The aim of this study was to report the pooled prevalence of post-traumatic osteoarthritis (PTOA) and examine whether the risk of developing PTOA after anterior cruciate ligament (ACL) injury has decreased in recent decades. The PubMed and Web of Science databases were searched from 1 January 1980 to 11 May 2022. Patient series, observational studies, and clinical trials having reported the prevalence of radiologically confirmed PTOA after ACL injury, with at least a ten-year follow-up, were included. All studies were analyzed simultaneously, and separate analyses of the operative and nonoperative knees were performed. The prevalence of PTOA was calculated separately for each study, and pooled prevalence was reported with 95% confidence intervals (CIs) using either a fixed or random effects model. To examine the effect of the year of injury on the prevalence, a logit transformed meta-regression analysis was used with a maximum-likelihood estimator. Results from meta-regression analyses were reported with the unstandardized coefficient (β).Aims
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The use of artificial intelligence (AI) is rapidly growing across many domains, of which the medical field is no exception. AI is an umbrella term defining the practical application of algorithms to generate useful output, without the need of human cognition. Owing to the expanding volume of patient information collected, known as ‘big data’, AI is showing promise as a useful tool in healthcare research and across all aspects of patient care pathways. Practical applications in orthopaedic surgery include: diagnostics, such as fracture recognition and tumour detection; predictive models of clinical and patient-reported outcome measures, such as calculating mortality rates and length of hospital stay; and real-time rehabilitation monitoring and surgical training. However, clinicians should remain cognizant of AI’s limitations, as the development of robust reporting and validation frameworks is of paramount importance to prevent avoidable errors and biases. The aim of this review article is to provide a comprehensive understanding of AI and its subfields, as well as to delineate its existing clinical applications in trauma and orthopaedic surgery. Furthermore, this narrative review expands upon the limitations of AI and future direction. Cite this article:
The aims of this study were: 1) to describe extended restricted kinematic alignment (E-rKA), a novel alignment strategy during robotic-assisted total knee arthroplasty (RA-TKA); 2) to compare residual medial compartment tightness following virtual surgical planning during RA-TKA using mechanical alignment (MA) and E-rKA, in the same set of osteoarthritic varus knees; 3) to assess the requirement of soft-tissue releases during RA-TKA using E-rKA; and 4) to compare the accuracy of surgical plan execution between knees managed with adjustments in component positioning alone, and those which require additional soft-tissue releases. Patients who underwent RA-TKA between January and December 2022 for primary varus osteoarthritis were included. Safe boundaries for E-rKA were defined. Residual medial compartment tightness was compared following virtual surgical planning using E-rKA and MA, in the same set of knees. Soft-tissue releases were documented. Errors in postoperative alignment in relation to planned alignment were compared between patients who did (group A) and did not (group B) require soft-tissue releases.Aims
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This study aimed to investigate the optimal sagittal positioning of the uncemented femoral component in total knee arthroplasty to minimize the risk of aseptic loosening and periprosthetic fracture. Ten different sagittal placements of the femoral component, ranging from -5 mm (causing anterior notch) to +4 mm (causing anterior gap), were analyzed using finite element analysis. Both gait and squat loading conditions were simulated, and Von Mises stress and interface micromotion were evaluated to assess fracture and loosening risk.Aims
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Revision total knee arthroplasty (rTKA) and revision total hip arthroplasty (rTHA) are complex procedures with higher rates of re-revision, complications, and mortality compared to primary TKA and THA. We report the effects of the establishment of a revision arthroplasty network (the East Midlands Specialist Orthopaedic Network; EMSON) on outcomes of rTKA and rTHA. The revision arthroplasty network was established in January 2015 and covered five hospitals in the Nottinghamshire and Lincolnshire areas of the East Midlands of England. This comprises a collaborative weekly multidisciplinary meeting where upcoming rTKA and rTHA procedures are discussed, and a plan agreed. Using the Hospital Episode Statistics database, revision procedures carried out between April 2011 and March 2018 (allowing two-year follow-up) from the five network hospitals were compared to all other hospitals in England. Age, sex, and mean Hospital Frailty Risk scores were used as covariates. The primary outcome was re-revision surgery within one year of the index revision. Secondary outcomes were re-revision surgery within two years, any complication within one and two years, and median length of hospital stay.Aims
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To examine whether natural language processing (NLP) using a clinically based large language model (LLM) could be used to predict patient selection for total hip or total knee arthroplasty (THA/TKA) from routinely available free-text radiology reports. Data pre-processing and analyses were conducted according to the Artificial intelligence to Revolutionize the patient Care pathway in Hip and knEe aRthroplastY (ARCHERY) project protocol. This included use of de-identified Scottish regional clinical data of patients referred for consideration of THA/TKA, held in a secure data environment designed for artificial intelligence (AI) inference. Only preoperative radiology reports were included. NLP algorithms were based on the freely available GatorTron model, a LLM trained on over 82 billion words of de-identified clinical text. Two inference tasks were performed: assessment after model-fine tuning (50 Epochs and three cycles of k-fold cross validation), and external validation.Aims
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While internet search engines have been the primary information source for patients’ questions, artificial intelligence large language models like ChatGPT are trending towards becoming the new primary source. The purpose of this study was to determine if ChatGPT can answer patient questions about total hip (THA) and knee arthroplasty (TKA) with consistent accuracy, comprehensiveness, and easy readability. We posed the 20 most Google-searched questions about THA and TKA, plus ten additional postoperative questions, to ChatGPT. Each question was asked twice to evaluate for consistency in quality. Following each response, we responded with, “Please explain so it is easier to understand,” to evaluate ChatGPT’s ability to reduce response reading grade level, measured as Flesch-Kincaid Grade Level (FKGL). Five resident physicians rated the 120 responses on 1 to 5 accuracy and comprehensiveness scales. Additionally, they answered a “yes” or “no” question regarding acceptability. Mean scores were calculated for each question, and responses were deemed acceptable if ≥ four raters answered “yes.”Aims
Methods
In the UK, the agricultural, military, and construction sectors have stringent rules about the use of hearing protection due to the risk of noise-induced hearing loss. Orthopaedic staff may also be at risk due to the use of power tools. The UK Health and Safety Executive (HSE) have clear standards as to what are deemed acceptable occupational levels of noise on A-weighted and C-weighted scales. The aims of this review were to assess the current evidence on the testing of exposure to noise in orthopaedic operating theatres to see if it exceeds these regulations. A search of PubMed and EMBASE databases was conducted using PRISMA guidelines. The review was registered prospectively in PROSPERO. Studies which assessed the exposure to noise for orthopaedic staff in operating theatres were included. Data about the exposure to noise were extracted from these studies and compared with the A-weighted and C-weighted acceptable levels described in the HSE regulations.Aims
Methods
Patient dissatisfaction following primary total knee arthroplasty (TKA) with manual jig-based instruments has been reported to be as high as 30%. Robotic-assisted total knee arthroplasty (RA-TKA) has been increasingly used in an effort to improve patient outcomes, however there is a paucity of literature examining patient satisfaction after RA-TKA. This study aims to identify the incidence of patients who were not satisfied following RA-TKA and to determine factors associated with higher levels of dissatisfaction. This was a retrospective review of 674 patients who underwent primary TKA between October 2016 and September 2020 with a minimum two-year follow-up. A five-point Likert satisfaction score was used to place patients into two groups: Group A were those who were very dissatisfied, dissatisfied, or neutral (Likert score 1 to 3) and Group B were those who were satisfied or very satisfied (Likert score 4 to 5). Patient demographic data, as well as preoperative and postoperative patient-reported outcome measures, were compared between groups.Aims
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To investigate the impact of consecutive perioperative care transitions on in-hospital recovery of patients who had primary total knee arthroplasty (TKA) over an 11-year period. This observational cohort study used electronic health record data from all patients undergoing preoperative screening for primary TKA at a Northern Netherlands hospital between 2009 and 2020. In this timeframe, three perioperative care transitions were divided into four periods: Baseline care (Joint Care, n = 171; May 2009 to August 2010), Function-tailored (n = 404; September 2010 to October 2013), Fast-track (n = 721; November 2013 to May 2018), and Prehabilitation (n = 601; June 2018 to December 2020). In-hospital recovery was measured using inpatient recovery of activities (IROA), length of stay (LOS), and discharge to preoperative living situation (PLS). Multivariable regression models were used to analyze the impact of each perioperative care transition on in-hospital recovery.Aims
Methods
The aim of this study was to report the meaningful values of the EuroQol five-dimension three-level questionnaire (EQ-5D-3L) and EuroQol visual analogue scale (EQ-VAS) in patients undergoing primary knee arthroplasty (KA). This is a retrospective study of patients undergoing primary KA for osteoarthritis in a university teaching hospital (Royal Infirmary of Edinburgh) (1 January 2013 to 31 December 2019). Pre- and postoperative (one-year) data were prospectively collected for 3,181 patients (median age 69.9 years (interquartile range (IQR) 64.2 to 76.1); females, n = 1,745 (54.9%); median BMI 30.1 kg/m2 (IQR 26.6 to 34.2)). The reliability of the EQ-5D-3L was measured using Cronbach’s alpha. Responsiveness was determined by calculating the anchor-based minimal clinically important difference (MCID), the minimal important change (MIC) (cohort and individual), the patient-acceptable symptom state (PASS) predictive of satisfaction, and the minimal detectable change at 90% confidence intervals (MDC-90).Aims
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The aim of this study was to determine the risk of tibial eminence avulsion intraoperatively for bi-unicondylar knee arthroplasty (Bi-UKA), with consideration of the effect of implant positioning, overstuffing, and sex, compared to the risk for isolated medial unicondylar knee arthroplasty (UKA-M) and bicruciate-retaining total knee arthroplasty (BCR-TKA). Two experimentally validated finite element models of tibia were implanted with UKA-M, Bi-UKA, and BCR-TKA. Intraoperative loads were applied through the condyles, anterior cruciate ligament (ACL), medial collateral ligament (MCL), and lateral collateral ligament (LCL), and the risk of fracture (ROF) was evaluated in the spine as the ratio of the 95th percentile maximum principal elastic strains over the tensile yield strain of proximal tibial bone.Aims
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Unicompartmental and total knee arthroplasty (UKA and TKA) are successful treatments for osteoarthritis, but the solid metal implants disrupt the natural distribution of stress and strain which can lead to bone loss over time. This generates problems if the implant needs to be revised. This study investigates whether titanium lattice UKA and TKA implants can maintain natural load transfer in the proximal tibia. In a cadaveric model, UKA and TKA procedures were performed on eight fresh-frozen knee specimens, using conventional (solid) and titanium lattice tibial implants. Stress at the bone-implant interfaces were measured and compared to the native knee.Aims
Methods
Although readmission has historically been of primary interest, emergency department (ED) visits are increasingly a point of focus and can serve as a potentially unnecessary gateway to readmission. This study aims to analyze the difference between primary and revision total joint arthroplasty (TJA) cases in terms of the rate and reasons associated with 90-day ED visits. We retrospectively reviewed all patients who underwent TJA from 2011 to 2021 at a single, large, tertiary urban institution. Patients were separated into two cohorts based on whether they underwent primary or revision TJA (rTJA). Outcomes of interest included ED visit within 90-days of surgery, as well as reasons for ED visit and readmission rate. Multivariable logistic regressions were performed to compare the two groups while accounting for all statistically significant demographic variables.Aims
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