Custom flange acetabular components (CFACs) are a patient-specific option for addressing large acetabular defects at revision total hip arthroplasty (THA), but patient and implant characteristics that affect survivorship remain unknown. This study aimed to identify patient and design factors related to survivorship. A retrospective review of 91 patients who underwent revision THA using 96 CFACs was undertaken, comparing features between radiologically failed and successful cases. Patient characteristics (demographic, clinical, and radiological) and implant features (design characteristics and intraoperative features) were collected. There were 74 women and 22 men; their mean age was 62 years (31 to 85). The mean follow-up was 24.9 months (Aims
Patients and Methods
Custom flanged acetabular components (CFAC) have been shown to be effective in treating complex acetabular reconstructions in revision total hip arthroplasty (THA). However, the specific patient factors and CFAC design characteristics that affect the overall survivorship remain unclear. Once the surgeon opts to follow this treatment pathway, numerous decisions need to be made during the pre-operative design phase and during implantation, which may influence the ultimate success of CFAC. The goal of this study was to retrospectively review the entire cohort of CFAC cases performed at a large volume institution and to identify any patient, surgeon, or design factors that may be related to the long-term survival of these prostheses. We reviewed 96 CFAC cases performed in 91 patients between 2004 and 2017, from which 36 variables were collected spanning patient demographics, pre-operative clinical and radiographic features, intraoperative information, and implant design characteristics. Patient demographics and relevant clinical features were collected from individual medical records. Radiographic review included analysis of pre-operative radiographs, computer tomographic (CT) scans, and serial post-operative radiographs. Radiographic failure was defined as loosening or gross migration as determined by a board-certified orthopedic surgeon. CFAC implant design characteristics and intra-operative features were collected from the design record, surgical record and post-operative radiograph for each case respectively. Two sets of statistical analyses were performed with this dataset. First, univariate analyses were performed for each variable, comprising of a Pearson chi-square test for categorical variables and an independent t-test for continuous variables. Second, a random forest supervised machine learning method was applied to identify the most influential variables within the dataset, which were then used to perform a bivariable logistic regression to generate odds ratios. Statistical significance for this study was set at p < 0.05.Introduction
Methods
