ORIGINAL RESEARCH

Prospective Clinical Assessment Using Sideline Concussion Assessment Tool-2 Testing in the Evaluation of Sport-Related Concussion in College Athletes Margot Putukian, MD,*† Ruben Echemendia, PhD,‡§ Annegret Dettwiler-Danspeckgruber, EdD,¶ Tawny Duliba,¶ Jared Bruce, PhD,‡ John L. Furtado, PT, ATC,* and Murali Murugavel, PhD¶

Objective: To evaluate the utility of the Sideline Concussion Assessment Tool (SCAT)-2 in collegiate athletes with sport-related concussion.

Design: Prospective cross-sectional study with baseline testing and serial repeat testing after concussion in contact sport athletes and non-concussed control athletes.

Setting: Division I University. Participants: Male and female club rugby and varsity athletes. Interventions: Baseline measures of concussion symptoms,

different. No differences in baseline SCAT-2 scores were seen based on self-reported history of concussion. At baseline, anxiety and depression screening scores were associated with higher symptom scores. When compared with baseline, a 3.5-point drop in SCAT-2 score had 96% sensitivity and 81% specificity in detecting concussion. When examined to exclude baseline scores, a cutoff value of 74.5 was associated with 83% sensitivity and 91% specificity in predicting concussion versus control status.

Conclusions: The SCAT-2 total composite score and each subcomponent are useful in the assessment of concussion. As SCAT-3 is similar to SCAT-2, it is expected that it too will be a useful tool.

cognitive function, and balance were obtained using the SCAT-2. Serial postinjury testing was conducted as clinically indicated.

Key Words: concussion, SCAT-2, SCAT-3, sideline assessment, college athlete, sport-related concussion

Main Outcome Measures: The SCAT-2 total and subset scores were calculated and evaluated at baseline and after injury.

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Results: The total SCAT-2 score and the composite scores of symptoms, symptom severity, and balance were significantly different in concussed groups after injury when compared with baseline. When comparing performance in concussed versus control athletes, all subcomponents of the SCAT-2 were significantly Submitted for publication July 17, 2013; accepted January 10, 2014. From the *Princeton University, Athletic Medicine, University Health Services, Princeton, New Jersey; †Robert Wood Johnson Medical School, University of Medicine and Dentistry of New Jersey, Princeton, New Jersey; ‡Department of Psychology, University of Missouri—Kansas City, Kansas City, Missouri; §University Orthopedics Comprehensive Concussion Care Clinic, State College, Pennsylvania; and ¶Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey. Supported by the American Medical Society for Sports Medicine Foundation and the New Jersey Commission on Brain Injury Research. All authors participated in the design, implementation, and/or review of the manuscript. M. Putukian reports nonfinancial support from National Football League Head Neck and Spine Committee, US Lacrosse Sports Science Safety Committee, and National Collegiate Athletics Association during the conduct of the study. R. Echemendia reports personal fees from Princeton University and personal fees and nonfinancial support from National Hockey League and Major League Soccer during the conduct of the study. J. Bruce reports personal fees from Princeton University, National Hockey League, and Archives of Clinical Neuropsychology during the conduct of the study. The remaining authors report no conflicts of interest. Corresponding Author: Margot Putukian, MD, University Health Services, Princeton University, Washington Rd, Princeton, NJ 08544 (putukian@ princeton.edu). Copyright © 2014 Wolters Kluwer Health, Inc. All rights reserved.

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INTRODUCTION Sport-related concussion (SRC) is a common injury in athletes participating in contact and collision sports and a challenging injury for clinicians to evaluate and manage.1–3 Acute changes that occur in the first few days after injury and typically resolve within the first few weeks after injury include self-reported symptoms, cognitive dysfunction, and balance problems.4–19 The diagnosis of SRC is particularly challenging because the symptoms are not specific to concussion, may be delayed in onset,20 and no definitive biomarker exists that determines if an injury has occurred or to track recovery. Over the past decades, there has been a significant amount of research dedicated to standardizing a sideline evaluation, including the development of the Sideline Concussion Assessment Tool (SCAT)21 and the SCAT-2.2 Several other sporting groups have promoted the utility of a standardized sideline assessment22–24; yet, little information exists regarding the validity of these tools. The SCAT-2 incorporates several subcomponents that have been validated, including a symptom checklist,25–28 a brief cognitive evaluation [the Standardized Assessment of Concussion (SAC)],29 the Maddocks questions,30 and balance assessment31 [modified Balance Error Scoring System (m-BESS)]. The SCAT-2 also includes a Glasgow Coma Scale and signs of concussion (loss of consciousness, balance dysfunction). Though the SCAT-2 was developed as Clin J Sport Med  Volume 25, Number 1, January 2015

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a postinjury tool, it has been modified and used as part of a baseline assessment22,23 before injury and repeated after injury. Studies using SCAT-2 demonstrate individual differences at baseline,32,33 but to our knowledge, there are no studies to assess the validity of the SCAT-2 in evaluating concussion. Consequently, the sensitivity and specificity of the SCAT-2, with and without a baseline assessment, are unclear. Whether concussion “modifiers” described recently,34 such as age, gender, migraine history, depression or other mental health disorders, Attention Deficit Hyperactivity Disorder (ADHD) or learning disabilities, and history of concussion, are associated with baseline or postinjury SCAT-2 scores is also unclear. The purpose of this prospective study was to evaluate the utility of a standardized assessment tool, the SCAT-2, in the assessment of SRC among male and female college athletes. In addition, we evaluated whether the modifiers of age, gender, history of concussion, loss of consciousness, and screening surveys for depression and anxiety were associated with SCAT-2 scores. Finally, we examined whether having a baseline assessment is useful when diagnosing SRC.

METHODS Varsity athletes from several sports and club rugby with baseline SCAT-2 assessment and those who subsequently sustained SRC were included in the study. All subjects (concussed and controls) gave written consent to participate in the study, which was approved by the Institutional Review Panel for Human Subjects Research of Princeton University. Athletes with SRC had the SCAT-2 administered as soon as possible after the injury. The Princeton Concussion Program incorporates SCAT-2, questions regarding concussion modifiers (history of concussion, migraine, learning disabilities, and mental health issues), screening surveys for anxiety and depression [Generalized Anxiety Disorder—7 Item (GAD-7) and Patient Health Questionnaire—9 (PHQ-9), respectively],35,36 and neuropsychological testing.37 All athletes diagnosed with SRC are evaluated by a team physician. A progressive return to play (RTP) progression, taking into account individual factors and modifiers, is used by the medical staff to advance athletes back into full academic and sport activity.1,2 This article will report on the baseline and postinjury validity of the SCAT-2 as it relates to the clinical diagnosis of concussion. Nonconcussed contact sport athletes served as control subjects and had repeat SCAT-2 testing administered 3 to 6 months after their baseline assessment.

Data Analytic Strategy We examined demographic and clinical factors that could be associated with baseline SCAT-2 performance. To control for the risk of familywise error, subscale differences were only examined if significant differences emerged on the SCAT-2 total score. Familywise error refers to the probability of erroneously finding a significant difference among groups, or type I error, when performing multiple hypothesis tests. We also wanted to examine how concussion influences SCAT-2 performance from both the standpoint of statistical change and clinical decision making. Mixed factor analysis of Copyright  2014 Wolters Kluwer Health, Inc. All rights reserved.

SCAT-2 Testing in SRC College Athletes

variance (ANOVA) (group · time) was used to examine how a diagnosis of concussion is related to performance on the SCAT-2 in a group of concussed athletes and matched nonconcussed athlete controls. To examine the utility of SCAT-2 in clinical decision making, we evaluated participant performance in 2 primary ways. First, the test–retest reliability of the SCAT-2 was calculated in the control group using Pearson correlations, Spearman correlations, and single measures 2-way random effects ANOVA intraclass correlation coefficient (sICC) with absolute agreement. Pearson reliabilities and the SDs of the control sample were then used to construct reliable change indices (RCI) using the Iverson adaptation of the formula of Jacobson and Truax.38,39 Clinical decision making was also examined using normative data described in the first part of the study. Finally, receiver operating characteristic (ROC) curves were created to demonstrate the most appropriate SCAT-2 cut points for the identification of concussed and control groups using both the test–retest and the postinjury assessment paradigms.

RESULTS Preliminary Analyses Two hundred eighty collegiate athletes with baseline SCAT-2 assessments were included in the study. Seven participants were excluded because of a self-reported history of learning disability and 10 because of incomplete data leaving a total sample of 263 athletes. The majority of subjects were men (66.9%). On average, participants were 20.33 years old (SD = 1.74) at the time of baseline testing. Athletes from a variety of sports were represented. The most common sports included football (15.6%), men’s rugby (12.5%), women’s rugby (11.8%), women’s volleyball (6.1%), sprint football (5.3%), and men’s crew (5%). Additional sports represented and the baseline descriptive information for participants on the SCAT-2, GAD-7, and PHQ-9 are presented in Table 1. One hundred seventy-eight participants reported no history of concussion. Forty-nine reported a history of 1 concussion and 36 a history of 2 or more concussions. Thirty participants reported a history of loss of consciousness.

Baseline Analyses We examined demographic variables that might account for differences in baseline SCAT-2 performance. Spearman correlations revealed a modest relationship between age and SCAT-2 performance. Older participants performed better on the SCAT-2 than younger participants (r = 0.15, P , 0.05). Exploratory follow-up analyses of SCAT-2 scales revealed an association between age and symptom severity (r = 20.14, P , 0.05), symptom score (r = 0.14, P , 0.05), and SAC performance (r = 0.15, P , 0.05). Age was not significantly correlated with m-BESS performance (r = 0.10, P . 0.1). Given these very small effects, age was not thought to be a clinically significant modifier of SCAT-2 performance in this collegiate sample. Older participants also reported less depression and anxiety as measured by the PHQ-9 (r = 20.17, P , 0.05) and GAD-7 (r = 20.15, www.cjsportmed.com |

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TABLE 1. Descriptive Data for Participants Assessed at Baseline Percentile Cut Points 1 5 10 15 25 50 75 90 95 Mean SD Median Min Max Percentile Percentile Percentile Percentile Percentile Percentile Percentile Percentile Percentile SCAT-2 total Symptom severity Total symptoms Balance total SAC GAD-7 PHQ-9

76.11 4.85 2.11 4.51

77 0

55 0

83 52

59 19

66 8

69 6

72 4

74 3

77 0

80 0

81 0

82 0

1.48 2.44

0

0

17

13

6

5

4

2

0

0

0

0

27 28 0 0

10 23 0 0

30 30 22 13

16 24 10 10

20 25 4 4

22 26 3 3

24 26 3 2

25 27 1 1

27 28 0 0

29 29 0 0

30 30 0 0

30 30 0 0

26.55 28.05 1.07 1.03

3.21 1.60 2.12 1.85

Items relevant to Glasgow Coma Scale and physical signs were not administered at baseline. As such, total possible score was 83. Percentile cut points are not exact but are provided as a nearest estimate using SPSS percentile identification in the frequencies menu. These percentile classifications can be used to make RTP decisions if athletes do not have baseline data. For example, a concussed athlete with a SCAT-2 total score of 77 would be at the 50% and in the average range compared with nonconcussed peers; in contrast, a person with a total score of 66 after a concussion is scoring worse than 95% of nonconcussed peers and likely is still symptomatic. Most common sports included football (15.6%), men’s rugby (12.5%), women’s rugby (11.8%), women’s volleyball (6.1%), sprint football (5.3%), and men’s crew (5%). Additional women’s sports represented (,5% each) included soccer, water polo, hockey, lacrosse, basketball, and field hockey. Additional men’s sports included wrestling, water polo, hockey, lacrosse, soccer, track, swimming, and diving.

P , 0.05), respectively. No significant gender differences emerged on the SCAT-2 [t (261) = 1.62, P . 0.1], GAD-7 [t (260) = 0.25, P . 0.1], or PHQ-9 [t (260) = 0.53, P . 0.1]. Thirteen athletes reported a history of migraine at baseline. No significant differences emerged among athletes with and without a history of migraine on the SCAT-2 [t (261) = 1.45, P . 0.1], GAD-7 [t (260) = 0.12, P . 0.1], or PHQ-9 [t (260) = 1.17, P . 0.1]. Next we examined whether previous self-report of concussion was associated with baseline performance on the SCAT-2. No significant differences emerged between athletes with and without a self-reported history of concussion on the SCAT-2 [t (261) = 1.13, P . 0.1], GAD-7 [t (260) = 0.88, P . 0.1], or PHQ-9 [t (260) = 1.25, P . 0.1]. Similarly, no significant differences emerged among athletes with and without a history of loss of consciousness on the SCAT-2 [t (261) = 1.49, P . 0.1], GAD-7 [t (260) = 0.55, P . 0.1], or PHQ-9 [t (260) = 0.01, P . 0.1]. Depression and anxiety were both significantly associated with SCAT-2 performance. Increased depression as measured by the PHQ-9 was associated with worse performance on the SCAT-2 (r = 20.27, P , 0.001). Follow-up analyses revealed that athletes endorsing depressive symptoms reported worse symptom severity (r = 0.48, P , 0.001) and more symptoms (r = 0.48, P , 0.001). Increased anxiety as measured by the GAD-9 was also associated with worse performance on the SCAT-2 (r = 20.23, P , 0.001). Follow-up analyses revealed that patients endorsing symptoms of anxiety and worry reported worse symptom severity (r = 0.44, P , 0.001) and more symptoms (r = 20.48, P , 0.001). Depression and anxiety scores were not significantly associated with SAC or balance subtests.

athletes were 20.79 6 1.02 years old. Mean time from baseline testing to postinjury testing was 283.32 6 259.56 days. Mean time from concussion to postinjury testing was 0.52 6 1.18 days. Eleven newly concussed athletes reported a history of at least 1 concussion. The concussed athletes played football (n = 7), men’s rugby (n = 5), men’s ice hockey (n = 4), men’s water polo (n = 3), women’s rugby (n = 3), men’s sprint football (n = 2), men’s basketball (n = 2), men’s lacrosse (n = 1), men’s soccer (n = 2), women’s ice hockey (n = 1), field hockey (n = 1), and wrestling (n = 1). Twentythree athlete controls (17 men) were recruited for the study. The mean age of the controls was 20.81 6 0.78 years, and the average time from baseline to retest was 243.87 6 126.47 days. Ten of the controls reported a history of 1 or more concussions. The controls played men’s basketball (n = 6), men’s ice hockey (n = 4), field hockey (n = 1), men’s lacrosse (n = 2), men’s soccer (n = 3), women’s ice hockey (n = 5), and wrestling (n = 2). No significant demographic or clinical differences were uncovered between the concussed and the control group.

Comparison of Concussed and Control Athletes

Over the course of the study, 32 athletes (27 men) with baseline test data sustained a concussion. On an average,

Mixed factor (time · group) ANOVAs revealed significant interaction effects for the SCAT-2 total score [F (1,53) = 35.54, P , 0.001], symptom severity score [F (1,53) = 25.81, P , 0.001], total symptoms [F (1,53) = 23.19, P , 0.001], balance [F (1,53) = 7.57, P , 0.01], and SAC [F (1,53) = 5.99, P , 0.05]. As seen in Table 2, concussed athletes showed increased postconcussive symptoms from baseline to postinjury testing for the SCAT-2 total score [t (31) = 5.61, P , 0.001], symptom severity score [t (31) = 5.80, P , 0.001], total symptoms [t (31) = 5.84, P , 0.001], and m-BESS [t (31) = 2.28, P , 0.05]. No significant change was noted for concussed athletes on the SAC [t (31) = 1.05, P . 0.1]. In contrast, controls demonstrated practice effects on the SCAT-2 total score [t (22) = 2.70, P , 0.05] and the SAC [t (22) = 3.41, P , 0.01]. Table 2 shows descriptive information for participants on the SCAT-2.

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Post-Concussion Analyses Demographics

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TABLE 2. Baseline and Retest Performance for Concussed and Control Groups Concussed

Control

Time 1 SCAT-2 total Symptom severity Total symptoms Balance total SAC GAD-7 PHQ-9

Time 2

Time 1

Time 2

Mean

SD

Mean

SD

Mean

SD

Mean

SD

F

P

74.16 2.53 2.56 26.13 27.63 1.53 1.16

7.57 4.83 4.83 3.99 2.14 2.01 1.43

65.28 19.41 9.00 24.544 27.13 2.63 2.21

8.39 16.97 5.11 5.44 2.58 3.42 2.53

73.83 2.87 1.70 25.17 27.35 0.67 1.22

6.08 6.03 3.66 4.48 2.19 0.97 2.18

76.74 1.39 1.13 26.39 28.48 1.28 1.06

5.15 2.21 1.87 4.53 1.16 2.08 1.16

34.54 25.82 23.19 7.57 5.99 0.38 2.77

,0.001 ,0.001 ,0.001 ,0.01 ,0.05 .0.1 .0.1

Items relevant to Glasgow Coma Scale and physical signs were not administered at baseline. As such, total possible score was 83. F and P values represent group by time interaction effects. A subsample of 19 concussed and 18 control participants received the GAD-7 and PHQ-9. Given the skewed distribution of the data, we also examined this interaction by comparing the 2 groups’ difference scores with Mann–Whitney tests. This nonparametric approach produced similar results. Balance total, total score from the BESS.

Clinical Decision Making Using Reliable Change Indices, Normative Data, and Postinjury Receiver Operating Characteristic Curves

Association With Outcomes

We examined the test–retest reliability of the SCAT-2 and its subscales among athlete controls using Pearson correlations, Spearman correlations, and sICC with absolute agreement. Table 3 shows test–retest correlations for these measures and RCI calculated using the Iverson adaptation of the formula of Jacobson and Truax.38,39 Next we examined the percentage of concussed athletes and controls who would be identified as having experienced decline when using RCI and a postinjury normative approach (Table 4). The Figure shows ROC curves for both postinjury data alone and change from baseline to postinjury for the SCAT-2 total score. Receiver operating characteristic curves are commonly used in research to identify optimal cut points for medical instruments. These curves demonstrate how sensitivity (the likelihood of correctly identifying concussed athletes as having experienced concussion) and specificity (the likelihood of correctly identifying controls as not having experienced concussion) vary with different SCAT-2 cut points. In clinical concussion management practice, it is common to attempt to maximize sensitivity, even if this causes specificity to decline. This ensures that concussed athletes are managed conservatively and do not RTP before complete recovery.

Athlete outcomes were tracked clinically for the number of days until symptoms cleared, resumed restricted activities, and cleared to RTP. One athlete was lost to followup and 1 scored more than 5 SDs above the mean for all 3 outcome measures, leaving 30 athletes for outcome analyses. Mean and median numbers of days until symptom free were 12.90 6 28.45 and 7 days, respectively. Mean and median numbers of days until being advised to resume restricted activities were 17.47 6 34.06 and 10 days, respectively. Mean and median numbers of days until being advised to return to competition were 26.77 6 49.85 and 15 days, respectively. Outcome data were not significantly associated with concussion history or gender. As can be seen in Table 5, more symptoms at the initial postinjury evaluation were significantly associated with worse participant outcomes.

DISCUSSION The SCAT-2 was developed as a sideline assessment tool that incorporates several assessment modalities in an attempt to provide clinicians a standardized assessment of concussive injury. This study evaluated a large population of collegiate varsity and club rugby athletes using the SCAT-2 at

TABLE 3. Test–Retest Reliabilities and RCI for Athlete Controls RCI Confidence Intervals Test SCAT-2 total Symptom severity Total symptoms Balance total SAC

SEM1

SEM2

Sdiff

Single ICC

r

rs

0.70

0.80

0.90

3.35 6.02 3.36 2.30 1.71

2.24 1.98 1.46 2.16 0.88

4.03 6.33 3.66 3.16 1.92

0.38* 0.02 0.11 0.49† 0.29

0.51* 0.04 0.14 0.52* 0.41

0.46* 0.07 0.11 0.55† 0.50*

4.18 6.56 3.79 3.27 1.99

5.16 8.11 4.68 4.04 2.46

6.61 10.39 6.00 5.18 3.15

Reliable change confidence intervals were calculated using the Iverson adaptation of the formula of Jacobson and Truax. Items relevant to Glasgow Coma Scale and physical signs were not administered at baseline. As such, total possible score was 83. *P , 0.05. †P , 0.01. Balance total, total score from the BESS; r, Pearson product moment correlation coefficient; rs, Spearman Rho; Sdiff, Standard Error of the Difference; SEM, Standard Error of Measure; Single ICC, single intraclass correlation coefficient.

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TABLE 4. Percentage of Athletes in the Concussed (n = 32) and Control Groups (n = 23) Who Would be Categorized as Having Declined Using 0.70, 0.80, and 0.90 RCIs and Normative Performance Below the 15th, 10th, 5th, and 1st Percentiles 0.70 RCI SCAT-2 total Symptom severity Total symptoms Balance total SAC

0.80 RCI

0.90 RCI

,15th Percentile

,10th Percentile

,5th Percentile

,1st Percentile

Cx

Cntl

Cx

Cntl

Cx

Cntl

Cx

Cntl

Cx

Cntl

Cx

Cntl

Cx

Cntl

81 78 63 28 34

4 4 9 17 0

75 72 63 22 34

4 0 4 13 0

65 66 53 22 19

0 0 0 1 0

78 91 88 47 41

4 9 9 22 9

66 88 81 34 41

0 9 9 9 9

53 84 75 25 28

0 0 0 0 0

22 34 22 9 13

0 0 0 0 0

Percentages are rounded to nearest whole number. Items relevant to Glasgow Coma Scale and physical signs were not administered at baseline. As such, total possible score was 83. Balance total, total score from the BESS; Cntl, control; Cx, concussed.

baseline and immediately after injury. At baseline, no differences were detected in SCAT-2, GAD-7, or PHQ-9 score between athletes with or without a history of concussion or loss of consciousness. In addition, there were no gender differences noted at baseline in measures of total SCAT-2, GAD-7, or PHQ-9 score. There was an association between elevated GAD-7 and PHQ-9 scores and SCAT-2 total score, and when these were evaluated more closely, both associations were because of an increased symptom report and not because of decreases in SAC or m-BESS score. Additional research is underway to determine differences in clinical recovery, baseline and postinjury reporting of symptoms between genders, and specific sport subgroups and association with postinjury anxiety and depression scores. This study demonstrates the utility of SCAT-2 in a college population in assessing SRC when used acutely. In this study, the mean time from concussion to SCAT-2 testing was 0.52 6 1.18 days. The total SCAT-2 score, total symptom score, symptom severity score, and the modified BESS were all statistically significant in demonstrating a difference between the baseline and postinjury assessments in the concussed group. In addition, though the SAC was not different in the concussed group postinjury compared to their baseline measures, when compared to the controls, who showed an improvement in their repeat test from their baseline, the difference was significant. This finding underscores that the lack of improvement observed in the

SAC among concussed group athletes is suggestive of concussive injury. This “lack of a practice effect” has been discussed previously as consistent with concussive injury with tests where a practice effect is typically seen.19 The m-BESS was also associated with a slight improvement in scores in the control group, consistent with a practice effect, which has also been demonstrated previously.40 Though the BESS has been shown to be sensitive to SRC,9,15–17,31,41 the m-BESS has not been evaluated,42 and this study provides initial support for the utility of m-BESS in the evaluation of SRC among college athletes. The clinical outcomes of this study, time to become symptom free, resume restricted activities, and return to full play, were slightly longer than what has been reported previously,15 most likely reflecting a more conservative approach to concussive injury that has been endorsed at all levels of play.1,2,22,24 Clinical outcome data (RTP) in this study were not associated with self-reported concussion history or gender. These have both been raised as possible risk factors for delayed recovery,34 and the effect of gender on recovery has been especially controversial. As was demonstrated in Table 5, a larger number of total symptoms at the initial postinjury evaluation was significantly associated with worse participant outcomes. This is in agreement with other studies demonstrating that the initial burden of symptoms is associated with more severe outcome.34,43

FIGURE. Receiver operating characteristic curves predicting group membership using baseline to retest changed scores (left) and postinjury scores alone (right). For the curve on the left, 96% sensitivity and 81% specificity were obtained using a baseline to postinjury decline cut point of 3.5 on the SCAT-2 (area under the curve = 0.93). For the curve on the right, 83% sensitivity and 91% specificity were obtained using a postinjury score cut point of 74.5 on the SCAT-2 (area under the curve = 0.91). Ninety-one percent sensitivity and 78% specificity were obtained using a cut point of 72.5 on the postinjury SCAT-2.

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TABLE 5. Spearman Correlations Between Postinjury SCAT-2 Scores and Outcome Variables

SCAT-2 total Symptom severity Total symptoms Balance total SAC

Days Until Symptom Free

Days Until Return to Restricted Activity

Days Until RTP

20.36* 0.48†

20.42* 0.40*

20.35 0.58†

20.47†

20.49†

20.56*

0.07 20.35

0.00 20.36

0.18 20.24

Items relevant to Glasgow Coma Scale and physical signs were not administered at baseline. As such, total possible score was 83. One additional athlete was excluded from analyses examining restricted activity because the season ended before he was advised to resume restricted activity. Similarly, 3 athletes were kept out for the rest of the season before being advised to RTP and 2 either left the sport or left school, leaving 25 participants remaining for RTP analyses. *P , 0.05. †P , 0.01. Balance total, total score from the BESS.

Understanding the sensitivity and specificity of the SCAT-2 with and without a baseline is particularly useful information for clinicians. Our study demonstrated 96% sensitivity and 81% specificity of the SCAT-2 when a baseline is present with an average 3.5-point decrease in the SCAT-2 score between baseline and postinjury. In the absence of a baseline, the specificity increases to 91% when using a cutoff value of 74.5 for the SCAT-2, whereas the sensitivity decreases to 83%. Naturally, changing the cutoff value can alter these sensitivity and specificity measures. These values are clinically acceptable, especially when compared with the sensitivity and specificity of computerized neuropsychological testing which have been shown to be quite limited in isolation.41 These data may be used to support arguments for using a baseline measure against which to compare an individual’s performance. For example, the baseline performance on the SAC and the lack of a practice effect on the SAC score after injury in our population is different than what has been reported previously.29 Without knowing this baseline information, it would be challenging to evaluate SAC scores. Once baseline information has been obtained, the use of a specific population-based normative data then allows for evaluation without baseline data. However, these data also clearly support the utility of SCAT-2, even if no baseline is present, similar to recent data on the utility of baseline neuropsychological testing.44 Given the expense and the difficulties involved in conducting baseline testing with all players, the utility versus cost of baseline testing should be carefully evaluated at the local level. An important take-home message from this study is that for the clinician evaluating SRC in the college-aged athlete, using a standardized multimodal tool such as the SCAT-2 that incorporates symptoms, a cognitive and balance assessment is preferable to using only one of these in isolation. In other words, our data support the use of combining these assessments. In addition, the results of our study also point out that for all the clinician-diagnosed concussions that occurred, only a very small decrease in SCAT-2 score occurred, and therefore, the SCAT-2 should not be used to make a diagnosis but as an adjunct to clinical decision making. Copyright  2014 Wolters Kluwer Health, Inc. All rights reserved.

SCAT-2 Testing in SRC College Athletes

This study was completed just before the Fourth International Consensus Conference on Concussion in Sport and the development of the SCAT-3. Although this study technically used the SCAT-2, all components of the SCAT-3 were incorporated into the current study, making this study equally relevant to the utility and validity of both SCAT-3 and SCAT-2.

LIMITATIONS One limitation of this study is that only one time point after injury was used to complete SCAT-2. Generalized Anxiety Disorder—7 Item and PHQ-9 scores were obtained at baseline during the preseason sports physical examination and might be expected to be high at this time. Another limitation is that control subjects were contact sport athletes reevaluated at a time frame considered “typical” and not matched specifically to concussed subjects. It would be useful to use both contact and noncontact sport athletes matched at the time of injury and at identical times during the academic year to better assess for possible differences between groups.

CONCLUSIONS The SCAT-2 (and thus SCAT-3) seems to be a useful tool in the acute assessment of SRC. Both a total SCAT-2 score and each subcomponent demonstrated significant differences between baseline and postinjury, which is critical in assessing SRC. Both self-reported history of concussion and loss of consciousness were not associated with a lower SCAT-2 score. Gender and history of concussion were not associated with clinical measures of resolution of symptoms or return to limited or full play. Finally, though the preinjury measures of depression and anxiety correlated with an increased report of baseline symptoms, they were not associated with SCAT-2 scores or poor clinical outcome after concussion. The use of these measures at baseline with repeat measures after injury has a 96% sensitivity and 83% specificity in identifying athletes with concussion when a drop in 3.5 point is seen in the total SCAT-2 score, and without a baseline, the SCAT-2 has a sensitivity of 83% and a specificity of 91% when a cutoff of 74.5 was used for the SCAT-2 score. This supports the concept of using a baseline and postinjury assessment model, yet also demonstrates utility in situations where a baseline SCAT-2 is not available but group normative data exist for a college population. ACKNOWLEDGMENTS The authors acknowledge the important contributions of the athletic training staff and the Neuroscience Institute for their support of this project. REFERENCES 1. Herring S, Cantu R, Kibler WB, et al. Concussion (mild traumatic brain injury) and the team physician: a consensus statement. Med Sci Sports Exerc. 2011;43:2412–2422. 2. McCrory P, Meeuwisse W, Johnston K, et al. Consensus statement on concussion in sport, 3rd International Conference on concussion in sport, held in Zurich, November 2008. Clin J Sport Med. 2009;19:185–200.

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3. McCrory P, Meeuwisse W, Aubry M, et al. Consensus statement on concussion in sport—4th International Conference on concussion in sport, held in Zurich, November 2012. Clin J Sport Med. 2013;23: 89–117. 4. Barr WB, McCrea M. Sensitivity and specificity of standardized neurocognitive testing immediately following sports concussion. J Int Neuropsychol Soc. 2001;7:693–702. 5. Collins MW, Grindel SH, Lovell MR, et al. Relationship between concussion and neuropsychological performance in college football players. JAMA. 1999;282:964–970. 6. Delaney JS, Lacroix VJ, Gagne C, et al. Concussions among university football and soccer players: a pilot study. Clin J Sport Med. 2001;11: 234–240. 7. Erlanger D, Feldman D, Kutner K, et al. Development and validation of a web-based neuropsychological test protocol for sports-related return-to-play decision-making. Arch Clin Neuropsychol. 2003;18: 293–316. 8. Erlanger D, Saliba E, Barth J, et al. Monitoring resolution of postconcussion symptoms in athletes: preliminary results of a web-based neuropsychological test protocol. J Athl Train. 2001;36:280–287. 9. Guskiewicz KM, Ross SE, Marshall SW. Postural stability and neuropsychological deficits after concussion in collegiate athletes. J Athl Train. 2001;36:263–273. 10. Macciocchi SN, Barth JT, Alves W, et al. Neuropsychological functioning and recovery after mild head injury in collegiate athletes. Neurosurgery. 1996;39:510–514. 11. Makdissi M, Collie A, Maruff P, et al. Computerised cognitive assessment of concussed Australian rules footballers. Br J Sports Med. 2001; 35:354–360. 12. Matser JT, Kessels AG, Lezak MD, et al. A dose-response relation of headers and concussions with cognitive impairment in professional soccer players. J Clin Exp Neuropsychol. 2001;23:770–774. 13. McCrea M, Kelly JP, Randolph C, et al. Immediate neurocognitive effects of concussion. Neurosurgery. 2002;50:1032–1040; discussion 1040–1032. 14. Warden DL, Bleiberg J, Cameron KL, et al. Persistent prolongation of simple reaction time in sports concussion. Neurology. 2001;57: 524–526. 15. McCrea M, Guskiewicz KM, Marshall SW, et al. Acute effects and recovery time following concussion in collegiate football players: the NCAA Concussion Study. JAMA. 2003;290:2556–2563. 16. Peterson CL, Ferrara MS, Mrazik M, et al. Evaluation of neuropsychological domain scores and postural stability following cerebral concussion in sports. Clin J Sport Med. 2003;13:230–237. 17. Riemann BL, Guskiewicz KM. Effects of mild head injury on postural stability as measured through clinical balance testing. J Athl Train. 2000; 35:19–25. 18. McCrea M, Iverson GL, McAllister TW, et al. An integrated review of recovery after mild traumatic brain injury (MTBI): implications for clinical management. Clin Neuropsychol. 2009;23:1368–1390. 19. Echemendia RJ, Putukian M, Mackin RS, et al. Neuropsychological test performance prior to and following sports-related mild traumatic brain injury. Clin J Sport Med. 2001;11:23–31. 20. Putukian M. The acute symptoms of sport-related concussion: diagnosis and on-field management. Clin Sports Med. 2011;30:49–61. 21. SCAT; McCrory P, Johnston K, Meeuwisse W, et al. Summary and agreement statement of the 2nd International Conference on Concussion in Sport, Prague 2004. Clin J Sport Med. 2005;15:48–55. 22. NFL Sideline Concussion Assessment Tool [online]. http:// nflhealthandsafety.files.wordpress.com/2011/01/nfl-concussion-toolpost-injury.pdf. Accessed June 12, 2013.

23. International Ice Hockey Federation (IIHF) medical care guide 2012. Regulations 2012. http://www.iih.com/medical/medical care regulations. Accessed January 2, 2013. 24. Runkle D. Concussion management Plan. NCAA health and safety website. NCAA required concussion management Plan. http://www.ncaa.org/wps/ myportal/ncaahome?WCM_GLOBAL_CONTEXT=/ncaa/ncaa/academics +and+athletes/personal+welfare/health+and+safety/concussion. Accessed June 15, 2010. 25. Alla S, Sullivan SJ, Hale L, et al. Self-report scales/checklists for the measurement of concussion symptoms a systematic review. Br J Sports Med. 2009;43:i3–i12. 26. Maroon JC, Lovell MR, Norwig J, et al. Cerebral concussion in athletes: evaluation and neuropsychological testing. Neurosurgery. 2000;47:659– 669; discussion 69–72. 27. Lovell MR, Collins MW. Neuropsychological assessment of the college football player. J Head Trauma Rehabil. 1998;13:9–26. 28. Piland SG, Motl RW, Ferrara MS, et al. Evidence for the factorial and construct validity of a self-report concussion symptoms scale. J Athl Train. 2003;38:104–112. 29. McCrea M. Standardized mental status assessment of sports concussion. Clin J Sport Med. 2001;11:176–181. 30. Maddocks D, Dicker G. An objective measure of recovery from concussion in Australian rules footballers. Sport Health. 1989;7(suppl):6–7. 31. Guskiewicz KM. Postural stability assessment following concussion: one piece of the puzzle. Clin J Sport Med. 2001;11:182–189. 32. Shehata N, Wiley JP, Richea S, et al. Sport concussion assessment tool: baseline values for varsity collision sport athlete. Br J Sports Med. 2009; 43:730–734. 33. Schneider KJ, Emery CA, Kang J, et al. Examining sport concussion assessment ratings for male and female youth hockey players with and without a history of concussion. Br J Sports Med. 2010;44:1112–1117. 34. Makdissi M, Davis G, Jordan B, et al. Revisiting the modifiers: how should the evaluation and management of acute concussions differ in specific groups? Br J Sports Med. 2013;47:314–320. 35. Spitzer RL, Kroenke K, Williams JB, et al. A brief measure for assessing generalized anxiety disorder. Arch Intern Med. 2006;166:1092–1097. 36. Kroenke K, Spitzer R, Williams W. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16:606–613. 37. Iverson GL, Lovell MR, Collins MW. Interpreting change on ImPACT following sports concussion. Clin Neuropsychol. 2003;17:460–467. 38. Jacobson NS, Truax P. Clinical significance: a statistical approach to defining meaningful change in psychotherapy research. J Consult Clin Psychol. 1991;59:12–19. 39. Iverson GL, Lovell MR, Collins MW. Interpreting change on ImPACT following sport concussion. Comparative study. Clin Neuropsychol. 2003;17:460–467. 40. Valovich McLeod TC, Perrin DH, Guskiewicz K, et al. Serial administration of clinical concussion assessments and learning effects in healthy youth sports participants. Clin J Sport Med. 2004;14:287–295. 41. Broglio SP, Macciocchi SN, Ferrara MS. Sensitivity of the concussion assessment battery. Neurosurgery. 2007;60:1050–1057; discussion 1057–1058. 42. McCrea M, Iverson GL, Echemendia RJ, et al. Day of injury assessment of sport-related concussion. Br J Sports Med. 2013;47:272–284. 43. Makdissi M, Darby D, Maruff P, et al. Natural history of concussion in sport: markers of severity and implications for management. Am J Sports Med. 2010;38:464–471. 44. Echemendia RJ, Bruce J, Bailey C, et al. The utility of post-concussion neuropsychological data in identifying cognitive change following sports related MTBI in the absence of baseline data. Clin Neuropyschol. 2012; 26:1077–1091.

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