Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-26T04:41:21.133Z Has data issue: false hasContentIssue false
  • English
  • Français

Utility of the Neuropsychological Assessment Battery in detecting cognitive impairment after unilateral stroke

Published online by Cambridge University Press:  01 July 2010

NIKKI H. STRICKER ,
JOSHUA M. TYBUR ,
JOSEPH R. SADEK  and
KATHLEEN Y. HAALAND
NIKKI H. STRICKER
Affiliation:
Psychology Service, Boston VA Healthcare System, Boston, Massachusetts Department of Psychiatry, Boston University School of Medicine, Boston, Massachusetts
JOSHUA M. TYBUR
Affiliation:
Department of Psychology, University of New Mexico, Albuquerque, New Mexico
JOSEPH R. SADEK
Affiliation:
Behavioral Healthcare Line, New Mexico VA Healthcare System, Albuquerque, New Mexico Department of Psychiatry, University of New Mexico School of Medicine, Albuquerque, New Mexico
KATHLEEN Y. HAALAND*
Affiliation:
Department of Psychiatry, University of New Mexico School of Medicine, Albuquerque, New Mexico Research Service, New Mexico VA Healthcare System, Albuquerque, New Mexico Department of Neurology, University of New Mexico School of Medicine, Albuquerque, New Mexico
*
*Correspondence and reprint requests to: Kathleen Y. Haaland, Research Service (151), New Mexico VA Healthcare System, 1501 San Pedro SE, Albuquerque, NM 87108. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

This study evaluated the clinical utility of the Neuropsychological Assessment Battery (NAB) in a stroke sample by examining the NAB’s ability to differentiate a chronic stroke group with radiologically confirmed unilateral damage (n = 42) and a demographically matched healthy control (HC) group (n = 36). The stroke group performed more poorly than the control group across NAB Total score and all five Domain scores. Receiver operator curves (ROC) were derived and area under the curve (AUC) showed moderate diagnostic effectiveness (AUC .70 to .90) for NAB Total score, all five Domain scores, a motor composite, and a Global Deficit Score (GDS) that has been shown to closely approximate clinical ratings of neuropsychological impairment. The NAB Total, GDS, and motor composite had comparable clinical utility, whereas the Attention and Executive domain scores demonstrated better classification utility compared with the Memory domain. Because 90.5% of our stroke sample had middle cerebral artery territory strokes, the comparison of motor and cognitive classification utility may be biased. However, follow-up analyses showed that the NAB accounted for additional variance even when motor composite was included in the model. Sensitivity, specificity, and odds ratios at various clinical cutoffs are provided. These results suggest that the NAB is a useful clinical tool for detection of cognitive deficits in individuals with chronic unilateral stroke, although lenient clinical cutoffs appear warranted to maximize sensitivity. (JINS, 2010, 16, 813–821.)

Keywords

NeuropsychologyNeuropsychological testsAgingCerebrovascular disordersCognition disordersHemiparesis
Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 16 , Issue 5 , September 2010 , pp. 813 - 821
Copyright
Copyright © The International Neuropsychological Society 2010

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

Barba, R., Martinez-Espinosa, S., Rodriguez-Garcia, E., Pondal, M., Vivancos, J., & Del Ser, T. (2000). Poststroke dementia: Clinical features and risk factors. Stroke, 31, 1494–1501.Google Scholar
Brown, L.B., Stern, R.A., Cahn-Weiner, D.A., Rogers, B., Messer, M.A., Lannon, M.C., et al. . (2005). Driving scenes test of the Neuropsychological Assessment Battery (NAB) and on-road driving performance in aging and very mild dementia. Archives of Clinical Neuropsychology, 20, 209–215.CrossRefGoogle ScholarPubMed
Carey, C.L., Woods, S.P., Gonzalez, R., Conover, E., Marcotte, T.D., Grant, I., et al. . (2004). Predictive validity of global deficit scores in detecting neuropsychological impairment in HIV infection. Journal of Clinical and Experimental Neuropsychology, 26, 307–319.CrossRefGoogle ScholarPubMed
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Desmond, D.W., Moroney, J.T., Paik, M.C., Sano, M., Mohr, J.P., Aboumatar, S., et al. . (2000). Frequency and clinical determinants of dementia after ischemic stroke. Neurology, 54, 1124–1131.CrossRefGoogle ScholarPubMed
Fischer, J.E., Bachmann, L.M., & Jaeschke, R. (2003). A readers’ guide to the interpretation of diagnostic test properties: Clinical example of sepsis. Intensive Care Medicine, 29, 1043–1051.CrossRefGoogle Scholar
Gavett, B.E., Poon, S.J., Ozonoff, A., Jefferson, A.L., Nair, A.K., Green, R.C., et al. . (2009). Diagnostic utility of the NAB List Learning test in Alzheimer’s disease and amnestic mild cognitive impairment. Journal of the International Neuropsychological Society, 15, 121–129.Google Scholar
Gonzalez, R., Heaton, R.K., Moore, D.J., Letendre, S., Ellis, R.J., Wolfson, T., et al. . (2003). Computerized reaction time battery versus a traditional neuropsychological battery: Detecting HIV-related impairments. Journal of the International Neuropsychological Society, 9, 64–71.CrossRefGoogle ScholarPubMed
Grohman, K., & Fals-Stewart, W. (2004). The detection of cognitive impairment among substance-abusing patients: The accuracy of the neuropsychological assessment battery-screening module. Experimental and Clinical Psychopharmacology, 12, 200–207.CrossRefGoogle Scholar
Haaland, K.Y., & Flaherty, D. (1984). The different types of limb apraxia errors made by patients with left vs. right hemisphere damage. Brain and Cognition, 3, 370–384.Google Scholar
Haaland, K.Y., Harrington, D.L., & Knight, R.T. (2000). Neural representations of skilled movement. Brain, 123(Pt 11), 2306–2313.CrossRefGoogle ScholarPubMed
Haaland, K.Y., Prestopnik, J.L., Knight, R.T., & Lee, R.R. (2004). Hemispheric asymmetries for kinematic and positional aspects of reaching. Brain, 127(Pt 5), 1145–1158.Google Scholar
Hachinski, V., Iadecola, C., Petersen, R.C., Breteler, M.M., Nyenhuis, D.L., Black, S.E., et al. . (2006). National Institute of Neurological Disorders and Stroke-Canadian Stroke Network vascular cognitive impairment harmonization standards. Stroke, 37, 2220–2241.Google Scholar
Hanley, J.A., & McNeil, B.J. (1983). A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology, 148, 839–843.Google Scholar
Heaton, R.K., Grant, I., Butters, N., White, D.A., Kirson, D., Atkinson, J.H., et al. . (1995). The HNRC 500–neuropsychology of HIV infection at different disease stages. HIV Neurobehavioral Research Center. Journal of the International Neuropsychological Society, 1, 231–251.CrossRefGoogle ScholarPubMed
Heaton, R.K., Miller, S.W., Taylor, M.J., & Grant, I. (2004). Revised comprehensive norms for an expanded Halstead-Reitan Battery: Demographically adjusted neuropsychological norms for African American and Caucasian Adults. Lutz, FL: Psychological Assessment Resources, Inc.Google Scholar
Hom, J., & Reitan, R.M. (1990). Generalized cognitive function after stroke. Journal of Clinical and Experimental Neuropsychology, 12, 644–654.Google Scholar
Iverson, G.L., Mendrek, A., & Adams, R.L. (2004). The persistent belief that VIQ-PIQ splits suggest lateralized brain damage. Applied Neuropsychology, 11, 85–90.Google Scholar
Iverson, G.L., Williamson, D.J., Ropacki, M., & Reilly, K.J. (2007). Frequency of abnormal scores on the Neuropsychological Assessment Battery Screening Module (S-NAB) in a mixed neurological sample. Applied Neuropsychology, 14, 178–182.CrossRefGoogle Scholar
Jaillard, A., Naegele, B., Trabucco-Miguel, S., LeBas, J.F., & Hommel, M. (2009). Hidden dysfunctioning in subacute stroke. Stroke, 40, 2473–2479.CrossRefGoogle ScholarPubMed
Lesniak, M., Bak, T., Czepiel, W., Seniow, J., & Czlonkowska, A. (2008). Frequency and prognostic value of cognitive disorders in stroke patients. Dementia and Geriatric Cognitive Disorders, 26, 356–363.Google Scholar
Linden, T., Skoog, I., Fagerberg, B., Steen, B., & Blomstrand, C. (2004). Cognitive impairment and dementia 20 months after stroke. Neuroepidemiology, 23, 45–52.Google Scholar
Madureira, S., Guerreiro, M., & Ferro, J.M. (2001). Dementia and cognitive impairment three months after stroke. European Journal of Neurology, 8, 621–627.CrossRefGoogle ScholarPubMed
McKay, C., Wertheimer, J.C., Fichtenberg, N.L., & Casey, J.E. (2008). The repeatable battery for the assessment of neuropsychological status (RBANS): Clinical utility in a traumatic brain injury sample. The Clinical Neuropsychologist, 22, 228–241.CrossRefGoogle Scholar
Nys, G.M., van Zandvoort, M.J., de Kort, P.L., van der Worp, H.B., Jansen, B.P., Algra, A., et al. . (2005). The prognostic value of domain-specific cognitive abilities in acute first-ever stroke. Neurology, 64, 821–827.Google Scholar
Rasquin, S.M., Lodder, J., Ponds, R.W., Winkens, I., Jolles, J., & Verhey, F.R. (2004). Cognitive functioning after stroke: A one-year follow-up study. Dementia and Geriatric Cognitive Disorders, 18, 138–144.Google Scholar
Rinehart, J.K., Singleton, R.D., Adair, J.C., Sadek, J.R., & Haaland, K.Y. (2009). Arm use after left or right hemiparesis is influenced by hand preference. Stroke, 40, 545–550.Google Scholar
Sachdev, P.S., Brodaty, H., Valenzuela, M.J., Lorentz, L., Looi, J.C., Berman, K., et al. . (2006). Clinical determinants of dementia and mild cognitive impairment following ischaemic stroke: The Sydney Stroke Study. Dementia and Geriatric Cognitive Disorders, 21, 275–283.CrossRefGoogle ScholarPubMed
Sachdev, P.S., Brodaty, H., Valenzuela, M.J., Lorentz, L., Looi, J.C., Wen, W., et al. . (2004). The neuropsychological profile of vascular cognitive impairment in stroke and TIA patients. Neurology, 62, 912–919.CrossRefGoogle ScholarPubMed
Sadek, J.R., Stricker, N.H., Adair, J.C., & Haaland, K.Y. (2010). Performance based assessment of instrumental activities of daily living in stroke. Manuscript submitted for publication.Google Scholar
Srikanth, V.K., Anderson, J.F., Donnan, G.A., Saling, M.M., Didus, E., Alpitsis, R., et al. . (2004). Progressive dementia after first-ever stroke: A community-based follow-up study. Neurology, 63, 785–792.Google Scholar
Stephens, S., Kenny, R.A., Rowan, E., Allan, L., Kalaria, R.N., Bradbury, M., et al. . (2004). Neuropsychological characteristics of mild vascular cognitive impairment and dementia after stroke. International Journal of Geriatric Psychiatry, 19, 1053–1057.Google Scholar
Stern, R.A., & White, T. (2003a). Neuropsychological assessment battery. Lutz, FL: Psychological Assessment Resources.Google Scholar
Stern, R.A., & White, T. (2003b). Neuropsychological assessment battery: Administration, scoring, and interpretation manual. Lutz, FL: Psychological Assessment Resources.Google Scholar
Stricker, N.H., Sadek, J.R., Leiphart, S.S., Stapp, L.H., & Haaland, K.Y. (2009). Neuropsychological performance best predicts performance-based instrumental activities of daily living after stroke. ’Abstracts for the AACN Scientific Poster Session.’ The Clinical Neuropsychologist, 23, 605.Google Scholar
Temple, R.O., Zgaljardic, D.J., Abreu, B.C., Seale, G.S., Ostir, G.V., & Ottenbacher, K.J. (2009). Ecological validity of the neuropsychological assessment battery screening module in post-acute brain injury rehabilitation. Brain Injury, 23, 45–50.CrossRefGoogle ScholarPubMed
White, T., & Stern, R.A. (2003). Neuropsychological assessment battery: Psychometric and technical manual. Lutz, FL: Psychological Assessment Resources.Google Scholar