Intraobserver and interobserver reliability of the computed tomography-based radiographic classification of primary elbow osteoarthritis: comparison with plain radiograph-based classification and clinical assessment.

OBJECTIVES: To develop a staging system that could better reflect symptoms by the spurs quantification in the fossa and joint space narrowing using computed tomography (CT) for elbow arthritis and to evaluate its reproducibility with multiple readers. METHODS: This retrospective study evaluated 81 cases of primary elbow osteoarthritis using both plain radiography and CT. Qualitative and quantitative analyses were independently performed by four orthopedic surgeons using previous and newly proposed staging systems. The reproducibility of the new system was analyzed with intraclass correlation coefficients (ICC). Correlations between symptoms and radiologic classification were assessed using Pearson's correlation coefficient (PCC). RESULTS: The interobserver agreement (1) and intraobserver agreement (2) among the four evaluators was present by ICC. (1) The system of Hastings and Rettig [first observation, 0.544 (95% confidence interval (CI), 0.436-0.649); second observation, 0.582 (95% CI, 0.478-0.682)] and Broberg and Morrey's staging system [first observation, 0.620 (95% CI, 0.521-0.714); second observation, 0.656 (95% CI, 0.562-0.743)] showed substantial and moderate retrospective agreement, whereas the CT-based staging system showed almost perfect agreement [first observation, 0.867 (95% CI, 0.820-0.906); second observation, 0.909 (95% CI, 0.875-0.936)]. (2) The intraobserver agreement was almost perfect in the Brogerg and Morrey's and CT-based staging systems. CT-based staging showed high correlation with visual analogue scale (PCC 0.754, P < 0.001) and Mayo elbow performance score (PCC -0.614, P < 0.001) and moderate correlation with range of motion (PCC -0.458, P < 0.001). CONCLUSIONS: CT-based staging system was highly reproducible and clinically feasible than previous plain radiograph-based staging systems.