Abstract
We studied the relationship between osteolysis and polyethylene wear, age at surgery, body mass index and height in 463 subjects (180 osteolysis and 283 controls) after cemented Charnley total hip arthroplasty (THA), in order to develop a kernel-based Bayesian model to quantitate risk of osteolysis. Such tools may be integrated into decision-making algorithms to help personalize clinical decision-making. A predictive model was constructed, and the estimated posterior probability of the implant failure calculated. Annual wear provided the greatest discriminatory information. Age at surgery provided additional predictive information and was added to the model. Body mass index and height did not contain valuable discriminatory information over the range in which observations were densely sampled. The robustness and misclassification rate of the predictive model was evaluated by a five-times cross-validation method. This yielded a 70% correct predictive classification of subjects into osteolysis versus non-osteolysis groups at a mean of 11 years after THA. Finally, the data were divided into male and female subsets to further explore the relationship between wear rate, age at surgery and incidence of osteolysis. The correct classification rate using age and wear rate in the model was approximately 66% for males and 74% for females.
Highlights
Osteolysis, resulting in aseptic loosening, is the most common factor limiting the survival of modern total hip arthroplasty (THA)
We can infer that the probability of a patient with annual wear 0.2 being in control group is 0.45
We examined the potential role of a kernelbased Bayesian model in the prediction of osteolysis in the late period after cemented THA
Summary
Osteolysis, resulting in aseptic loosening, is the most common factor limiting the survival of modern total hip arthroplasty (THA). The relationship between wear rate and osteolysis has been quantified using logistic regression analysis and expressing the results as odds of osteolysis per unit change in wear [2], and using a population wear quintile-based approach to characterize the dose–response relationship between annual wear rate and osteolysis [3,4]. Wear measurement made in the mid-term after THA have the potential to provide a tool for personalizing the need for later implant surveillance after THA. While these methods give useful information on the epidemiological association between wear and osteolysis, they do not provide risk data that would be directly clinically applicable to individual patients, and they do not predict risk of osteolysis in the setting of other clinical risk factors, such as age and sex
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