Esther L. Yuh

Magnetic resonance imaging improves 3‐month outcome prediction in mild traumatic brain injury

To determine the clinical relevance, if any, of traumatic intracranial findings on early head computed tomography (CT) and brain magnetic resonance imaging (MRI) to 3‐month outcome in mild traumatic brain injury (MTBI).

Transforming Research and Clinical Knowledge in Traumatic Brain Injury Pilot: Multicenter Implementation of the Common Data Elements for Traumatic Brain Injury

Traumatic brain injury (TBI) is among the leading causes of death and disability worldwide, with enormous negative social and economic impacts. The heterogeneity of TBI combined with the lack of precise outcome measures have been central to the discouraging results from clinical trials. Current approaches to the characterization of disease severity and outcome have not changed in more than three decades. This prospective multicenter observational pilot study aimed to validate the feasibility of implementing the TBI Common Data Elements (TBI-CDEs). A total of 650 subjects who underwent computed tomography (CT) scans in the emergency department within 24 h of injury were enrolled at three level I trauma centers and one rehabilitation center. The TBI-CDE components collected included: 1) demographic, social and clinical data; 2) biospecimens from blood drawn for genetic and proteomic biomarker analyses; 3) neuroimaging studies at 2 weeks using 3T magnetic resonance imaging (MRI); and 4) outcome assessments at 3 and 6 months. We describe how the infrastructure was established for building data repositories for clinical data, plasma biomarkers, genetics, neuroimaging, and multidimensional outcome measures to create a high quality and accessible information commons for TBI research. Risk factors for poor follow-up, TBI-CDE limitations, and implementation strategies are described. Having demonstrated the feasibility of implementing the TBI-CDEs through successful recruitment and multidimensional data collection, we aim to expand to additional study sites. Furthermore, interested researchers will be provided early access to the Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) data set for collaborative opportunities to more precisely characterize TBI and improve the design of future clinical treatment trials. (ClinicalTrials.gov Identifier NCT01565551.).

Diffusion Tensor Imaging for Outcome Prediction in Mild Traumatic Brain Injury: A TRACK-TBI Study

We evaluated 3T diffusion tensor imaging (DTI) for white matter injury in 76 adult mild traumatic brain injury (mTBI) patients at the semiacute stage (11.2±3.3 days), employing both whole-brain voxel-wise and region-of-interest (ROI) approaches. The subgroup of 32 patients with any traumatic intracranial lesion on either day-of-injury computed tomography (CT) or semiacute magnetic resonance imaging (MRI) demonstrated reduced fractional anisotropy (FA) in numerous white matter tracts, compared to 50 control subjects. In contrast, 44 CT/MRI-negative mTBI patients demonstrated no significant difference in any DTI parameter, compared to controls. To determine the clinical relevance of DTI, we evaluated correlations between 3- and 6-month outcome and imaging, demographic/socioeconomic, and clinical predictors. Statistically significant univariable predictors of 3-month Glasgow Outcome Scale-Extended (GOS-E) included MRI evidence for contusion (odds ratio [OR] 4.9 per unit decrease in GOS-E; p=0.01), ≥1 ROI with severely reduced FA (OR, 3.9; p=0.005), neuropsychiatric history (OR, 3.3; p=0.02), age (OR, 1.07/year; p=0.002), and years of education (OR, 0.79/year; p=0.01). Significant predictors of 6-month GOS-E included ≥1 ROI with severely reduced FA (OR, 2.7; p=0.048), neuropsychiatric history (OR, 3.7; p=0.01), and years of education (OR, 0.82/year; p=0.03). For the subset of 37 patients lacking neuropsychiatric and substance abuse history, MRI surpassed all other predictors for both 3- and 6-month outcome prediction. This is the first study to compare DTI in individual mTBI patients to conventional imaging, clinical, and demographic/socioeconomic characteristics for outcome prediction. DTI demonstrated utility in an inclusive group of patients with heterogeneous backgrounds, as well as in a subset of patients without neuropsychiatric or substance abuse history.

Imaging of ependymomas: MRI and CT.

The imaging features of intracranial and spinal ependymoma are reviewed with an emphasis on conventional magnetic resonance imaging (MRI), perfusion MRI and proton magnetic resonance spectroscopy, and computed tomography. Imaging manifestations of leptomeningeal dissemination of disease are described. Finally, salient imaging features obtained in the postoperative period to evaluate completeness of surgical resection, and thereafter for long-term surveillance for disease recurrence, are reviewed.

Study of focused ultrasound tissue damage using MRI and histology.

This paper reports on an experimental study of in vivo tissue damage in the rabbit brain with focused ultrasound (FUS) using magnetic resonance imaging (MRI) and histopathological analysis. Ten ultrasonic lesions (tissue damage) were created in five rabbits using a focused ultrasound beam of 1.5 MHz, electrical power input to the transducer of 70–85 W, and an exposure duration of 15–20 seconds. T1‐ and T2‐weighted fast spin‐echo (FSE) and Fluid attenuated inversion recovery (FLAIR) sequences were used to detect the ultrasonic lesions after treatment. Imaging was performed for 4–8 hours after treatment, after which the animals were immediately sacrificed. Ultrasonic lesion diameter was measured on MRI and histological sections after correction for tissue shrinkage during the histological processing. The T1‐weighted images showed lesions poorly, whereas both T2‐weighted and FLAIR images showed lesions clearly. The lesion diameters on both T2 and FLAIR imaging correlated well with measurements from histology. The time delay before lesions appeared on T2‐weighted imaging was 15 minutes to 1 hour, depending on the exposure location in the brain. J. Magn. Reson. Imaging 1999;10:146–153.

Outcome Prediction after Mild and Complicated Mild Traumatic Brain Injury: External Validation of Existing Models and Identification of New Predictors Using the TRACK-TBI Pilot Study

Although the majority of patients with mild traumatic brain injury (mTBI) recover completely, some still suffer from disabling ailments at 3 or 6 months. We validated existing prognostic models for mTBI and explored predictors of poor outcome after mTBI. We selected patients with mTBI from TRACK-TBI Pilot, an unselected observational cohort of TBI patients from three centers in the United States. We validated two prognostic models for the Glasgow Outcome Scale Extended (GOS-E) at 6 months after injury. One model was based on the CRASH study data and another from Nijmegen, The Netherlands. Possible predictors of 3- and 6-month GOS-E were analyzed with univariate and multi-variable proportional odds regression models. Of the 386 of 485 patients included in the study (median age, 44 years; interquartile range, 27-58), 75% (n=290) presented with a Glasgow Coma Score (GCS) of 15. In this mTBI population, both previously developed models had a poor performance (area under the receiver operating characteristic curve, 0.49-0.56). In multivariable analyses, the strongest predictors of lower 3- and 6-month GOS-E were older age, pre-existing psychiatric conditions, and lower education. Injury caused by assault, extracranial injuries, and lower GCS were also predictive of lower GOS-E. Existing models for mTBI performed unsatisfactorily. Our study shows that, for mTBI, different predictors are relevant as for moderate and severe TBI. These include age, pre-existing psychiatric conditions, and lower education. Development of a valid prediction model for mTBI patients requires further research efforts.

Quantitative CT improves outcome prediction in acute traumatic brain injury.

The admission noncontrast head computed tomography (CT) scan has been demonstrated to be one of several key early clinical and imaging features in the challenging problem of prediction of long-term outcome after acute traumatic brain injury (TBI). In this study, we employ two novel approaches to the problem of imaging classification and outcome prediction in acute TBI. First, we employ the novel technique of quantitative CT (qCT) image analysis to provide more objective, reproducible measures of the abnormal features of the admission head CT in acute TBI. We show that the incorporation of quantitative, rather than qualitative, CT features results in a significant improvement in prediction of the 6-month Extended Glasgow Outcome Scale (GOS-E) score over a wide spectrum of injury severity. Second, we employ principal components analysis (PCA) to demonstrate the interdependence of certain predictive variables. Relatively few prior studies of outcome prediction in acute TBI have used a multivariate approach that explicitly takes into account the potential covariance among clinical and CT predictive variables. We demonstrate that several predictors, including midline shift, cistern effacement, subdural hematoma volume, and Glasgow Coma Scale (GCS) score are related to one another. Rather than being independent features, their importance may be related to their status as surrogate measures of a more fundamental underlying clinical feature, such as the severity of intracranial mass effect. We believe that objective computational tools and data-driven analytical methods hold great promise for neurotrauma research, and may ultimately have a role in image analysis for clinical care.

Circulating Brain-Derived Neurotrophic Factor Has Diagnostic and Prognostic Value in Traumatic Brain Injury.

Brain-derived neurotrophic factor (BDNF) is important for neuronal survival and regeneration. We investigated the diagnostic and prognostic values of serum BDNF in traumatic brain injury (TBI). We examined serum BDNF in two independent cohorts of TBI cases presenting to the emergency departments (EDs) of the Johns Hopkins Hospital (JHH; n = 76) and San Francisco General Hospital (SFGH, n = 80), and a control group of JHH ED patients without TBI (n = 150). Findings were subsequently validated in the prospective, multi-center Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) Pilot study (n = 159). We investigated the association between BDNF, glial fibrillary acidic protein (GFAP), and ubiquitin C-terminal hydrolase-L1 (UCH-L1) and recovery from TBI at 6 months in the TRACK-TBI Pilot cohort. Incomplete recovery was defined as having either post-concussive syndrome or a Glasgow Outcome Scale Extended score <8 at 6 months. Median day-of-injury BDNF concentrations (ng/mL) were lower among TBI cases (JHH TBI, 17.5 and SFGH TBI, 13.8) than in JHH controls (60.3; p = 0.0001). Among TRACK-TBI Pilot subjects, median BDNF concentrations (ng/mL) were higher in mild (8.3) than in moderate (4.3) or severe TBI (4.0; p = 0.004. In the TRACK-TBI cohort, the 75 (71.4%) subjects with very low BDNF values (i.e., <the 1st percentile for non-TBI controls, <14.2 ng/mL) had higher odds of incomplete recovery than those who did not have very low values (odds ratio, 4.0; 95% confidence interval [CI]: 1.5-11.0). The area under the receiver operator curve for discriminating complete and incomplete recovery was 0.65 (95% CI: 0.52-0.78) for BDNF, 0.61 (95% CI: 0.49-0.73) for GFAP, and 0.55 (95% CI: 0.43-0.66) for UCH-L1. The addition of GFAP/UCH-L1 to BDNF did not improve outcome prediction significantly. Day-of-injury serum BDNF is associated with TBI diagnosis and also provides 6-month prognostic information regarding recovery from TBI. Thus, day-of-injury BDNF values may aid in TBI risk stratification.

Imaging concussion: a review.

Concussion is a significant public health problem that is receiving increased attention from physicians, the media, and the public. Recent studies suggest that persistent symptoms after concussion/mild traumatic brain injury (mTBI) may arise from structural or metabolic alterations in the brain, despite normal conventional neuroimaging studies. New, advanced neuroimaging techniques show promise in refining diagnosis and outcome prediction in concussion/mTBI. Here, we review some of these techniques, including diffusion tensor imaging, task-based and resting-state functional magnetic resonance imaging, as well as positron emission tomography, H magnetic resonance spectroscopy, and perfusion imaging. Although further validation is needed through large prospective studies that correlate these techniques with patient outcome, it is likely that the definitions of pathoanatomic lesions, and a better understanding of their relationship to symptoms and prognosis, will continue to evolve as neuroimaging techniques continue to progress.Concussion is a significant public health problem that is receiving increased attention from physicians, the media, and the public. Recent studies suggest that persistent symptoms after concussion/mild traumatic brain injury (mTBI) may arise from structural or metabolic alterations in the brain, despite normal conventional neuroimaging studies. New, advanced neuroimaging techniques show promise in refining diagnosis and outcome prediction in concussion/mTBI. Here, we review some of these techniques, including diffusion tensor imaging, task-based and resting-state functional magnetic resonance imaging, as well as positron emission tomography, 1 H magnetic resonance spectroscopy, and perfusion imaging. Although further validation is needed through large prospective studies that correlate these techniques with patient outcome, it is likely that the definitions of pathoanatomic lesions, and a better understanding of their relationship to symptoms and prognosis, will continue to evolve as neuroimaging techniques continue to progress.

Computer-Aided Assessment of Head Computed Tomography (CT) Studies in Patients with Suspected Traumatic Brain Injury

In this study, we sought to determine the accuracy of a computer algorithm that automatically assesses head computed tomography (CT) studies in patients with suspected traumatic brain injury (TBI) for features of intracranial hemorrhage and mass effect, employing a neuroradiologists interpretation as the gold standard. To this end, we designed a suite of computer algorithms that evaluates in a fully automated fashion the presence of intracranial blood and/or mass effect based on the following CT findings: (1) presence or absence of a subdural or epidural hematoma, (2) presence or absence of subarachnoid hemorrhage, (3) presence or absence of an intraparenchymal hematoma, (4) presence or absence of clinically significant midline shift (>or=5 mm), and (5) normal, partly effaced, or completely effaced basal cisterns. The algorithm displays abnormal findings as color overlays on the original head CT images, and calculates the volume of each type of blood collection, the midline shift, and the volume of the basal cisterns, based on the above-described features. Thresholds and parameters yielding optimal accuracy of the computer algorithm were determined using a development sample of 33 selected, nonconsecutive patients. The software was then applied to a validation sample of 250 consecutive patients evaluated for suspicion of acute TBI at our institution in 2006-2007. Software detection of the presence of at least one noncontrast CT (NCT) feature of acute TBI demonstrated high sensitivity of 98% and high negative predictive value (NPV) of 99%. There was actually only one false negative case, where a very subtle subdural hematoma, extending exclusively along the falx, was diagnosed by the neuroradiologist, while the case was considered as normal by the computer algorithm. The software was excellent at detecting the presence of mass effect and intracranial hemorrhage, but showed some disagreements with the neuroradiologist in quantifying the degree of mass effect and characterizing the type of intracranial hemorrhage. In summary, we have developed a fully automated computer algorithm that demonstrated excellent sensitivity for acute intracranial hemorrhage and clinically significant midline shift, while maintaining intermediate specificity. Further studies are required to evaluate the potential favorable impact of this software on facilitating workflow and improving diagnostic accuracy when used as a screening aid by physicians with different levels of experience.