Chapter 5 Classification of Traumatic Brain Injury Severity: A Neuropsychological Approach

Transcription

1 Chapter 5 Classification of Traumatic Brain Injury Severity: A Neuropsychological Approach Daniel N. Allen, Nicholas S. Thaler, Chad L. Cross, and Joan Mayfield Introduction Traumatic brain injury (TBI) continues to be one of the most common causes of disability and death among children and adolescents in the United States each year, with many going on to have permanent disabilities (Faul, Xu, Wald, & Coronado, 2010 ; Rivara et al., 2012 ). However, outcomes vary dramatically such that some children demonstrate minimal long-term impairment, while others evidence significant continuing disability. The classification of injury severity is important then, because it may provide one means of predicting long- term outcomes and prescribing treatment. In this way, severity classification may assist in identifying those children who are at increased risk for long-term disability following TBI and suggest specific interventions that might assist in the recovery process. D.N. Allen, Ph.D. (*) N.S. Thaler, M.A. Lincy Professor of Psychology, Department of Psychology, University of Nevada Las Vegas, 4505 Maryland Parkway, Box , Las Vegas, NV 89154, USA daniel.allen@unlv.edu; nick.thaler@gmail.com C.L. Cross, Ph.D., P.Stat, L.C.A.D.C., M.F.T. Veterans Health Administration, Office of Informatics and Analytics, Las Vegas, NV, USA School of Community Health Sciences, University of Nevada, Las Vegas, 4505 Maryland Parkway, Las Vegas, NV 89154, USA chad.cross@rocketmail.com J. Mayfield, Ph.D. Our Children s House at Baylor, 3301 Swiss Avenue, Dallas, TX 75204, USA JoanM@baylorhealth.edu D.N. Allen and G. Goldstein (eds.), Cluster Analysis in Neuropsychological Research: Recent Applications, DOI / _5, Springer Science+Business Media New York

2 96 D.N. Allen et al. Despite its importance, classifying the severity of TBI has presented unique challenges from very early on. For some disorders, where neuropathology is relatively uniform across patients and where disease progression follows a relatively well-defined course, classification efforts have been successful in staging the disease as it progresses from mild to severe, including a prototypical characterization of both symptom expression and cognitive decline. In contrast, for disorders where neuropathology is not uniform or heterogeneous, classifying the severity of brain injury or dysfunction has been particularly challenging. In Chap. 3 the case for schizophrenia is discussed, which although not an acquired disorder or typically considered a neurological condition, is characterized by heterogeneity across a number of domains, such as symptoms and outcomes. More relevant to the current discussion is the observation that schizophrenia is also characterized by heterogeneous neurocognitive deficits. As is apparent from studies of schizophrenia, cluster analysis can be particularly useful in identifying profiles of performance on neuropsychological testing that may be related to important disorder-related variables such as treatment outcomes, medication response, and longer-term prognosis. In the current chapter, literature is reviewed that demonstrates cluster analysis is a useful approach to investigate neurocognitive heterogeneity present in TBI and the case if made for the potential usefulness of tests such as the Trail Making Test (TMT) to aid in classifying the severity of the injury. Heterogeneity and TBI Severity Classification Saatman et al. ( 2008 ) recently underscored the problem that heterogeneity poses to the classification of TBI through conventional methods. Their paper summarized the preliminary deliberations of a workgroup tasked with developing a classification system for TBI. They state, The heterogeneity of traumatic brain injury (TBI) is considered one of the most significant barriers to finding effective therapeutic interventions with a pressing need to.develop a reliable, efficient, and valid classification system for TBI that could be used to link specific patterns of brain and neurovascular injury with appropriate therapeutic interventions (p. 719). While the focus of their efforts was primarily on developing classification for therapeutic interventions, their point is more generally relevant to classification for other purposes, such as to predict educational and vocational outcomes, which is similarly troubled by heterogeneity. Heterogeneity in outcomes arises from a number of sources, and variability in neuropathology resulting from TBI is a major contributing factor. This heterogeneity can be seen in Fig. 5.1, which presents computed tomography scans (CTs) of six individuals who sustained a severe TBI. Each case highlights a different pathology, ranging from localized contusions to diffuse axonal injury, which in turn may or may not be indicative of subsequent cognitive impairment and functional disability. Furthermore, some children who sustain injuries with little to no corroborating evidence from neuroimaging indicating the presence of cerebral damage may experience substantial declines, while others with profound

3 5 Classification of Traumatic Brain Injury Severity: A Neuropsychological Approach 97 Fig. 5.1 Heterogeneity of severe traumatic brain injury (TBI). Note: computed tomography (CT) scans of six different patients with severe TBI, defined as a Glasgow Coma Scale score of 8, highlighting the significant heterogeneity of pathological findings. CT scans represent patients with epidural hematomas (EDH), contusions and parenchymal hematomas (contusion/hematoma), diffuse axonal injury (DAI), subdural hematoma (SDH), subarachnoid hemorrhage and intraventricular hemorrhage (SAH/IVH), and diffuse brain swelling (diffuse swelling) (From: Saatman, K. E., Duhaime, A. C., Bullock, R., Maas, A. I., Valadka, A., & Manley, G. T. (2008). Workshop Scientific Team and Advisory Panel Members. Classification of traumatic brain injury for targeted therapies. Journal of Neurotrauma, 25 (7), Used with permission. All rights reserved) visible injuries go on to make adequate recoveries (Suskauer & Huisman, 2009 ). Therefore, classification methods should incorporate both clinical and neuroimaging data as well as subsequent assessments of behavioral and cognitive functions, which are typically obtained through neuropsychological evaluation. Current Classification Approaches As detailed by Saatman et al. ( 2008 ) and others, there are a number of different approaches to classify TBI. For example, pathoanatomic classification focuses on common neuropathological features of the injury, including both lesion location and the underlying causative processes. This approach has identified four general types of neuropathology associated with TBI that include (1) hematomas, (2) subarachnoid hemorrhage, (3) contusions, and (4) diffuse axonal injury. To the extent

4 98 D.N. Allen et al. that neuropsychological tests are differentially sensitive to these various types of neuropathology, they may prove useful as part of a multidimensional classification system. The physical mechanism causing TBI has also been used to classify injury severity. This approach utilizes magnitude and direction of forces acting on the brain to predict pattern of injury ( Gennarelli & Thibault, 1985 ) and classifies injuries based on whether they were caused by the head striking or being struck by an object (impact loadings) or from the brain moving within the intracranial space (inertial loadings). It has been observed that inertial loading is often associated with diffuse injuries (e.g., DAI), while impact loading is more often associated with focal injuries (brain contusion), providing some support for the validity of this approach. Others have classified brain injury by distinguishing between primary and secondary injuries resulting from specific pathophysiological mechanisms, where primary injuries result directly from the trauma (e.g., contusions), while secondary injuries develop following the initial injury as a result of other mechanisms (e.g., edema causing herniation). Of import, secondary injuries are particularly viable targets for treatment, since some may be avoided with proper and timely intervention. Finally, classifications have been developed based on clinical signs present at the time of injury or soon thereafter in order to predict injury severity. These clinical signs often include length of unconsciousness and post-traumatic amnesia (PTA), neurological signs, and confusion and disorientation following injury (Ruff, Iverson, Barth, Bush, & Broshek, 2009 ). Different professional organizations have used combinations of these signs to develop criteria for classifying severity of brain injury (e.g., American Congress of Rehabilitation Medicine, 1993 ; Carroll, Cassidy, Holm, Kraus, & Coronado, 2004 ), although there is some variability in the criteria used across disciplines and sites. One of the most commonly used clinical indicators is the Glasgow Coma Scale score (GCS; Teasdale & Jennett, 1974 ), which reflects the depth of coma. The GCS is a 15-point scale with severe injury defined as a score of 8 or less, moderate injury as a score of 9 12, and a mild injury as reflected by scores of 13 or greater. GCS scores have demonstrated usefulness in predicting a number of important outcomes including the probability of cognitive recovery and the development of cerebral atrophy, among others (Cifu et al., 1997 ; Dikmen & Machamer, 1995 ; Ghosh et al., 2009 ), although the GCS is not without limitations (Saatman et al., 2008 ). Another clinical indicator is the length of PTA, or the time period following injury during which continuous memory or the ability to store current events is impaired (Russell & Smith, 1961 ; Wrightson & Gronwall, 1981 ). Russell first proposed the use of PTA in 1932, and variations of this method continue to be used today to predict clinical outcomes (e.g., Brown et al., 2010 ). Length of coma or unconsciousness has been used in a similar manner, although criteria that identify when a person has officially regained consciousness do vary. Some recommend a combination of these and other indicators to improve prediction of outcomes (e.g., American Congress of Rehabilitation Medicine, 1993 ; Carroll et al., 2004 ; Saatman et al., 2008 ; Sherer, Struchen, Yablon, Wang, & Nick, 2008 ).

5 5 Classification of Traumatic Brain Injury Severity: A Neuropsychological Approach 99 While these methods have proven useful, they do have limitations, including that some individuals who are initially classified as having severe injuries demonstrate minimal long-term impairments. For example, when severity classification is made using the GCS or other similar procedures, some children initially classified as having severe TBI do not demonstrate significant neurocognitive or behavioral deficits when examined after a period of recovery, and other factors including age at injury and premorbid functioning may account for more variance in neurocognitive outcomes (Fay et al., 2009 ; Lieh-Lai et al., 1992 ; Wells, Minnes, & Phillips, 2009 ). Indeed, the GCS has been described as only a gross predictor of TBI severity and functional outcome (Ghosh et al., 2009 ; Hackbarth et al., 2002 ; Hiekkanen, Kurki, Brandstack, Kairisto, & Tenovuo, 2009 ). Saatman et al. ( 2008 ) also point out that the GCS relies primarily on acute behavioral responses post-injury including best eye, verbal, and motor response, but provides little information about the pathophysiologic mechanisms underlying injury, which may provide additional insights on the nature and severity of the injury. They propose that an alternative, multidimensional classification system that expands upon current qualitative observations of behavior may be useful for future TBI clinical trials. In this regard, neuropsychological test results, particularly when used in combination with other indicators, may afford a powerful method to help classify injury severity. Neuropsychological Approaches While acknowledged that neuropsychological testing cannot be used to classify brain injury severity in the acute stages of moderate to severe injury for obvious reasons, brief computerized neuropsychological batteries that are administered shortly after mild TBI in athletes may show some promise in predicting protracted recovery (e.g., Lau, Collins, & Lovell, 2012 ) and in this way may provide one means for classifying severity of more mild injuries. Additionally, neuropsychological approaches to classification of severity of injury have been attempted and progressed along a number of lines. Some advocate for the usefulness of impairment indexes that represent an average of impaired scores across a battery of tests sensitive to brain injury (Reitan & Wolfson, 2009 ; Russell, Neuringer, & Goldstein, 1970 ). Such approaches have been shown to be predictive of the presence or absence of brain injury, as well as the severity of injury. Others have utilized scores from an individual measure or a limited number of measures, to screen for brain impairment following injury (e.g., Reitan & Wolfson, 1995, 2004 ). In this approach, neuropsychological tests are administered some time after injury, and results are used to establish the severity of impairment in specific abilities that are ostensibly the result of brain injury. For TBI, the feasibility of this approach has support from studies that demonstrate neuropsychological testing with brief test batteries can be conducted within weeks following moderate to severe TBI (Boake et al., 2001 ; Sherer et al., 2002 ), even before PTA has fully resolved in some patients (Kalmar et al., 2008 ; Wilson et al., 1999 ). Kalmar and colleagues (2008) found that 32 % ( n = 112)

6 100 D.N. Allen et al. of their patients with moderate to severe TBI who were still experiencing PTA were able to complete a brief battery of neuropsychological tests designed to take min to administer. Performance on this battery was predictive of important outcomes such as functional independence and disability (Hanks et al., 2008 ). Neuropsychological approaches may also provide additional information on the pathophysiological changes that occur post-injury through the repeated measurement of cognition and behavior over time (Goethe & Levin, 1986 ). These approaches are therefore useful for broadly classifying injury severity, but do not typically capture the heterogeneity of neurocognitive deficits that results from TBI. In contrast, cluster analysis may provide a unique approach to reflect both severity and heterogeneity of injury. While we do not provide an exhaustive review of the TBI cluster analysis literature here, some representative studies are helpful to illustrate this point. To date, cluster analytic studies have provided a number of unique insights into TBI, and one organizing theme to these studies is that they address the issue of neuropsychological heterogeneity. Illustrative of this, a recent study by Allen et al. ( 2010 ) investigated attention and memory heterogeneity in 150 children and adolescents with TBI using the Test of Memory and Learning (TOMAL; Reynolds & Bigler, 1994 ). The children with TBI were on average 11.7 years old (SD = 3.7), 52.1 % male, and 56.3 % Caucasian and were assessed 6.9 months (SD = 3.1) following injury. Clusters derived from this sample were compared to clusters derived from 150 age- and sex-matched normal controls to determine whether differing patterns of learning, memory, and attention/concentration would be evident among the groups. Also, the TBI clusters were compared on a number of important clinical, cognitive, and behavioral variables, to determine whether cluster membership might be associated with unique patterns of cognitive and behavioral disturbances. Results of the cluster analyses for the TBI and control groups are presented in Fig. 5.2a, b. As can be seen from the figure, cluster analyses indicated that a four-cluster solution was optimal for the control group (Fig. 5.2a ), while a five-cluster solution was optimal for the TBI group (Fig. 5.2b ). Not only were there differences in the number of clusters, the profiles of performance differed between the groups with the control group clusters being primarily differentiated by level of performance, while the TBI clusters were characterized by both level and pattern of performance differences. Differences were also present among the TBI clusters for neurocognitive, achievement, and behavioral variables not included in the cluster analysis, which provided additional support for the validity of the cluster solution and its potential value in predicting outcomes. Clusters characterized by impairment in verbal, nonverbal, or global memory impairment generally had poorer neurocognitive and academic achievement outcomes than clusters characterized by average memory performance or attention deficits. In addition, the cluster characterized by global memory impairment had increased parent- and teacher-reported behavioral problems. Thus, the findings indicate that unique patterns of neurocognitive impairment are observed in children with TBI that distinguish them from non-brain-injured children, these patterns of impairment are not accounted for by expected variation in test performance observed in normal populations, and cluster membership is associated with

7 5 Classification of Traumatic Brain Injury Severity: A Neuropsychological Approach 101 Fig. 5.2 Normal control and traumatic brain injury clusters on the Test of Memory and Learning. Panel ( a ): normal controls. Panel ( b ): traumatic brain injury (From: Allen, D. N., Leany, B. D., Thaler, N. S., Cross, C., Sutton, G. P., & Mayfield, J. (2010). Memory and attention profiles in pediatric traumatic brain injury. Archives of Clinical Neuropsychology, 25 (7), Used with permission. All rights reserved) patterns of outcome on some important clinical variables. These neurocognitive profiles identified using cluster analysis may prove useful for identifying homogeneous subgroups of children with TBI that are differentiated by a number of important clinical, cognitive, and behavioral variables associated with treatment and outcomes. The validity and usefulness of cluster solutions such as those identified by Allen et al. ( 2010 ) is dependent not only on demonstrating between-cluster differences on external variables but also on the degree to which they are generalizable from one sample to another. Comparison of two studies of the Wechsler Intelligence Scale for Children, Third Edition (WISC-III; Wechsler, 1991 ) conducted at different sites by different investigators on separate study samples provides some support for the generalizability of neuropsychological clusters (Donders & Warschausky, 1997 ; Thaler et al., 2010 ). Figure 5.3a, b provide WISC-III profiles obtained in these two studies. Figure 5.3a presents results reported by Donders and Warschausky ( 1997 ) who examined WISC-III performance of 153 children who sustained mild, moderate, or severe closed head injuries. The sample was on average 11.8 years old, 52 % male, and 87 % Caucasian and had a Full Scale IQ of When the WISC-III scores

8 102 D.N. Allen et al. Fig. 5.3 WISC-III cluster analysis results from Donders & Warschausky ( 1997 ) and Thaler et al. ( 2010 ). Panel ( a ): Donders et al. Panel ( b ): Thaler et al. (Panel ( a ) from: Donders, J., & Warschausky, S. (1997). WISC-III factor index score patterns after traumatic head injury in children. Child Neuropsychology, 3 (1), Used with permission. All rights reserved. Panel ( b ) from: Thaler, N. S., Bello, D. T., Randall, C., Goldstein, G., Mayfield, J., & Allen, D. N. (2010). IQ profiles are associated with differences in behavioral and emotional functioning following pediatric traumatic brain Injury. Archives of Clinical Neuropsychology, 25 (8), ) were subjected to cluster analysis, four clusters were identified (see Fig. 5.3a ). Three of the clusters were characterized by either above average, average, or low average index scores and in this way were differentiated primarily by level of performance differences. The fourth cluster exhibited low average scores on verbal and attention indexes and impaired scores on the nonverbal and processing speed indexes. No differences were present between the clusters on demographic variables, although significant differences were present on the GCS and neuroimaging data, with the fourth cluster demonstrating more severe brain injury than the other clusters. Thaler et al. ( 2010 ) also examined WISC-III clusters in 123 children with TBI who were on average 11.6 years old, 58 % male, and had Full Scale IQ scores of The majority of these children sustained closed head injuries in the moderate to severe range (Mean GCS = 7.1; median = 7). Cluster analysis of the WISC-III scores also identified four clusters that were similar in many respects to those identified by Donders and Warschausky ( 1997 ), as can be seen in Fig. 5.3b. Comparisons between the clusters on behavioral ratings generally indicated that the most severely impaired cluster typically exhibited the most severe behavioral disturbances. The samples for these two studies were comparable in many respects.

9 5 Classification of Traumatic Brain Injury Severity: A Neuropsychological Approach 103 Both studies identified average and low average clusters, as well as a more severely impaired cluster with selective impairment on perceptual organization and processing speed. The Donders and Warschausky ( 1997 ) sample contained more children with mild TBI, which may account for the overall higher performance and the high average cluster identified in that study, which was not identified by Thaler et al. ( 2010 ). These studies illustrate the potential usefulness of neuropsychological tests in addressing the question of neuropsychological heterogeneity in TBI and demonstrate that in addition to differences in pattern of performance, there are also level of performance differences between neuropsychological clusters. These level of performance differences range from mild to severe impairment and may correspond to injuries also ranging from mild to severe. For practical purposes as it pertains to classifying severity of brain injury, a brief measure that is quickly and easily administered and also sensitive to brain damage has certain advantages over more extensive assessments like the TOMAL and WISC. The TMT is one such measure and the focus of the investigation described later in this chapter. Trail Making Test In the investigation, we examined TMT performance as an indicator of brain injury severity approximately one year following injury in children who sustained a TBI. The TMT consists of Part A (TMT-A) and Part B (TMT-B). For adults, TMT-A consists of a series of 25 numbered circles which the test subject is instructed to connect in sequence by drawing a line from one circle to the next (i.e., start at 1, draw a line to 2, then 3, and so on). TMT-B is similar to TMT-A, except that the 25 circles contain both letters and numbers. For TMT-B, the test subject is instructed to connect the circles by alternating between the numerical and alphabetical sequences (i.e., start at 1, and then draw a line to A, then 2, then B, and so on). Performance is timed on both sections and the score is the amount of time (in seconds) taken to complete each part. Errors are also recorded, although they are not typically used when interpreting test performance. Although well over 60 years old, the TMT continues to be one of the most frequently administered neuropsychological tests in research and clinical practice (Rabin, Barr, & Burton, 2005 ). The TMT was originally developed in 1938 as a test of intelligence called the Test of Distributed Attention, and was then renamed the Partington s Pathways Test (Partington, 1949 ; Partington & Leiter, 1949 ; Watson, 1949 ). Later, it was included in the Army Individual Test of General Ability (U. S. War Department, 1944 ) where it was called the TMT. The TMT was subsequently incorporated into the Halstead Reitan Neuropsychological Battery (Reitan & Wolfson, 1992 ) but is also commonly used outside of this battery. In fact, a recent survey of the members from the National Academy of Neuropsychology, APA Division 40, and the International Neuropsychological Society found that the TMT ranked third among the most frequently used instruments for clinical neuropsychological evaluation (Rabin et al., 2005 ). Consistent with its original design as

10 104 D.N. Allen et al. a measure of intelligence, early studies indicated that the TMT was indeed significantly correlated with tests of intelligence. For example, Partington and Leiter ( 1949 ) found a correlation of 0.68 between the Standford Binet 1937 Edition and the Partington s Pathways Test in a sample of 256 World War II veterans. However, numerous studies thereafter established its sensitivity to brain dysfunction resulting from a wide variety of psychiatric and neurologic conditions, including TBI in children and adults (Armitage, 1946 ; Barth et al., 1983 ; Levin, Benton, & Grossman, 1982 ; Periáñez et al., 2007 ; Reitan, 1955, 1958, 1971 ; Reitan & Wolfson, 1992 ). As recently reviewed by Allen, Thaler, Ringdahl, Barney, and Mayfield ( 2012 ), the TMT in adults achieves overall correct classification rates of approximately 84 % when normal controls are compared to mixed neurological samples (Reitan, 1955, 1958 ). For older children, classification accuracies of 0.82 and 0.80 were found for TMT-A and TMT-B, respectively, when normal controls were compared to children with mixed neurological disorders (Reitan & Herring, 1985 ). Similarly, a TMT-B cut score of 37/38 achieved a correct classification rate of 78.0 % when normal children were compared to those with brain damage (Reitan & Wolfson, 2004 ). Comparable classification rates were also obtained when children classified as either slow or normal learners were examined (Mittelmeier, Rossi, & Berman, 1989 ). Some alternative versions of the original TMT, such as the Comprehensive TMT (Reynolds, 2002 ), also show comparable classification rates (Allen et al., 2012 ; Armstrong, Allen, Donohue, & Mayfield, 2008 ). As a result, rather than being considered a test of intelligence as originally envisioned by Partington, a TMT has gained popularity and widespread use because of its sensitivity to brain injury and the recognition that successful performance requires a number of abilities, including psychomotor speed, complex attention, visual scanning, and mental flexibility. Differential associations between Parts A and B of the TMT with other neuropsychological tests provide evidence that the two parts are assessing somewhat different constructs. These correlational studies suggest TMT-A is more reliant on perceptual abilities, visuoperceptual processing speed, and motor speed, while TMT-B is more reliant on working memory, inhibition, and executive functions (Langenecker, Zubieta, Young, Akil, & Nielson, 2007 ; Ríos, Periáñez, & Muñoz-Céspedes, 2004 ; Sánchez-Cubillo et al., 2009 ; Thaler et al., 2012 ). The popularity of the TMT is based in part on its brief administration time and ease of administration, as well as its well-documented sensitivity to brain dysfunction. Accordingly, despite its simplicity, the TMT may also be useful for establishing the severity of brain injury. This is particularly true when one considers that motor and sensory deficits are common following TBI, as is slowed information processing, although deficits in attention, concentration, and memory are also common (Babikian & Asarnow, 2009 ; Felmingham, Baguley, & Green, 2004 ). Also, because neurocognitive deficits can present great challenges for rehabilitation and educational placement (Kraemer & Blancher, 1997 ; Lowther & Mayfield, 2004 ) and the TMT requires abilities that are often impaired by TBI, TMT performance is expected to have some predictive power in this regard. We present the results of a study that examined classification of brain injury severity via TMT performance in a sample of children with TBI. Given its

11 5 Classification of Traumatic Brain Injury Severity: A Neuropsychological Approach 105 sensitivity to brain damage, the TMT was used as an indicator of brain injury severity, and cluster analysis was used as an empirical statistical approach to derive injury severity subgroups. Following identification of an optimal cluster solution, comparisons were made among the clusters on important outcome domains including intellectual, academic, and memory function to determine whether brain injury severity classification derived from cluster analyzing the TMT scores would exhibit expected associations with these outcomes. It was hypothesized that those classified as exhibiting greater severity of impairment would also demonstrate greater deficits in intellect, poorer academic achievement, and greater impairment of memory abilities. Comparisons were also made between the TMT clusters and classifications made at the time of injury using the GCS. Given studies indicating inconsistent correspondence between GCS scores at time of injury and later neurocognitive and functional outcomes (Fay et al., 2009 ; Salorio et al., 2005 ), we did not anticipate that there would be a high degree of consistency between the TMT and GCS classifications and that the TMT classifications would be more strongly associated with intellectual, academic, and memory functioning compared to the GCS classifications. Method Participants Participants included 152 children and adolescents who had sustained a TBI that were on average 12.9 years of age (SD = 2.8) with Full Scale IQ scores of 93.9 (SD = 14.8). They were 59.2 % male and 86.2 % right-hand dominant, and ethnicity included 59.8 % Anglo/European, 20.6 % African American, 16.7 % Hispanic, 1.9 % Asian American, and 2.0 % others. They were assessed on average 13.8 months following injury. Of these children, 92.8 % had sustained closed head injuries, with the most common causes of head injury including motor vehicle accidents (50.7 %), pedestrian struck by a motor vehicle (21.1 %), four-wheeler accidents (8.6 %), skiing accident (5.9 %), gunshot wound (3.3 %), bicycle accident (3.3 %), falls (1.3 %), and other causes (6.0 %). The GCS (Teasdale & Jennett, 1974 ) had been completed for 97 of the children, either by first responders or after the children were transported to the hospital, and indicated that overall they had sustained moderate to severe TBI (median = 7.0; mean = 7.2, SD = 2.9). Measures TMT Parts A and B The TMT assesses psychomotor speed, visual scanning, complex attention, and mental flexibility. As previously discussed, the adult version of the TMT consists of two parts, A and B, and both parts include 25 circles that are distributed across an

12 106 D.N. Allen et al. 8.5 by 11 in. sheet of paper. For Part A, the circles are numbered 1 to 25. The test subject is given a pencil and instructed to draw a line as quickly as possible that connects the 25 numbered circles in order. The older child version of the test, which was used in the current study, is designed for children aged 9 15 years. It is identical to the adult version except that it includes only the first 15 circles for Parts A and B. Wechsler Intelligence Scales Intelligence was assessed using several versions of the Wechsler Intelligence Scales because the children were tested over a number of years and were of different ages. Most were administered the WISC-III ( n = 129), although other versions of the test were also administered. Because these versions of the Wechsler scales share many common subtests and these subtests were designed to assess similar abilities, data from the various versions were combined. Subtests that were selected for analysis are strong indicators of their representative index scores, including Vocabulary (verbal comprehension index), Block Design (perceptual organization/reasoning index), Digit Span (working memory index), and Digit Symbol/Coding (processing speed index). We also evaluated group differences for the Full Scale IQ. The Woodcock Johnson Psycho-educational Battery Tests of Achievement (WJ; Woodcock & Johnson, 1989 ; Woodcock, McGrew, & Mather, 2001 ) Academic achievement was assessed with the Woodcock Johnson Psychoeducational Battery Tests of Achievement Revised (Woodcock & Johnson, 1989 ) or Third (Woodcock et al., 2001 ) Version. The Broad Reading and Broad Math cluster scores were selected for analysis because these were completed by most participants and are generally consistent across the two versions of the test. The Test of Memory and Learning (TOMAL; Reynolds & Bigler, 1994 ) The TOMAL is a broad-based measure of memory and attention normed for children between 5 and 19 years of age. Ten core subtests and four supplemental subtests form indexes for verbal memory (VMI), nonverbal memory (NMI), delayed recall (DRI), and attention/concentration (ACI), as well as an overall composite memory (CMI). These indexes were compared across TMT clusters and GCS groups. The Glasgow Coma Scale (Teasdale & Jennett, 1974 ) The GCS is commonly used for assessing the severity of brain injury in TBI. The GCS allows for ratings of three areas including Best Eye Response (score 1 4), Best Verbal Response (score 1 5), and Best Motor Response (score 1 6). GCS

13 5 Classification of Traumatic Brain Injury Severity: A Neuropsychological Approach 107 scores range between 3 and 15, with scores of 3 8 indicating severe injury, scores of 9 12 indicating moderate injury, and scores of indicating mild injury. The GCS is popular for TBI severity classification for a number of reasons including that it is objective and simple to complete. Furthermore, inter-rater reliability is high. Changes in GCS scores from one assessment to the next suggest a corresponding change in level of consciousness ( Jennett & Teasdale, 1977 ; Teasdale & Jennett, 1974 ). Data Analysis In order to develop severity classifications based on TMT performance, Part A and Part B raw scores (time in seconds) were submitted to hierarchical cluster analysis using Ward s method with squared Euclidean distance as the distance measure. Ward s method of cluster analysis was selected because it is consistent with the cluster analytic methodology of previous studies of neuropsychological variables in children with TBI (Allen et al., 2010 ; Curtiss, Vanderploeg, Spencer, & Salazar, 2001 ; Mottram & Donders, 2006 ; Thaler et al., 2010 ), its results are consistent with those produced by other agglomerative clustering methods, and compared to these other methods, it is less affected by outliers (Morris, Blashfield, & Satz, 1981 ). The squared Euclidian distance coefficient was selected because it provides a direct measure of distance between objects in Euclidean space and is sensitive to both pattern and level of performance differences (Everitt, Landau, & Leese, 2001 ). Three-, four-, and five-cluster solutions were specified, and we followed the approach suggested by Aldenderfer and Blashfield ( 1984 ) to determine the appropriate number of clusters. DFA was used to examine the proportion of cases correctly classified into their cluster solutions as well as to determine the overlap between clusters. By graphing the clusters in discriminant function space, the overlap between each cluster was inspected (as suggested by Aldenderfer & Blashfield, 1984 ). In this case, the clusters should be fairly well separated when plotted in discriminant function space, though there will often be some overlap. Additionally, Beale s F statistic was used as a measure of parsimony to determine if more complex cluster solutions accounted for significantly more variance. Finally, cluster solutions that yielded clusters that had <5 % of the sample were discarded, as such clusters are unlikely to be generalizable or meaningful for other samples. The stability of each of the cluster solutions were examined using K-means iterative partitioning cluster analysis, with the number of clusters and initial cluster centers specified based on results from the Ward s method solutions. Degree of correspondence between the cluster solutions derived using Ward s method and K-means iterative partitioning method was then evaluated using Cohen s kappa. Based on these methods, the most appropriate number of clusters was identified for the children with TBI. After identifying the appropriate number of TMT clusters, comparisons among the clusters were conducted on variables that were not included in the cluster

14 108 D.N. Allen et al. analysis, but that represented outcomes in other important domains, including intelligence, academic achievement, and memory. This was accomplished to determine the extent to which the TMT severity clusters identified using cluster analysis were associated with other variables of interest. It was hypothesized that if the TMT clusters represented a reliable method of classifying the sample, those with more severe injuries would demonstrate greater impairment on these other indicators of outcome. These comparisons were made using analysis of variance (ANOVA) or multivariate ANOVA (MANOVA) in which cluster membership served as a between-subjects variable and test scores as the dependent variables. Finally, in order to examine the correspondence between severity classifications produced by cluster analysis of the TMT and those produced by the GCS that was completed at the time of injury, the sample was divided into three groups based on GCS scores using accepted cutoffs. For mild, moderate, and severe brain injury (Jennett & Teasdale, 1977 ; Teasdale & Jennett, 1974 ). Correspondence between the TMT and GCS classifications was accomplished using chi-squared analysis and kappa coefficients, and comparisons were also made among the GCS groups on the intellectual, academic, and memory variables with ANOVA or MANOVA to determine whether similar differences would be present for the GCS classifications as for the TMT classifications. Procedure Participants in the TBI group were selected from a consecutive series of cases that were referred for neuropsychological assessment to a pediatric specialty care hospital. Children from this series were included in this study if they had sustained a TBI, completed the TMT as part of the neuropsychological evaluation, and had evidence of structural brain damage based on appropriate neuroimaging, laboratory, and other examinational findings including neurological evaluation. All tests were administered according to standard procedures by a pediatric neuropsychologist or by doctoral-level technicians under the supervision of the neuropsychologist. Children were evaluated 3 98 months following TBI (mean = 13.8; SD = 15.3). The study was conducted in compliance with IRB regulations. Results Cluster Analysis of the TMT Overall results for the measures used in the study are presented in Table 5.1. As can be seen from the table, not all variables were available for all participants. When data were missing, comparisons were made on the reduced number of cases.

15 5 Classification of Traumatic Brain Injury Severity: A Neuropsychological Approach 109 Table 5.1 Results on primary variables for all participants Variable N Mean SD TMT-A (sec) TMT-B (sec) Full Scale IQ Vocabulary Block Design Digit Span Coding WJ Broad Reading WJ Broad Math TOMAL verbal TOMAL nonverbal TOMAL composite TOMAL delayed TOMAL attention Note : Results are reported in standard scores except where noted. TMT-A Trail Making Test Part A, TMT-B Trail Making Test Part B, WJ Woodcock Johnson Fig. 5.4 Three-, four-, and five-cluster solutions for the Trail Making Test Parts A and B The three-, four-, and five-cluster solutions for the TMT are presented in Fig Examination of the profiles based on TMT-A and TMT-B indicates that clusters in the three-cluster solution were differentiated primarily by level of performance. Discrepancies between the clusters were larger for TMT-B than for TMT-A, although each cluster maintained its position with regard to overall performance. The cluster that performed the best obtained scores that were in the average range, and it was the largest cluster ( n = 80). The second cluster was similar to the first on TMT-A, but performed almost four SDs poorer on TMT-B ( n = 42). The third cluster was the smallest of the clusters ( n = 30) and exhibited the worst performance overall, obtaining scores in the impaired range on both TMT-A and TMT-B. In the fourcluster solution, the impaired cluster (C3) was divided into two clusters with the fourth cluster (C4) exhibiting marked impairment on TMT-B and comparable or somewhat better performance on TMT-A. However, this cluster consisted of only

16 110 D.N. Allen et al. three participants. Finally, for the five-cluster solution, C4 was maintained and the impaired cluster (C3) was divided again to form a fifth cluster (C5) that demonstrated worse performance on TMT-A and TMT-B compared to C3. This cluster consisted of only six participants. Discriminant function analysis (DFA) results indicated that when the TMT scores were used to predict cluster membership, there was a negligible difference in the correct classification rates for the three-, four-, and five-cluster solutions, with correct classification rates of 96.1 %, 95.4 %, and 97.4 %, respectively. The K-means iterative partitioning method was then used to examine the stability of the three-, four-, and five-cluster solutions. Cohen s kappa indicated that the level of agreement between the Ward s and K -means results for the three-, four-, and five-cluster solutions were 0.90, 0.92, and 0.96, respectively. Kappas above 0.80 are considered to indicate excellent agreement (Landis & Koch, 1977 ), suggesting that all solutions are stable as indicated by level of agreement between the Ward s and K-means methods. Beale s F tests were nonsignificant when comparing the three-cluster solution to the four-cluster solution, F = 1.66, p = 0.19; the three-cluster to the fivecluster solution, F = 1.58, p = 0.18; or the four-cluster to the five-cluster solution, F = 1.29, p = The Beale s F test results suggest that compared to the threecluster solution, the four- and five-cluster solutions did not explain significantly more variance. Finally, the additional clusters in the four- and five-cluster solutions (C4 and C5) only accounted for 2.0 % and 3.9 % of the entire sample, respectively. Considering these results, we turned our attention to the three-cluster solution. While we considered the other clusters (particularly C5) theoretically interesting, given what appears to be somewhat unique impairment on TMT-A relative to TMT- B, we decided that grouping these more severely impaired clusters into one severe cluster (C3) would provide a better approach with regard to cluster stability, generalizability to other samples, and power to make comparisons between the clusters on external validity variables. Also, from a parsimony perspective, the three-cluster solution appeared to be the best given the negligible differences between the three-, four-, and five-cluster solutions from the DFA and K-means analyses, as well as from the Beale s F test results. A straightforward interpretation of the clusters based on the TMT Part B scores would suggest that C1 could be considered a mild severity cluster, as scores for this cluster are generally in the normal range. Mild severity is used to characterize this cluster rather than normal because of the fact that they had sustained a TBI. A moderate cluster is also present (C2), as well as a more severely impaired cluster (C3). Comparisons Between the GCS Groups and TMT Clusters One of the primary goals of the study was to compare the severity classifications of the TBI sample that resulted from the GCS scores and the TMT clusters. This goal was addressed in two ways, including a direct comparison of agreement between the two different approaches to severity classification, as well as a comparison of the GCS and TMT groups on the external validity variables.

17 5 Classification of Traumatic Brain Injury Severity: A Neuropsychological Approach 111 Table 5.2 Cross-tabulation of severity classification for the Trail Making Test (TMT) clusters and Glasgow Coma Scale (CGS) severity groups TMT clusters Mild Moderate Severe GCS group Mild 2 (2.1%) 2 (2.1%) 1 (1.0%) Moderate 12 (12.4%) 7 (7.2%) 4 (4.1%) Severe 37 (38.1%) 19 (19.6%) 13 (13.4%) Agreement Between the TMT and GCS Classifications To form GCS severity groups, the GCS scores were used to classify the 97 participants with available GCS data based on established cutoffs. When classified in this manner, 71.1 % were identified as severe, 23.7 % were identified as moderate, and 5.2 % were identified as mild. Given that GCS scores were missing for 55 of the children, we compared those with GCS scores to those without GCS scores on all of the demographic, clinical, and neuropsychological variables used in this study, in order to determine if the groups were comparable. No significant differences were present for any of the variables, with the exception of the Coding subtest of the Wechsler scales, F (1, 149) = 9.18, p < The group without GCS scores was lower on this variable than the group with GCS scores (means = 5.6 and 7.4, respectively), suggesting that the group without GCS scores may have had more severe neurocognitive impairment. If that was the case, then the group might be most accurately characterized as having sustained severe injuries. However, there were no significant differences on TMT-A and TMT-B, which is arguably the most sensitive test to brain dysfunction that we administered. Table 5.2 presents the severity classification agreement rates between the TMT clusters and the GCS. Visual inspection of the table indicated low agreement between the TMT and GCS groups, which was confirmed using Cohen s kappa, which suggested very poor agreement between the two methods (kappa = 0.02, p = 0.98). For example, cases in the mild TMT cluster (C1) were distributed across all GCS levels, while cases classified as severe by the GCS were distributed across all of the TMT clusters, with most ( n = 37) falling in the mild TMT cluster. In addition to the low correspondence between the two approaches, examination of the mean TMT and GCS scores for the classification groups also demonstrates expected absence of differences. Table 5.3 presents descriptive statistics for each classification approach as well as results of between-group analyses (ANOVA). For the TMT clusters, there were large between-group differences for TMT Part A and B, with similar results for the GCS scores of the GCS groups. These differences were expected because the TMT and GCS scores were used to develop the TMT clusters and GCS groups, respectively. However, the analyses further indicated that the TMT clusters did not significantly differ with regard to GCS scores, while the GCS clusters did not differ on TMT variables. The absence of differences provides additional evidence that the TMT and GCS approaches are yielding quite different classification results.

18 112 D.N. Allen et al. Table 5.3 Differences between Trail Making Test (TMT) clusters and Glasgow Coma Scale (CGS) groups on classification variables Mild Moderate Severe Mean SD Mean SD Mean SD F p TMT clusters GCS TMT-A <0.01 TMT-B <0.01 GCS groups GCS <0.01 TMT-A TMT-B Note : TMT-A Trail Making Test Part A, TMT-B Trail Making Test Part B TMT and GCS Classification Comparisons on Demographic and Clinical Variables Next, the TMT clusters and GCS groups were compared across demographic, clinical, intellectual, achievement, and memory variables. These variables are considered external validity variables in this analysis since they were not included in the cluster analysis, and differences between groups on these variables would provide support for the validity of the two classification approaches. Comparisons on demographic and clinical variables are presented in Table 5.4. None of the differences between the groups were significant, suggesting that TMT cluster membership and GCS group membership is not substantially influenced by these variables. TMT and GCS Classification Comparisons on IQ Variables Descriptive statistics for IQ variables are presented in Table 5.5 for the TMT clusters and GCS groups. For the TMT clusters, significant differences emerged for all the Wechsler subtests as well as the Full Scale IQ. However, no differences were present for the GCS groups on the Wechsler subtests, although there was a significant difference for Full Scale IQ. Figure 5.5 presents the Wechsler subtest profiles for the GCS groups ( Fig. 5.5a ) and the TMT clusters (Fig. 5.5b ). As seen in the figure, there was some overlap across the four subtests among GCS groups, particularly with the Digit Span subtest in which all three groups converged in level of performance. The mild group otherwise appeared to perform better than the moderate and severe groups, which exhibited little distinction from each other with the exception of the Digit Symbol subtest where clearer distinction between the three groups was apparent. Post hoc analyses indicated that for the Full Scale IQ, the severe and moderate groups performed significantly worse than the mild group, but did not differ from each other. Regarding the TMT clusters, the overlap was reduced and particularly differentiated the severe cluster from the mild and moderate