Abstract
Periprosthetic osteolysis is associated with accelerated wear rates. The goal of this study was to investigate the influence of demographic and technical variables on wear rates and size of osteolytic lesions. Eighty retrieved ABG I prostheses were analyzed according to prospectively established criteria. There were 22 men and 58 women with an average age of 52 years (34-65) at the time of revision. The average time from index surgery to revision was 67 months (26 to 106). Polyethylene wear measurements were performed using a Universal-type measuring microscope. The average linear wear and volumetric wear rate was 0.363 mm per year (0-0.939, SD 0.241) and 161 mm(3) per year (0-467, SD 118.2), respectively. The wear rates were significantly higher (a) in patients with primary osteoarthritis in comparison with postdysplastic hips, (b) in hips where zirconia prosthetic heads articulated against the polyethylene liner, and (c) in cups placed laterally to Kohler's line. Risk that linear wear rate could be more than 0.2 mm per year was three times higher in patients who were operated in 1997 and later (OR 3.0, 95 % CI 1.126-7.993, p = 0.03). A strong association was revealed between magnitude of wear and size of femoral osteolysis.
Highlights
Periprosthetic osteolysis is considered a key problem of total hip arthroplasty[1]
Patient-related factors Significantly higher wear rates were found in patients whose primary diagnosis was osteoarthritis compared to postdysplastic hip (ANOVA, for linear wear rate p = 0.011, for volumetric wear rate p = 0.036)
Polyethylene wear has been considered a key parameter for periprosthetic osteolysis development, and its multifactorial origin is generally accepted[4]
Summary
Periprosthetic osteolysis is considered a key problem of total hip arthroplasty[1]. It can be defined as excessive bone resorption caused by predominantly polyethylene particles that trigger an adverse host response against the bone bed[2]. Orishimo et al.[9] investigated patients with bilateral hip arthroplasty to determine patient influence on wear rates. These authors found that patient factors accounted for 61 % of the variance in wear rate. Hopper et al.[10] from the same institution reported that all of the variables identified in their study as statistically significant including three patient-related factors (age, BMI, primary diagnosis) accounted for only 26 % of the variance in the wear data
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