Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-23T21:05:33.008Z Has data issue: false hasContentIssue false
  • English
  • Français

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 ,
Alison R. Burns ,
Eric A. Youngstrom ,
Christopher G. Vaughan ,
Maegan D. Sady  and
Gerard A. Gioia
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: [email protected]
Get access Rights & Permissions [Opens in a new window]

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)

Keywords

Brain injuryPost-concussion symptomsPediatricsReceiver operating characteristicAcademicsEvidence based practice
Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 22 , Special Issue 10: Special Issue: Preclinical Prediction , November 2016 , pp. 1038 - 1049
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

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, 41–44.Google Scholar
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), 186–193. http://doi.org/10.1093/arclin/act083 Google 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), 454–467. http://doi.org/10.1080/13854040802360574 CrossRefGoogle ScholarPubMed
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), 294–298. http://doi.org/10.1136/bjsports-2013-092186 Google Scholar
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), 669–679. http://doi.org/10.1111/bdi.12100 Google Scholar
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), 195–206. http://doi.org/10.3109/02699052.2014.965210 CrossRefGoogle ScholarPubMed
Gioia, G.A., Isquith, P.K., Guy, S.C., & Kenworthy, L. (2000). TEST REVIEW behavior rating inventory of executive function. Child Neuropsychology, 6(3), 235–238. http://doi.org/10.1076/chin.6.3.235.3152 Google Scholar
Evidence-Based Medicine Working Group. (1992). Evidence-based medicine: A new approach to teaching the practice of medicine. JAMA, 268(17), 2420–2425. http://doi.org/10.1001/jama.1992.03490170092032 Google Scholar
Hagenaars, J.A., & McCutcheon, A.L. (2002). Applied latent class analysis. New York: Cambridge University Press.Google Scholar
Halstead, M.E., McAvoy, K., Devore, C.D., Carl, R., Lee, M., & Logan, K. (2013). Returning to learning following a concussion. Pediatrics, 948–957. 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), 29–51. http://doi.org/10.1146/annurev.clinpsy.3.022806.091419 Google Scholar
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), 17–37. http://doi.org/10.1080/13854040903190797 Google Scholar
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), 257–271. http://doi.org/10.1037/1082-989X.4.3.257 Google 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), 250–258. http://doi.org/10.1136/bjsports-2013-092313 CrossRefGoogle ScholarPubMed
McCutcheon, A. (1987). Latent class analysis. Thousand Oaks, CA: Sage.CrossRefGoogle Scholar
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), 200–204.Google Scholar
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), 779–793. http://doi.org/10.1080/13854046.2013.789552 Google Scholar
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 Scholar
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, 1043–1050. http://doi.org/10.1542/peds.2014-3434 Google 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), 615–620. http://doi.org/10.1007/s10979-005-6832-7 Google Scholar
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), 701–719, ix. http://doi.org/10.1016/j.pmr.2011.08.008 Google Scholar
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), 348–363. http://doi.org/10.1093/arclin/acu014 Google Scholar
Schatz, P., & Maerlender, A. (2013). A two-factor theory for concussion assessment using ImPACT: Memory and speed. Archives of Clinical Neuropsychology, 28(8), 791–797. http://doi.org/10.1093/arclin/act077 Google 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), 835–848. http://doi.org/10.1093/biomet/83.4.835 Google 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), 139–159. http://doi.org/10.1080/15374416.2012.736358 Google 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, 204–221. http://doi.org/10.1093/jpepsy/jst062 Google Scholar
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), 989–1021.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 Google Scholar