Predicting poor physical performance after total knee arthroplasty

J Orthop Res. 2012 Nov;30(11):1805-10. doi: 10.1002/jor.22140. Epub 2012 Apr 26.


The purpose of this study was to develop a preliminary decision algorithm predicting functional performance outcomes to aid in the decision of when to undergo total knee arthroplasty (TKA). One hundred and nineteen patients undergoing primary unilateral TKA were evaluated before and 6 months after TKA. A regression tree analysis using a recursive partitioning function was performed with the Timed Up and Go (TUG) time, Six-Minute Walk (6MW) distance, and Stair Climbing Test (SCT) time as measured 6 months after TKA as the primary outcomes. Preoperative measures of functional performance, joint performance, anthropometrics, demographics, and self-reported status were evaluated as predictors of the primary outcomes 6 months after surgery. Individuals taking ≥10.1 s on the TUG and aged 72 years or older before surgery had the poorest performance on the TUG 6 months after surgery. Individuals walking <314 meters on the 6MW before surgery had the poorest performance on the 6MW test 6 months after surgery. Individuals taking ≥17 s to complete the SCT and scoring <40 on the SF-36 mental component score before surgery had the poorest performance on the SCT 6 months after surgery. Poorer performance preoperatively on the 6MW, SCT, and TUG, was related to poorer performance in the same measure after TKA. Age and decreased mental health were secondary predictors of poorer performance at 6 months on the TUG and SCT, respectively. These measures may help further develop models predicting thresholds for poor outcomes after TKA.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Algorithms
  • Anthropometry
  • Arthroplasty, Replacement, Knee*
  • Decision Trees
  • Female
  • Forecasting
  • Humans
  • Knee Joint / physiology*
  • Male
  • Middle Aged
  • Self Report
  • Treatment Failure