Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-02T19:15:56.911Z Has data issue: false hasContentIssue false
  • English
  • Français

Empirical Derivation and Validation of a Clinical Case Definition for Neuropsychological Impairment in Children and Adolescents

Published online by Cambridge University Press:  26 August 2015

Miriam H. Beauchamp ,
Brian L. Brooks ,
Nick Barrowman ,
Mary Aglipay ,
Michelle Keightley ,
Peter Anderson ,
Keith O. Yeates ,
Martin H. Osmond  and
Roger Zemek
Miriam H. Beauchamp*
Affiliation:
Department of Psychology, University of Montreal, Montreal, Quebec, Canada Ste-Justine Hospital Research Center, Montreal, Quebec, Canada
Brian L. Brooks
Affiliation:
Neurosciences program (Brain Injury and Rehabilitation), Alberta Children’s Hospital, Calgary, Alberta, Canada Departments of Pediatrics and Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada Department of Psychology, University of Calgary, Calgary, Alberta, Canada
Nick Barrowman
Affiliation:
Department of Psychology, University of Calgary, Calgary, Alberta, Canada
Mary Aglipay
Affiliation:
Clinical Research Unit, Children’s Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada
Michelle Keightley
Affiliation:
Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada Departments of Occupational Science and Occupational Therapy and Rehabilitation Sciences Institute, University of Toronto, Toronto, Ontario, Canada Toronto Rehabilitation Institute, Toronto, Ontario, Canada
Peter Anderson
Affiliation:
Behavioural Neurosciences & Consultation-Liaison program, Children’s Hospital of Eastern Ontario, Ottawa, ON Children’s Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada
Keith O. Yeates
Affiliation:
Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada Department of Psychology, University of Calgary, Calgary, Alberta, Canada Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
Martin H. Osmond
Affiliation:
Children’s Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada Departments of Pediatrics and Emergency Medicine, University of Ottawa, Ottawa, Ontario, Canada
Roger Zemek
Affiliation:
Children’s Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada Departments of Pediatrics and Emergency Medicine, University of Ottawa, Ottawa, Ontario, Canada
*
Correspondence and reprint requests to: Miriam Beauchamp, Department of Psychology, University of Montreal, C.P. Succursale Centre-Ville, Montréal, Québec, Canada, H3C 3J7. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Neuropsychological assessment aims to identify individual performance profiles in multiple domains of cognitive functioning; however, substantial variation exists in how deficits are defined and what cutoffs are used, and there is no universally accepted definition of neuropsychological impairment. The aim of this study was to derive and validate a clinical case definition rule to identify neuropsychological impairment in children and adolescents. An existing normative pediatric sample was used to calculate base rates of abnormal functioning on eight measures covering six domains of neuropsychological functioning. The dataset was analyzed by varying the range of cutoff levels [1, 1.5, and 2 standard deviations (SDs) below the mean] and number of indicators of impairment. The derived rule was evaluated by bootstrap, internal and external clinical validation (orthopedic and traumatic brain injury). Our neuropsychological impairment (NPI) rule was defined as “two or more test scores that fall 1.5 SDs below the mean.” The rule identifies 5.1% of the total sample as impaired in the assessment battery and consistently targets between 3 and 7% of the population as impaired even when age, domains, and number of tests are varied. The NPI rate increases in groups known to exhibit cognitive deficits. The NPI rule provides a psychometrically derived method for interpreting performance across multiple tests and may be used in children 6–18 years. The rule may be useful to clinicians and scientists who wish to establish whether specific individuals or clinical populations present within expected norms versus impaired function across a battery of neuropsychological tests. (JINS, 2015, 21, 596–609)

Keywords

NeuropsychologyAssessmentPediatricCognitionPerformanceBrain injury
Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 21 , Issue 8 , September 2015 , pp. 596 - 609
Copyright
Copyright © The International Neuropsychological Society 2015 

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

Achenbach, T.M. (1991). Integrative guide for the 1991 CBCL/4-18, YSR, and TRF profiles. Burlington, VT: Department of Psychiatry, University of Vermont.Google Scholar
Anderson, V., Spencer-Smith, M., Leventer, R., Coleman, L., Anderson, P., Williams, J., & Jacobs, R. (2009). Childhood brain insult: Can age at insult help us predict outcome? Brain, 132(1), 45–56. doi:10.1093/brain/awn293CrossRefGoogle ScholarPubMed
Babikian, T., & Asarnow, R. (2009). Neurocognitive outcomes and recovery after pediatric TBI: Meta-analytic review of the literature. Neuropsychology, 23(3), 283–296. doi:10.1037/a0015268CrossRefGoogle ScholarPubMed
Babikian, T., Satz, P., Zaucha, K., Light, R., Lewis, R.S., & Asarnow, R.F. (2011). The UCLA longitudinal study of neurocognitive outcomes following mild pediatric traumatic brain injury. Journal of the International Neuropsychological Society, 17(5), 886–895. doi:10.1017/s1355617711000907CrossRefGoogle ScholarPubMed
Beauchamp, M.H., & Anderson, V. (2010). SOCIAL: An integrative framework for the development of social skills. Psychological Bulletin, 136(1), 39–64. doi:10.1037/a0017768CrossRefGoogle ScholarPubMed
Beauchamp, M.H., & Anderson, V. (2013). Cognitive and psychopathological sequelae of pediatric traumatic brain injury. In O. Dulac, S. Di Mauro & M. Lassonde (Eds.), Handbook of clinical pediatric neurology, Vol. 112, (pp. 913–920). Amsterdam: Elsevier.Google Scholar
Binder, L.M., Iverson, G.L., & Brooks, B.L. (2009). To err is human: “Abnormal” neuropsychological scores and variability are common in healthy adults. Archives of Clinical Neuropsychology, 24(1), 31–46. doi:10.1093/arclin/acn001CrossRefGoogle Scholar
Brooks, B.L. (2010). Seeing the forest for the trees: Prevalence of low scores on the Wechsler Intelligence Scale for Children, fourth edition (WISC-IV). Psychological Assessment, 22(3), 650–656. doi:10.1037/a0019781CrossRefGoogle ScholarPubMed
Brooks, B.L., & Iverson, G.L. (2012). Improving accuracy when identifying cognitive impairment in pediatric neuropsychological assessments. In E.M.S. Sherman & B.L. Brooks (Eds.), Pediatric forensic neuropsychology (pp. 66–88). New York, NY: Oxford University Press.Google Scholar
Brooks, B.L., Iverson, G.L., Feldman, H.H., & Holdnack, J.A. (2009). Minimizing misdiagnosis: Psychometric criteria for possible or probable memory impairment. Dementia and Geriatric Cognitive Disorders, 27(5), 439–450. doi:10.1159/000215390CrossRefGoogle ScholarPubMed
Brooks, B.L., Iverson, G.L., Koushik, N.S., Mazur-Mosiewicz, A., Horton, A., & Reynolds, C.R. (2013). Prevalence of low scores in children and adolescents on the test of verbal conceptualization and fluency. Applied Neuropsychology; Child, 2(1), 70–77. doi:10.1080/21622965.2012.742792CrossRefGoogle ScholarPubMed
Brooks, B.L., Iverson, G.L., Sherman, E.M., & Holdnack, J.A. (2009). Healthy children and adolescents obtain some low scores across a battery of memory tests. Journal of the International Neuropsychological Society, 15(4), 613–617. doi:10.1017/S1355617709090651CrossRefGoogle ScholarPubMed
Brooks, B.L., Iverson, G.L., Sherman, E.M., & Roberge, M.-C. (2010). Identifying cognitive problems in children and adolescents with depression using computerized neuropsychological testing. Applied Neuropsychology, 17(1), 37–43. doi:10.1080/09084280903526083CrossRefGoogle ScholarPubMed
Brooks, B.L., Sherman, E.M., & Iverson, G.L. (2010). Healthy children get low scores too: Prevalence of low scores on the NEPSY-II in preschoolers, children, and adolescents. Archives of Clinical Neuropsychology, 25(3), 182–190. doi:10.1093/arclin/acq005CrossRefGoogle ScholarPubMed
Carey, C.L., Woods, S.P., Gonzalez, R., Conover, E., Marcotte, T.D., Grant, I., & Heaton, R.K. (2004). Predictive validity of global deficit scores in detecting neuropsychological impairment in HIV infection. Journal of Clinical and Experimental Psychology, 26(3), 307–319. doi:10.1080/13803390490510031Google ScholarPubMed
Casey, B.J., Jones, R.M., & Hare, T.A. (2008). The adolescent brain. Annals of the New York Academy of Sciences, 1124(1), 111–126. doi:10.1196/annals.1440.010CrossRefGoogle ScholarPubMed
Conklin, H.M., Salorio, C.F., & Slomine, B.S. (2008). Working memory performance following paediatric traumatic brain injury. Brain Injury, 22(11), 847–857. doi:10.1080/02699050802403565CrossRefGoogle ScholarPubMed
Crawford, J.R., Garthwaite, P.H., & Gault, C.B. (2007). Estimating the percentage of the population with abnormally low scores (or abnormally large score differences) on standardized neuropsychological test batteries: A generic method with applications. Neuropsychology, 21(4), 419–430. doi:10.1037/0894-4105.21.4.419CrossRefGoogle ScholarPubMed
Crowe, L.M., Catroppa, C., Babl, F.E., & Anderson, V. (2012). Intellectual, behavioral, and social outcomes of accidental traumatic brain injury in early childhood. Pediatrics, 129(2), e262–e268. doi:10.1542/peds.2011-0438CrossRefGoogle ScholarPubMed
Delis, D.C., Kramer, J.H., Kaplan, E., & Ober, B.A. (1994). CVLT-C: California Verbal Learning Test. San Antonio, TX: The Psychological Corporation.Google Scholar
Diamond, A. (2000). Close interrelation of motor development and cognitive development and of the cerebellum and prefrontal cortex. Child Development, 71(1), 44–56.CrossRefGoogle ScholarPubMed
Evans, A.C., Brain Development Cooperative Group. (2006). The NIH MRI study of normal brain development. Neuroimage, 30(1), 184–202.CrossRefGoogle ScholarPubMed
Ewing-Cobbs, L., Barnes, M.A., & Fletcher, J.M. (2003). Early brain injury in children: Development and reorganization of cognitive function. Developmental Neuropsychology, 24(2-3), 669–704. doi:10.1080/87565641.2003.9651915CrossRefGoogle ScholarPubMed
Fay, T.B., Yeates, K.O., Wade, S.L., Drotar, D., Stancin, T., & Taylor, H.G. (2009). Predicting longitudinal patterns of functional deficits in children with traumatic brain injury. Neuropsychology, 23(3), 271–282. doi:10.1037/a0014936CrossRefGoogle ScholarPubMed
Gathercole, S.E. (1998). The development of memory. Journal of Child Psychology and Psychiatry, 39(1), 3–27.CrossRefGoogle ScholarPubMed
Gazzaley, A., & Nobre, A.C. (2012). Top-down modulation: Bridging selective attention and working memory. Trends in Cognitive Science, 16(2), 129–135. doi:10.1016/j.tics.2011.11.014CrossRefGoogle ScholarPubMed
Hackman, D.A., Farah, M.J., & Meaney, M.J. (2014). Socioeconomic status and the brain: Mechanistic insights from human and animal research. Nature Reviews Neuroscience, 11(9), 651–659. doi:10.1038/nrn2897CrossRefGoogle Scholar
Hung, R., Carroll, L.J., Cancelliere, C., Cote, P., Rumney, P., Keightley, M., & Cassidy, J.D. (2014). Systematic review of the clinical course, natural history, and prognosis for pediatric mild traumatic brain injury: Results of the International Collaboration on Mild Traumatic Brain Injury Prognosis. Archives of Physical Medicine and Rehabilitation, 95(3 Suppl.), S174–S191. doi:10.1016/j.apmr.2013.08.301CrossRefGoogle ScholarPubMed
Hurks, P., Hendriksen, J., Dek, J., & Kooij, A. (2013). Normal variability of children’s scaled scores on subtests of the Dutch Wechsler Preschool and Primary Scale of Intelligence-Third Edition. The Clinical Neuropsychologist, 27(6), 988–1003. doi:10.1080/13854046.2013.797502CrossRefGoogle ScholarPubMed
Huttenlocher, P.R. (1979). Synaptic density in human frontal cortex - Developmental changes and effects of aging. Brain Research, 163(2), 195–205.Google ScholarPubMed
Huttenlocher, P.R., & Dabholkar, A. (1997). Developmental anatomy of the prefrontal cortex. In N. Krasnegor, G. Reid, & P. Goldman-Rakic (Eds.), Development of the prefrontal cortex: Evolution, neurobiology and behaviour (pp. 69–83). Baltimore: Paul H. Brookes Publishing Company.Google Scholar
Isquith, P.K., Crawford, J.S., Espy, K.A., & Gioia, G.A. (2005). Assessment of executive function in preschool-aged children. Mental Retardation and Developmental Disabilities Research Reviews, 11(3), 209–215. doi:10.1002/mrdd.20075CrossRefGoogle ScholarPubMed
Kolb, B., Pellis, S., & Robinson, T.E. (2004). Plasticity and functions of the orbital frontal cortex. Brain and Cognition, 55(1), 104–115. doi:10.1016/S0278-2626(03)00278-1CrossRefGoogle ScholarPubMed
Korkman, M., Kirk, U., & Kemp, S. (1998). NEPSY: A developmental neuropsychological assessment. San Antonio, TX: The Psychological Corporation.Google Scholar
Levin, H.S., Li, X., McCauley, S.R., Hanten, G., Wilde, E.A., & Swank, P. (2013). Neuropsychological outcome of mTBI: A principal component analysis approach. Journal of Neurotrauma, 30(8), 625–632. doi:10.1089/neu.2012.2627CrossRefGoogle ScholarPubMed
Lezak, M.D. (1995). Neuropsychological assessment. New York: Oxford University Press.Google Scholar
Mather, N., & Woodcock, R.W. (2001). Woodcock-Johnson III Tests of Achievement: Examiner’s manual. Rolling Meadows, IL: Riverside Publishing.Google Scholar
Rieger, B.P., Lewandowski, L.J., Callahan, J.M., Spenceley, L., Truckenmiller, A., Gathje, R., & Miller, L.A. (2013). A prospective study of symptoms and neurocognitive outcomes in youth with concussion vs orthopaedic injuries. Brain Injury, 27(2), 169–178. doi:10.3109/02699052.2012.729290CrossRefGoogle 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), 348–363. doi:10.1093/arclin/acu014CrossRefGoogle ScholarPubMed
Sahakian, B.J., Morris, R.G., Evenden, J.L., Heald, A., Levy, R., Philpot, M., & Robbins, T.W. (1988). A comparative study of visuospatial memory and learning in Alzheimer-type dementia and Parkinson’s disease. Brain, 111(3), 695–718.CrossRefGoogle ScholarPubMed
Sahakian, B.J., & Owen, A.M. (1992). Computerized assessment in neuropsychiatry using CANTAB: Discussion paper. Journal of the Royal Society of Medicine, 85(7), 399–402.Google ScholarPubMed
Schoenberg, M.R., Lange, R.T., Brickell, T.A., & Saklofske, D.H. (2007). Estimating premorbid general cognitive functioning for children and adolescents using the American Wechsler Intelligence Scale for Children-Fourth Edition: Demographic and current performance approaches. Child Neurology, 22(4), 379–388.CrossRefGoogle ScholarPubMed
Tiffin, J. (1968). Purdue pegboard: Examiner manual. Chicago: Science Research Associates.Google Scholar
Tiffin, J., & Asher, E.J. (1948). The Purdue Pegboard: Norms and studies of reliability and validity. Journal of Applied Psychology, 32(3), 234–247.CrossRefGoogle ScholarPubMed
Tsujimoto, S. (2008). The prefrontal cortex: Functional neural development during early childhood. Neuroscientist, 14(4), 345–358. doi:10.1177/1073858408316002CrossRefGoogle ScholarPubMed
van der Sluis, S., Willemsen, G., de Geus, E.J., Boomsma, D.I., & Posthuma, D. (2008). Gene-environment interaction in adults’ IQ scores: Measures of past and present environment. Behavioral Genetics, 38(4), 348–360. doi:10.1007/s10519-008-9212-5CrossRefGoogle ScholarPubMed
Waber, D.P., De Moor, C., Forbes, P.W., Almli, C.R., Botteron, K.N., Leonard, G., & Group, B.D.C. (2007). The NIH MRI study of normal brain development: Performance of a population based sample of healthy children aged 6 to 18 years on a neuropsychological battery. Journal of the International Neuropsychological Society, 13(5), 729–746. doi:10.1017/S1355617707070841CrossRefGoogle ScholarPubMed
Wechsler, D. (1999). Wechsler Abbreviated Scale of Intelligence. New York, NY: The Psychological Corporation.Google Scholar
Yeates, K.O. (2010). Mild traumatic brain injury and postconcussive symptoms in children and adolescents. Journal of the International Neuropsychological Society, 16(6), 953–960. doi:10.1017/s1355617710000986CrossRefGoogle ScholarPubMed
Yeates, K.O., Taylor, H.G., Wade, S., Drotar, D., Stancin, T., & Minich, N. (2002). A prospective study of short- and long-term neuropsychological outcomes after traumatic brain injury in children. Neuropsychology, 16(4), 514–523.CrossRefGoogle Scholar
Zemek, R., Osmond, M.H., & Barrowman, N., Pediatric Emergency Research Canada Concussion Team. (2013). Predicting and preventing postconcussive problems in paediatrics (5P) study: Protocol for a prospective multicentre clinical prediction rule derivation study in children with concussion. British Medical Journal Open, 3(8), 1–10. doi:10.1136/bmjopen-2013-003550Google ScholarPubMed
Zimmerman, I.L., Steiner, V.G., & Pond, R.E. (2002). Preschool language scale (3rd ed.). San Antonio, TX: The Psychological Corporation.Google Scholar