A correlation study of the American Shoulder and Elbow Society Score and the Oxford Shoulder Score with the use of regression analysis to predict one score from the other in patients undergoing reverse shoulder joint arthroplasty for cuff tear arthropathy. Issue 2 (April 2014)

• Record Type: Journal Article
• Authors: Hapuarachchi, Kamal S; Poon, Peter C
• Is Part Of: Shoulder & elbow. Volume 6:Issue 2(2014)
• Page Start: 81
• Page End: 89
• Publication Date: 2014-04
• Subjects: Shoulder -- outcome -- American Shoulder and Elbow Society Score -- Oxford Shoulder Score -- patient-reported outcome -- regression analysis -- correlating scores -- reverse shoulder joint arthroplasty; Elbow -- Diseases -- Periodicals; Elbow -- Surgery -- Periodicals; Shoulder -- Diseases -- Periodicals; Shoulder -- Surgery -- Periodicals; Shoulder -- Periodicals; Elbow -- Periodicals; Shoulder Joint -- Periodicals; Elbow Joint -- Periodicals; 617.572005
• DOI: 10.1177/1758573213518499
• Languages: English
• ISSNs: 1758-5732

Abstract

Background: More than 30 different scoring systems are available for evaluating outcomes of shoulder surgery. Unfortunately, given the multitude of scoring systems, there is no objective method to compare results between studies when different scoring systems are utilized. Methods: We compared the American Shoulder and Elbow Society score (ASES) and the Oxford Shoulder Score (OSS) in patients undergoing reverse shoulder arthroplasty for cuff tear arthropathy. Twenty-nine patients had the ASES and OSS recorded pre-operatively, and at 6 and 12 months follow-up. The paired scores were assessed for their degree of correlation and sensitivity to change over time. Linear regression analysis was used to formulate a regression equation to predict one score from the other. Results: The ASES and OSS correlated well with a Pearson's correlation coefficient of 0.91 ( p < 0.0001, n = 87). Both scores were sensitive to change. Regression analysis yielded a formula to predict the ASES from the OSS and vice versa with good accuracy ( r 2 = 0.83, F 1, 85 = 422.6, p < 0.0001). Conclusions: Where good correlation exists, regression formulae can be used to accurately predict one score from the other in a specific population that it has been validated for. This can be of benefit when objectively comparing outcomes between studies using these two scoring systems.