Symptoms are the Most Effective Child SCAT5 Component for Recognizing Concussion on the Day of Injury

doi:10.21203/rs.3.rs-957510/v1

Background: The Child Sport Concussion Assessment Tool 5th Edition (Child SCAT5) was developed to evaluate children between 5-12 years of age for a suspected concussion. However, limited empirical evidence exists demonstrating the value of the Child SCAT5 for acute concussion assessment. Therefore, the purpose of our study was to examine differences and assess the diagnostic properties of Child SCAT5 scores among concussed and non-concussed middle school children on the same day as a suspected concussion.

Methods: Our participants included 34 concussed (21 boys, 13 girls; age=12.8±0.86 years) and 44 non-concussed (31 boys, 13 girls; age=12.4±0.76 years) middle school children who were administered the Child SCAT5 upon suspicion of a concussion. Child SCAT5 scores were calculated from the symptom evaluation (total symptoms, total severity), child version of the Standardized Assessment of Concussion (SAC-C), and modified Balance Error Scoring System (mBESS). The Child SCAT5 scores were compared between the concussed and non-concussed groups. Non-parametric effect sizes (r=z/√n) were calculated to assess the magnitude of difference for each comparison. The diagnostic properties (sensitivity, specificity, diagnostic accuracy, predictive values, likelihood ratios, and diagnostic odds ratio) of each Child SCAT5 score were also calculated.

Results: Concussed children endorsed more symptoms (p<0.001, r=0.45), higher symptom severity (p<0.001, r=0.44), and had higher double leg (p=0.046, r=0.23), single leg (p=0.035, r=0.24), and total scores (p=0.022, r=0.26) for the mBESS than non-concussed children. No significant differences were observed for the SAC-C scores (p’s≥0.542). The quantity and severity of endorsed symptoms had the best diagnostic accuracy (AUC=0.76–0.77), negative predictive values (NPV=0.84–0.88), and negative likelihood ratios (-LR=0.22–0.31) of the Child SCAT5 scores.

Conclusions: The symptom evaluation was the most effective component of the Child SCAT5 for differentiating between concussed and non-concussed middle school children on the same day as a suspected concussion.

Sports Medicine and Kinesiology

Neurocognitive

balance

symptomology

children

diagnostic accuracy

Concussed middle school athletes endorsed more symptoms, reported greater symptom severity, and had worse balance performance as compared to middle school athletes who were not diagnosed with a concussion on the same day as the suspected injury.
The number and severity of self-endorsed symptoms were effective at recognizing concussed middle school athletes on the same day as their suspected injury.
Highly variable diagnostic properties were observed for the individual assessment of symptoms, neurocognition, and balance when administered to middle school athletes on the same day as a suspected concussion.

There are approximately 12.3 million middle school (grade level 6-8) age children in the United States and an estimated 36% (≈4.4 million) will participate in organized intramural or interscholastic sport annually.[1, 2] Participation in organized sport for children under 14 years of age is the leading cause of concussion (43%)[3] and has been associated with a six times greater risk of concussion as compared to other leisure physical activities.[4] Limited on-site medical coverage of school-sanctioned sports[5] contributes to a majority (86%) of children seeking medical care from healthcare professionals in direct access settings (e.g., emergency department, outpatient clinics)[6, 7] where follow up visits are uncommon (1-3%).[8–10]

Several governing bodies[11–14] recommend the implementation of a multimodal assessment for the evaluation of children following a suspected concussion. The Sport Concussion Assessment Tool 5th edition (SCAT5)[15] and the Child Sport Concussion Assessment Tool 5th Edition (Child SCAT5)[16] are two of the most commonly used multimodal assessments.[17–20] The Child SCAT5 is a modified version of the SCAT5 which was developed for administration to children between 5-12 years of age.[15] Previous literature has demonstrated poor psychometric (e.g., test-retest reliability) and diagnostic properties (e.g., sensitivity, specificity, predictive value, likelihood ratios) for the individual components of prior iterations of the SCAT[16, 21–40] and the Child SCAT 3rd Edition (Child SCAT3)[41–44]. Despite consensus recommendation and wide adoption, scant evidence exists regarding the psychometric and diagnostic properties of the Child SCAT5.[45]

The lack of empirical evidence regarding the Child SCAT5 necessitates that healthcare professionals rely upon their subjective interpretation of Child SCAT5 scores to inform acute clinical management. An approach which may contribute to misdiagnosis and inconsistent treatment for those children acutely evaluated for concussion using the Child SCAT5.[46] Therefore, the purpose of our study was to evaluate for differences and assess the diagnostic properties of Child-SCAT5 scores on the same day as a suspected concussion among middle school children who were (“concussed”) or were not (“non-concussed”) subsequently diagnosed with a concussion. We hypothesized that (i) the concussed children would endorse significantly more symptoms and report a significantly higher symptom severity as compared to the non-concussed children; (ii) the concussed children would score lower on the SAC-C and commit significantly more errors on the mBESS as compared to the non-concussed children; and (iii) the symptom evaluation (total symptoms endorsed, symptom severity) would demonstrate better diagnostic properties than the SAC-C or mBESS on the same day as the suspected concussion.

Design and Settings

The Advancing Healthcare Initiatives for Underserved Students (ACHIEVES) Project provided on-site medical care to sixteen middle schools within a large socio-demographically diverse school district in Virginia, USA.[47] The George Mason University Institutional Review Board approved the construction of the deidentified database for research purposes as part of the ACHIEVES Project and waived assent and consent. All participants in our study competed in school-sanctioned sports between the 2017-2018 and 2019-2020 academic years.

Participants

Our sample originally consisted of 186 (103 concussed, 83 non-concussed) middle school children that were evaluated for a suspected concussion (Figure 1). Exclusion criteria were set to ensure that injury and assessment data was available from the concussion assessment that was administered at the time of the suspected concussion. Participants were excluded if their suspected concussion occurred outside of sport, the initial assessment was not completed on the same day as the suspected concussion, or there were any missing data elements from the Child SCAT5 (Figure 1). After applying all of our exclusion criteria, our final sample consisted of 78 (34 concussed, 44 non-concussed) middle school children that were evaluated for a suspected concussion while participating in school-sanctioned sports.

Testing Procedures

Consistent with the school system’s concussion management protocol, the Child SCAT5 was administered to middle school children after removing them from participation in school-sanctioned sports due to the suspicion of a concussion. The children in our study were allocated into groups dependent on whether they were (“concussed”) or were not (“non-concussed”) subsequently diagnosed with a concussion. The definition of a concussion was consistent with the most recent international consensus statement on concussion in sport.[11] Regardless of their Child SCAT5 scores, the children were not permitted to return to sport participation on the same day as the suspected concussion which is in alignment with international consensus statements, several governing bodies, and the state law of Virginia.[11–14, 48, 49]

Outcomes

The administration and scoring of each component of the Child SCAT5 (symptom evaluation, child version of the Standardized Assessment of Concussion [SAC-C], modified version of the balance error scoring system [mBESS]) are described in detail elsewhere.[16] For the symptom evaluation, the total number of symptoms endorsed (range=0–21) and the total symptom severity (range=0–63) endorsed by the children were calculated. For the SAC-C, points earned for the immediate memory (range=0–15 points), concentration (range=0–6 points), and delayed recall (range=0–5 points) domains were recorded and summed to calculate a composite score (range=0–26 points). For the mBESS, errors committed (range=0–10) during each stance (double leg, single leg, tandem) were recorded and summed to calculate the total score (range=0–30). Lower scores on the SAC-C and higher scores on the mBESS were indicative of worse performance on the respective components of the Child SCAT5.

Statistical Analyses

Nonparametric analyses were performed due to the non-normal distribution (Shapiro-Wilk=0.78-0.94, p’s<0.05) of the Child SCAT5 scores. Mann-Whitney U-tests were used to assess for differences between the concussed and non-concussed children for each Child SCAT5 score. Nonparametric effect sizes[50] (\(r=\frac{Z}{\sqrt{N}}\)) were calculated and interpreted as small (r=0.10–0.30), moderate (r=0.30–0.50), or large (r≥0.50).[51]

Receiver operator curve analyses were performed to calculate the diagnostic accuracy (area under the curve [AUC]) of each Child SCAT5 score.[21, 24, 25, 52–55] Youden’s Index (J)[56] was calculated to determine the cutoff score that optimized the combination of sensitivity (Sn) and specificity (Sp) for each Child SCAT5 score.[57–60] Values closer to 1.0 for the Youden Index are indicative of a greater combination of sensitivity and specificity.[56]

The sensitivity and specificity values of each Child SCAT5 score was used to calculate their positive (PPV) and negative (NPV) predictive values, positive (+LR) and negative (-LR) likelihood ratios, and diagnostic odds ratios (DOR). Positive and negative predictive values that are closer to 1.0 are indicative of a more valid test result.[61] Higher positive likelihood ratios indicate a greater likelihood that the patient has the test condition (e.g., concussed) while lower negative likelihood ratios indicate a greater likelihood that the patient does not have the test condition (e.g., not concussed).[62] Higher diagnostic odds ratios indicate that the test result has a greater ability to differentiate between patients with (e.g., concussed) and without (e.g., non-concussed) the test condition. All analyses were performed using SPSS (Version 27, IBM Corp., NY, USA). Alpha was set a priori at p<0.05.

Our sample consisted of 34 concussed (21 male [62%], age=12.8±0.86 years) and 44 non-concussed (31 male [70%], age=12.4±0.76 years) middle school children who participated in a variety of school-sanctioned sports including football, wrestling, baseball, softball, volleyball, cheerleading, basketball, soccer, and track (Figure 1).

The concussed children endorsed significantly more symptoms (median=6.0 [range=1-20] vs. median=2.0 [range=0-19], p<0.001, r=0.45) and reported greater symptom severity (median=7.5 [range=1-47] vs. median=3.0 [range=0-31], p<0.001, r=0.44) than the non-concussed group (Table 1, Figure 2). The total number and severity of endorsed symptoms resulted in the highest diagnostic accuracy (AUC=0.76-0.77) and negative predictive values (NPV=0.84-0.88) and the lowest negative likelihood ratios (-LR=0.22-0.31) of the Child SCAT5 scores (Table 2, Figure 3A).

Table 1

Descriptive statistics for the Child Sport Concussion Assessment Tool – 5^th Edition (Child SCAT5) scores.

	Concussed					Non-Concussed					Group Comparisons
Child SCAT5 Component	Range	M	SD	Md	IQR	Range	M	SD	Md	IQR	p	r
Symptoms
Total Number	1-20	8.0	5.50	6.0	4-12	0-19	3.72	3.97	2.0	1-5	<.001	0.45
Total Severity	1-47	12.1	10.5	7.5	5-17	0-31	5.0	6.01	3.0	1-9	<.001	0.44
SAC-C
Immediate Memory	6-15	13.6	1.46	14.0	13-15	9-15	13.7	1.47	14.0	13-15	.582	0.06
Concentration	2-6	3.9	0.65	4.0	4-4	2-6	4.0	1.02	4.0	3-5	.896	0.01
Delayed Recall	2-5	3.5	1.38	4.0	3-5	1-5	3.7	1.06	4.0	3-5	.684	0.05
Composite Score	13-24	21.0	2.61	21.5	20-23	16-26	21.5	2.48	22.0	19-23	.542	0.07
mBESS
Double Leg	0-2	0.2	0.50	0.0	0-0	0-0	0.0	0.00	0.0	0-0	.046	0.23
Single Leg	1-10	5.5	3.12	5.5	3-8	0-10	4.0	2.85	3.0	2-6	.035	0.24
Tandem Leg	0-10	2.2	2.36	1.5	1-3	0-10	1.6	1.83	1.0	0-2	.398	0.10
Total Score	1-20	7.8	4.53	7.5	4-11	1-20	5.6	4.17	5.0	2-7	.022	0.26
Note. SAC-C = Standardized Assessment of Concussion - Child Version, mBESS = Modified Balance Error Scoring System, M = mean, SD = standard deviation, Md = median, IQR = interquartile range (25^th to 75^th percentile), r = Pearson correlation coefficient.

None of the SAC-C scores were not significantly different between the concussed and non-concussed children (p’s≥.542) and had the lowest diagnostic accuracy values (AUC=0.51–0.54) of the Child SCAT5 scores (Table 2, Figure 3B).

Table 2

Diagnostic properties for the Child Sport Concussion Assessment Tool 5^th Edition (Child SCAT5) scores.

Outcome Measure

Cutoff Score

Sn

Sp

J

AUC

PPV

NPV

+LR

-LR

DOR

Symptoms

Total Endorsed

Total Severity

≥ 3 symptoms

≥ 5 points

0.88

0.79

0.54

0.67

0.47

0.42

0.77

0.76

0.54

0.60

0.88

0.84

1.90

2.44

0.22

0.31

8.60

7.97

SAC-C

Immediate Memory

Concentration

Delayed Recall

Composite

≤ 12 points

≤ 4 points

≤ 1 point

≤ 18 points

0.24

0.85

0.15

0.88

0.86

0.30

0.98

0.23

0.10

0.15

0.12

0.11

0.54

0.51

0.53

0.54

0.51

0.43

0.80

0.41

0.65

0.77

0.65

0.76

1.73

1.21

6.39

1.14

0.89

0.50

0.87

0.52

1.95

2.43

7.32

2.19

mBESS

Double Leg

Single Leg

Tandem

Total

≥ 1 error

≥ 7 errors

≥ 2 errors

≥ 9 errors

0.09

0.44

0.50

0.47

1.00

0.81

0.61

0.86

0.09

0.11

0.26

0.33

0.54

0.55

0.63

0.64

0.98

0.59

0.44

0.67

0.64

0.70

0.66

0.73

88.00

2.37

1.27

3.36

0.91

0.69

0.83

0.62

96.39

3.45

1.53

5.47

SAC-C = child version of the Standardized Assessment of Concussion, mBESS = modified version of the balance error scoring system, Sn = sensitivity, Sp = specificity, J = Youden Index, AUC = area under the curve, PPV/NPV = positive and negative predictive values, +LR/-LR = positive and negative likelihood ratios, DOR = diagnostic odds ratios.

The concussed children committed significantly more errors in the double leg (median=0.0 [range=0-2] vs median=0.0 [range=0-0], p=0.046, \(r\)=0.23) and single leg (median=5.5 [range=1-10] vs median=3.0 [range=0-10], p=0.035, \(r\)=0.24) stances of the mBESS as compared to the non-concussed children (Table 1). Significantly higher total scores on the mBESS (median=7.5 [range=1-20] vs median=5.0 [range=1-20], p=0.022, \(r\)=0.26) were observed for the concussed children as compared to the non-concussed children (Table 1). Committing at least one error in the double leg stance resulted in the highest specificity (Sp=1.00) but the lowest sensitivity (Sn=0.09; Table 2) values. A total mBESS score of at least nine errors had higher diagnostic accuracy (AUC=0.64) than interpretation of the individual stances (AUC=0.54–0.63; Table 2, Figure 3C).

Our study evaluated for differences in Child SCAT5 scores, and their subsequent diagnostic properties, among concussed and non-concussed middle school children on the day that they were evaluated for a suspected concussion. Our findings demonstrate that concussed children endorse a greater number and severity of symptoms and suggest that the symptom evaluation is the most effective component of the Child SCAT5 for differentiating between concussed and non-concussed children on the same day as a suspected concussion. The concussed children also committed significantly more errors on the mBESS than the non-concussed children, however, the magnitude of these differences were relatively small as supported by the effect sizes. Our data suggests that the SAC-C is the least meaningful component of the Child SCAT5 as no significant differences were observed between the concussed and non-concussed children which resulted in poor diagnostic accuracy values. Overall, our study reinforces the importance of the symptom evaluation as an integral part of the clinical assessment of children on the same day as a suspected concussion.

Self-reported symptomology has been a cornerstone of concussion evaluation and remains the key component for informing clinical judgement.[11] Our data demonstrates that concussed middle school children endorse a significantly greater number and severity of symptoms than children who are not diagnosed with a concussion. Specifically, the concussed children in our sample endorsed nearly double the number of symptoms (8.0±5.50 vs. 3.7±3.97) and symptom severity (12.1±10.5 vs. 12.1±10.9) as compared to the non-concussed children. Previous literature has demonstrated that concussed children evaluated in the emergency department using the Child SCAT3 endorsed significantly more symptoms and greater severity than non-concussed children, which is alignment with our results.[44] Our findings also align well with previous literature that have reported elevated endorsement and severity of symptoms in older athletic populations (e.g., high school, collegiate) following a diagnosed concussion as compared to preinjury (baseline) data or matched comparisons.[23, 63, 64] We did not incorporate baseline assessments or matched comparisons, thus, we encourage future research to investigate these differences in symptom reporting of middle school children following a suspected concussion.

Our study also observed that concussed children committed significantly more errors in two stances (double leg, single leg) of the mBESS and had significantly higher mBESS total scores as compared to the non-concussed children. Furthermore, our mBESS total scores for the concussed children (7.8±4.53 errors) were slightly elevated compared to normative reference values for this population (5.0±3.7 errors).[65] However, the small effect sizes (r=0.23–0.26) calculated for the concussed versus non-concussed comparisons suggest that the differences may not be clinically meaningful. Findings from our sample of middle school children align well with extensive literature that have reported that concussed high school and collegiate athletes perform worse on the BESS than non-concussed athletes.[22, 23, 63, 64, 66, 67] Collectively, our findings and those of previous literature that assessed older athletic populations suggest that balance assessment following a suspected concussion may elicit subtle deficits to inform clinical diagnosis and management.

The concussed children in our study did not perform significantly different on any component of the SAC-C as compared to the non-concussed children on the same day as the suspected concussion. The SAC-C composite scores were also not significantly different between the concussed and non-concussed groups in our sample and the values for each group align within the “broadly normal” interpretation of normative reference values for this population.[65] The lack of significant findings between the concussed and non-concussed groups is different than previous literature that evaluated older athletic populations.[22, 23, 63, 64, 66, 67] Possible rationales for the lack of significant differences in SAC-C scores include discrepancies in the age of the participants in our study (middle school children) as compared to those of previous literature study populations (high school or collegiate athletes) and different ranges of composite scores for the SAC (range=0-25) and SAC-C (range=0-26).[15]

To our knowledge, our study is the first to provide evidence of the diagnostic properties of the Child SCAT5 in the population for which it was designed. The total number and severity of endorsed symptoms were found to have the highest levels of diagnostic accuracy (AUC=0.76–0.77) and sensitivity (Sn=0.79–0.88) of the Child SCAT5 scores. Based on the calculated cutoff scores, children who endorse less than four symptoms or report a severity less than six points on the same day as the suspected concussion were less likely (-LR=0.22–0.31) to be diagnosed with a concussion. As mentioned previously, it is important to note that none of the children assessed for a suspected concussion were permitted to return to sport participation on the same day as the assessment regardless of their Child SCAT5 scores. This is in alignment with the recommendations from the leading international consensus group on concussion in sport,[11] position statements from several governing bodies,[12–14] and the legal requirements of state laws[49].

The highest positive predictive values and likelihood ratios were observed for interpretation of the double leg stance of the mBESS followed by the delayed recall domain of the SAC-C. However, inordinately low thresholds for a positive test result (e.g., diagnosed concussion) for the double leg stance of the mBESS (≤1 error) and the delayed recall domain of the SAC-C (≤1 point) yielded high specificity values (Sp=0.98–1.00) which artificially elevated the calculated positive predictive values and likelihood ratios. Therefore, we caution healthcare professionals from independent interpretation of the double leg stance of the mBESS or the delayed recall domain of the SAC-C in their clinical decision-making at this time. Future research is warranted to validate our findings related to the diagnostic properties of the individual components (symptom evaluation, SAC-C, mBESS) of the Child SCAT5 in an independent sample of children on the same day as a suspected concussion.

The calculated diagnostic properties of the Child SCAT5 scores in our study align with those reported for previous iterations of the SCAT and the Child SCAT3.[23, 44, 63, 67] More specifically, the values of sensitivity, specificity, and diagnostic accuracy observed in our study are similar to those calculated for the individual components of the SCAT for the assessment of older athletes.[23, 63, 67] A similar methodology as our study has been implemented to evaluate for differences and assess the diagnostic properties of Child SCAT3 scores among children who were evaluated for a suspected concussion in the emergency department.[44] The authors of this prior study reported similar diagnostic accuracy values for Child SCAT3 scores to those observed in our study which utilized the Child SCAT5.[44] Overall, our findings support those of previous literature[23, 44, 63, 67] which suggest that the symptom evaluation has the best combination of diagnostic properties and is the most effective component of the Child SCAT for differentiating between concussed and non-concussed children.

Healthcare professionals in direct access settings (e.g., emergency department, outpatient clinics) may be the first to evaluate a child following a suspected concussion[6] and likely will not have access to preinjury (baseline) scores for comparison. It is vital that healthcare professionals in direct access settings are equipped with age-appropriate assessment tools that can effectively differentiate between those who are and are not concussed in order to appropriately inform patient care. Our findings reinforce the importance of the symptom evaluation of the Child SCAT5 and suggest that healthcare professionals can be confident in the clinical interpretation of acute symptom reporting of children following a suspected concussion. The poorer diagnostic accuracy of the SAC-C and mBESS highlights the inability of these assessments to adequately differentiate between concussed and non-concussed children which limits their clinical utility on the same day as a suspected concussion. Future research should investigate alternative assessment tools (e.g., tandem gait test[68]) or strategies (e.g., the dual task paradigm[69]) for the acute evaluation of children following a suspected concussion. Findings from this future research may provide additional objective data to assist in the evaluation of children with a suspected concussion.

We recognize that our study is not without limitations. All of the children in our study were participating in school-sanctioned sports at middle schools within a single county in the northern Virginia which limits our generalizability. However, the middle school student population in our study has a unique socio-demographic profile including students of diverse racial backgrounds (e.g., 36.7% Hispanic, 27.8% White/Caucasian, 20.7% Black/African-American) and high academic achievement (e.g., less than a 2% course failure rate overall).[47] Another limitation is the variability in the time between the removal from sport and the concussion evaluation, however, all participants were evaluated on the same day as the suspected concussive event. Lastly, the diagnosis of a concussion was made by the on-site healthcare professional using their own clinical decision-making which improves the external validity of our study. The healthcare professionals participating in the ACHIVES Project also completed annual training on concussion assessment using the Child SCAT5 and followed an established concussion management protocol which limited variability in their clinical evaluation.

The concussed middle school children endorsed more symptoms, reported greater symptom severity, and committed more errors during administration of the mBESS on the same day as the suspected concussion than children who were not diagnosed with a concussion. Clinical interpretation of the total quantity and severity of the endorsed symptoms resulted in the greatest combination of diagnostic properties as supported by the best sensitivity, diagnostic accuracy, negative predictive values, and negative likelihood ratios. Our data does not support the clinical interpretation of the SAC-C on the same day as a suspected concussion due to the lack of significant differences observed between the concussed and non-concussed groups and highly variable diagnostic properties. Overall, our findings demonstrate that the symptom evaluation may be the most effective component of the Child SCAT5 for differentiating between concussed and non-concussed middle school children on the day of a suspected concussive event.

Ethics Approval:

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Additionally, our study was approved by the George Mason University Institutional Review Board.

Consent to Participate:

Participants signed informed consent regarding publishing their data.

Consent for Publication:

Informed consent was obtained from all individual participants included in the study.

Availability of Data and Materials:

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing Interests:

The authors have no competing interests to disclose.

Funding:

Dr. Shane V. Caswell has received funding from the Virginia Department of Health (Grant #709I832700) and the Centers for Disease Control and Prevention (Grant #NU17CE924832) as well as support from the Prince William County Public Schools.

Author’s Contributions:

Dr. Nicholas K. Erdman conceptualized the design of the study, carried out the initial analyses, drafted the initial manuscript, and revised the manuscript. Dr. Patricia M. Kelshaw conceptualized the design of the study, designed the data collection instruments, collected data, and critically reviewed the manuscript for important intellectual content. Ms. Samantha Hacherl designed the data collection instruments, collected data, and critically reviewed the manuscript for important intellectual content. Dr. Shane V. Caswell conceptualized the design of the study, designed the data collection instruments, and critically reviewed the manuscript for important intellectual content. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

School Enrollment in the United States. October 2018 - Detailed Tables [Internet]. 2019 [cited 2021 Jan 15]. Available from: https://www.census.gov/content/census/en/data/tables/2018/demo/school-enrollment/2018-cps.html.
Kanters MA, Bocarro JN, Edwards MB, Casper JM, Floyd MF. School Sport Participation Under Two School Sport Policies: Comparisons by Race/Ethnicity, Gender, and Socioeconomic Status. Ann Behav Med. 2013;45:113–21.
Sarmiento K, Daugherty J, DePadilla L, Breiding MJ. Examination of sports and recreation-related concussion among youth ages 12–17: results from the 2018 YouthStyles survey. Brain Inj [Internet]. 2020 [cited 2020 Nov 10];34:357–62. Available from: https://www.tandfonline.com/doi/full/10.1080/02699052.2020.1723165.
Browne GJ. Concussive head injury in children and adolescents related to sports and other leisure physical activities. Br J Sports Med [Internet]. 2006 [cited 2020 Nov 9];40:163–8. Available from: https://bjsm.bmj.com/lookup/doi/10.1136/bjsm.2005.021220.
Pike AM, Pryor RR, Vandermark LW, Mazerolle SM, Casa DJ. Athletic Trainer Services in Public and Private Secondary Schools. J Athl Train [Internet]. 2017 [cited 2021 Jan 14];52:5–11. Available from: https://meridian.allenpress.com/jat/article/52/1/5/111645/Athletic-Trainer-Services-in-Public-and-Private.
DePadilla L, Miller GF, Jones SE, Peterson AB, Breiding MJ. Self-Reported Concussions from Playing a Sport or Being Physically Active Among High School Students — United States, 2017. MMWR Morb Mortal Wkly Rep [Internet]. 2018 [cited 2020 Nov 9];67:682–5. Available from: http://www.cdc.gov/mmwr/volumes/67/wr/mm6724a3.htm?s_cid=mm6724a3_w.
Arbogast KB, Curry AE, Pfeiffer MR, Zonfrillo MR, Haarbauer-Krupa J, Breiding MJ, et al. Point of Health Care Entry for Youth With Concussion Within a Large Pediatric Care Network. JAMA Pediatr [Internet]. 2016 [cited 2019 Jun 18];170:e160294. Available from: http://archpedi.jamanetwork.com/article.aspx?doi=10.1001/jamapediatrics.2016.0294.
Arora R, Shukla M, McQuillen E, Sethuraman U. Pediatric minor head injury related return visits to the emergency department and their outcome. Am J Emerg Med [Internet]. 2021 [cited 2021 Mar 9];45:71–4. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0735675721000395.
Bressan S, Berlese P, Arpone M, Steiner I, Titomanlio L, Da Dalt L. Missed intracranial injuries are rare in emergency departments using the PECARN head injury decision rules. Childs Nerv Syst [Internet]. 2021 [cited 2021 Mar 9];37:55–62. Available from: http://link.springer.com/10.1007/s00381-020-04660-0.
Nigrovic LE, Stack AM, Mannix RC, Lyons TW, Samnaliev M, Bachur RG, et al. Quality Improvement Effort to Reduce Cranial CTs for Children With Minor Blunt Head Trauma. PEDIATRICS [Internet]. 2015 [cited 2021 Mar 9];136:e227–33. Available from: http://pediatrics.aappublications.org/cgi/doi/10.1542/peds.2014-3588.
McCrory P, Meeuwisse W, Dvorak J, Aubry M, Bailes J, Broglio S, et al. Consensus statement on concussion in sport—the 5th international conference on concussion in sport held in Berlin, October 2016. Br J Sports Med [Internet]. 2017 [cited 2018 Aug 24];1–10. Available from: http://bjsm.bmj.com/lookup/doi/10.1136/bjsports-2017-097699.
NASN Position Statement: Concussions—The Role of the School Nurse. NASN Sch Nurse [Internet]. 2013 [cited 2020 Mar 3];28:110–1. Available from: http://journals.sagepub.com/doi/10.1177/1942602X12473949.
Broglio SP, Cantu RC, Gioia GA, Guskiewicz KM, Kutcher J, Palm M, et al. National athletic trainers’ association position statement: management of sport concussion. J Athl Train [Internet]. 2014 [cited 2018 Aug 21];49:245–65. Available from: http://natajournals.org/doi/10.4085/1062-6050-49.1.07.
Harmon KG, Clugston JR, Dec K, Hainline B, Herring S, Kane SF, et al. American Medical Society for Sports Medicine position statement on concussion in sport. Br J Sports Med [Internet]. 2019 [cited 2020 Mar 3];53:213–25. Available from: http://bjsm.bmj.com/lookup/doi/10.1136/bjsports-2018-100338.
Echemendia RJ, Meeuwisse W, McCrory P, Davis GA, Putukian M, Leddy JJ, et al. The Sport Concussion Assessment Tool 5th Edition (SCAT5): Background and rationale. 2017. 2017;51:851–8.
Davis GA, Purcell L, Schneider KJ, Yeates KO, Gioia GA, Anderson V, et al. The Child Sport Concussion Assessment Tool 5th Edition (Child SCAT5). Br J Sports Med [Internet]. 2017 [cited 2020 Oct 30];859–61. Available from: https://bjsm.bmj.com/lookup/doi/10.1136/bjsports-2017-097492.
Lempke LB, Schmidt JD, Lynall RC. Athletic Trainers’ Concussion-Assessment and Concussion-Management Practices: An Update. J Athl Train [Internet]. 2020 [cited 2020 Mar 4];55:17–26. Available from: http://natajournals.org/doi/10.4085/1062-6050-322-18.
Lynall RC, Laudner KG, Mihalik JP, Stanek JM. Concussion-assessment and -management techniques used by athletic trainers. J Athl Train [Internet]. 2013 [cited 2018 Nov 27];48:844–50. Available from: http://natajournals.org/doi/10.4085/1062-6050-48.6.04.
Buckley TA, Burdette G, Kelly K. Concussion-Management Practice Patterns of National Collegiate Athletic Association Division II and III Athletic Trainers: How the Other Half Lives. J Athl Train [Internet]. 2015 [cited 2019 Oct 18];50:879–88. Available from: http://natajournals.org/doi/10.4085/1062-6050-50.7.04.
Kelly KC, Jordan EM, Joyner AB, Burdette GT, Buckley TA. National Collegiate Athletic Association Division I Athletic Trainers’ Concussion-Management Practice Patterns. J Athl Train [Internet]. 2014 [cited 2019 Dec 9];49:665–73. Available from: http://natajournals.org/doi/10.4085/1062-6050-49.3.25.
Barr WB, McCrea M. Sensitivity and specificity of standardized neurocognitive testing immediately following sports concussion. J Int Neuropsychol Soc [Internet]. 2001 [cited 2018 Aug 21];7:693–702. Available from: http://www.journals.cambridge.org/abstract_S1355617701766052.
McLeod TCV, Barr WB, McCrea M, Guskiewicz KM. Psychometric and measurement properties of concussion assessment tools in youth sports. J Athl Train. 2006;41:399–408.
Chin EY, Nelson LD, Barr WB, McCrory P, McCrea MA. Reliability and validity of the sport concussion assessment tool–3 (SCAT3) in high school and collegiate athletes. Am J Sports Med [Internet]. 2016 [cited 2018 Aug 21];44:2276–85. Available from: http://journals.sagepub.com/doi/10.1177/0363546516648141.
Resch JE, Brown CN, Schmidt J, Macciocchi SN, Blueitt D, Cullum CM, et al. The sensitivity and specificity of clinical measures of sport concussion: three tests are better than one. BMJ Open Sport Exerc Med [Internet]. 2016 [cited 2018 Oct 4];2:e000012. Available from: http://bmjopensem.bmj.com/lookup/doi/10.1136/bmjsem-2015-000012.
Broglio SP, Macciocchi SN, Ferrara MS. Sensitivity of the Concussion Assessment Battery. Neurosurgery [Internet]. 2007 [cited 2020 Mar 2];60:1050–8. Available from: https://academic.oup.com/neurosurgery/article/60/6/1050/3775791.
Broglio SP, Guskiewicz KM. Concussion in Sports: The Sideline Assessment. Sports Health Multidiscip Approach [Internet]. 2009 [cited 2018 Aug 21];1:361–9. Available from: http://journals.sagepub.com/doi/10.1177/1941738109343158.
Investigators CAREC, Garcia G-GP, Broglio SP, Lavieri MS, McCrea M, McAllister T. Quantifying the Value of Multidimensional Assessment Models for Acute Concussion: An Analysis of Data from the NCAA-DoD Care Consortium. Sports Med [Internet]. 2018 [cited 2019 Sep 5];48:1739–49. Available from: http://link.springer.com/10.1007/s40279-018-0880-x.
Sullivan SJ, Hammond-Tooke GD, Schneiders AG, Gray AR, McCrory P. The diagnostic accuracy of selected neurological tests. J Clin Neurosci [Internet]. 2012 [cited 2018 Aug 24];19:423–7. Available from: http://linkinghub.elsevier.com/retrieve/pii/S096758681100573X.
Buckley TA, Munkasy BA, Clouse BP. Sensitivity and specificity of the modified balance error scoring system in concussed collegiate student athletes. Clin J Sport Med [Internet]. 2017 [cited 2018 Aug 21];1–3. Available from: http://Insights.ovid.com/crossref?an=00042752-900000000-99477.
Register-Mihalik JK, Guskiewicz KM, Mihalik JP, Schmidt JD, Kerr ZY, McCrea MA. Reliable Change, Sensitivity, and Specificity of a Multidimensional Concussion Assessment Battery: Implications for Caution in Clinical Practice. J Head Trauma Rehabil [Internet]. 2013 [cited 2019 May 17];28:274–83. Available from: http://content.wkhealth.com/linkback/openurl?sid=WKPTLP:landingpage&an=00001199-201307000-00005.
Putukian M, Echemendia R, Dettwiler-Danspeckgruber A, Duliba T, Bruce J, Furtado JL, et al. Prospective Clinical Assessment Using Sideline Concussion Assessment Tool-2 Testing in the Evaluation of Sport-Related Concussion in College Athletes. Clin J Sport Med. 2015;25:7.
Glaviano NR, Benson S, Goodkin H, Broshek D, Saliba S. Baseline SCAT2 Assessment of Healthy Youth Student-Athletes. Clin J Sport Med. 2015;25:373–9.
Norheim N, Kissinger-Knox A, Cheatham M, Webbe F. Performance of college athletes on the 10-item word list of SCAT5. BMJ Open Sport Exerc Med [Internet]. 2018 [cited 2019 Aug 8];4:e000412. Available from: http://bmjopensem.bmj.com/lookup/doi/10.1136/bmjsem-2018-000412.
Snedden TR, Brooks MA, Hetzel S, McGuine T. Normative Values of the Sport Concussion Assessment Tool 3 (SCAT3) in High School Athletes: Clin J Sport Med [Internet]. 2017 [cited 2018 Aug 21];27:462–7. Available from: http://Insights.ovid.com/crossref?an=00042752-201709000-00007.
Thomas RE, Alves J, Vaska MM, Magalhães R. SCAT2 and SCAT3 scores at baseline and after sports-related mild brain injury/concussion: qualitative synthesis with weighted means. BMJ Open Sport Exerc Med [Internet]. 2016 [cited 2018 Aug 21];2:e000095. Available from: http://bmjopensem.bmj.com/lookup/doi/10.1136/bmjsem-2015-000095.
Valovich McLeod TC, Bay RC, Lam KC, Chhabra A. Representative Baseline Values on the Sport Concussion Assessment Tool 2 (SCAT2) in Adolescent Athletes Vary by Gender, Grade, and Concussion History. Am J Sports Med [Internet]. 2012 [cited 2020 Mar 3];40:927–33. Available from: http://journals.sagepub.com/doi/10.1177/0363546511431573.
Zimmer A, Marcinak J, Hibyan S, Webbe F. Normative Values of Major SCAT2 and SCAT3 Components for a College Athlete Population. Appl Neuropsychol Adult [Internet]. 2015 [cited 2018 Sep 26];22:132–40. Available from: http://www.tandfonline.com/doi/abs/10.1080/23279095.2013.867265.
Kontos AP. Testretest reliability of the Vestibular Ocular Motor Screening (VOMS) tool and modified Balance Error Scoring System (mBESS) in US military personnel. J Sci Med Sport. 2021;5.
Cushman D, Hendrick J, Teramoto M, Fogg B, Bradley S, Hansen C. Reliability of the balance error scoring system in a population with protracted recovery from mild traumatic brain injury. 2018. 2018;5:569–74.
Hänninen T, Parkkari J, Howell DR, Palola V, Seppänen A, Tuominen M, et al. Reliability of the Sport Concussion Assessment Tool 5 baseline testing: A 2-week test–retest study. J Sci Med Sport [Internet]. 2021 [cited 2021 Mar 16];24:129–34. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1440244020307179.
Porter S, Smith-Forrester J, Alhajri N, Kusch C, Sun J, Barrable B, et al. The Child Sport Concussion Assessment Tool (Child SCAT3): normative values and correspondence between child and parent symptom scores in male child athletes. BMJ Open Sport Exerc Med [Internet]. 2015 [cited 2020 Nov 10];1:e000029. Available from: https://bmjopensem.bmj.com/lookup/doi/10.1136/bmjsem-2015-000029.
Brooks MA, Snedden TR, Mixis B, Hetzel S, McGuine TA. Establishing Baseline Normative Values for the Child Sport Concussion Assessment Tool. JAMA Pediatr [Internet]. 2017 [cited 2020 Nov 10];171:670. Available from: http://archpedi.jamanetwork.com/article.aspx?doi=10.1001/jamapediatrics.2017.0592.
Yengo-Kahn AM, Hale AT, Zalneraitis BH, Zuckerman SL, Sills AK, Solomon GS. The Sport Concussion Assessment Tool: a systematic review. Neurosurg Focus. 2016;40:14.
Babl FE, Dionisio D, Davenport L, Baylis A, Hearps SJC, Bressan S, et al. Accuracy of Components of SCAT to Identify Children With Concussion. Pediatrics [Internet]. 2017 [cited 2021 Mar 2];140:e20163258. Available from: http://pediatrics.aappublications.org/lookup/doi/10.1542/peds.2016-3258.
Cook NE, Kelshaw PM, Caswell SV, Iverson GL. Children with Attention-Deficit/Hyperactivity Disorder Perform Differently on Pediatric Concussion Assessment. J Pediatr. 2019;214:168–74, e1.
Stern RA, Seichepine D, Tschoe C, Fritts NG, Alosco ML, Berkowitz O, et al. Concussion Care Practices and Utilization of Evidence-Based Guidelines in the Evaluation and Management of Concussion: A Survey of New England Emergency Departments. J Neurotrauma [Internet]. 2017 [cited 2021 Mar 16];34:861–8. Available from: http://www.liebertpub.com/doi/10.1089/neu.2016.4475.
Middle Sch. Profiles - Prince William Cty. Public Sch. [cited 2020 Jan 14]. Available from: https://www.pwcs.edu/cms/One.aspx?portalId=340225&pageId=658553.
Virginia General Assembly. § 22.1-271.5. Guidelines and policies and procedures on concussions in student-athletes. [Internet]. Code Va. [cited 2021 Jan 14]. Available from: https://law.lis.virginia.gov/vacode/title22.1/chapter14/section22.1-271.5/.
National Conference of State Legislatures. Traumatic brain injury legislation [Internet]. [cited 2021 Feb 16]. Available from: http://www.ncsl.org/research/health/traumatic-brain-injury-legislation.aspx.
Fritz C, Morris P, Richler J. Effect size estimates: current use, calculations, and interpretation. J Exp Psychol Gen. 2012;141:2–18.
Cohen J. Statistical Power Analysis for the Behvaioral Sciences. 2nd ed. Hillsdale: Erlbaum; 1988.
Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology [Internet]. 1982 [cited 2020 Feb 27];143:29–36. Available from: http://pubs.rsna.org/doi/10.1148/radiology.143.1.7063747.
Rochefort C, Walters-Stewart C, Aglipay M, Barrowman N, Zemek R, Sveistrup H. Balance Markers in Adolescents at 1 Month Postconcussion. Orthop J Sports Med [Internet]. 2017 [cited 2019 May 17];5:232596711769550. Available from: http://journals.sagepub.com/doi/10.1177/2325967117695507.
Nelson LD, Furger RE, Gikas P, Lerner EB, Barr WB, Hammeke TA, et al. Prospective, Head-to-Head Study of Three Computerized Neurocognitive Assessment Tools Part 2: Utility for Assessment of Mild Traumatic Brain Injury in Emergency Department Patients. J Int Neuropsychol Soc [Internet]. 2017 [cited 2019 May 17];23:293–303. Available from: https://www.cambridge.org/core/product/identifier/S1355617717000157/type/journal_article.
Resch JE, McCrea MA, Cullum CM. Computerized Neurocognitive Testing in the Management of Sport-Related Concussion: An Update. Neuropsychol Rev [Internet]. 2013 [cited 2018 Aug 21];23:335–49. Available from: http://link.springer.com/10.1007/s11065-013-9242-5.
Youden WJ. Index for rating diagnostic tests. Cancer. 1950;3:32–5.
Eagle SR, Womble MN, Elbin RJ, Pan R, Collins MW, Kontos AP. Concussion Symptom Cutoffs for Identification and Prognosis of Sports-Related Concussion: Role of Time Since Injury. Am J Sports Med [Internet]. 2020 [cited 2021 Feb 15];48:2544–51. Available from: http://journals.sagepub.com/doi/10.1177/0363546520937291.
Habibzadeh F, Habibzadeh P, Yadollahie M. On determining the most appropriate test cut-off value: the case of tests with continuous results. Biochem Medica [Internet]. 2016 [cited 2021 Feb 15];297–307. Available from: http://www.biochemia-medica.com/en/journal/26/3/10.11613/BM.2016.034.
Linens SW, Ross SE, Arnold BL, Gayle R, Pidcoe P. Postural-Stability Tests That Identify Individuals With Chronic Ankle Instability. J Athl Train [Internet]. 2014 [cited 2018 Aug 21];49:15–23. Available from: http://natajournals.org/doi/10.4085/1062-6050-48.6.09.
Glas AS, Lijmer JG, Prins MH, Bonsel GJ, Bossuyt PMM. The diagnostic odds ratio: a single indicator of test performance. J Clin Epidemiol [Internet]. 2003 [cited 2020 Apr 4];56:1129–35. Available from: https://linkinghub.elsevier.com/retrieve/pii/S089543560300177X.
Biggerstaff BJ. Comparing diagnostic tests: a simple graphic using likelihood ratios. Stat Med. 2000;19:649–63.
McGee S. Simplifying likelihood ratios. J Gen Intern Med [Internet]. 2002 [cited 2021 Mar 16];17:647–50. Available from: http://link.springer.com/10.1046/j.1525-1497.2002.10750.x.
McCrea M, Barr WB, Guskiewicz K, Randolph C, Marshall SW, Cantu R, et al. Standard regression-based methods for measuring recovery after sport-related concussion. J Int Neuropsychol Soc [Internet]. 2005 [cited 2019 May 17];11:58–69. Available from: https://www.cambridge.org/core/product/identifier/S1355617705050083/type/journal_article.
Nelson LD, Guskiewicz KM, Barr WB, Hammeke TA, Randolph C, Ahn KW, et al. Age Differences in Recovery After Sport-Related Concussion: A Comparison of High School and Collegiate Athletes. J Athl Train [Internet]. 2016 [cited 2018 Aug 21];51:142–52. Available from: http://natajournals.org/doi/10.4085/1062-6050-51.4.04.
Kelshaw PM, Cook NE, Terry DP, Iverson GL, Caswell SV. Child Sport Concussion Assessment Tool 5th Edition: Normative Reference Values in Demographically Diverse Youth. Clin J Sport Med. 2021;Ahead of Print.
McCrea M, Kelly JP, Kluge J, Ackley B, Randolph C. Standardized Assessment of Concussion in football players. Neurology [Internet]. 1997 [cited 2019 Jan 31];48:586–8. Available from: http://www.neurology.org/cgi/doi/10.1212/WNL.48.3.586.
Oldham JR, Difabio MS, Kaminski TW, Dewolf RM, Howell DR, Buckley TA. Efficacy of Tandem Gait to Identify Impaired Postural Control after Concussion: Med Sci Sports Exerc [Internet]. 2018 [cited 2018 Aug 21];50:1162–8. Available from: http://Insights.ovid.com/crossref?an=00005768-201806000-00005.
Schneiders AG, Sullivan SJ, McCrory PR, Gray A, Maruthayanar S, Singh P, et al. The effect of exercise on motor performance tasks used in the neurological assessment of sports-related concussion. Br J Sports Med [Internet]. 2007 [cited 2018 Aug 24];42:1011–3. Available from: http://bjsm.bmj.com/cgi/doi/10.1136/bjsm.2007.041665.
Register-Mihalik JK, Littleton AC, Guskiewicz KM. Are Divided Attention Tasks Useful in the Assessment and Management of Sport-Related Concussion? Neuropsychol Rev [Internet]. 2013 [cited 2019 May 17];23:300–13. Available from: http://link.springer.com/10.1007/s11065-013-9238-1.

Symptoms are the Most Effective Child SCAT5 Component for Recognizing Concussion on the Day of Injury

Status:

Journal Publication

Version 1

Abstract

Figures

Key Points

Background

Methods

Design and Settings

Participants

Testing Procedures

Outcomes

Statistical Analyses

Results

Discussion

Conclusion

Declarations

Ethics Approval:

Consent to Participate:

Consent for Publication:

Availability of Data and Materials:

Competing Interests:

Funding:

Author’s Contributions:

References

Status:

Journal Publication

Version 1