Innovations in orthopaedic trauma: Top advancements of the past two decades and predictions for the next two

Utility of Machine Learning, Natural Language Processing, and Artificial Intelligence in Predicting Hospital Readmissions After Orthopaedic Surgery: A Systematic Review and Meta-Analysis

BACKGROUND

Numerous applications and strategies have been utilized to help assess the trends and patterns of readmissions after orthopaedic surgery in an attempt to extrapolate possible risk factors and causative agents. The aim of this work is to systematically summarize the available literature on the extent to which natural language processing, machine learning, and artificial intelligence (AI) can help improve the predictability of hospital readmissions after orthopaedic and spine surgeries.

METHODS

This is a systematic review and meta-analysis. PubMed, Embase and Google Scholar were searched, up until August 30, 2023, for studies that explore the use of AI, natural language processing, and machine learning tools for the prediction of readmission rates after orthopedic procedures. Data regarding surgery type, patient population, readmission outcomes, advanced models utilized, comparison methods, predictor sets, the inclusion of perioperative predictors, validation method, size of training and testing sample, accuracy, and receiver operating characteristics (C-statistic), among other factors, were extracted and assessed.

RESULTS

A total of 26 studies were included in our final dataset. The overall summary C-statistic showed a mean of 0.71 across all models, indicating a reasonable level of predictiveness. A total of 15 articles (57%) were attributed to the spine, making it the most commonly explored orthopaedic field in our study. When comparing accuracy of prediction models between different fields, models predicting readmissions after hip/knee arthroplasty procedures had a higher prediction accuracy (mean C-statistic = 0.79) than spine (mean C-statistic = 0.7) and shoulder (mean C-statistic = 0.67). In addition, models that used single institution data, and those that included intraoperative and/or postoperative outcomes, had a higher mean C-statistic than those utilizing other data sources, and that include only preoperative predictors. According to the Prediction model Risk of Bias Assessment Tool, the majority of the articles in our study had a high risk of bias.

CONCLUSION

AI tools perform reasonably well in predicting readmissions after orthopaedic procedures. Future work should focus on standardizing study methodologies and designs, and improving the data analysis process, in an attempt to produce more reliable and tangible results.

LEVEL OF EVIDENCE

Level III. See Instructions for Authors for a complete description of levels of evidence.

Reduced Isocyanate Release Using a Waterproof, Resin-Based Cast Alternative Relative to Fiberglass Casts

The effects of occupational isocyanate exposure range from asthma and contact dermatitis to neurotoxicity and cancer. Respiratory sensitization due to orthopedic cast application has been well documented. This study aims to compare the safety of standard-of-care fiberglass casts and a novel waterproof cast alternative by measuring the amount of isocyanate released during off-gassing over time. A 3D-printed arm simulator with comparable casing material amounts was placed in a sealed chamber. An isocyanate-sensing color-changing (SafeAir) tag was used to measure the levels of toxic exposure. Triplicate trials were conducted across all time periods (15 min, 1 h, and 24 h) and conditions. The bare arm simulator and freshly opened tags served as negative controls. Normalized pixel intensity indexes and isocyanate release estimates in ppb were derived from ImageJ-analyzed SafeAir tag photos. Fiberglass casts exhibited greater isocyanate release than both the waterproof alternative (p = 0.0002) and no-cast controls (p = 0.0006), particularly at 24 h. The waterproof alternative and no-cast control did not statistically differ (p = 0.1603). Therefore, the waterproof alternative released less isocyanate than the fiberglass casts. Waterproof cast alternatives may be safer than fiberglass by limiting medical professionals' exposure to toxic isocyanates and, thus, decreasing their risk of suffering occupational asthma.

Patient perspective on the use of carbon fibre plates for extremity fracture fixation

INTRODUCTION

Carbon fibre-reinforced polyetheretherketone (CFR-PEEK) plates represent an exciting development within trauma and orthopaedic surgery, offering advantages including radiolucency, material properties similar to bone, and lack of localised tissue reaction. As more call for trials examining their use, there is no data available as to the acceptability of these implants to patients. This study aimed to therefore examine the acceptability of CFR-PEEK plates to patients undergoing fracture surgery.

METHODS

This was a prospective cross-sectional survey of patients undergoing surgery for a fracture of the ankle, distal femur, distal radius, or proximal humerus. Once a decision had been made to pursue operative fixation with a plate, patients were provided with descriptions of both CFR-PEEK and stainless steel and titanium metal implants alongside the current clinical evidence. All patients undertook a questionnaire examining their views as to the advantages and disadvantages of CFR-PEEK plates, and whether they would be happy to participate in a trial comparing both.

RESULTS

Ninety-nine patients were happy to participate (64 females, mean age 50). Eighty-seven patients reported that they would want a CFR-PEEK implant for their fracture, and 76 reported that they would be willing to participate in an RCT comparing their use. Commonly reported advantages included radiolucency, low weight and biocompatibility. Disadvantages reported included cost and concerns regarding durability.

CONCLUSIONS

This study demonstrates that CFR-PEEK implants would be acceptable to patients undergoing fracture surgery, with high numbers of patients stating that they would be willing to participate in a randomised study examining their use.

Strategies to Mitigate and Treat Orthopaedic Device-Associated Infections

Orthopaedic device implants play a crucial role in restoring functionality to patients suffering from debilitating musculoskeletal diseases or to those who have experienced traumatic injury. However, the surgical implantation of these devices carries a risk of infection, which represents a significant burden for patients and healthcare providers. This review delineates the pathogenesis of orthopaedic implant infections and the challenges that arise due to biofilm formation and the implications for treatment. It focuses on research advancements in the development of next-generation orthopaedic medical devices to mitigate against implant-related infections. Key considerations impacting the development of devices, which must often perform multiple biological and mechanical roles, are delineated. We review technologies designed to exert spatial and temporal control over antimicrobial presentation and the use of antimicrobial surfaces with intrinsic antibacterial activity. A range of measures to control bio-interfacial interactions including approaches that modify implant surface chemistry or topography to reduce the capacity of bacteria to colonise the surface, form biofilms and cause infections at the device interface and surrounding tissues are also reviewed.