Trajectories of arm recovery early after stroke: an exploratory study using latent class growth analysis

References

• Lawrence ES, Coshall C, Dundas R, et al. Estimates of the prevalence of acute stroke impairments and disability in a multiethnic population. Stroke. 2001;32(6):1279–1284.
   PubMed Web of Science ®Google Scholar
• Nakayama H, Jørgensen HS, Raaschou HO, et al. Recovery of upper extremity function in stroke patients: the Copenhagen stroke study. Arch Phys Med Rehabil. 1994;75(4):394–398.
   PubMed Web of Science ®Google Scholar
• Persson HC, Parziali M, Danielsson A, et al. Outcome and upper extremity function within 72 hours after first occasion of stroke in an unselected population at a stroke unit. A part of the SALGOT study. BMC Neurol. 2012;12(1):1–6.
   PubMedGoogle Scholar
• National Stroke Foundation. National stroke audit-rehabilitation services report 2018. Melbourne (Australia): National Stroke Foundation; 2018.
   Google Scholar
• Kim B, Winstein C. Can neurological biomarkers of brain impairment be used to predict poststroke motor recovery? A systematic review. Neurorehabil Neural Repair. 2017;31(1):3–24.
   PubMed Web of Science ®Google Scholar
• Chen SY, Winstein CJ. A systematic review of voluntary arm recovery in hemiparetic stroke: critical predictors for meaningful outcomes using the international classification of functioning, disability, and health. J Neurol Phys Ther. 2009;33(1):2–13.
   PubMed Web of Science ®Google Scholar
• Coupar F, Pollock A, Rowe P, et al. Predictors of upper limb recovery after stroke: a systematic review and meta-analysis. Clin Rehabil. 2012;26(4):291–313.
   PubMed Web of Science ®Google Scholar
• Nijland RH, van Wegen EE, Harmeling-van der Wel BC, EPOS Investigators, et al. Presence of finger extension and shoulder abduction within 72 hours after stroke predicts functional recovery: early prediction of functional outcome after stroke: the EPOS cohort study. Stroke. 2010;41(4):745–750.
   PubMed Web of Science ®Google Scholar
• Prabhakaran S, Zarahn E, Riley C, et al. Inter-individual variability in the capacity for motor recovery after ischemic stroke. Neurorehabil Neural Repair. 2008;22(1):64–71.
   PubMed Web of Science ®Google Scholar
• Stinear CM, Byblow WD, Ackerley SJ, et al. PREP2: a biomarker-based algorithm for predicting upper limb function after stroke. Ann Clin Transl Neurol. 2017;4(11):811–820.
   PubMed Web of Science ®Google Scholar
• Stinear CM, Barber PA, Petoe M, et al. The PREP algorithm predicts potential for upper limb recovery after stroke. Brain. 2012;135(Pt 8):2527–2535.
   PubMed Web of Science ®Google Scholar
• Kwah L, Herbert R. Prediction of walking and arm recovery after stroke: a critical review. Brain Sci. 2016;6(4):53.
   PubMed Web of Science ®Google Scholar
• Jung T, Wickrama KA. An introduction to latent class growth analysis and growth mixture modeling. Soc Personal Psychol. 2008;2(1):302–317.
   Google Scholar
• Nylund-Gibson K, Choi AY. Ten frequently asked questions about latent class analysis. Transl Issues Psychol Sci. 2018;4(4):440–461.
   Google Scholar
• Kongsted A, Kent P, Axen I, et al. What have we learned from ten years of trajectory research in low back pain? BMC Musculoskelet Disord. 2016;17(1):1–11.
   PubMedGoogle Scholar
• Pan JH, Song XY, Lee SY, et al. Longitudinal analysis of quality of life for stroke survivors using latent curve models. Stroke. 2008;39(10):2795–2802.
   PubMed Web of Science ®Google Scholar
• Munyombwe T, West R, Hill K, et al. P2-205 heterogeneity in functional recovery after stroke: an exploratory study using longitudinal latent class analysis. J Epidemiol Community Health. 2011;65(Suppl 1):A278–A278.
   Google Scholar
• Arling G, Ofner S, Reeves MJ, et al. Care trajectories of veterans in the 12 months after hospitalization for acute ischemic stroke. Circ Cardiovasc Qual Outcomes. 2015;8(6 Suppl 3):S131–S140.
   PubMed Web of Science ®Google Scholar
• West R, Hill K, Hewison J, et al. Psychological disorders after stroke are an important influence on functional outcomes: a prospective cohort study. Stroke. 2010;41(8):1723–1727.
   PubMed Web of Science ®Google Scholar
• Kjeverud A, Østlie K, Schanke A-K, et al. Trajectories of fatigue among stroke patients from the acute phase to 18 months post-injury: a latent class analysis. PLoS One. 2020;15(4):e0231709.
   PubMed Web of Science ®Google Scholar
• Mirbagheri MM, Rymer WZ. Time-course of changes in arm impairment after stroke: variables predicting motor recovery over 12 months. Arch Phys Med Rehabil. 2008;89(8):1507–1513.
   PubMed Web of Science ®Google Scholar
• Berlin KS, Williams NA, Parra GR. An introduction to latent variable mixture modeling (part 1): overview and cross-sectional latent class and latent profile analyses. J Pediatr Psychol. 2014;39(2):174–187.
   PubMed Web of Science ®Google Scholar
• Vratsistas-Curto A, Sherrington C, McCluskey A. Dosage and predictors of arm practice during inpatient stroke rehabilitation: an inception cohort study. Disabil Rehabil. 2021;43(5):640–647.
   PubMed Web of Science ®Google Scholar
• Callahan CM, Unverzagt FW, Hui SL, et al. Six-item screener to identify cognitive impairment among potential subjects for clinical research. Med Care. 2002;40(9):771–781.
   PubMed Web of Science ®Google Scholar
• Scrivener K, Sherrington C, Schurr K, et al. Many participants in inpatient rehabilitation can quantify their exercise dosage accurately: an observational study. J Physiother. 2011;57(2):117–122.
   PubMed Web of Science ®Google Scholar
• Stroke Foundation. Clinical guidelines for stroke management. Melbourne, Australia: stroke Foundation; 2021.
   Google Scholar
• Charlson ME, Pompei P, Ales KL, et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–383.
   PubMedGoogle Scholar
• van Swieten JC, Koudstaal PJ, Visser MC, et al. Interobserver agreement for the assessment of handicap in stroke patients. Stroke. 1988;19(5):604–607.
   PubMed Web of Science ®Google Scholar
• Lannin NA. Reliability, validity and factor structure of the Upper Limb subscale of the Motor Assessment Scale (UL-MAS) in adults following stroke. Disabil Rehabil. 2004;26(2):109–116.
   PubMed Web of Science ®Google Scholar
• Carr JH, Shepherd RB, Nordholm L, et al. Investigation of a new motor assessment scale for stroke patients. Phys Ther. 1985;65(2):175–180.
   PubMed Web of Science ®Google Scholar
• Herbison GJ, Isaac Z, Cohen ME, et al. Strength post-spinal cord injury: myometer vs manual muscle test. Spinal Cord. 1996;34(9):543–548.
   PubMed Web of Science ®Google Scholar
• Marx RG, Bombardier C, Wright JG. What do we know about the reliability and validity of physical examination tests used to examine the upper extremity? J Hand Surg Am. 1999;24(1):185–193.
   PubMedGoogle Scholar
• Schwartz S, Cohen ME, Herbison GJ, et al. Relationship between two measures of upper extremity strength: manual muscle test compared to hand-held myometry. Arch Phys Med Rehabil. 1992;73(11):1063–1068.
   PubMed Web of Science ®Google Scholar
• Mathiowetz V, Volland G, Kashman N, et al. Adult norms for the box and block test of manual dexterity. Am J Occup Ther. 1985;39(6):386–391.
   PubMed Web of Science ®Google Scholar
• Tardieu G, Rondot P, Mensch J, et al. Electromyographic response to muscular stretching in normal subjects. Rev Fr Etud Clin Biol. 1957;2(10):998–1004.
   PubMedGoogle Scholar
• Tardieu G, Shentoub S, Delarue R. Research on a technic for measurement of spasticity. Rev Neurol. 1954;91(2):143–144.
   PubMedGoogle Scholar
• Kwakkel G, van Peppen R, Wagenaar RC, et al. Effects of augmented exercise therapy time after stroke: a meta-analysis. Stroke. 2004;35(11):2529–2539.
   PubMed Web of Science ®Google Scholar
• Lohse KR, Lang CE, Boyd LA. Is more better? Using metadata to explore dose–response relationships in stroke rehabilitation. Stroke. 2014;45(7):2053–2058.
   PubMed Web of Science ®Google Scholar
• Veerbeek JM, Koolstra M, Ket JC, et al. Effects of augmented exercise therapy on outcome of gait and gait-related activities in the first 6 months after stroke: a meta-analysis. Stroke. 2011;42(11):3311–3315.
   PubMed Web of Science ®Google Scholar
• Wang J, Wang X. Structural equation modeling: applications using mplus. New York: John Wiley & Sons; 2019.
   Google Scholar
• Van De Schoot R, Sijbrandij M, Winter SD, et al. The GRoLTS-checklist: guidelines for reporting on latent trajectory studies. Struct Equ Modeling. 2017;24(3):451–467.
   Web of Science ®Google Scholar
• Bauer DJ, Curran PJ. Distributional assumptions of growth mixture models: implications for overextraction of latent trajectory classes. Psychol Methods. 2003;8(3):338–363.
   PubMed Web of Science ®Google Scholar
• Depaoli S, Winter SD, Lai K, et al. Implementing continuous non-normal skewed distributions in latent growth mixture modeling: an assessment of specification errors and class enumeration. Multivariate Behav Res. 2019;54(6):795–821.
   PubMed Web of Science ®Google Scholar
• Dunn KM, Jordan K, Croft PR. Characterizing the course of low back pain: a latent class analysis. Am J Epidemiol. 2006;163(8):754–761.
   PubMed Web of Science ®Google Scholar
• Downie AS, Hancock MJ, Rzewuska M, et al. Trajectories of acute low back pain: a latent class growth analysis. Pain. 2016;157(1):225–234.
   PubMed Web of Science ®Google Scholar
• Kongsted A, Kent P, Hestbaek L, et al. Patients with low back pain had distinct clinical course patterns that were typically neither complete recovery nor constant pain. A latent class analysis of longitudinal data. Spine J. 2015;15(5):885–894.
   PubMed Web of Science ®Google Scholar
• Li C. Little’s test of missing completely at random. The Stata Journal. 2013;13(4):795–809.
   Web of Science ®Google Scholar
• Lo Y, Mendell NR, Rubin DB. Testing the number of components in a normal mixture. Biometrika. 2001;88(3):767–778.
   Web of Science ®Google Scholar
• Chen Q, Luo W, Palardy GJ, et al. The efficacy of common fit indices for enumerating classes in growth mixture models when nested data structure is ignored: a Monte Carlo study. Sage Open. 2017;7(1):215824401770045.
   Web of Science ®Google Scholar
• Celeux G, Soromenho G. An entropy criterion for assessing the number of clusters in a mixture model. J Classif. 1996;13(2):195–212.
   Web of Science ®Google Scholar
• Nagin DS, Nagin D. Group-based modeling of development. Cambridge (MA): Harvard University Press; 2005.
   Google Scholar
• Nagin DS, Land KC. Age, criminal careers, and population heterogeneity: specification and estimation of a nonparametric, mixed Poisson model. Criminology. 1993;31(3):327–362.
   Web of Science ®Google Scholar
• Tsivgoulis G, Apostolidou N, Giannopoulos S, et al. Hemodynamic causes of deterioration in acute ischemic stroke. Perspect Med. 2012;1(1-12):177–184.
   Google Scholar
• Jackson H, Engelman M, Bandeen-Roche K. Robust respondents and lost limitations: the implications of nonrandom missingness for the estimation of health trajectories. J Aging Health. 2019;31(4):685–708.
   PubMed Web of Science ®Google Scholar
• Vratsistas-Curto A, Sherrington C, McCluskey A. Responsiveness of five measures of arm function in acute stroke rehabilitation. Clin Rehabil. 2018;32(8):1098–1107.
   PubMed Web of Science ®Google Scholar
• Ballester BR, Ward NS, Brander F, et al. Relationship between intensity and recovery in post-stroke rehabilitation: a retrospective analysis. J Neurol Neurosurg Psychiatry. 2022;93(2):226–228.
   PubMed Web of Science ®Google Scholar
• Borschmann KN, Hayward KS. Recovery of upper limb function is greatest early after stroke but does continue to improve during the chronic phase: a two-year, observational study. Physiotherapy. 2020;107:216–223.
   PubMed Web of Science ®Google Scholar
• Davies CE, Giles LC, Glonek GF. Performance of methods for estimating the effect of covariates on group membership probabilities in group-based trajectory models. Stat Methods Med Res. 2018;27(10):2918–2932.
   PubMed Web of Science ®Google Scholar