Latent Class Analysis to Predict Outcomes of Early High-Intensity Physiotherapy After Total Knee Arthroplasty Based on Longitudinal Trajectories of Walking Speed

Autor: Rana Dandis, Maria W.G. Nijhuis-van der Sanden, Thomas J. Hoogeboom, Jacco M Westeneng, Steven Teerenstra, Karen E. M. Harmelink, Joanna IntHout
Rok vydání: 2021
Předmět:
Zdroj: Journal of Orthopaedic and Sports Physical Therapy, 51, 7, pp. 362-371
Journal of Orthopaedic and Sports Physical Therapy, 51, 362-371
ISSN: 1938-1344
0190-6011
Popis: Item does not contain fulltext OBJECTIVE: To (1) classify patients who are recovering from total knee arthroplasty (TKA) based on walking speed during an early physical therapy program, and (2) assess whether walking-speed trajectory predicts performance on the timed up-and-go (TUG) test. DESIGN: Cohort study. METHODS: We included 218 patients from a 10-day physical therapy program after TKA. A latent class mixed model was used to classify patients according to their walking-speed trajectory during the program. We assessed the change in TUG test score from pre-TKA to 6 weeks and 1 year after TKA. The association between change in TUG test score and walking-speed trajectory was assessed using multivariable regression. RESULTS: There were 2 groups with distinct walking-speed trajectories: a high-gain group (46%) and a low-gain group (54%). There was no significant association between change in TUG test score and walking-speed trajectory after TKA and physical therapy. Function (based on TUG test performance) improved for all patients 1 year after TKA, irrespective of walking-speed trajectory (ie, high or low gain) early in postoperative physical therapy. CONCLUSION: Although we distinguished different groups based on functional outcomes during physical therapy, the clinical relevance of classifying patients based on walking speed remains unclear, as it did not predict short- and long-term functional outcomes. J Orthop Sports Phys Ther 2021;51(7):362-371. Epub 10 May 2021. doi:10.2519/jospt.2021.10299.
Databáze: OpenAIRE