Estimating Minimal Clinically Important Differences for Knee Range of Motion after Stroke
Tóm tắt
The importance of knee sagittal kinematic parameters, as a predictor of walking performance in post-stroke gait has been emphasised by numerous researchers. However, no studies so far were designed to determine the minimal clinically important differences (MCID), i.e., the smallest difference in the relevant score for the kinematic gait parameters, which are perceived as beneficial for patients with stroke. Studies focusing on clinically important difference are useful because they can identify the clinical relevance of changes in the scores. The purpose of the study was to estimate the MCID for knee range of motion (ROM) in the sagittal plane for the affected and unaffected side at a chronic stage post-stroke. Fifty individuals were identified in a database of a rehabilitation clinic. We estimated MCID values using: an anchor-based method, distribution-based method, linear regression analysis and specification of the receiver operating characteristic (ROC) curve. In the anchor-based study, the mean change in knee flexion/extension ROM for the affected/unaffected side in the MCID group amounted to 8.48°/6.81° (the first MCID estimate). In the distribution-based study, the standard error of measurement for the no-change group was 1.86°/5.63° (the second MCID estimate). Method 3 analyses showed 7.71°/4.66° change in the ROM corresponding to 1.85-point change in the Barthel Index. The best cut-off point, determined with ROC curve, was the value corresponding to 3.9°/3.8° of change in the knee sagittal ROM for the affected/unaffected side (the fourth MCID estimate). We have determined that, in chronic stroke, MCID estimates of knee sagittal ROM for the affected side amount to 8.48° and for the unaffected side to 6.81°. These findings will assist clinicians and researchers in interpreting the significance of changes observed in kinematic sagittal plane parameters of the knee. The data are part of the following clinical trial: Australian New Zealand Clinical Trials Registry: ACTRN12617000436370
Từ khóa
Tài liệu tham khảo
An, 2015, Gait velocity and walking distance to predict community walking after stroke, Nurs. Health Sci., 17, 533, 10.1111/nhs.12234
2012, Walking tests for stroke survivors: A systematic review of their measurement properties, Disabil. Rehabil., 34, 2207, 10.3109/09638288.2012.680649
Kosak, 2005, Comparison of the 2-, 6-, and 12-minute walk tests in patients with stroke, J. Rehabil. Res. Dev., 42, 103
Rensink, 2014, Clinimetric properties of the Timed Up and Go Test for patients with stroke: A systematic review, Top. Stroke. Rehabil., 21, 197, 10.1310/tsr2103-197
Wonsetler, 2017, A systematic review of mechanisms of gait speed change post-stroke. Part 1: Spatiotemporal parameters and asymmetry ratios, Top. Stroke Rehabil., 24, 435, 10.1080/10749357.2017.1285746
Patterson, 2012, Temporal gait symmetry and velocity differ in their relationship to age, Gait Posture, 35, 590, 10.1016/j.gaitpost.2011.11.030
Barak, 2006, Issues in selecting outcome measures to assess functional recovery after stroke, NeuroRx, 3, 505, 10.1016/j.nurx.2006.07.009
Jaeschke, 1989, Measurement of health status. Ascertaining the minimal clinically important difference, Control Clin. Trials, 10, 407, 10.1016/0197-2456(89)90005-6
Bohannon, 2013, Minimal clinically important difference for comfortable speed as a measure of gait performance in patients undergoing inpatient rehabilitation after stroke, J. Phys. Ther. Sci., 25, 1223, 10.1589/jpts.25.1223
Bushnell, 2015, Chronic Stroke Outcome Measures for Motor Function Intervention Trials: Expert Panel Recommendations, Circ. Cardiovasc. Qual. Outcomes, 8, S163, 10.1161/CIRCOUTCOMES.115.002098
Fulk, 2018, Minimal Clinically Important Difference of the 6-Minute Walk Test in People with Stroke, J. Neurol. Phys. Ther., 42, 235, 10.1097/NPT.0000000000000236
Baker, 2009, The gait profile score and movement analysis profile, Gait Posture, 30, 265, 10.1016/j.gaitpost.2009.05.020
Kazemi, 2013, Recent Advances in Computational Mechanics of the Human Knee Joint, Comput. Math. Methods. Med., 2013, 718423, 10.1155/2013/718423
Reicher, M., and Bochenek, A. (2008). Human Anathomy. General Anatomy. Bones, Joints and Ligaments, PZWL Press.
Perry, 1995, Classification of walking handicap in the stroke population, Stroke, 26, 982, 10.1161/01.STR.26.6.982
McGinley, 2009, The reliability of three-dimensional kinematic gait measurements: A systematic review, Gait Posture, 29, 360, 10.1016/j.gaitpost.2008.09.003
Nadeau, 2013, Gait analysis for poststroke rehabilitation: The relevance of biomechanical analysis and the impact of gait speed, Phys. Med. Rehabil. Clin. N. Am., 24, 265, 10.1016/j.pmr.2012.11.007
Boudarham, J., Roche, N., Pradon, D., Bonnyaud, C., Bensmail, D., and Zory, R. (2013). Variations in kinematics during clinical gait analysis in stroke patients. PLoS ONE, 8.
Cooper, 2012, The relationship of lower limb muscle strength and knee joint hyperextension during the stance phase of gait in hemiparetic stroke patients, Physiother. Res. Int., 17, 150, 10.1002/pri.528
Kaczmarczyk, 2009, Gait classification in post-stroke patients using artificial neural networks, Gait Posture, 30, 207, 10.1016/j.gaitpost.2009.04.010
Mun, 2014, Study on the usefulness of sit to stand training in self-directed treatment of stroke patients, J. Phys. Ther. Sci., 26, 483, 10.1589/jpts.26.483
Beyaert, 2015, Gait post-stroke: Pathophysiology and rehabilitation strategies, Neurophysiol. Clin., 45, 335, 10.1016/j.neucli.2015.09.005
Simon, 1996, Gait pattern in the early recovery period after stroke, J. Bone Joint Surg. Am., 78, 1506, 10.2106/00004623-199610000-00008
Kim, 2004, Magnitude and pattern of 3D kinematic and kinetic gait profiles in persons with stroke: Relationship to walking speed, Gait Posture, 20, 140, 10.1016/j.gaitpost.2003.07.002
Mulroy, 2003, Use of cluster analysis for gait pattern classification of patients in the early and late recovery phases following stroke, Gait Posture, 18, 114, 10.1016/S0966-6362(02)00165-0
Olney, 1991, Work and power in gait of stroke patients, Arch. Phys. Med. Rehabil., 72, 309
Kinsella, 2008, Gait pattern categorization of stroke participants with equinus deformity of the foot, Gait Posture, 27, 144, 10.1016/j.gaitpost.2007.03.008
Olney, 1998, Multivariate examination of data from gait analysis of persons with stroke, Phys. Ther., 78, 814, 10.1093/ptj/78.8.814
Davis, 1991, A gait analysis data collection and reduction technique, Hum. Mov. Sci., 10, 575, 10.1016/0167-9457(91)90046-Z
Collin, 1998, The Barthel ADL Index: A reliability study, Int. Disabil. Stud., 10, 61, 10.3109/09638288809164103
Hsueh, 2001, Psychometric characteristics of the Barthel Activities of Daily Living Index in stroke patients, J. Formos. Med. Assoc., 100, 526
Hsieh, 2007, Establishing the minimal clinically important difference of the Barthel Index in stroke patients, Neurorehabil. Neural. Repair., 21, 233, 10.1177/1545968306294729
Beaton, 2001, Looking for important change/differences in studies of responsiveness. OMERACT MCID Working Group. Outcome Measures in Rheumatology. Minimal Clinically Important Difference, J. Rheumatol., 28, 400
Hagg, 2003, The clinical importance of changes in outcome scores after treatment for chronic low back pain, Eur. Spine J., 12, 12, 10.1007/s00586-002-0464-0
Lydick, 1993, Interpretation of quality of life changes, Qual. Life Res., 2, 221, 10.1007/BF00435226
Wyrwich, 2004, Minimal important difference thresholds and the standard error of measurement: Is there a connection?, J. Biopharm. Stat., 14, 97, 10.1081/BIP-120028508
Crosby, 2003, Defining clinically meaningful change in health-related quality of life, J. Clin. Epidemiol., 56, 395, 10.1016/S0895-4356(03)00044-1
Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Press.
Hoffman, J.I.E. (2015). Variations Based on Linear Regression, in Biostatistics for Medical and Biomedical Practitioners, Academic Press.
Beninato, 2006, Determination of the minimal clinically important difference in the FIM instrument in patients with stroke, Arch. Phys. Med. Rehabil., 87, 32, 10.1016/j.apmr.2005.08.130
Stratford, 1996, Health status measures: Strategies and analytic methods for assessing change scores, Phys. Ther., 76, 1109, 10.1093/ptj/76.10.1109
Guzik, 2020, Application of the Gait Deviation Index in the analysis of post-stroke hemiparetic gait, J. Biomech., 99, 109575, 10.1016/j.jbiomech.2019.109575
Carmo, 2012, Three-dimensional kinematic analysis of upper and lower limb motion during gait of post-stroke patients, Braz. J. Med. Biol. Res., 45, 537, 10.1590/S0100-879X2012007500051
Guzik, 2016, Changes in Gait Symmetry After Training on a Treadmill with Biofeedback in Chronic Stroke Patients: A 6-Month Follow-Up from a Randomized Controlled Trial, Med. Sci. Monit., 22, 4859, 10.12659/MSM.898420
Duncan, 2000, Defining post-stroke recovery: Implications for design and interpretation of drug trials, Neuropharmacology, 39, 835, 10.1016/S0028-3908(00)00003-4
Lee, 2015, Six-month functional recovery of stroke patients: A multi-time-point study, Int. J. Rehabil. Res., 38, 173, 10.1097/MRR.0000000000000108
Czajka, 2019, Brain Functional Reserve in the Context of Neuroplasticity after Stroke, Neural. Plast., 2019, 9708905