IntroductionThe focus in reverse shoulder arthroplasty (RSA) has been on the lateralization and distalization of prosthesis positioning, influenced by implant design and surgical technique. There's no consensus on the optimal amount of lateralization and distalization or the best radiographic parameters for evaluating placement. This study examines the correlation and the predictive value between previously described modified distalization shoulder angle (DSA) and lateralization shoulder angle (LSA) with postoperative outcomes, which aim to differentiate the contributions of the humeral and glenoid components in the global distalization and lateralization of the RSA. The LSA was divided into the glenoid lateralization angle (GLA) and the humeral lateralization angle (HLA); the modified distalization shoulder angle (mDSA) was divided into the glenoid distalization angle (GDA) and the humeral distalization angle (HDA). Our hypothesis was that these new angles play a marginal role in predicting clinical outcomes. Materials and MethodsRetrospective analysis of 83 RSA patients from 2017-2021 at San Pietro Fatebenefratelli Hospital, Rome. Angles were measured using true anteroposterior radiographs. Clinical outcomes were assessed using the Constant-Murley Score (CS), a visual analogue scale (VAS) for pain, and range of motion (ROM). ResultsA weak correlation was found between the modified angles and clinical outcomes. Modified DSA (mDSA) correlated positively with internal rotation and negatively with VAS score. Glenoid distalization angle (GDA) correlated positively with abduction, forward flexion, and CS. Humeral distalization angle (HDA) correlated positively with internal rotation and negatively with VAS. Glenoid lateralization angle (GLA) and humeral lateralization angle (HLA) showed negative correlation with internal rotation and positive correlation with VAS. ConclusionModified DSA and LSA show marginal correlation with postoperative outcomes and have limited predictive value. Further research with larger, diverse populations is needed to refine these metrics and their clinical utility.
Read full abstract