Validation of a novel 3-dimensional classification for degenerative arthritis of the shoulder

INTRODUCTION: A novel three-dimensional classification to comprehensively describe degenerative arthritis of the shoulder (DAS) was recently published by our group. The purpose of the present work was to investigate intra- and interobserver agreement as well as validity for the three-dimensional classification. MATERIALS AND METHODS: Preoperative computed tomography (CT) scans of 100 patients who had undergone shoulder arthroplasty for DAS were randomly selected. Four observers independently classified the CT scans twice, with an interval of 4 weeks, after prior three-dimensional reconstruction of the scapula plane using a clinical image viewing software. Shoulders were classified according to biplanar humeroscapular alignment as posterior, centered or anterior (> 20% posterior, centered, > 5% anterior subluxation of humeral head radius) and superior, centered or inferior (> 5% inferior, centered, > 20% superior subluxation of humeral head radius). Glenoid erosion was graded 1–3. Gold-standard values based on precise measurements from the primary study were used for validity calculations. Observers timed themselves during classification. Cohen’s weighted κ was employed for agreement analysis. RESULTS: Intraobserver agreement was substantial (κ = 0.71). Interobserver agreement was moderate with a mean κ of 0.46. When the additional descriptors extra-posterior and extra-superior were included, agreement did not change substantially (κ = 0.44). When agreement for biplanar alignment alone was analyzed, κ was 0.55. The validity analysis reached moderate agreement (κ = 0.48). Observers took on average 2 min and 47 s (range 45 s to 4 min and 1 s) per CT for classification. CONCLUSIONS: The three-dimensional classification for DAS is valid. Despite being more comprehensive, the classification shows intra- and interobserver agreement comparable to previously established classifications for DAS. Being quantifiable, this has potential for improvement with automated algorithm-based software analysis in the future. The classification can be applied in under 5 min and thus can be used in clinical practice.