Hyaluronic acid-British anti-Lewisite as a safer chelation therapy for the treatment of arthroplasty-related metallosis.

Cobalt-containing alloys are useful for orthopedic applications due to their low volumetric wear rates, corrosion resistance, high mechanical strength, hardness, and fatigue resistance. Unfortunately, these prosthetics release significant levels of cobalt ions, which was only discovered after their widespread implantation into patients requiring hip replacements. These cobalt ions can result in local toxic effects-including peri-implant toxicity, aseptic loosening, and pseudotumor-as well as systemic toxic effects-including neurological, cardiovascular, and endocrine disorders. Failing metal-on-metal (MoM) implants usually necessitate painful, risky, and costly revision surgeries. To treat metallosis arising from failing MoM implants, a synovial fluid-mimicking chelator was designed to remove these metal ions. Hyaluronic acid (HA), the major chemical component of synovial fluid, was functionalized with British anti-Lewisite (BAL) to create a chelator (BAL-HA). BAL-HA effectively binds cobalt and rescues in vitro cell vitality (up to 370% of cells exposed to IC50 levels of cobalt) and enhances the rate of clearance of cobalt in vivo (t1/2 from 48 h to 6 h). A metallosis model was also created to investigate our therapy. Results demonstrate that BAL-HA chelator system is biocompatible and capable of capturing significant amounts of cobalt ions from the hip joint within 30 min, with no risk of kidney failure. This chelation therapy has the potential to mitigate cobalt toxicity from failing MoM implants through noninvasive injections into the joint.

Neural network models accurately predict discharge disposition after revision total knee arthroplasty?

PURPOSE: Based on the rising incidence of revision total knee arthroplasty (TKA), bundled payment models may be applied to revision TKA in the near future. Facility discharge represents a significant cost factor for those bundled payment models; however, accurately predicting discharge disposition remains a clinical challenge. The purpose of this study was to develop and validate artificial intelligence algorithms to predict discharge disposition following revision total knee arthroplasty. METHODS: A retrospective review of electronic patient records was conducted to identify patients who underwent revision total knee arthroplasty. Discharge disposition was defined as either home discharge or non-home discharge, which included rehabilitation and skilled nursing facilities. Four artificial intelligence algorithms were developed to predict this outcome and were assessed by discrimination, calibration and decision curve analysis. RESULTS: A total of 2228 patients underwent revision TKA, of which 1405 patients (63.1%) were discharged home, whereas 823 patients (36.9%) were discharged to a non-home facility. The strongest predictors for non-home discharge following revision TKA were American Society of Anesthesiologist (ASA) score, Medicare insurance type and revision surgery for peri-prosthetic joint infection, non-white ethnicity and social status (living alone). The best performing artificial intelligence algorithm was the neural network model which achieved excellent performance across discrimination (AUC = 0.87), calibration and decision curve analysis. CONCLUSION: This study developed four artificial intelligence algorithms for the prediction of non-home discharge disposition for patients following revision total knee arthroplasty. The study findings show excellent performance on discrimination, calibration and decision curve analysis for all four candidate algorithms. Therefore, these models have the potential to guide preoperative patient counselling and improve the value (clinical and functional outcomes divided by costs) of revision total knee arthroplasty patients. LEVEL OF EVIDENCE: IV.

Machine learning models accurately predict recurrent infection following revision total knee arthroplasty for periprosthetic joint infection.

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. RESULTS: The 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.

Elevated ESR and CRP Prior to Second-Stage Reimplantation Knee Revision Surgery for Periprosthetic Joint Infection Are Associated with Increased Reinfection Rates.

Although two-stage revision surgery is considered as the most effective treatment for managing chronic periprosthetic joint infection (PJI), there is no current consensus on the predictors of optimal timing to second-stage reimplantation. This study aimed to compare clinical outcomes between patients with elevated erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) prior to second-stage reimplantation and those with normalized ESR and CRP prior to second-stage reimplantation. We retrospectively reviewed 198 patients treated with two-stage revision total knee arthroplasty for chronic PJI. Cohorts included patients with: (1) normal level of serum ESR and CRP (n = 96) and (2) elevated level of serum ESR and CRP prior to second-stage reimplantation (n = 102). Outcomes including reinfection rates and readmission rates were compared between both cohorts. At a mean follow-up of 4.4 years (2.8-6.5 years), the elevated ESR and CRP cohort demonstrated significantly higher reinfection rates compared with patients with normalized ESR and CRP prior to second-stage reimplantation (33.3% vs. 14.5%, p < 0.01). Patients with both elevated ESR and CRP demonstrated significantly higher reinfection rates, when compared with patients with elevated ESR and normalized CRP (33.3% vs. 27.6%, p = 0.02) as well as normalized ESR and elevated CRP (33.3% vs. 26.3%, p < 0.01). This study demonstrates that elevated serum ESR and/or CRP levels prior to reimplantation in two-stage knee revision surgery for chronic PJI are associated with increased reinfection rate after surgery. Elevation of both ESR and CRP were associated with a higher risk of reinfection compared with elevation of either ESR or CRP, suggesting the potential benefits of normalizing ESR and CRP prior to reimplantation in treatment of chronic PJI.

The Utility of Machine Learning Algorithms for the Prediction of Early Revision Surgery After Primary Total Hip Arthroplasty.

BACKGROUND: Revision total hip arthroplasty (THA) is associated with increased morbidity, mortality, and healthcare costs due to a technically more demanding surgical procedure when compared with primary THA. Therefore, a better understanding of risk factors for early revision THA is essential to develop strategies for mitigating the risk of patients undergoing early revision. This study aimed to develop and validate novel machine learning (ML) models for the prediction of early revision after primary THA. METHODS: A total of 7,397 consecutive patients who underwent primary THA were evaluated, including 566 patients (6.6%) with confirmed early revision THA (<2 years from index THA). Electronic patient records were manually reviewed to identify patient demographics, implant characteristics, and surgical variables that may be associated with early revision THA. Six ML algorithms were developed to predict early revision THA, and these models were assessed by discrimination, calibration, and decision curve analysis. RESULTS: The strongest predictors for early revision after primary THA were Charlson Comorbidity Index, body mass index >35 kg/m2, and depression. The six ML models all achieved excellent performance across discrimination (area under the curve >0.80), calibration, and decision curve analysis. CONCLUSION: This study developed ML models for the prediction of early revision surgery for patients after primary THA. The study findings show excellent performance on discrimination, calibration, and decision curve analysis for all six candidate models, highlighting the potential of these models to assist in clinical practice patient-specific preoperative quantification of increased risk of early revision THA.

The Mechanism of Metallosis After Total Hip Arthroplasty.

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.

Surgeon and Patient Upper Extremity Dominance Does Not Influence Clinical Outcomes After Total Shoulder Arthroplasty.

BACKGROUND: Surgeon- and patient-specific characteristics as they pertain to total shoulder arthroplasty (TSA) are limited in the literature. The influence of surgeon upper extremity dominance in TSA and whether outcomes vary among patients undergoing right or left TSA with respect to surgeon handedness have yet to be investigated. PURPOSE: To determine whether surgeon or patient upper extremity dominance has an effect on clinical outcomes after primary TSA at short-term follow-up. STUDY DESIGN: Case series; Level of evidence, 4. METHODS: A retrospective chart review was performed on prospectively collected data from an institutional shoulder registry. Patients who underwent primary TSA for glenohumeral osteoarthritis from June 2008 to August 2012 were included in the study. Preoperative and postoperative American Shoulder and Elbow Surgeons (ASES), Simple Shoulder Test (SST), and visual analog scale (VAS) pain scores were evaluated. To determine the clinical relevance of ASES scores, the minimal clinically important difference (MCID), the substantial clinical benefit (SCB), and the patient acceptable symptom state (PASS) were used. Active forward elevation, abduction, and external rotation were recorded for each patient. Glenoid version was also evaluated preoperatively on standard radiographs. RESULTS: Included in this study were 40 patients (n = 44 shoulders; mean age, 69.0 ± 7.3 years) with a mean follow-up of 36.5 ± 16.2 months. Final active range of motion between patients who underwent dominant versus nondominant and left versus right TSA by a right-handed surgeon was not significantly different. Clinical outcomes including the ASES, SST, and VAS pain scores were compared, and no statistical significance was identified between groups. With regard to the ASES score, 89% of patients achieved the MCID, 64% achieved the SCB, and 60% reached or exceeded the PASS. No significant difference in preoperative glenoid version between groups could be found. CONCLUSION: With the numbers available, neither patient nor surgeon upper extremity dominance had a significant influence on clinical outcomes after primary TSA at short-term follow-up. CLINICAL RELEVANCE: The influence of surgeon and patient upper extremity dominance on TSA outcomes is an important consideration, given the preferential use of the dominant extremity exhibited by most patients during activities of daily living. To this, operating on a right shoulder might be technically more demanding for a right-handed surgeon and vice versa, as it is considered in other subspecialties.

Superporous hydrogels for cartilage repair: Evaluation of the morphological and mechanical properties.

Despite extensive research in the design of biomaterials for articular cartilage repair, there remains a need for the development of materials with the mechanical compliance to function synergistically with healthy cartilage, but porous enough to allow for tissue integration. In this study, superporous hydrogels of poly(vinyl alcohol) and poly(vinyl pyrrolidone) were prepared using a novel technique consisting of a double emulsion process. The hydrogel emulsions were physically cross-linked by freeze-thaw cycling. The hydrogels had a high degree of porosity, determined using environmental scanning electron microscopy, a technique superior to any method that involves dehydrating the samples. Increasing the volume of organic solvent increased porosity, due to cross-linking of the hydrogel solution around the droplets in the emulsion, leaving pores where the organic solvent was present. Poly(lactic-co-glyclic acid) microparticles formed and were embedded in the matrix. The mechanical properties, measured in confined creep and in unconfined, uniaxial compression, were similar to native articular cartilage. The permeability of the samples was unaffected by changing solvent content, despite changes in porosity. These materials are good candidates for tissue engineering of cartilage because they can mimic mature cartilage mechanically while providing a porous matrix through which cells can migrate and proliferate.

Characterization of the behavior of porous hydrogels in model osmotically-conditioned articular cartilage systems.

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.