Factors Driving Wear and Implant Failure in Total Shoulder Arthroplasty

About This Grant

Polyethylene (PE) wear and implant-related failure remain leading causes of revision in total shoulder arthroplasty (TSA), a procedure which now surpasses the growth rate of hip and knee arthroplasty. Both anatomic (aTSA) and reverse (rTSA) TSA outcomes are heavily influenced by complex interactions between rotator cuff function, scapular motion, implant design, and patient-specific loading—factors not adequately captured in current preclinical implant testing standards. Emerging evidence suggests that PE wear progression in TSA is highly dependent on shoulder kinematics, joint loading, implant positioning, and individual patient factors. Nonetheless, data on in vivo motion and load profiles remain sparse, and few tools exist to link these profiles to clinically relevant wear patterns or associated periprosthetic inflammatory tissue responses. Accordingly, the primary objective of this project is to develop validated, patient-specific models that predict PE wear in TSA and identify modifiable surgical, design, and rehabilitation targets to improve implant longevity and restore patient mobility. Additionally, we will establish histopathological hallmarks that indicate TSA failure caused by PE wear debris. Our central hypothesis is that specific shoulder kinematics and joint loading drive distinct PE wear patterns in TSA associated with mechanical failure or inflammatory-mediated osteolysis, depending on implant design and positioning. To achieve the overall objective of this work, shoulder motions and muscle excitations across 25 activities of daily living will be collected at pre-op and post-op (>6 months) in both aTSA and rTSA patients, with long-term follow-up of patient-reported outcomes via validated surveys (5 years). Unsupervised machine learning will categorize patients into movement-based phenotypes, which will then inform a multi-scale modeling framework to estimate in vivo shoulder joint loads and implant wear across the varying movement strategies. Predicted wear patterns will be validated using state-of-the-art preclinical wear simulators. Simultaneously, we will quantify how patient, surgical, and implant factors contribute to wear in retrieved TSA components (>400 samples), correlating imaging-based wear patterns with clinical outcomes, patient-reported function, inflammatory tissue responses, and radiographic indications of loosening. For that purpose, we will establish benchmarks of TSA wear rates and introduce a new histopathological approach augmented by infrared spectroscopic imaging. This work is innovative because we are linking patient-specific movement patterns following TSA with multi-scale computational models to predict PE wear, breaking the current approaches of using generic motions and loads in existing testing standards. This work will produce the first integrated, publicly available database of TSA kinematics, joint loading, and PE wear patterns and rates, along with validated computational tools to inform implant design, surgical planning, rehabilitation strategies, and personalized risk assessment. Ultimately, these advances will improve functional outcomes and long-term success for TSA patients and enable better preclinical testing methods and standards.