Investigating the relationship between proteomic, compositional, and histologic biomarkers and cartilage biomechanics using artificial neural networks

Artificial intelligence in total and unicompartmental knee arthroplasty

The application of Artificial intelligence (AI) and machine learning (ML) tools in total (TKA) and unicompartmental knee arthroplasty (UKA) emerges with the potential to improve patient-centered decision-making and outcome prediction in orthopedics, as ML algorithms can generate patient-specific risk models. This review aims to evaluate the potential of the application of AI/ML models in the prediction of TKA outcomes and the identification of populations at risk.An extensive search in the following databases: MEDLINE, Scopus, Cinahl, Google Scholar, and EMBASE was conducted using the PIOS approach to formulate the research question. The PRISMA guideline was used for reporting the evidence of the data extracted. A modified eight-item MINORS checklist was employed for the quality assessment. The databases were screened from the inception to June 2022.Forty-four out of the 542 initially selected articles were eligible for the data analysis; 5 further articles were identified and added to the review from the PUBMED database, for a total of 49 articles included. A total of 2,595,780 patients were identified, with an overall average age of the patients of 70.2 years ± 7.9 years old. The five most common AI/ML models identified in the selected articles were: RF, in 38.77% of studies; GBM, in 36.73% of studies; ANN in 34.7% of articles; LR, in 32.65%; SVM in 26.53% of articles.This systematic review evaluated the possible uses of AI/ML models in TKA, highlighting their potential to lead to more accurate predictions, less time-consuming data processing, and improved decision-making, all while minimizing user input bias to provide risk-based patient-specific care.

An Injectable Containing Morphine, Ropivacaine, Epinephrine, and Ketorolac Is Not Cytotoxic to Articular Cartilage Explants From Degenerative Knees

PURPOSE

The purpose of this study was to determine the effects of a multidrug injectate containing morphine, ropivacaine, epinephrine, and ketorolac, commonly referred to as the "Orthococktail," on cartilage tissue viability and metabolic responses using an established in vitro model.

METHODS

With institutional review board approval and informed patient consent, tissues normally discarded after total knee arthroplasty (TKA) were recovered. Full-thickness cartilage explants (n = 72, Outerbridge grade 1 to 3) were created and bisected. Paired explant halves were treated with either 1 mL Orthococktail or 1 mL of saline and cultured for 8 hours at 37°C, with 0.5 mL of the treatment being removed and replaced with tissue culture media every hour. Explants were cultured for 6 days, and media were changed and collected on days 3 and 6. After day 6, tissues were processed for cell viability, weighed, and processed for histologic grading. Outcome measures were compared for significant differences between treated and untreated samples.

RESULTS

There were no significant differences in cartilage viability between control and Orthococktail-treated samples across a spectrum of cartilage pathologies. Orthococktail treatment consistently resulted in a significant decrease in the release of PGE2, MCP-1, MMP-7, and MMP-8 on day 3 of culture and PGE2, MMP-3, MMP-7, and MMP-8 on day 6 of culture, compared with saline controls.

CONCLUSION

The results of the present study indicate that an Orthococktail injection composed of morphine, ropivacaine, epinephrine, and ketorolac is associated with a transient decrease in degradative and inflammatory mediators produced by more severely affected articular cartilage and may mitigate perioperative joint pain such that postoperative narcotic drug use could be reduced.

CLINICAL RELEVANCE

The Orthococktail solution used in this study may be a safe intraoperative, intra-articular injection option for patients undergoing joint arthroplasty and other joint preservation surgical procedures.

Machine learning in knee arthroplasty: specific data are key-a systematic review

PURPOSE

Artificial intelligence (AI) in healthcare is rapidly growing and offers novel options of data analysis. Machine learning (ML) represents a distinct application of AI, which is capable of generating predictions and has already been tested in different medical specialties with various approaches such as diagnostic applications, cost predictions or identification of risk factors. In orthopaedics, this technology has only recently been introduced and the literature on ML in knee arthroplasty is scarce. In this review, we aim to investigate which predictions are already feasible using ML models in knee arthroplasty to identify prerequisites for the effective use of this novel approach. For this reason, we conducted a systematic review of ML algorithms for outcome prediction in knee arthroplasty.

METHODS

A comprehensive search of PubMed, Medline database and the Cochrane Library was conducted to find ML applications for knee arthroplasty. All relevant articles were systematically retrieved and evaluated by an orthopaedic surgeon and a data scientist on the basis of the PRISMA statement. The search strategy yielded 225 articles of which 19 were finally assessed as eligible. A modified Coleman Methodology Score (mCMS) was applied to account for a methodological evaluation.

RESULTS

The studies presented in this review demonstrated fair to good results (AUC median 0.76/range 0.57-0.98), while heterogeneous prediction models were analysed: complications (6), costs (4), functional outcome (3), revision (2), postoperative satisfaction (2), surgical technique (1) and biomechanical properties (1) were investigated. The median mCMS was 65 (range 40-80) points.

CONCLUSION

The prediction of distinct outcomes with ML models applying specific data is already feasible; however, the prediction of more complex outcomes is still inaccurate. Registry data on knee arthroplasty have not been fully analysed yet so that specific parameters have not been sufficiently evaluated. The inclusion of specific input data as well as the collaboration of orthopaedic surgeons and data scientists are essential prerequisites to fully utilize the capacity of ML in knee arthroplasty. Future studies should investigate prospective data with specific and longitudinally recorded parameters.

LEVEL OF EVIDENCE

III.

Artificial neural network prediction of same-day discharge following primary total knee arthroplasty based on preoperative and intraoperative variables

AIMS

This study used an artificial neural network (ANN) model to determine the most important pre- and perioperative variables to predict same-day discharge in patients undergoing total knee arthroplasty (TKA).

METHODS

Data for this study were collected from the National Surgery Quality Improvement Program (NSQIP) database from the year 2018. Patients who received a primary, elective, unilateral TKA with a diagnosis of primary osteoarthritis were included. Demographic, preoperative, and intraoperative variables were analyzed. The ANN model was compared to a logistic regression model, which is a conventional machine-learning algorithm. Variables collected from 28,742 patients were analyzed based on their contribution to hospital length of stay.

RESULTS

The predictability of the ANN model, area under the curve (AUC) = 0.801, was similar to the logistic regression model (AUC = 0.796) and identified certain variables as important factors to predict same-day discharge. The ten most important factors favouring same-day discharge in the ANN model include preoperative sodium, preoperative international normalized ratio, BMI, age, anaesthesia type, operating time, dyspnoea status, functional status, race, anaemia status, and chronic obstructive pulmonary disease (COPD). Six of these variables were also found to be significant on logistic regression analysis.

CONCLUSION

Both ANN modelling and logistic regression analysis revealed clinically important factors in predicting patients who can undergo safely undergo same-day discharge from an outpatient TKA. The ANN model provides a beneficial approach to help determine which perioperative factors can predict same-day discharge as of 2018 perioperative recovery protocols. Cite this article: Bone Joint J 2021;103-B(8):1358-1366.

Osteoarthritis year in review 2019: mechanics

Mechanics play a critical - but not sole - role in the pathogenesis of osteoarthritis, and recent research has highlighted how mechanical constructs are relevant at the cellular, joint, and whole-body level related to osteoarthritis outcomes. This review examined papers from April 2018 to April 2019 that reported on the role of mechanics in osteoarthritis etiology, with a particular emphasis on studies that focused on the interaction between movement and tissue biomechanics with other clinical outcomes relevant to the pathophysiology of osteoarthritis. Studies were grouped by themes that were particularly prevalent from the past year. Results of the search highlighted the large exposure of knee-related research relative to other body areas, as well as studies utilizing laboratory-based motion capture technology. New research from this past year highlighted the important role that rate of exerted loads and rate of muscle force development - rather than simply force capacity (strength) - have in OA etiology and treatment. Further, the role of muscle activation patterns in functional and structural aspects of joint health has received much interest, though findings remain equivocal. Finally, new research has identified potential mechanical outcome measures that may be related to osteoarthritis disease progression. Future research should continue to combine knowledge of mechanics with other relevant research techniques, and to identify mechanical markers of joint health and structural and functional disease progression that are needed to best inform disease prevention, monitoring, and treatment.