-200 kHz ultrasound-enhanced ischemic stroke treatment.
The objective of this paper is to demonstrate the effective deployment of a robotic assessment tool for the evaluation of mild traumatic brain injury (mTBI) patients in a busy, resource-constrained, urban emergency department (ED). Methods: Functional integration of new robotic technology for research in the ED presented several obstacles that required a multidisciplinary approach, including participation from electrical and computer engineers, emergency medicine clinicians, and clinical operations staff of the hospital. Our team addressed many challenges in deployment of this advanced technology including: 1) adapting the investigational device for the unique clinical environment; 2) acquisition and maintenance of appropriate testing space for point-of-care assessment; and 3) dedicated technical support and upkeep of the device. Upon successful placement of the robotic device in the ED, the clinical study required screening of all patients presenting to the ED with complaints of head injury. Eligible patients were enrolled and tested using a robot-assisted test battery. Three weeks after the injury, patients were contacted to complete follow-up assessments. Results: Adapting the existing technology to meet anticipated physical constraints of the ED was performed by engineering a mobile platform. Due to the large footprint of the device, it was frequently moved before ultimately being fully integrated into the ED. Over 14 months, 1423 patients were screened. Twenty-eight patients could not be enrolled because the device was unavailable due to operations limitations. Technical problems with the device resulted in failure to include 20 patients. A total of 66 mTBI patients were enrolled and 42 of them completed both robot-assisted testing and follow-up assessment. Successful completion of screening and enrollment demonstrated that the challenges associated with integration of investigational devices into the ED can be effectively addressed through a collaborative patient-oriented research model. Conclusion: Effective deployment and use of new robotic technology for research in an urban academic ED required significant planning, coordination, and collaboration with key personnel from multiple disciplines. Clinical Impact: A pilot clinical study on mTBI patients using the robotic device provided useful data without disrupting the ED workflow. Integration of this technology into the ED serves as an important step toward pursing active clinical research in an acute care setting.
Objectives
Postconcussion symptoms (PCS) are a common complication of mild traumatic brain injury (TBI). Currently, there is no validated clinically available method to reliably predict at the time of injury who will subsequently develop PCS. The purpose of this study was to determine if PCS following mild TBI can be predicted during the initial presentation to an emergency department (ED) using a novel robotic-assisted assessment of neurologic function.
Methods
All patients presenting to an urban ED with a chief complaint of head injury within the preceding 24 hours were screened for inclusion from March 2013 to April 2014. The enrollment criteria were as follows: 1) age of 18 years or greater, 2) ability and willingness to provide written informed consent, 3) blunt head trauma and clinical diagnosis of isolated mild TBI by the treating physician, and 4) blood alcohol level of <100 mg/dL. Eligible mild TBI patients were enrolled and their neuromotor function was assessed in the ED using a battery of five tests that cover a range of proprioceptive, visuomotor, visuospatial, and executive function performance metrics. At 3 weeks postinjury, participants were contacted via telephone to complete the Rivermead Post-Concussion Symptoms Questionnaire to assess the presence of significant PCS.
Results
A total of 66 mild TBI patients were enrolled in the study with 42 of them completing both the ED assessment and the follow-up; 40 patients were included in the analyses. The area under the receiver operating characteristic curve (AUC) for the entire test battery was 0.72 (95% confidence interval [CI] = 0.54 to 0.90). The AUC for tests that primarily measure visuomotor and proprioceptive performance were 0.80 (95% CI = 0.65 to 0.95) and 0.71 (95% CI = 0.53 to 0.89), respectively.
Conclusions
The robotic-assisted test battery has the ability to discriminate between subjects who developed PCS and those who did not. Additionally, poor visuomotor and proprioceptive performance were most strongly associated with subsequent PCS.
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