Skip to main content Accessibility help
×
Home
Hostname: page-component-5c569c448b-r8t2r Total loading time: 0.521 Render date: 2022-07-06T11:11:30.374Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "useNewApi": true } hasContentIssue true

Applying an Evidence-Based Assessment Model to Identify Students at Risk for Perceived Academic Problems following Concussion

Published online by Cambridge University Press:  01 December 2016

Danielle M. Ransom
Affiliation:
University of Miami Miller School of Medicine, Miami, Florida
Alison R. Burns
Affiliation:
Children’s National Health System, Washington, DC George Washington University School of Medicine, Washington, DC
Eric A. Youngstrom
Affiliation:
University of North Carolina, Chapel Hill, North Carolina
Christopher G. Vaughan
Affiliation:
Children’s National Health System, Washington, DC George Washington University School of Medicine, Washington, DC
Maegan D. Sady
Affiliation:
Children’s National Health System, Washington, DC George Washington University School of Medicine, Washington, DC
Gerard A. Gioia*
Affiliation:
Children’s National Health System, Washington, DC George Washington University School of Medicine, Washington, DC
*
Correspondence and reprint requests to: Gerard Gioia, Chief, Division of Neuropsychology, Children’s National Medical Center, 15425 Shady Grove Road, Suite 350, Rockville, MD 20850. E-mail: ggioia@childrensnational.org

Abstract

Objectives: The aim of this study was to demonstrate the utility of an evidence-based assessment (EBA) model to establish a multimodal set of tools for identifying students at risk for perceived post-injury academic problems. Methods: Participants included 142 students diagnosed with concussion (age: M=14.95; SD=1.80; 59% male), evaluated within 4 weeks of injury (median=16 days). Demographics, pre-injury history, self- and parent-report measures assessing symptom severity and executive functions, and cognitive test performance were examined as predictors of self-reported post-injury academic problems. Results: Latent class analysis categorized participants into “high” (44%) and “low” (56%) levels of self-reported academic problems. Receiver operating characteristic analyses revealed significant discriminative validity for self- and parent-reported symptom severity and executive dysfunction and self-reported exertional response for identifying students reporting low versus high academic problems. Parent-reported symptom ratings [area under the receiver operating characteristic curve (AUC)=.79] and executive dysfunction (AUC=.74), and self-reported ratings of executive dysfunction (AUC=.84), symptoms (AUC=.80), and exertional response (AUC=.70) each classified students significantly better than chance (ps<.001). Hierarchical logistic regression indicated that, of the above, self-reported symptoms and executive dysfunction accounted for the most variance in the prediction of self-reported academic problems. Conclusions: Post-concussion symptom severity and executive dysfunction significantly predict perceived post-injury academic problems. EBA modeling identified the strongest set of predictors of academic challenges, offering an important perspective in the management of concussion by applying traditional strengths of neuropsychological assessment to clinical decision making. (JINS, 2016, 22, 1038–1049)

Type
Research Articles
Copyright
Copyright © The International Neuropsychological Society 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bossuyt, P.M., Reitsma, J.B., Bruns, D.E., Gatsonis, C.A., Glasziou, P.P., Irwig, L.M., & de Vet, H.C.W. (2003). Towards complete and accurate reporting of studies of diagnostic accuracy: The STARD initiative. British Medical Journal, 326, 4144.CrossRefGoogle ScholarPubMed
Brooks, B.L., Kadoura, B., Turley, B., Crawford, S., Mikrogianakis, A., & Barlow, K.M. (2014). Perception of recovery after pediatric mild traumatic brain injury is influenced by the “good old days” bias: Tangible implications for clinical practice and outcomes research. Archives of Clinical Neuropsychology, 29(2), 186193. http://doi.org/10.1093/arclin/act083 CrossRefGoogle Scholar
Center for Disease Control and Prevention, & National Center for Injury Prevention and Control. (2015, February 26). What are the signs and symptoms of concussion? Retrieved from http://www.cdc.gov/concussion/signs_symptoms.html Google Scholar
Chelune, G.J. (2010). Evidence-based research and practice in clinical neuropsychology. The Clinical Neuropsychologist, 24(3), 454467. http://doi.org/10.1080/13854040802360574 CrossRefGoogle Scholar
Echemendia, R.J., Iverson, G.L., McCrea, M., Macciocchi, S.N., Gioia, G.A., Putukian, M., & Comper, P. (2013). Advances in neuropsychological assessment of sport-related concussion. British Journal of Sports Medicine, 47(5), 294298. http://doi.org/10.1136/bjsports-2013-092186 CrossRefGoogle ScholarPubMed
Findling, R.L., Jo, B., Frazier, T.W., Youngstrom, E.A., Demeter, C.A., Fristad, M.A., & Horwitz, S.M. (2013). The 24-month course of manic symptoms in children. Bipolar Disorders, 15(6), 669679. http://doi.org/10.1111/bdi.12100 CrossRefGoogle ScholarPubMed
Gioia, G.A. (2014). Medical-school partnership in guiding return to school following mild traumatic brain injury in youth. Journal of Child Neurology, 883073814555604. http://doi.org/10.1177/0883073814555604 Google Scholar
Gioia, G.A. (2015). Multimodal evaluation and management of children with concussion: Using our heads and available evidence. Brain Injury, 29(2), 195206. http://doi.org/10.3109/02699052.2014.965210 CrossRefGoogle Scholar
Gioia, G.A., Isquith, P.K., Guy, S.C., & Kenworthy, L. (2000). TEST REVIEW behavior rating inventory of executive function. Child Neuropsychology, 6(3), 235238. http://doi.org/10.1076/chin.6.3.235.3152 CrossRefGoogle Scholar
Evidence-Based Medicine Working Group. (1992). Evidence-based medicine: A new approach to teaching the practice of medicine. JAMA, 268(17), 24202425. http://doi.org/10.1001/jama.1992.03490170092032 CrossRefGoogle Scholar
Hagenaars, J.A., & McCutcheon, A.L. (2002). Applied latent class analysis. New York: Cambridge University Press.CrossRefGoogle Scholar
Halstead, M.E., McAvoy, K., Devore, C.D., Carl, R., Lee, M., & Logan, K. (2013). Returning to learning following a concussion. Pediatrics, 948957. http://doi.org/10.1542/peds.2013–2867 CrossRefGoogle ScholarPubMed
Hunsley, J., & Mash, E.J. (2007). Evidence-based assessment. Annual Review of Clinical Psychology, 3(1), 2951. http://doi.org/10.1146/annurev.clinpsy.3.022806.091419 CrossRefGoogle ScholarPubMed
Iverson, G.L., Lange, R.T., Brooks, B.L., & Rennison, V.L.A. (2010). “Good old days” bias following mild traumatic brain injury. The Clinical Neuropsychologist, 24(1), 1737. http://doi.org/10.1080/13854040903190797 CrossRefGoogle ScholarPubMed
Kraemer, H.C. (1992). Evaluating medical tests: Objective and quantitative guidelines. Newbury Park, CA: Sage.Google Scholar
Kraemer, H.C., Kazdin, A.E., Offord, D.R., Kessler, R.C., Jensen, P.S., & Kupfer, D.J. (1999). Measuring the potency of risk factors for clinical or policy significance. Psychological Methods, 4(3), 257271. http://doi.org/10.1037/1082-989X.4.3.257 CrossRefGoogle Scholar
Lovell, M.R., Collins, M., Podell, K., Powell, J., & Maroon, J. (2000). ImPACT: Immediate post-concussion assessment and cognitive testing. Pittsburgh, PA: Neurohealth Systems, LLC.Google Scholar
McCrory, P., Meeuwisse, W.H., Aubry, M., Cantu, B., Dvořák, J., Echemendia, R.J., & Turner, M. (2013). Consensus statement on concussion in sport: The 4th International Conference on Concussion in Sport held in Zurich, November 2012. British Journal of Sports Medicine, 47(5), 250258. http://doi.org/10.1136/bjsports-2013-092313 CrossRefGoogle ScholarPubMed
McCutcheon, A. (1987). Latent class analysis. Thousand Oaks, CA: Sage.CrossRefGoogle ScholarPubMed
McGill, C., Gerst, E., Isquith, P., & Gioia, G. (2011). Evidence of validity for a monitoring version of the Behavior Rating Inventory of Executive Function (BRIEF). Journal of the International Neuropsychological Society, 17(s1), 297.Google Scholar
Mittenberg, W., DiGiulio, D.V., Perrin, S., & Bass, A.E. (1992). Symptoms following mild head injury: Expectation as aetiology. Journal of Neurology, Neurosurgery, and Psychiatry, 55(3), 200204.CrossRefGoogle ScholarPubMed
Newman, J.B., Reesman, J.H., Vaughan, C.G., & Gioia, G.A. (2013). Assessment of processing speed in children with mild TBI: A “first look” at the validity of pediatric ImPACT. The Clinical Neuropsychologist, 27(5), 779793. http://doi.org/10.1080/13854046.2013.789552 CrossRefGoogle ScholarPubMed
Pepe, M.S. (2004). The statistical evaluation of medical tests for classification and prediction. New York, NY: Oxford University Press.Google Scholar
R Core Team. (2013). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from, http://www.R-project.org/ Google ScholarPubMed
Ransom, D.M., Vaughan, C.G., Pratson, L., Sady, M.D., McGill, C.A., & Gioia, G.A. (2015). Academic effects of concussion in children and adolescents. Pediatrics, 135, 10431050. http://doi.org/10.1542/peds.2014-3434 CrossRefGoogle Scholar
Rice, M.E., & Harris, G.T. (2005). Comparing effect sizes in follow-up studies: ROC Area, Cohen’s d, and r. Law and Human Behavior, 29(5), 615620. http://doi.org/10.1007/s10979-005-6832-7 CrossRefGoogle ScholarPubMed
Sady, M.D., McGill, C.A., Gerst, E.H., & Gioia, G.A. (2013). Standardized assessment of cognitive exertion in mTBI and non-injured children. Journal of the International Neuropsychological Society, 19(S1), 194. http://doi.org/10.1017/S1355617713000362 Google Scholar
Sady, M.D., Vaughan, C.G., & Gioia, G.A. (2011). School and the concussed youth: Recommendations for concussion education and management. Physical Medicine and Rehabilitation Clinics of North America, 22(4), 701719, ix. http://doi.org/10.1016/j.pmr.2011.08.008 CrossRefGoogle ScholarPubMed
Sady, M.D., Vaughan, C.G., & Gioia, G.A. (2014). Psychometric characteristics of the postconcussion symptom inventory in children and adolescents. Archives of Clinical Neuropsychology, 29(4), 348363. http://doi.org/10.1093/arclin/acu014 CrossRefGoogle ScholarPubMed
Schatz, P., & Maerlender, A. (2013). A two-factor theory for concussion assessment using ImPACT: Memory and speed. Archives of Clinical Neuropsychology, 28(8), 791797. http://doi.org/10.1093/arclin/act077 CrossRefGoogle Scholar
Straus, S.E., Glasziou, P., Richardson, W.S., & Haynes, R.B. (2011). Evidence-based medicine: How to practice and teach it (4th ed.), Edinburgh: Churchill Livingstone.Google Scholar
Venkatraman, E.S., & Begg, C.B. (1996). A distribution-free procedure for comparing receiver operating characteristic curves from a paired experiment. Biometrika, 83(4), 835848. http://doi.org/10.1093/biomet/83.4.835 CrossRefGoogle Scholar
Vermunt, J., & Magidson, J. (2005). Latent GOLD Choice 4.0 User’s Guide. Belmont, MA: Statistical Innovations Inc.Google Scholar
Youngstrom, E.A. (2013). Future directions in psychological assessment: Combining evidence-based medicine innovations with psychology’s historical strengths to enhance utility. Journal of Clinical Child and Adolescent Psychology, 42(1), 139159. http://doi.org/10.1080/15374416.2012.736358 CrossRefGoogle Scholar
Youngstrom, E.A. (2014). A primer on receiver operating characteristic analysis and diagnostic efficiency statistics for pediatric psychology: We are ready to ROC. Journal of Pediatric Psychology, 39, 204221. http://doi.org/10.1093/jpepsy/jst062 CrossRefGoogle ScholarPubMed
Youngstrom, E.A., Meyers, O., Youngstrom, J.K., Calabrese, J.R., & Findling, R.L. (2006). Diagnostic and measurement issues in the assessment of pediatric bipolar disorder: implications for understanding mood disorder across the life cycle. Developmental Psychopathology, 18(4), 9891021.CrossRefGoogle ScholarPubMed
Zhou, X.-H., Obuchowski, N., & McClish, D. (2002). Statistical methods in diagnostic medicine. New York: Wiley. Retrieved from http://onlinelibrary.wiley.com/doi/10.1111/1541-0420.00266/abstract CrossRefGoogle Scholar
24
Cited by

Save article to Kindle

To save this article to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Applying an Evidence-Based Assessment Model to Identify Students at Risk for Perceived Academic Problems following Concussion
Available formats
×

Save article to Dropbox

To save this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about saving content to Dropbox.

Applying an Evidence-Based Assessment Model to Identify Students at Risk for Perceived Academic Problems following Concussion
Available formats
×

Save article to Google Drive

To save this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about saving content to Google Drive.

Applying an Evidence-Based Assessment Model to Identify Students at Risk for Perceived Academic Problems following Concussion
Available formats
×
×

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *