European Society for Surgery of the Shoulder and Elbow (SECEC) rotator cuff tear registry Delphi consensus

Abstract

The purpose of this study was to establish consensus statements via a Delphi process on the factors that should be included in a registry for those patients undergoing rotator cuff tear treatment. A consensus process on the treatment of rotator cuff utilizing a modified Delphi technique was conducted. Fifty-seven surgeons completed these consensus statements and 9 surgeons declined. The participants were members of the European Society for Surgery of the Shoulder and Elbow committees representing 23 European countries. Thirteen questions were generated regarding the diagnosis and follow-up of rotator cuff tears were distributed, with 3 rounds of questionnaires and final voting occurring. Consensus was defined as achieving 80%-89% agreement, whereas strong consensus was defined as 90%-99% agreement, and unanimous consensus was defined by 100% agreement with a proposed statement. Of the 13 total questions and consensus statements on rotator cuff tears, 1 achieved unanimous consensus, 6 achieved strong consensus, 5 achieved consensus, and 1 did not achieve consensus. The statement that reached unanimous consensus was that the factors in the patient history that should be evaluated and recorded in the setting of suspected/known rotator cuff tear are age, gender, comorbidities, smoking, traumatic etiology, prior treatment including physical therapy/injections, pain, sleep disturbance, sports, occupation, workmen's compensation, hand dominance, and functional limitations. The statement that did not achieve consensus was related to the role of ultrasound in the initial diagnosis of patients with rotator cuff tears. Nearly all questions reached consensus among 57 European Society for Surgery of the Shoulder and Elbow members representing 23 different European countries. We encourage surgeons to use this minimum set of variables to establish rotator cuff registries and multicenter studies. By adapting and using compatible variables, data can more easily be compared and eventually merged across countries.