Archives of Physical Medicine and Rehabilitation
Volume 89, Issue 8 , Pages 1507-1513 , August 2008

Time-Course of Changes in Arm Impairment After Stroke: Variables Predicting Motor Recovery Over 12 Months

  • Mehdi M. Mirbagheri, PhD

      Affiliations

    • Corresponding Author InformationReprint requests to Mehdi M. Mirbagheri, PhD, Dept of Physical Medicine and Rehabilitation, Northwestern University, Sensory Motor Performance Program Rehabilitation Institute of Chicago, 345 E Superior St, Ste 1408, Chicago, IL 60611
  • ,
  • W. Zev Rymer, MD, PhD

References 

  1. Duncan PW, Goldstein LB, Horner RD, Landsman PB, Samsa GP, Matchar DB. Similar motor recovery of upper and lower extremities after stroke. Stroke. 1994;25:1181–1188
  2. Jorgensen HS, Nakayama H, Raaschou HO, Vive-Larsen J, Stoier M, Olsen TS. Outcome and time course recovery in stroke, part II: time course of recovery (The Copenhagen Stroke Study). Arch Phys Med Rehabil. 1995;76:406–412
  3. Duncan PW, Goldstein LB, Matchar D, Divine GW, Feussner J. Measurement of motor recovery after stroke: outcome assessment and sample size requirements. Stroke. 1992;23:1084–1089
  4. Gilman S. Time course of outcome of recovery from stroke: relevance to stem cell treatment. Exp Neurol. 2006;199:37–41
  5. Kwakkel G, Boudewijn K, Twisk J. Impact of time on improvement of outcome after stroke. Stroke. 2006;37:2348–2353
  6. Newman M. The process of recovery after hemiplegia. Stroke. 1972;3:702–710
  7. Winward CE, Halligan PW, Wade DT. Somotosensory recovery: a longitudinal study of the first 6 months after unilateral stroke. Disabil Rehabil. 2007;29:293–299
  8. Fugl-Meyer A. Assessment of motor function in the hemiparetic patients. In:  Buerger AA,  Tobis JS editor. Neurophysiologic aspects of rehabilitation medicine. Springfield: CC Thomas; 1976;p. 231–250
  9. Lai SM, Duncan PW, Keighley J. Prediction of functional outcome after stroke: comparison of the Orpington Prognostic Scale and the NIH Stroke Scale. Stroke. 1998;29:1838–1842
  10. Schiemanck SK, Kwakkel G, Post MW, Prevo AJ. Predictive value of ischemic lesion volume assessed with magnetic resonance imaging for neurological deficits and functional outcome poststroke: a critical review of the literature. Neurorehabil Neural Repair. 2006;20:492–502
  11. Ashworth B. Preliminary trial of carisoprodol in multiple sclerosis. Practitioner. 1964;192:540–542
  12. Tsao C, Mirbagheri MM. Upper limb impairments associated with spasticity in neurological disorders. J Neuroeng Rehabil. 2007;4(45):1–15
  13. Rohrer B, Fasoli S, Krebs HI, et al. Movement smoothness changes during stroke recovery. J Neurosci. 2002;22:8297–8304
  14. Wagner JM, Lang CE, Bastian AJ, et al. Relationships between reaching deficits and clinical impairments in acute hemiparesis. Neurorehabil Neural Repair. 2006;20:406–416
  15. Kreuter F, Muthen B. Longitudinal modeling of population heterogeneity: methodological challenges to the analysis of empirically derived criminal trajectory profiles. In:  Hancock GR,  Samuelsen KM editor. Advances in latent variable mixture models. Charlotte: Information Age Publishing; 2007;p. 53–75
  16. Muthen B. Latent variable analysis: growth mixture modeling and related techniques for longitudinal data. In:  Kaplan D editors. Handbook of quantitative methodology for the social sciences. Newbury Park: Sage; 2004;p. 345–368
  17. Muthen B, Asparouhov T. Growth mixture analysis: models with non-Gaussian random effects. In:  Fitzmaurice G,  Davidian M,  Verbeke G,  Molenberghs G editor. Advances in longitudinal data analysis. Boca Raton: Chapman & Hall/CRC Pr; 2006;p. 1–23
  18. Muthén B, Brown CH, Masyn K, et al. General growth mixture modeling for randomized preventive interventions. Biostatistics. 2002;3:459–475
  19. Li F, Duncan TE, Hops H. Examining developmental trajectories in adolescent alcohol use using piecewise growth mixture modeling analysis. J Stud Alcohol. 2001;62:199–210
  20. Proust-Lima C, Leternneur L, Jacqmin-Gadda H. A nonlinear latent class model for joint analysis of multivariate longitudinal data and a binary outcome. Stat Med. 2006;26:2229–2245
  21. Mirbagheri MM, Tsao C, Rymer WZ. Changes of elbow kinematics and kinetics during one year after stroke. Muscle Nerve. 2008;37:387–395
  22. Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc B. 1977;39:1–38
  23. Duncan P, Propst M, Nelson S. Reliability of Fugl-Meyer assessment of sensorimotor recovery following cerebrovascular accident. Phys Ther. 1983;63:1606–1610

 Published online June 30, 2008 at www.archives-pmr.org.Supported by the National Institutes of Health (grant no. 1 R21 NS45005-01A1), the American Heart Association (grant no. SDG 0330166N), and the National Science Foundation (grant no. NSF 0302313).No commercial party having a direct financial interest in the results of the research supporting this article has or will confer a benefit upon the authors or upon any organization with which the authors are associated.

PII: S0003-9993(08)00306-7

doi: 10.1016/j.apmr.2008.02.017

Archives of Physical Medicine and Rehabilitation
Volume 89, Issue 8 , Pages 1507-1513 , August 2008