No abstract

Metallosis is defined as the accumulation and deposition of metallic particles secondary to abnormal wear from prosthetic implants that may be visualized as abnormal macroscopic staining of periprosthetic soft tissues. This phenomenon occurs secondary to the release of metal ions and particles from metal-on-metal hip implants in patients with end-stage osteoarthritis. Ions and particles shed from implants can lead to local inflammation of surrounding tissue and less commonly, very rare systemic manifestations may occur in various organ systems. With the incidence of total hip arthroplasty increasing as well as rates of revisions due to prosthesis failure from previous metal-on-metal implants, metallosis has become an important area of research. Bodily fluids are electrochemically active and react with biomedical implants. Particles, especially cobalt and chromium, are released from implants as they abrade against one another into the surrounding tissues. The body’s normal defense mechanism becomes activated, which can elicit a cascade of events, leading to inflammation of the immediate surrounding tissues and eventually implant failure. In this review, various mechanisms of metallosis are explored. Focus was placed on the atomic and molecular makeup of medical implants, the component/surgical associated factors, cellular responses, wear, tribocorrosion, joint loading, and fluid pressure associated with implantation. Current treatment guidelines for failed implants include revision surgery. An alternative treatment could be chelation therapy, which may drive future studies. Lay Summary Arthroplasty is an invasive procedure which disrupts surrounding joint tissues, and can greatly perturb the joint’s immune homeostasis. In some instances, this may pose a difficult challenge to implant integration. Particles released from implants into the surrounding joint tissues activate the body’s defense mechanism, eliciting a cascade of events, which leads to biotribocorrosion and electrochemical attacks on the implant. This process may lead to the release of even more particles. Besides, implant makeup and designs, frictions between bearing surfaces, corrosion of non-moving parts with modular junctions, surgical mistakes, patient factor, comorbidities, and loosened components can alter the expected function of implants. High accumulations of these ions and particulates result in metallosis, with accompanying adverse complications. Current recommended treatment for failed prosthesis is revision surgeries. However, chelation therapy as a prophylactic intervention may be useful in future efforts but more investigation is required.

The evaluation of hydrogel swelling behavior is a vital step in development of new materials for biomedical applications. Phosphate-buffered saline (PBS) is the most commonly chosen swelling medium to model hydrogel behavior in articular cartilage (AC). However, the use of PBS does not fully elucidate the osmotic pressure hydrogels will face in many tissues in vivo. Thus, there is a critical need to assess the performance of hydrogels in a model system that can better reflect the native tissues for a specified application. The aim of this study was to evaluate the mechanical properties, porosity, and swelling behavior of poly(vinyl alcohol) hydrogels with a degradable poly(lactic-co-glycolic acid) (PLGA) phase in synthetic models and in ex vivo AC model systems. The controlled degradation of the PLGA phase reflected the dynamic nature of native tissues and allowed for the assessment of hydrogel swelling characteristics under fluctuating osmotic pressures. When hydrogels were implanted ex vivo into bovine AC, their swelling ratios and water contents significantly decreased. This response was matched by hydrogels immersed in a solution of PEG having an osmotic pressure matching AC. The hydrogels were further characterized over 6 weeks in PEG and in PBS, with each system having unique affects on the hydrogel swelling behavior and material properties. The results show that a PEG solution conditioned to an osmotic pressure of AC is a strong model for the effects of the osmotic environment on hydrogels and that PBS is an ineffective predictor of swelling changes in vivo.

Purpose This study aimed to develop and validate machine-learning models for the prediction of recurrent infection in patients following revision total knee arthroplasty for periprosthetic joint infection. Methods A total of 618 consecutive patients underwent revision total knee arthroplasty for periprosthetic joint infection. The patient cohort included 165 patients with confirmed recurrent periprosthetic joint infection (PJI). Potential risk factors including patient demographics and surgical characteristics served as input to three machine-learning models which were developed to predict recurrent periprosthetic joint. The machine-learning models were assessed by discrimination, calibration and decision curve analysis. ResultsThe factors most significantly associated with recurrent PJI in patients following revision total knee arthroplasty for PJI included irrigation and debridement with/without modular component exchange (p < 0.001), > 4 prior open surgeries (p < 0.001), metastatic disease (p < 0.001), drug abuse (p < 0.001), HIV/AIDS (p < 0.01), presence of Enterococcus species (p < 0.01) and obesity (p < 0.01). The machine-learning models all achieved excellent performance across discrimination (AUC range 0.81-0.84). Conclusion This study developed three machine-learning models for the prediction of recurrent infections in patients following revision total knee arthroplasty for periprosthetic joint infection. The strongest predictors were previous irrigation and debridement with or without modular component exchange and prior open surgeries. The study findings show excellent model performance, highlighting the potential of these computational tools in quantifying increased risks of recurrent PJI to optimize patient outcomes. Level of evidence IV.