Revision shoulder arthroplasty and proximal humeral bone loss: a comprehensive review and proposal of a new algorithm of management

Abstract

AbstractWith the rising prevalence of shoulder arthroplasty, the incidence of revision shoulder arthroplasty is also increasing. The complexity of these revision procedures poses significant challenges, with bone loss being a critical factor impacting treatment outcomes. Addressing substantial humeral bone defects is crucial for ensuring implant stability and functionality. A comprehensive literature review was conducted using PubMed, Medline, and Google Scholar to identify existing classification systems for proximal humeral bone loss in the context of revision shoulder arthroplasty. The study assessed the advantages and limitations of these classifications, using this information to propose a new diagnostic and therapeutic algorithm. Several classification systems for proximal humeral bone loss were identified. McLendon et al. classify proximal humeral bone loss based on a 5-cm bone loss threshold and suggest an allograft prosthesis composite for losses exceeding this limit. Boileau’s system stratifies bone loss into four types based on the extent of loss, with specific recommendations for each category. The PHAROS classification provides a detailed anatomical assessment but lacks quantitative precision. The proposed PHBL-SCORe system offers a novel algorithm incorporating preoperative radiographic measurements to determine the percentage of bone loss and guide treatment options. Proximal humeral bone loss presents significant challenges in revision shoulder arthroplasty, necessitating precise preoperative planning and classification to guide surgical intervention. Existing classification systems provide valuable frameworks but often rely on average population values, neglecting individual anatomical variations. The proposed PHBL-SCORe system offers a patient-specific approach, improving the accuracy of bone loss assessment and optimizing treatment strategies. Implementing this classification in clinical practice could enhance surgical outcomes and reduce complications associated with rRSA (revision Reverse Shoulder arthroplasty). Further studies are required to validate this algorithm and explore its long-term efficacy in diverse patient populations.