baseline measures, Doctor of Philosophy, Health, Hip Replacement, Indianapolis, Journal of Bone and Joint Surgery, lower extremity functional scale, Medicine, Physical therapy, Research, science, UNited States, University of Indianapolis
Prediction of Functional Outcome at Six Months Following Total Hip Arthroplasty.
Prediction of Functional Outcome at Six Months Following Total Hip Arthroplasty
E.J. Slaven, PT, PhD, OCS, FAAOMPT, CertMDT, Krannert School of Physical Therapy, University of Indianapolis, Indianapolis, IN 46227 (USA).
Background Recovery of function such as the ability to walk without an assistive device after total hip arthroplasty (THA) is not always automatic.
Objective This study investigated whether predetermined variables could be used to identify those patients who might have functional limitations at 6 months following THA.
Design Prospective observational cohort design.
Method Demographics and baseline measures including age, sex, and preoperative Lower Extremity Functional Scale (LEFS) score were collected 1 to 3 weeks prior to surgery from 40 participants who were scheduled to undergo THA. Six weeks after surgery a second LEFS score was recorded along with the participant’s BMI and the THA procedure performed; walking speed and balance were also assessed at this time using the 10-meter walking test, the timed up and go test (TUG), and the functional reach test. At 6 months following surgery, each participant’s functional outcome was determined from the final LEFS score and the need for an assistive device. Classification and regression tree (CART) analyses and logistic regression were used to establish which of the variables could predict outcome at 6 months.
Results BMI, sex, and age were identified by CART analysis as predictors to classify participants who did not reach successful outcome status. Logistic regression revealed that sex (female) was the only individual variable that predicted outcome at 6 months (p = 0.039). Walking speed was the only performance variable identified as a predictor for outcome using CART analysis.
Limitations Only a limited number of variables were observed due to the study size.
Conclusion It is possible to identity those patients who are at risk for an unsuccessful outcome through the use of variables such as BMI, age, and sex.
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