Statistical shape modelling provides a responsive measure of morphological change in knee osteoarthritis over 12 months.

ABSTRACT

OBJECTIVES: Responsive biomarkers are needed to assess the progression of OA and their lack has hampered previous clinical trials. Statistical shape modelling (SSM) from radiographic images identifies those at greatest risk of fast-progression or joint replacement, but its sensitivity to change has not previously been measured. This study evaluates the responsiveness of SSM in knee OA in a 12-month observational study. METHODS: A total of 109 people were recruited who had undergone knee radiographs in the previous 12 months, and were grouped based on severity of radiographic OA (Kellgren-Lawrence grading). An SSM was built from three dual-energy X-ray absorptiometry scans at 6-month intervals. Change-over-time and OA were assessed using generalized estimating equations, standardized response means (SRM) and reliable change indices. RESULTS: Mode 1 showed typical features of radiographic OA and had a strong link with Kellgren-Lawrence grading but did not change significantly during the study. Mode 3 showed asymmetrical changes consistent with medial cartilage loss, osteophytes and joint malalignment, and was responsive to change, with a 12-month SRM of 0.63. The greatest change was observed in the moderate radiographic OA group (SRM 0.92) compared with the controls (SRM 0.21), and the reliable change index identified 14% of this group whose progression was clinically significant. CONCLUSION: Shape changes linked the progression of osteophytosis with increasing malalignment within the joint. Modelling of the whole joint enabled quantification of change beyond the point where bone-to-bone contact has been made. The knee SSM is, therefore, a responsive biomarker for radiographic change in knees over 12 months.