Developing and Validating a Model of Humeral Stem Primary Stability, Intended for In Silico Clinical Trials

Area of Science:

• Biomechanical Engineering
• Computational Modeling
• Medical Device Performance Evaluation

Background:

• In silico clinical trials (ISCT) offer a promising approach for evaluating medical device performance using computational models and virtual cohorts.
• Establishing the credibility of these computational models is crucial for their reliable application in regulatory submissions and clinical decision-making.
• Total shoulder arthroplasty (TSA) is a common procedure, and understanding the risk factors for humeral stem loosening is critical for improving patient outcomes.

Purpose of the Study:

• To establish the credibility of a computational model for assessing the risk of humeral stem loosening in TSA for ISCT applications.
• To validate the model through a twofold scheme involving both benchtop and clinical validation.
• To ensure the model accurately captures the physics of bone-implant micromotion and predicts clinical performance.

Main Methods:

• A finite element model was developed to compute bone-implant micromotion and quantitatively compared to benchtop foam micromotion tests.
• The model was expanded to a population-based approach and qualitatively evaluated against published clinical study findings.
• Model sensitivities to surgical variation and implant design were assessed.

Main Results:

• The computational model accurately replicated benchtop micromotion measurements, showing minimal impact from press-fit variations.
• In silico simulations demonstrated that grit-blasted stems exhibited significantly larger micromotions compared to porous-coated stems.
• These simulation results aligned with established clinical findings regarding stem loosening risk.

Conclusions:

• This study provides a concrete framework for evaluating the credibility of ISCT models.
• Validation against both benchtop and clinical data establishes model credibility for ISCT applications.
• The validated model can enrich clinical data for regulatory submissions, aiding in device performance assessment.