Alexander Meynen1, Georges Vles2,3, Mark Roussot4, Anthony Van Eemeren2,3, Hazem Wafa2,3, Michiel Mulier2,3, Lennart Scheys2,3. 1. Institute of Orthopaedic Research and Training, Gasthuisberg, University Hospitals Leuven/Catholic University of Leuven, Herestraat 49, 3000, Leuven, Belgium. alexander.meynen@kuleuven.be. 2. Institute of Orthopaedic Research and Training, Gasthuisberg, University Hospitals Leuven/Catholic University of Leuven, Herestraat 49, 3000, Leuven, Belgium. 3. Division of Orthopaedics, Gasthuisberg, University Hospitals Leuven, Herestraat 49, 3000, Leuven, Belgium. 4. Department of Trauma and Orthopaedics, University College Hospital London, London, NW1 2BU, UK.
Abstract
INTRODUCTION: Classifying complex acetabular defects in revision total hip arthroplasty (THA) by means of conventional radiographs comes with significant limitations. Statistical shape modelling allows the virtual reconstruction of the native pelvic morphology, hereby enabling an analytic acetabular defect assessment. Our objective was to evaluate the effect of advanced imaging augmented with analytic representations of the defect on (1) intra- and inter-rater reliability, and (2) up- or downscaling of classification scores when evaluating acetabular defects in patients undergoing revision THA. MATERIALS AND METHODS: The acetabular defects of 50 patients undergoing revision THA were evaluated by three independent, fellowship-trained orthopaedic surgeons. Defects were classified according to the acetabular defect classification (ADC) using four different imaging-based representations, namely, standard radiographs, CT imaging, a virtual three-dimensional (3D) model and a quantitative analytic representation of the defect based on a statistical shape model reconstruction. Intra- and inter-rater reliabilities were quantified using Fleiss' and Cohen's kappa scores, respectively. Up- and downscaling of classification scores were compared for each of the imaging-based representations and differences were tested. RESULTS: Overall inter-rater agreement across all imaging-based representations for the classification was fair (κ 0.29 95% CI 0.28-0.30). Inter-rater agreement was lowest for radiographs (κ 0.21 95% CI 0.19-0.22) and increased for other representations with agreement being highest when using analytic defect models (κ 0.46 95% CI 0.43-0.48). Overall intra-rater agreement was moderate (κ 0.51 95% CI 0.42-0.60). Intra-rater agreement was lowest for radiographs (κ 0.40 95% CI 0.23-0.57), and highest for ratings including analytic defect models (κ 0.64:95% CI 0.46-0.82). Virtual 3D models with quantitative analytic defect representations upscaled acetabular defect scores in comparison to standard radiographs. CONCLUSIONS: Using 3D CT imaging with statistical shape models doubles the intra- and inter-rater reliability and results in upscaling of acetabular defect classification when compared to standard radiographs. This method of evaluating defects will aid in planning surgical reconstruction and stimulate the development of new classification systems based on advanced imaging techniques.
INTRODUCTION: Classifying complex acetabular defects in revision total hip arthroplasty (THA) by means of conventional radiographs comes with significant limitations. Statistical shape modelling allows the virtual reconstruction of the native pelvic morphology, hereby enabling an analytic acetabular defect assessment. Our objective was to evaluate the effect of advanced imaging augmented with analytic representations of the defect on (1) intra- and inter-rater reliability, and (2) up- or downscaling of classification scores when evaluating acetabular defects in patients undergoing revision THA. MATERIALS AND METHODS: The acetabular defects of 50 patients undergoing revision THA were evaluated by three independent, fellowship-trained orthopaedic surgeons. Defects were classified according to the acetabular defect classification (ADC) using four different imaging-based representations, namely, standard radiographs, CT imaging, a virtual three-dimensional (3D) model and a quantitative analytic representation of the defect based on a statistical shape model reconstruction. Intra- and inter-rater reliabilities were quantified using Fleiss' and Cohen's kappa scores, respectively. Up- and downscaling of classification scores were compared for each of the imaging-based representations and differences were tested. RESULTS: Overall inter-rater agreement across all imaging-based representations for the classification was fair (κ 0.29 95% CI 0.28-0.30). Inter-rater agreement was lowest for radiographs (κ 0.21 95% CI 0.19-0.22) and increased for other representations with agreement being highest when using analytic defect models (κ 0.46 95% CI 0.43-0.48). Overall intra-rater agreement was moderate (κ 0.51 95% CI 0.42-0.60). Intra-rater agreement was lowest for radiographs (κ 0.40 95% CI 0.23-0.57), and highest for ratings including analytic defect models (κ 0.64:95% CI 0.46-0.82). Virtual 3D models with quantitative analytic defect representations upscaled acetabular defect scores in comparison to standard radiographs. CONCLUSIONS: Using 3D CT imaging with statistical shape models doubles the intra- and inter-rater reliability and results in upscaling of acetabular defect classification when compared to standard radiographs. This method of evaluating defects will aid in planning surgical reconstruction and stimulate the development of new classification systems based on advanced imaging techniques.
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