Aims. To identify variables independently associated with same-day discharge (SDD) of patients following revision total knee arthroplasty (rTKA) and to develop
This annotation briefly reviews the history of artificial intelligence and
Aims. The use of technology to assess balance and alignment during total knee surgery can provide an overload of numerical data to the surgeon. Meanwhile, this quantification holds the potential to clarify and guide the surgeon through the surgical decision process when selecting the appropriate bone recut or soft tissue adjustment when balancing a total knee. Therefore, this paper evaluates the potential of deploying supervised
Degenerative lumbar spondylolisthesis (DLS) is a common condition with many available treatment options. The Degenerative Spondylolisthesis Instability Classification (DSIC) scheme, based on a systematic review of best available evidence, was proposed by Simmonds et al. in 2015. This classification scheme proposes that the stability of the patient's pathology be determined by a surgeon based on quantitative and qualitative clinical and radiographic parameters. The purpose of the study is to utilise
Femoroacetabular Impingement (FAI) syndrome, characterised by abnormal hip contact causing symptoms and osteoarthritis, is measured using the International Hip Outcome Tool (iHOT). This study uses
Aims. Clinical decision support systems (CDSS) can support clinicians in selecting appropriate treatments for patients. The objective of this study was to examine if triaging patients with LBP to the most optimal treatment can be improved by using a data-driven approach with the help of
Introduction & Aims. Patient recovery after total knee arthroplasty remains highly variable. Despite the growing interest in and implementation of patient reported outcome measures (e.g. Knee Society Score, Oxford Knee Score), the recovery process of the individual patient is poorly monitored. Unfortunately, patient reported outcomes represent a complex interaction of multiple physiological and psychological aspects, they are also limited by the discrete time intervals at which they are administered. The use of wearable sensors presents a potential alternative by continuously monitoring a patient's physical activity. These sensors however present their own challenges. This paper deals with the interpretation of the high frequency time signals acquired when using accelerometer-based wearable sensors. Method. During a preliminary validation, five healthy subjects were equipped with two wireless inertial measurement units (IMUs). Using adhesive tape, these IMU sensors were attached to the thigh and shank respectively. All subjects performed a series of supervised activities of daily living (ADL) in their everyday environment (1: walking, 2: stair ascent, 3: stair descent, 4: sitting, 5: laying, 6: standing). The supervisor timestamped the performed activities, such that the raw IMU signals could be uniquely linked to the performed activities. Subsequently, the acquired signals were reduced in Python. Each five second time window was characterized by the minimum, maximum and mean acceleration per sensor node. In addition, the frequency response was analyzed per sensor node as well as the correlation between both sensor nodes. Various
Single level discectomy (SLD) is one of the most commonly performed spinal surgery procedures. Two key drivers of their cost-of-care are duration of surgery (DOS) and postoperative length of stay (LOS). Therefore, the ability to preoperatively predict SLD DOS and LOS has substantial implications for both hospital and healthcare system finances, scheduling and resource allocation. As such, the goal of this study was to predict DOS and LOS for SLD using
Precision health aims to develop personalised and proactive strategies for predicting, preventing, and treating complex diseases such as osteoarthritis (OA). Due to OA heterogeneity, which makes developing effective treatments challenging, identifying patients at risk for accelerated disease progression is essential for efficient clinical trial design and new treatment target discovery and development. To create a reliable and interpretable precision health tool that predicts rapid knee OA progression over a 2-year period from baseline patient characteristics using an advanced automated
Anterior approach total hip arthroplasty (AA-THA) has a steep learning curve, with higher complication rates in initial cases. Proper surgical case selection during the learning curve can reduce early risk. This study aims to identify patient and radiographic factors associated with AA-THA difficulty using
Abstract. Introduction. Precision health aims to develop personalised and proactive strategies for predicting, preventing, and treating complex diseases such as osteoarthritis (OA), a degenerative joint disease affecting over 300 million people worldwide. Due to OA heterogeneity, which makes developing effective treatments challenging, identifying patients at risk for accelerated disease progression is essential for efficient clinical trial design and new treatment target discovery and development. Objectives. This study aims to create a trustworthy and interpretable precision health tool that predicts rapid knee OA progression based on baseline patient characteristics using an advanced automated
Disorders of human joints manifest during dynamic movement, yet no objective tools are widely available for clinicians to assess or diagnose abnormal joint motion during functional activity.
Access to health care, including physiotherapy, is increasingly occurring through virtual formats. At-home adherence to physical therapy programs is often poor and few tools exist to objectively measure low back physiotherapy exercise participation without the direct supervision of a medical professional. The aim of this study was to develop and evaluate the potential for performing automatic, unsupervised video-based monitoring of at-home low back physiotherapy exercises using a single mobile phone camera. 24 healthy adult subjects performed seven exercises based on the McKenzie low back physiotherapy program while being filmed with two smartphone cameras. Joint locations were automatically extracted using an open-source pose estimation framework. Engineered features were extracted from the joint location time series and used to train a support vector machine classifier (SVC). A convolutional neural network (CNN) was trained directly on the joint location time series data to classify exercises based on a recording from a single camera. The models were evaluated using a 5-fold cross validation approach, stratified by subject, with the class-balanced accuracy used as the performance metric. Optimal performance was achieved when using a total of 12 pose estimation landmarks from the upper and lower body, with the SVC model achieving a classification accuracy of 96±4% and the CNN model an accuracy of 97±2%. This study demonstrates the feasibility of using a smartphone camera and a supervised
Background. The advent of value-based conscientiousness and rapid-recovery discharge pathways presents surgeons, hospitals, and payers with the challenge of providing the same total hip arthroplasty episode of care in the safest and most economic fashion for the same fee, despite patient differences. Various predictive analytic techniques have been applied to medical risk models, such as sepsis risk scores, but none have been applied or validated to the elective primary total hip arthroplasty (THA) setting for key payment-based metrics. The objective of this study was to develop and validate a predictive
Arthroplasties are widely performed to improve mobility and quality of life for symptomatic knee/hip osteoarthritis patients. With increasing rates of Total Joint Replacements in the United Kingdom, predicting length of stay is vital for hospitals to control costs, manage resources, and prevent postoperative complications. A longer Length of stay has been shown to negatively affect the quality of care, outcomes and patient satisfaction. Thus, predicting LOS enables us to make full use of medical resources. Clinical characteristics were retrospectively collected from 1,303 patients who received TKA and THR. A total of 21 variables were included, to develop predictive models for LOS by multiple
Objective. Wearable sensors have enabled objective functional data collection from patients before total knee replacement (TKR) and at clinical follow-ups post-surgery whereas traditional evaluation has solely relied on self-reported subjective measures. The timed-up-and-go (TUG) test has been used to evaluate function but is commonly measured using only total completion time, which does not assess joint function or test completion strategy. The current work employs
Total knee and hip arthroplasty (TKA and THA) are the most commonly performed surgical procedures, the costs of which constitute a significant healthcare burden. Improving access to care for THA/TKA requires better efficiency. It is hypothesized that this may be possible through a two-stage approach that utilizes prediction of surgical time to enable optimization of operating room (OR) schedules. Data from 499,432 elective unilateral arthroplasty procedures, including 302,490 TKAs, and 196,942 THAs, performed from 2014-2019 was extracted from the American College of Surgeons (ACS) National Surgical and Quality Improvement (NSQIP) database. A deep multilayer perceptron model was trained to predict duration of surgery (DOS) based on pre-operative clinical and biochemical patient factors. A two-stage approach, utilizing predicted DOS from a held out “test” dataset, was utilized to inform the daily OR schedule. The objective function of the optimization was the total OR utilization, with a penalty for overtime. The scheduling problem and constraints were simulated based on a high-volume elective arthroplasty centre in Canada. This approach was compared to current patient scheduling based on mean procedure DOS. Approaches were compared by performing 1000 simulated OR schedules. The predict then optimize approach achieved an 18% increase in OR utilization over the mean regressor. The two-stage approach reduced overtime by 25-minutes per OR day, however it created a 7-minute increase in underutilization. Better objective value was seen in 85.1% of the simulations. With deep learning prediction and mathematical optimization of patient scheduling it is possible to improve overall OR utilization compared to typical scheduling practices. Maximizing utilization of existing healthcare resources can, in limited resource environments, improve patient's access to arthritis care by increasing patient throughput, reducing surgical wait times and in the immediate future, help clear the backlog associated with the COVID-19 pandemic.
The success of a cementless Total Hip Arthroplasty (THA) depends not only on initial micromotion, but also on long-term failure mechanisms, e.g., implant-bone interface stresses and stress shielding. Any preclinical investigation aimed at designing femoral implant needs to account for temporal evolution of interfacial condition, while dealing with these failure mechanisms. The goal of the present multi-criteria optimization study was to search for optimum implant geometry by implementing a novel
Physiotherapy is a critical element in successful conservative management of low back pain (LBP). The aim of this study was to develop and evaluate a system with wearable inertial sensors to objectively detect sitting postures and performance of unsupervised exercises containing movement in multiple planes (flexion, extension, rotation). A set of 8 inertial sensors were placed on 19 healthy adult subjects. Data was acquired as they performed 7 McKenzie low-back exercises and 3 sitting posture positions. This data was used to train two models (Random Forest (RF) and XGBoost (XGB)) using engineered time series features. In addition, a convolutional neural network (CNN) was trained directly on the time series data. A feature importance analysis was performed to identify sensor locations and channels that contributed most to the models. Finally, a subset of sensor locations and channels was included in a hyperparameter grid search to identify the optimal sensor configuration and the best performing algorithm(s) for exercise classification. Models were evaluated using F1-score in a 10-fold cross validation approach. The optimal hardware configuration was identified as a 3-sensor setup using lower back, left thigh, and right ankle sensors with acceleration, gyroscope, and magnetometer channels. The XBG model achieved the highest exercise (F1=0.94±0.03) and posture (F1=0.90±0.11) classification scores. The CNN achieved similar results with the same sensor locations, using only the accelerometer and gyroscope channels for exercise classification (F1=0.94±0.02) and the accelerometer channel alone for posture classification (F1=0.91±0.03). This study demonstrates the potential of a 3-sensor lower body wearable solution (e.g. smart pants) that can identify proper sitting postures and exercises in multiple planes, suitable for low back pain. This technology has the potential to improve the effectiveness of LBP rehabilitation by facilitating quantitative feedback, early problem diagnosis, and possible remote monitoring.
To detect early signs of infection infrared thermography has been suggested to provide quantitative information. Our vision is to invent a pin site infection thermographic surveillance tool for patients at home. A preliminary step to this goal is the aim of this study, to automate the process of locating the pin and detecting the pin sites in thermal images efficiently, exactly, and reliably for extracting pin site temperatures. A total of 1708 pin sites was investigated with Thermography and augmented by 9 different methods in to totally 10.409 images. The dataset was divided into a training set (n=8325), a validation set (n=1040), and a test set (n=1044) of images. The Pin Detection Model (PDM) was developed as follows: A You Only Look Once (YOLOv5) based object detection model with a Complete Detection Intersection over Union (CDIoU), it was pre-trained and finetuned by the through transfer learning. The basic performance of the YOLOv5 with CDIoU model was compared with other conventional models (FCOS and YOLOv4) for deep and transition learning to improve performance and precision. Maximum Temperature Extraction (MTE) Based on Region of Interest (ROI) for all pin sites was generated by the model. Inference of MTE using PDM with infected and un-infected datasets was investigated. An automatic tool that can identify and annotate pin sites on conventional images using bounding boxes was established. The bounding box was transferred to the infrared image. The PMD algorithm was built on YOLOv5 with CDIoU and has a precision of 0.976. The model offers the pin site detection in 1.8 milliseconds. The thermal data from ROI at the pin site was automatically extracted. These results enable automatic pin site annotation on thermography. The model tracks the correlation between temperature and infection from the detected pin sites and demonstrates it is a promising tool for automatic pin site detection and maximum temperature extraction for further infection studies. Our work for automatic pin site annotation on thermography paves the way for future research on infection assessment using thermography.
Pedicle screw fixation is a technically demanding procedure with potential difficulties and reoperation rates are currently on the order of 11%. The most common intraoperative practice for position assessment of pedicle screws is biplanar fluoroscopic imaging that is limited to two- dimensions and is associated to low accuracies. We have previously introduced a full-dimensional position assessment framework based on registering intraoperative X-rays to preoperative volumetric images with sufficient accuracies. However, the framework requires a semi-manual process of pedicle screw segmentation and the intraoperative X-rays have to be taken from defined positions in space in order to avoid pedicle screws' head occlusion. This motivated us to develop advancements to the system to achieve higher levels of automation in the hope of higher clinical feasibility. In this study, we developed an automatic segmentation and X-ray adequacy assessment protocol. An artificial neural network was trained on a dataset that included a number of digitally reconstructed radiographs representing pedicle screw projections from different points of view. This model was able to segment the projection of any pedicle screw given an X-ray as its input with accuracy of 93% of the pixels. Once the pedicle screw was segmented, a number of descriptive geometric features were extracted from the isolated blob. These segmented images were manually labels as ‘adequate’ or ‘not adequate’ depending on the visibility of the screw axis. The extracted features along with their corresponding labels were used to train a decision tree model that could classify each X-ray based on its adequacy with accuracies on the order of 95%. In conclusion, we presented here a robust, fast and automated pedicle screw segmentation process, combined with an accurate and automatic algorithm for classifying views of pedicle screws as adequate or not. These tools represent a useful step towards full automation of our pedicle screw positioning assessment system.
Artificial intelligence (AI) is, in essence, the concept of ‘computer thinking’, encompassing methods that train computers to perform and learn from executing certain tasks, called
Aims. A substantial fraction of patients undergoing knee arthroplasty (KA) or hip arthroplasty (HA) do not achieve an improvement as high as the minimal clinically important difference (MCID), i.e. do not achieve a meaningful improvement. Using three patient-reported outcome measures (PROMs), our aim was: 1) to assess
Aims. The lack of disease-modifying treatments for osteoarthritis (OA) is linked to a shortage of suitable biomarkers. This study combines multi-molecule synovial fluid analysis with
In recent years,
Literature surrounding artificial intelligence (AI)-related applications for hip and knee arthroplasty has proliferated. However, meaningful advances that fundamentally transform the practice and delivery of joint arthroplasty are yet to be realized, despite the broad range of applications as we continue to search for meaningful and appropriate use of AI. AI literature in hip and knee arthroplasty between 2018 and 2021 regarding image-based analyses, value-based care, remote patient monitoring, and augmented reality was reviewed. Concerns surrounding meaningful use and appropriate methodological approaches of AI in joint arthroplasty research are summarized. Of the 233 AI-related orthopaedics articles published, 178 (76%) constituted original research, while the rest consisted of editorials or reviews. A total of 52% of original AI-related research concerns hip and knee arthroplasty (n = 92), and a narrative review is described. Three studies were externally validated. Pitfalls surrounding present-day research include conflating vernacular (“AI/machine learning”), repackaging limited registry data, prematurely releasing internally validated prediction models, appraising model architecture instead of inputted data, withholding code, and evaluating studies using antiquated regression-based guidelines. While AI has been applied to a variety of hip and knee arthroplasty applications with limited clinical impact, the future remains promising if the question is meaningful, the methodology is rigorous and transparent, the data are rich, and the model is externally validated. Simple checkpoints for meaningful AI adoption include ensuring applications focus on: administrative support over clinical evaluation and management; necessity of the advanced model; and the novelty of the question being answered. Cite this article:
This study was designed to develop a model for predicting bone mineral density (BMD) loss of the femur after total hip arthroplasty (THA) using artificial intelligence (AI), and to identify factors that influence the prediction. Additionally, we virtually examined the efficacy of administration of bisphosphonate for cases with severe BMD loss based on the predictive model. The study included 538 joints that underwent primary THA. The patients were divided into groups using unsupervised time series clustering for five-year BMD loss of Gruen zone 7 postoperatively, and a machine-learning model to predict the BMD loss was developed. Additionally, the predictor for BMD loss was extracted using SHapley Additive exPlanations (SHAP). The patient-specific efficacy of bisphosphonate, which is the most important categorical predictor for BMD loss, was examined by calculating the change in predictive probability when hypothetically switching between the inclusion and exclusion of bisphosphonate.Aims
Methods
To develop prediction models using machine-learning (ML) algorithms for 90-day and one-year mortality prediction in femoral neck fracture (FNF) patients aged 50 years or older based on the Hip fracture Evaluation with Alternatives of Total Hip arthroplasty versus Hemiarthroplasty (HEALTH) and Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trials. This study included 2,388 patients from the HEALTH and FAITH trials, with 90-day and one-year mortality proportions of 3.0% (71/2,388) and 6.4% (153/2,388), respectively. The mean age was 75.9 years (SD 10.8) and 65.9% of patients (1,574/2,388) were female. The algorithms included patient and injury characteristics. Six algorithms were developed, internally validated and evaluated across discrimination (c-statistic; discriminative ability between those with risk of mortality and those without), calibration (observed outcome compared to the predicted probability), and the Brier score (composite of discrimination and calibration).Aims
Methods
There is increasing popularity in the use of artificial intelligence and machine-learning techniques to provide diagnostic and prognostic models for various aspects of Trauma & Orthopaedic surgery. However, correct interpretation of these models is difficult for those without specific knowledge of computing or health data science methodology. Lack of current reporting standards leads to the potential for significant heterogeneity in the design and quality of published studies. We provide an overview of machine-learning techniques for the lay individual, including key terminology and best practice reporting guidelines. Cite this article:
Machine-learning (ML) prediction models in orthopaedic trauma hold great promise in assisting clinicians in various tasks, such as personalized risk stratification. However, an overview of current applications and critical appraisal to peer-reviewed guidelines is lacking. The objectives of this study are to 1) provide an overview of current ML prediction models in orthopaedic trauma; 2) evaluate the completeness of reporting following the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement; and 3) assess the risk of bias following the Prediction model Risk Of Bias Assessment Tool (PROBAST) tool. A systematic search screening 3,252 studies identified 45 ML-based prediction models in orthopaedic trauma up to January 2023. The TRIPOD statement assessed transparent reporting and the PROBAST tool the risk of bias.Aims
Methods
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:
Total hip arthroplasty (THA) and total knee arthroplasty (TKA) are common orthopaedic procedures requiring postoperative radiographs to confirm implant positioning and identify complications. Artificial intelligence (AI)-based image analysis has the potential to automate this postoperative surveillance. The aim of this study was to prepare a scoping review to investigate how AI is being used in the analysis of radiographs following THA and TKA, and how accurate these tools are. The Embase, MEDLINE, and PubMed libraries were systematically searched to identify relevant articles. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews and Arksey and O’Malley framework were followed. Study quality was assessed using a modified Methodological Index for Non-Randomized Studies tool. AI performance was reported using either the area under the curve (AUC) or accuracy.Aims
Methods
This study aimed to explore the biological and clinical importance of dysregulated key genes in osteoarthritis (OA) patients at the cartilage level to find potential biomarkers and targets for diagnosing and treating OA. Six sets of gene expression profiles were obtained from the Gene Expression Omnibus database. Differential expression analysis, weighted gene coexpression network analysis (WGCNA), and multiple machine-learning algorithms were used to screen crucial genes in osteoarthritic cartilage, and genome enrichment and functional annotation analyses were used to decipher the related categories of gene function. Single-sample gene set enrichment analysis was performed to analyze immune cell infiltration. Correlation analysis was used to explore the relationship among the hub genes and immune cells, as well as markers related to articular cartilage degradation and bone mineralization.Aims
Methods
Aims. To determine the major risk factors for unplanned reoperations (UROs) following corrective surgery for adult spinal deformity (ASD) and their interactions, using machine learning-based prediction algorithms and game theory. Methods. Patients who underwent surgery for ASD, with a minimum of two-year follow-up, were retrospectively reviewed. In total, 210 patients were included and randomly allocated into training (70% of the sample size) and test (the remaining 30%) sets to develop the
Aims. The aim of this study was to identify factors associated with five-year cancer-related mortality in patients with limb and trunk soft-tissue sarcoma (STS) and develop and validate
The number of convolutional neural networks (CNN) available for fracture detection and classification is rapidly increasing. External validation of a CNN on a temporally separate (separated by time) or geographically separate (separated by location) dataset is crucial to assess generalizability of the CNN before application to clinical practice in other institutions. We aimed to answer the following questions: are current CNNs for fracture recognition externally valid?; which methods are applied for external validation (EV)?; and, what are reported performances of the EV sets compared to the internal validation (IV) sets of these CNNs? The PubMed and Embase databases were systematically searched from January 2010 to October 2020 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The type of EV, characteristics of the external dataset, and diagnostic performance characteristics on the IV and EV datasets were collected and compared. Quality assessment was conducted using a seven-item checklist based on a modified Methodologic Index for NOn-Randomized Studies instrument (MINORS).Aims
Methods
Natural Language Processing (NLP) offers an automated method to extract data from unstructured free text fields for arthroplasty registry participation. Our objective was to investigate how accurately NLP can be used to extract structured clinical data from unstructured clinical notes when compared with manual data extraction. A group of 1,000 randomly selected clinical and hospital notes from eight different surgeons were collected for patients undergoing primary arthroplasty between 2012 and 2018. In all, 19 preoperative, 17 operative, and two postoperative variables of interest were manually extracted from these notes. A NLP algorithm was created to automatically extract these variables from a training sample of these notes, and the algorithm was tested on a random test sample of notes. Performance of the NLP algorithm was measured in Statistical Analysis System (SAS) by calculating the accuracy of the variables collected, the ability of the algorithm to collect the correct information when it was indeed in the note (sensitivity), and the ability of the algorithm to not collect a certain data element when it was not in the note (specificity).Aims
Methods
Accurate identification of the ankle joint centre is critical for estimating tibial coronal alignment in total knee arthroplasty (TKA). The purpose of the current study was to leverage artificial intelligence (AI) to determine the accuracy and effect of using different radiological anatomical landmarks to quantify mechanical alignment in relation to a traditionally defined radiological ankle centre. Patients with full-limb radiographs from the Osteoarthritis Initiative were included. A sub-cohort of 250 radiographs were annotated for landmarks relevant to knee alignment and used to train a deep learning (U-Net) workflow for angle calculation on the entire database. The radiological ankle centre was defined as the midpoint of the superior talus edge/tibial plafond. Knee alignment (hip-knee-ankle angle) was compared against 1) midpoint of the most prominent malleoli points, 2) midpoint of the soft-tissue overlying malleoli, and 3) midpoint of the soft-tissue sulcus above the malleoli.Aims
Methods
An objective technological solution for tracking adherence to at-home shoulder physiotherapy is important for improving patient engagement and rehabilitation outcomes, but remains a significant challenge. The aim of this research was to evaluate performance of machine-learning (ML) methodologies for detecting and classifying inertial data collected during in-clinic and at-home shoulder physiotherapy exercise. A smartwatch was used to collect inertial data from 42 patients performing shoulder physiotherapy exercises for rotator cuff injuries in both in-clinic and at-home settings. A two-stage ML approach was used to detect out-of-distribution (OOD) data (to remove non-exercise data) and subsequently for classification of exercises. We evaluated the performance impact of grouping exercises by motion type, inclusion of non-exercise data for algorithm training, and a patient-specific approach to exercise classification. Algorithm performance was evaluated using both in-clinic and at-home data.Aims
Methods
This study aimed to compare the performance of survival prediction models for bone metastases of the extremities (BM-E) with pathological fractures in an Asian cohort, and investigate patient characteristics associated with survival. This retrospective cohort study included 469 patients, who underwent surgery for BM-E between January 2009 and March 2022 at a tertiary hospital in South Korea. Postoperative survival was calculated using the PATHFx3.0, SPRING13, OPTIModel, SORG, and IOR models. Model performance was assessed with area under the curve (AUC), calibration curve, Brier score, and decision curve analysis. Cox regression analyses were performed to evaluate the factors contributing to survival.Aims
Methods
Identification of patients at risk of not achieving minimally clinically important differences (MCID) in patient reported outcome measures (PROMs) is important to ensure principled and informed pre-operative decision making.
Aims. Failure of irrigation and debridement (I&D) for prosthetic joint infection (PJI) is influenced by numerous host, surgical, and pathogen-related factors. We aimed to develop and validate a practical, easy-to-use tool based on
External validation of
Approximately 20% of patients feel unsatisfied 12 months after primary total knee arthroplasty (TKA). Current predictive tools for TKA focus on the clinician as the intended user rather than the patient. The aim of this study is to develop a tool that can be used by patients without clinician assistance, to predict health-related quality of life (HRQoL) outcomes 12 months after total knee arthroplasty (TKA). All patients with primary TKAs for osteoarthritis between 2012 and 2019 at a tertiary institutional registry were analysed. The predictive outcome was improvement in Veterans-RAND 12 utility score at 12 months after surgery. Potential predictors included patient demographics, co-morbidities, and patient reported outcome scores at baseline. Logistic regression and three
Background. Magnetic resonance imaging (MRI) algorithm identifies end stage severely degenerated disc as ‘black’, and a moderately degenerate to non-degenerated disc as ‘white’. MRI is based on signal intensity changes that identifies loss of proteoglycans, water, and general radial bulging but lacks association with microscopic features such as fissure, endplate damage, persistent inflammatory catabolism that facilitates proteoglycan loss leading to ultimate collapse of annulus with neo-innervation and vascularization, as an indicator of pain. Thus, we propose a novel
The December 2023 Research Roundup. 360. looks at: Tissue integration and chondroprotective potential of acetabular labral augmentation with autograft tendon: study of a porcine model; The Irish National Orthopaedic Register under cyberattack: what happened, and what were the consequences?; An overview of
The June 2023 Hip & Pelvis Roundup. 360. looks at:
Introduction.
The August 2023 Hip & Pelvis Roundup. 360. looks at: Using
The June 2024 Hip & Pelvis Roundup. 360. looks at:
Abstract. Objectives. Spinal disorders such as back pain incur a substantial societal and economic burden. Unfortunately, there is lack of understanding and treatment of these disorders are further impeded by the inability to assess spinal forces in vivo. The aim of this project is to address this challenge by developing and testing a novel image-driven approach that will assess the forces in an individual's spine in vivo by incorporating information acquired from multimodal imaging (magnetic resonance imaging (MRI) and biplane X-rays) in a subject-specific model. Methods. Magnetic resonance and biplane X-ray imaging are used to capture information about the anatomy, tissues, and motion of an individual's spine as they perform a range of everyday activities. This information is then utilised in a subject-specific computational model based on the finite element method to predict the forces in their spine. The project is also utilising novel
The August 2023 Knee Roundup. 360. looks at: Curettage and cementation of giant cell tumour of bone: is arthritis a given?; Anterior knee pain following total knee arthroplasty: does the patellar cement-bone interface affect postoperative anterior knee pain?; Nickel allergy and total knee arthroplasty; The use of artificial intelligence for the prediction of periprosthetic joint infection following aseptic revision total knee arthroplasty; Ambulatory unicompartmental knee arthroplasty: development of a patient selection tool using
Introduction. Instability remains a common complication following total hip arthroplasty (THA) and continues to account for the highest percentage of revisions in numerous registries. Many risk factors have been described, yet a patient-specific risk assessment tool remains elusive. The purpose of this study was to apply a
Background. Implant loosening is a common cause of a poor outcome and pain after total knee arthroplasty (TKA). Despite the increase in use of expensive techniques like arthrography, the detection of prosthetic loosening is often unclear pre-operatively, leading to diagnostic uncertainty and extensive workup. The objective of this study was to evaluate the ability of a
Aims. The aim of this study was to evaluate the ability of a machine-learning algorithm to diagnose prosthetic loosening from preoperative radiographs and to investigate the inputs that might improve its performance. Methods. A group of 697 patients underwent a first-time revision of a total hip (THA) or total knee arthroplasty (TKA) at our institution between 2012 and 2018. Preoperative anteroposterior (AP) and lateral radiographs, and historical and comorbidity information were collected from their electronic records. Each patient was defined as having loose or fixed components based on the operation notes. We trained a series of convolutional neural network (CNN) models to predict a diagnosis of loosening at the time of surgery from the preoperative radiographs. We then added historical data about the patients to the best performing model to create a final model and tested it on an independent dataset. Results. The convolutional neural network we built performed well when detecting loosening from radiographs alone. The first model built de novo with only the radiological image as input had an accuracy of 70%. The final model, which was built by fine-tuning a publicly available model named DenseNet, combining the AP and lateral radiographs, and incorporating information from the patient’s history, had an accuracy, sensitivity, and specificity of 88.3%, 70.2%, and 95.6% on the independent test dataset. It performed better for cases of revision THA with an accuracy of 90.1%, than for cases of revision TKA with an accuracy of 85.8%. Conclusion. This study showed that
Technological advancements in orthopaedic surgery have mainly focused on increasing precision during the operation however, there have been few developments in post-operative physiotherapy. We have developed a computer vision program using
Artificial Intelligence (AI) is becoming more powerful but is barely used to counter the growth in health care burden. AI applications to increase efficiency in orthopedics are rare. We questioned if (1) we could train
Currently implemented accuracy metrics in open-source libraries for segmentation by supervised
Background. Postoperative recovery after routine total hip arthroplasty (THA) can lead to the development of prolonged opioid use but there are few tools for predicting this adverse outcome. The purpose of this study was to develop
Serial section electron microscopy (SSEM) was initially developed to map the neural connections in the brain. SSEM eventually led to the term ‘Connectomics’ to be coined to describe process of following a cell or structure through a volume of tissue. This permits the true three-dimensionality to be appreciated and relationships between cells and structures. The purpose of this study was to utilize this methodology to interrogate S. aureus infected bone. Bone samples were harvested from mice tibia infected with S. aureus and were fixed, decalcified, and osmicated. The samples were paraffin embedded and 5-micron sections were cut to identify regions of bacterial invasion into the osteocyte-lacuna-canalicular-network (OLCN). This area was cut from the paraffin block, deparaffinized, post-fixed and reprocessed into epoxy resin. Serial sections were cut at 60nm and collected onto Kapton tape utilizing the Automated Tape-collecting Ultramicrotome (ATUMtome) system. Samples were mounted onto 4” silicon wafers and post-stained with 2% uranyl acetate followed by 0.3% lead citrate and carbon coated. A ZEISS GeminiSEM 450 scanning electron microscope fitted with an electron backscatter diffusion detector was used to image the sections. The image stack was aligned and segmented using the open-source software, VASTlite. 264 serial sections were imaged, representing approximately 40 × 45 × 15-micron (x, y, z) volume of tissue. 70% of the canaliculi demonstrated infiltration by S. aureus. This study demonstrates that SSEM can be applied to the skeletal system and provide a new solution to investigate the OLCN system. It is feasible that this methodology could be implemented to investigate why some canaliculi are resistant to colonization and potentially opens up a new direction for the prevention of chronic osteomyelitis. In order to make this a realistic target, automated segmentation methodologies utilizing
Gait analysis is an indispensable tool for scientific assessment and treatment of individuals whose ability to walk is impaired. The high cost of installation and operation are a major limitation for wide-spread use in clinical routine. Advances in Artificial Intelligence (AI) could significantly reduce the required instrumentation. A mobile phone could be all equipment necessary for 3D gait analysis. MediaPipe Pose provided by Google Research is such a
Periacetabular Osteotomy (PAO) has become the most important surgical procedure for patients with hip dysplasia, offering significant pain relief and improved joint function. This study focuses on recovery after PAO, specifically the return to sports (RTS) timeline, with the objective of identifying preoperative predictors to optimize patient outcomes. Our prospective, monocentric study from 2019 to 2023 included 698 hips from 606 patients undergoing PAO. Comprehensive preoperative data were collected, including demographic information, clinical assessments (Modified Harris Hip Score (mHHS), International Hip Outcome Tool-12 (iHot-12), Hip Disability and Osteoarthritis Outcome Score (HOOS), UCLA Activity Score) and psychological evaluations (Brief Symptom Inventory (BSI) and SF-36 Health Survey). Advanced logistic regression and
Advances in cancer therapy have prolonged patient survival even in the presence of disseminated disease and an increasing number of cancer patients are living with metastatic bone disease (MBD). The proximal femur is the most common long bone involved in MBD and pathologic fractures of the femur are associated with significant morbidity, mortality and loss of quality of life (QoL). Successful prophylactic surgery for an impending fracture of the proximal femur has been shown in multiple cohort studies to result in longer survival, preserved mobility, lower transfusion rates and shorter post-operative hospital stays. However, there is currently no optimal method to predict a pathologic fracture. The most well-known tool is Mirel's criteria, established in 1989 and is limited from guiding clinical practice due to poor specificity and sensitivity. The ideal clinical decision support tool will be of the highest sensitivity and specificity, non-invasive, generalizable to all patients, and not a burden on hospital resources or the patient's time. Our research uses novel
Over 8000 total hip arthroplasties (THA) in the UK were revised in 2019, half for aseptic loosening. It is believed that Artificial Intelligence (AI) could identify or predict failing THA and result in early recognition of poorly performing implants and reduce patient suffering. The aim of this study is to investigate whether Artificial Intelligence based
Excessive resident duty hours (RDH) are a recognized issue with implications for physician well-being and patient safety. A major component of the RDH concern is on-call duty. While considerable work has been done to reduce resident call workload, there is a paucity of research in optimizing resident call scheduling. Call coverage is scheduled manually rather than demand-based, which generally leads to over-scheduling to prevent a service gap.
Most cost containment efforts in public health systems have focused on regulating the use of hospital resources, especially operative time. As such, attempting to maximize the efficiency of limited operative time is important. Typically, hospital operating room (OR) scheduling of time is performed in two tiers: 1) master surgical scheduling (annual allocation of time between surgical services and surgeons) and 2) daily scheduling (a surgeon's selection of cases per operative day). Master surgical scheduling is based on a hospital's annual case mix and depends on the annual throughput rate per case type. This throughput rate depends on the efficiency of surgeons’ daily scheduling. However, daily scheduling is predominantly performed manually, which requires that the human planner simultaneously reasons about unknowns such as case-specific length-of-surgery and variability while attempting to maximize throughput. This often leads to OR overtime and likely sub-optimal throughput rate. In contrast, scheduling using mathematical and optimization methods can produce maximum systems efficiency, and is extensively used in the business world. As such, the purpose of our study was to compare the efficiency of 1) manual and 2) optimized OR scheduling at an academic-affiliated community hospital representative of most North American centres. Historic OR data was collected over a four year period for seven surgeons. The actual scheduling, surgical duration, overtime and number of OR days were extracted. This data was first configured to represent the historic manual scheduling process. Following this, the data was then used as the input to an integer linear programming model with the goal of determining the minimum number of OR days to complete the same number of cases while not exceeding the historic overtime values. Parameters included the use of a different quantile for each case type's surgical duration in order to ensure a schedule within five percent of the historic overtime value per OR day. All surgeons saw a median 10% (range: 9.2% to 18.3%) reduction in the number of OR days needed to complete their annual case-load compared to their historical scheduling practices. Meanwhile, the OR overtime varied by a maximum of 5%. The daily OR configurations differed from historic configurations in 87% of cases. In addition, the number of configurations per surgeon was reduced from an average of six to four. Our study demonstrates a significant increase in OR throughput rate (10%) with no change in operative time required. This has considerable implications in terms of cost reduction, surgical wait lists and surgeon satisfaction. A limitation of this study was that the potential gains are based on the efficiency of the pre-existing manual scheduling at our hospital. However, given the range of scenarios tested, number of surgeons included and the similarity of our hospital size and configuration to the majority of North American hospitals with an orthopedic service, these results are generalizable. Further optimization may be achieved by taking into account factors that could predict case duration such as surgeon experience, patients characteristics, and institutional attributes via
Abstract. Objectives. Biomechanics is an essential form of measurement in the understanding of the development and progression of osteoarthritis (OA). However, the number of participants in biomechanical studies are often small and there is limited ways to share or combine data from across institutions or studies. This is essential for applying modern
Total knee and hip arthroplasty (TKA and THA) are two of the highest volume and resource intensive surgical procedures. Key drivers of the cost of surgical care are duration of surgery (DOS) and postoperative inpatient length of stay (LOS). The ability to predict TKA and THA DOS and LOS has substantial implications for hospital finances, scheduling and resource allocation. The goal of this study was to predict DOS and LOS for elective unilateral TKAs and THAs using
Cobalt chrome alloy is commonly used in joint replacement surgery. However, it is recognised that some patients develop lymphocyte mediated delayed type hypersensitivity (DTH) responses to this material, which may result in extensive bone and soft tissue destruction. Phase 1. United Kingdom: From an existing database, we identified extreme phenotype patient groups following metal on metal (MoM) hip resurfacing or THR: ALVAL with low wearing prostheses; ALVAL with high wear; no ALVAL with high wear; and asymptomatic patients with implants in situ for longer than ten years. Class I and II HLA genotype frequency distributions were compared between these patients’ groups, and in silico peptide binding studies were carried out using validated methodology. Phase 2. United Kingdom: We expanded the study to include more patients, including those with intermediary phenotypes to test whether an algorithm could be developed incorporating “risk genotypes”, patient age, sex and metal exposure. This model was trained in phase 3. Phase 3. United Kingdom, Australia, United States. Patients from other centres were invited to give DNA samples. The data set was split in two. 70% was used to develop
Aim. While metagenomic (microbial DNA) sequencing technologies can detect the presence of microbes in a clinical sample, it is unknown whether this signal represents dead or live organisms. Metatranscriptomics (sequencing of RNA) offers the potential to detect transcriptionally “active” organisms within a microbial community, and map expressed genes to functional pathways of interest (e.g. antibiotic resistance). We used this approach to evaluate the utility of metatrancriptomics to diagnose PJI and predict antibiotic resistance. Method. In this prospective study, samples were collected from 20 patients undergoing revision TJA (10 aseptic and 10 infected) and 10 primary TJA. Synovial fluid and peripheral blood samples were obtained at the time of surgery, as well as negative field controls (skin swabs, air swabs, sterile water). All samples were shipped to the laboratory for metatranscriptomic analysis. Following microbial RNA extraction and host analyte subtraction, metatranscriptomic sequencing was performed. Bioinformatic analyses were implemented prior to mapping against curated microbial sequence databases– to generate taxonomic expression profiles. Principle Coordinates Analysis (PCoA) and Partial Least Squares-Discriminant Analysis were utilized to ordinate metatranscriptomic profiles, using the 2018 definition of PJI as the gold-standard. Results. After RNA metatranscriptomic analysis, blinded PCoA modeling revealed accurate and distinct clustering of samples into 3 separate cohorts (infected, aseptic, and primary joints) – based on their active transcriptomic profile, both in synovial fluid and blood (synovial anosim p=0.001; blood anosim p=0.034). Differential metatranscriptomic signatures for infected versus noninfected cohorts enabled us to train
During OA the homeostasis of healthy articular chondrocytes is dysregulated, which leads to a phenotypical transition of the cells, further influenced by external stimuli. Chondrocytes sense those stimuli, integrate them at the intracellular level and respond by modifying their secretory and molecular state. This process is controlled by a complex interplay of intracellular factors. Each factor is influenced by a myriad of feedback mechanisms, making the prediction of what will happen in case of external perturbation challenging. Hampering the hypertrophic phenotype has emerged as a potential therapeutic strategy to help OA patients (Ripmeester et al. 2018). Therefore, we developed a computational model of the chondrocyte's underlying regulatory network (RN) to identify key regulators as potential drug targets. A mechanistic mathematical model of articular chondrocyte differentiation was implemented with a semi-quantitative formalism. It is composed of a protein RN and a gene RN(GRN) and developed by combining two strategies. First, we established a mechanistic network based on accumulation of decades of biological knowledge. Second, we combined that mechanistic network with data-driven modelling by inferring an OA-GRN using an ensemble of
Background. Advanced technologies, like robotics, provide enhanced precision for implanting total knee arthroplasty (TKA) components; however, optimal component position and limb alignment remain unknown. This study purpose was to identify the ideal target sagittal component position and coronal limb alignment that produce optimal clinical outcomes. Methods. A retrospective review of 1,091 consecutive TKAs was performed. All TKAs were PCL retaining or sacrificing with anterior lipped (49.4%) or conforming bearings (50.6%) performed with modern perioperative protocols. Posterior tibial slope, femoral flexion, and tibiofemoral limb alignment were measured with a standardized protocols. Patients were grouped by the ‘how often does your knee feel normal?’ outcome score at latest follow-up.
Introduction. Clinical decision support tools are software that match the input characteristics of an individual patient to an established knowledge base to create patient-specific assessments that support and better inform individualized healthcare decisions. Clinical decision support tools can facilitate better evidence-based care and offer the potential for improved treatment quality and selection, shared decision making, while also standardizing patient expectations. Methods. Predict+ is a novel, clinical decision support tool that leverages clinical data from the Exactech Equinoxe shoulder clinical outcomes database, which is composed of >11,000 shoulder arthroplasty patients using one specific implant type from more than 30 different clinical sites using standardized forms. Predict+ utilizes multiple coordinated and locked supervised
To explore a novel
Participation in a physical therapy program is considered one of the greatest predictors for successful conservative management of common shoulder disorders, however, adherence to standard exercise protocols is often poor (around 50%) and typically worse for unsupervised home exercise programs. Currently, there are limited tools available for objective measurement of adherence and performance of shoulder rehabilitation in the home setting. The goal of this study was to develop and evaluate the potential for performing home shoulder physiotherapy monitoring using a commercial smartwatch. We hypothesize that shoulder physiotherapy exercises can be classified by analyzing the temporal sequence of inertial sensor outputs from a smartwatch worn on the extremity performing the exercise. Twenty healthy adult subjects with no prior shoulder disorders performed seven exercises from a standard evidence-based rotator cuff physiotherapy protocol: pendulum, abduction, forward elevation, internal/external rotation and trapezius extension with a resistance band, and a weighted bent-over row. Each participant performed 20 repetitions of each exercise bilaterally under the supervision of an orthopaedic surgeon, while 6-axis inertial sensor data was collected at 50 Hz from an Apple Watch. Using the scikit-learn and keras platforms, four supervised learning algorithms were trained to classify the exercises: k-nearest neighbour (k-NN), random forest (RF), support vector machine classifier (SVC), and a deep convolutional recurrent neural network (CRNN). Algorithm performance was evaluated using 5-fold cross-validation stratified first temporally and then by subject. Categorical classification accuracy was above 94% for all algorithms on the temporally stratified cross validation, with the best performance achieved by the CRNN algorithm (99.4± 0.2%). The subject stratified cross validation, which evaluated classifier performance on unseen subjects, yielded lower accuracies scores again with CRNN performing best (88.9 ± 1.6%). This proof-of concept study demonstrates the feasibility of a smartwatch device and
Quantitative assessment of metastatic involvement of the bony spine is important for assessing disease progression and treatment response. Quantification of metastatic involvement is challenging as tumours may appear as osteolytic (bone resorbing), osteoblastic (bone forming) or mixed. This investigation aimed to develop an automated method to accurately segment osteoblastic lesions in a animal model of metastatically involved vertebrae, imaged with micro computed tomography (μCT). Radiomics seeks to apply standardized features extracted from medical images for the purpose of decision-support as well as diagnosis and treatment planning. Here we investigate the application of radiomic-based features for the delineation of osteoblastic vertebral metastases. Osteoblastic lesions affect bone deposition and bone quality, resulting in a change in the texture of bony material physically seen through μCT imaging. We hypothesize that radiomics based features will be sensitive to changes in osteoblastic lesion bone texture and that these changes will be useful for automating segmentation. Osteoblastic metastases were generated via intracardiac injection of human ZR-75-1 breast cancer cells into a preclinical athymic rat model (n=3). Four months post inoculation, ex-vivo μCT images (µCT100, Scanco) were acquired of each rodent spine focused on the metastatically involved third lumbar vertebra (L3) at 7µm/voxel and resampled to 34µm/voxel. The trabecular bone within each vertebra was isolated using an atlas and level-set based segmentation approach previously developed by our group. Pyradiomics, an open source Radiomics library written in python, was used to calculate 3D image features at each voxel location within the vertebral bone. Thresholding of each radiomic feature map was used to isolate the osteoblastic lesions. The utility of radiomic feature-based segmentation of osteoblastic bone tissue was evaluated on randomly selected 2D sagittal and axial slices of the μCT volume. Feature segmentations were compared to ground truth osteoblastic lesion segmentations by calculating the Dice Similarity Coefficient (DSC). Manually defined ground truth osteoblastic tumor segmentations on the μCT slices were informed by histological confirmation of the lesions. The radiomic based features that best segmented osteoblastic tissue while optimizing computational time were derived from the Neighbouring Gray Tone Difference Matrix (NGTDM). Measures of coarseness yielded the best agreement with the manual segmentations (DSC=707%) followed by contrast, strength and complexity (DSC=6513%, 5428%, and 4826%, respectively). This pilot study using a radiomic based approach demonstrates the utility of the NGTDM features for segmentation of vertebral osteoblastic lesions. This investigation looked at the utility of isolated features to segment osteoblastic lesions and found modest performance in isolation. In future work we will explore combining these features using
This research sought a mathematical model to relate the postero-anterior (PA) and lateral (LAT) views of the spinal curve in scoliosis in an attempt to justify the acquisition of only One X-ray, thereby reducing patient exposure to harmful X-radiation while preserving complete 3D characterization of the spine. Using powerful developments in functional statistics and
Purposes and Background. Musculoskeletal disorders including as back and neck pain are leading causes of work disability. Effective interventions exist (i.e. functional restoration, multidisciplinary biopsychosocial rehabilitation, workplace-based interventions, etc.), but it is difficult to select the optimal intervention for specific patients. The Work Assessment Triage Tool (WATT) is a clinical decision support tool developed using
INTRODUCTION. Statistical shape models (SSM) have become a common tool to create reference models for design input and verification of total joint implants. In a recent discussion paper around Artificial Intelligence and
Our lab uses computer-aided design to build in silico libraries of surface topographies, which we reproduce on polymeric chips and analyse for cellular responses using high content imaging and
Advances in cancer therapy have prolonged cancer patient survival even in the presence of disseminated disease and an increasing number of cancer patients are living with metastatic bone disease (MBD). The proximal femur is the most common long bone involved in MBD and pathologic fractures of the femur are associated with significant morbidity, mortality and loss of quality of life (QoL). Successful prophylactic surgery for an impending fracture of the proximal femur has been shown in multiple cohort studies to result in patients more likely to walk after surgery, longer survival, lower transfusion rates and shorter post-operative hospital stays. However, there is currently no optimal method to predict a pathologic fracture. The most well-known tool is Mirel's criteria, established in 1989 and is limited from guiding clinical practice due to poor specificity and sensitivity. The goal of our study is to train a convolutional neural network (CNN) to predict fracture risk when metastatic bone disease is present in the proximal femur. Our fracture risk prediction tool was developed by analysis of prospectively collected data for MBD patients (2009–2016) in order to determine which features are most commonly associated with fracture. Patients with primary bone tumors, pathologic fractures at initial presentation, and hematologic malignancies were excluded. A total of 1146 patients comprising 224 pathologic fractures were included. Every patient had at least one Anterior-Posterior X-ray. The clinical data includes patient demographics, tumor biology, all previous radiation and chemotherapy received, multiple pain and function scores, medications and time to fracture or time to death. Each of Mirel's criteria has been further subdivided and recorded for each lesion. We have trained a convolutional neural network (CNN) with X-ray images of 1146 patients with metastatic bone disease of the proximal femur. The digital X-ray data is converted into a matrix representing the color information at each pixel. Our CNN contains five convolutional layers, a fully connected layers of 512 units and a final output layer. As the information passes through successive levels of the network, higher level features are abstracted from the data. This model converges on two fully connected deep neural network layers that output the fracture risk. This prediction is compared to the true outcome, and any errors are back-propagated through the network to accordingly adjust the weights between connections. Methods to improve learning included using stochastic gradient descent with a learning rate of 0.01 and a momentum rate of 0.9. We used average classification accuracy and the average F1 score across test sets to measure model performance. We compute F1 = 2 x (precision x recall)/(precision + recall). F1 is a measure of a test's accuracy in binary classification, in our case, whether a lesion would result in pathologic fracture or not. Five-fold cross validation testing of our fully trained model revealed accurate classification for 88.2% of patients with metastatic bone disease of the proximal femur. The F1 statistic is 0.87. This represents a 24% error reduction from using Mirel's criteria alone to classify the risk of fracture in this cohort. This is the first reported application of convolutional neural networks, a
Introduction. Identifying knee osteoarthritis patient phenotypes is relevant to assessing treatment efficacy. Biomechanical variability has not been applied to phenotyping, yet features may be related to outcomes of total knee arthroplasty (TKA), an inherently mechanical surgery. This study aimed to i) identify biomechanical phenotypes among TKA candidates based on demographic and gait mechanic similarities, and ii) compare objective gait improvements between phenotypes post-TKA. Methods. TKA patients underwent 3D gait analysis one-week pre (n=134) and one-year post-TKA (n=105). Principal component analysis was applied to frontal and sagittal knee angle and moment gait waveforms, extracting major patterns of variability. Demographics (age, sex, BMI), gait speed, and frontal and sagittal pre-TKA angle and moment principal component (PC) scores previously found to differentiate sex, osteoarthritis (OA) severity, and symptoms of TKA recipients were standardized (mean=0, SD=1, [134×15]) to perform multidimensional scaling and
INTRODUCTION. The advent of CT based 3D preoperative planning software for reverse total shoulder arthroplasty (RTSA) provides surgeons with more data than ever before to prepare for a case. Interestingly, as the usage of such software has increased, further questions have appeared over the optimal way to plan and place a glenoid implant for RTSA. In this study, a survey of shoulder specialists from the American Shoulder and Elbow Society (ASES) was conducted to examine thought patterns in current RTSA implant selection and placement. METHODS. 172 ASES members completed an 18-question survey on their thought process for how they select and place a RTSA glenoid implant. Data was collected using a custom online Survey Monkey survey. Surgeon answers were split into two cohorts based on number of arthroplasties performed per year: between 0–75 was considered low volume (LV), and between 75–200+ was considered high volume (HV). Data was analyzed for each cohort to examine differences in thought patterns, implant selection, and implant placement. RESULTS. 70 surgeons were grouped into the LV cohort, and 102 surgeons were grouped into the HV cohort. 46.1% of surgeons in the HV cohort reported using a preoperative planning software for the majority of cases, 48% reported seldom use, and 5.9% reported no use. In the LV cohort, 41.4% reported use for the majority of cases, 24.3% reported seldom use, and 34.3% reported no use (Figure 1). When questioned on what percentage of RTSA cases do surgeons use augmented glenoid implants, 26.7% in the HV cohort responded never using augments vs. 32.4% in the LV cohort, 32.7% responded using augments <15% of the time in the HV cohort vs. 30.9% in the LV cohort, 26.7% responded using augments between 15–45% of the time in the HV cohort vs. 27.9% in the LV cohort, and 13.8% responded using augments >45% of the time in the HV cohort vs. 8.8% in the LV cohort (Figure 2). When asked what the maximum allowable superior inclination for a RTSA glenoid implant is, surgeons answered 10° 20.6% of the time in the HV cohort vs. 30% in the LV cohort, 5° 18.6% of the time in the HV cohort vs. 25.7% in the LV cohort, 0° 38.2% of the time in the HV cohort vs. 25.7% in the LV cohort, and no fixed degree 22.5% of the time in the HV cohort vs. 18.6% in the LV cohort (Figure 3). CONCLUSION. The results of this study show that even within a group of highly trained surgeons, there are widely varying opinions on how to plan the optimal RTSA case. Variation between high and low volume surgeons reveals even greater differences, suggesting that experience affects thought pattern. Despite these differences, there is no way to prove the optimal implant selection and placement without consistent data collection and long-term clinical outcomes.
The COVID-19 pandemic has led to unprecedented times worldwide. From lockdowns to masks now being part of our everyday routine, to the halting of elective surgeries, the virus has touched everyone and every part of our personal and professional lives. Perhaps, now more than ever, our ability to adapt, change and persevere is critical to our survival. This year's closed meeting of The Knee Society demonstrated exactly those characteristics. When it became evident that an in-person meeting would not be feasible, The Knee Society leadership, under the direction of President John Callaghan, MD and Program Chair Craig Della Valle, MD created a unique and engaging meeting held on September 10–12, 2020. Special recognition should be given to Olga Foley and Cynthia Garcia at The Knee Society for their flexibility and creativeness in putting together a world-class flawless virtual program. The Bone & Joint Journal is very pleased to partner with The Knee Society to once again publish the proceedings of the closed meeting of the Knee Society. The Knee Society is a United States based society of highly selected members who have shown leadership in education and research in knee surgery. It invites up to 15% international members; this includes some of the key opinion leaders in knee surgery from outside the USA. Each year, the top research papers from The Knee Society meeting will be published and made available to the wider orthopaedic community in The Bone & Joint Journal. The first such proceedings were published in BJJ in 2019. International dissemination should help to fulfil the mission and vision of the Knee Society of advancing the care of patients with knee disorders through leadership, education and research. The quality of dissemination that The Bone & Joint Journal provides should enhance the profile of this work and allow a larger body of surgeons, associated healthcare professionals and patients to benefit from the expertise of the members of The Knee Society. The meeting is one of the highlights of the annual academic calendar for knee surgeons. With nearly every member in attendance virtually throughout the 3 days, the top research papers from the membership were presented and discussed in a virtual format that allowed for lively interaction and discussion. There are 75 abstracts presented. More selective proceedings with full papers will be available after a robust peer review process in 2021, both online and in The Bone & Joint Journal. The meeting commenced with the first group of scientific papers focused on Periprosthetic Joint Infection. Dr Berry and colleagues from the Mayo Clinic further help to clarify the issue of serology and aspirate results to diagnose TKA PJI in the acute postoperative setting. 177 TKA's had an aspiration within 12 weeks and 22 were proven to have PJI. Their results demonstrated that acute PJI after TKA should be suspected within 6 weeks if CRP is ≥81 mg/L, synovial WBCs are ≥8500 cells/μL, and/or synovial neutrophils≥86%. Between 6– 12 weeks, concerning thresholds include a CRP ≥ 32 mg/L, synovial WBC ≥7450, and synovial neutrophils ≥ 84%. While historically the results of a DAIR procedure for PJI have been variable, Tom Fehring's study showed promise with the local delivery of vancomycin through the Intraosseous route improved early results. New member Simon Young contrasted the efficacy of the DAIR procedure when comparing early infections to late acute hematogenous PJI. DAIR failed in 63% of late hematogenous PJIs (implant age>1 year) compared to 36% of early (<1year) PJIs. Dr Masri demonstrated in a small group of patients that those with well-functioning articulating spacers can retain their spacers for over 12 months with no difference in infection from those that had a formal two stage exchange. The mental toll of PJI was demonstrated in a longitudinal study by Doug Dennis, where patient being treated with 2 stage exchange had 4x higher rates of depression compared to patient undergoing aseptic revision. The second session focused on both postoperative issues with regards to anticoagulation and manipulation. Steven Haas demonstrated high complication rates with utilization of anticoagulation for treatment of postoperative pulmonary embolism with modern therapeutic anticoagulation (warfarin, enoxaparin, Xa inhibitors) with the Xa inhibitors demonstrating lower complication rates. Two papers focused on the topic of manipulation. Mark Pagnano presented data on timing of manipulation under anesthesia up to even past 12 months. While gains were modest, a subset of patients did achieve substantial gains in ROM > 20degrees even after 3 months post op. Dr Westrich's study demonstrated no difference in MUA outcomes with either IV sedation or neuraxial anesthesia although the length of stay was shorter in the IV sedation group. Several studies in Session II focused on kinematics and femoral component position. Dr Li's in vivo kinematic study during weightbearing flexion and gait demonstrated that several knees rotated with a lateral pivot motion and not all knees can be described with a single motion character. Dr Mayman and his group utilized a computational knee model to demonstrate that additional distal femoral resection results in increasing levels of mid -flexion instability and cautioned against the use of additional bony resection as the first line for flexion contractures. Using computer navigation, Dr Huddleston's study nicely outlined the variability in femoral component rotation to achieve a rectangular flexion gap utilizing a gap balanced method. The third session opened the meeting on Friday morning. The focus was on unicompartmental knee arthroplasty and the increasing utilization of robotic assisted total knee arthroplasty. David Murray showed using registry data that for patient with higher comorbidities (ASA >3), UKA was safer and more cost effective than TKA while Dr Della Valle's group demonstrated overall lower average healthcare costs in UKA patients compared to TKA in the first 10 years after surgery. Dr Geller assessed UKA survivorship among 3 international registries. While survivorship varied by nation and designs, certain designs consistently had better overall performance. Dr Nunley and his group showed robotic navigation UKA significantly reduced outliers in alignment and overhang compared to manual UKA. Dr Catani's data demonstrated that full thickness cartilage loss should still be considered a requirement for UKA success even with robotic assistance. Despite a high dislocation rate of 4%, Mr Dodd demonstrated high survivorship for lateral UKA despite historical contraindications. The growing evidence for robotics TKA was demonstrated in two studies. Professor Haddad showed less soft tissue injury, reduced bone trauma and improved accuracy or rTKA compared to manual TKA while Dr Gustke single surgeon study showed his rTKA had improved forgotten joint scores and less ligament releasing required for balancing. Despite these finding, Dr Lee's study demonstrated that a robotic TKA could not guarantee excellent pain relief and other factors such a patient expectations and psychological factors play a role. Our fourth session was devoted to
A total hip replacement (THR) patient's spinopelvic mobility might predispose them to an increased risk of impingement, instability and edge-loading. This risk can be minimised by considering their preoperative movement during planning of component alignment. However, the question of whether the preoperative, arthritic motion is representative of the postoperative mobility has been raised. We aimed to determine the change in functional pelvic tilt in a series of THR patients at one-year. Four-hundred and eleven patients had their pelvic tilt and lumbar lordotic angle (LLA) measured in the standing and flexed-seated (position when patients initiate rising from a seat) positions as part of routine planning for THR. All measurements were performed on lateral radiographs. At 12-months postoperatively, the same two lateral images were taken and pelvic tilt measured. Pearson correlation was used to investigate the linear relationship between pre-and post-op pelvic tilt. Furthermore, a predictive model of post-op pelvic tilt was developed using
Introduction. Custom flanged acetabular components (CFAC) have been shown to be effective in treating complex acetabular reconstructions in revision total hip arthroplasty (THA). However, the specific patient factors and CFAC design characteristics that affect the overall survivorship remain unclear. Once the surgeon opts to follow this treatment pathway, numerous decisions need to be made during the pre-operative design phase and during implantation, which may influence the ultimate success of CFAC. The goal of this study was to retrospectively review the entire cohort of CFAC cases performed at a large volume institution and to identify any patient, surgeon, or design factors that may be related to the long-term survival of these prostheses. Methods. We reviewed 96 CFAC cases performed in 91 patients between 2004 and 2017, from which 36 variables were collected spanning patient demographics, pre-operative clinical and radiographic features, intraoperative information, and implant design characteristics. Patient demographics and relevant clinical features were collected from individual medical records. Radiographic review included analysis of pre-operative radiographs, computer tomographic (CT) scans, and serial post-operative radiographs. Radiographic failure was defined as loosening or gross migration as determined by a board-certified orthopedic surgeon. CFAC implant design characteristics and intra-operative features were collected from the design record, surgical record and post-operative radiograph for each case respectively. Two sets of statistical analyses were performed with this dataset. First, univariate analyses were performed for each variable, comprising of a Pearson chi-square test for categorical variables and an independent t-test for continuous variables. Second, a random forest supervised
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In professional football a key factor regarding injury is the time to return to play. Accurate prediction of this would aid planning by the club in the event of injury. It would also aid the club medical staff. Gaussian processes may be used for
The February 2023 Knee Roundup360 looks at: Machine-learning models: are all complications predictable?; Positive cultures can be safely ignored in revision arthroplasty patients that do not meet the 2018 International Consensus Meeting Criteria; Spinal versus general anaesthesia in contemporary primary total knee arthroplasty; Preoperative pain and early arthritis are associated with poor outcomes in total knee arthroplasty; Risk factors for infection and revision surgery following patellar tendon and quadriceps tendon repairs; Supervised versus unsupervised rehabilitation following total knee arthroplasty; Kinematic alignment has similar outcomes to mechanical alignment: a systematic review and meta-analysis; Lifetime risk of revision after knee arthroplasty influenced by age, sex, and indication; Risk factors for knee osteoarthritis after traumatic knee injury.
The preoperative grading of chondrosarcomas of bone that accurately predicts surgical management is difficult for surgeons, radiologists, and pathologists. There are often discrepancies in grade between the initial biopsy and the final histology. Recent advances in the use of imaging methods have shown promise in the ability to predict the final grade. The most important clinical distinction is between grade 1 chondrosarcomas, which are amenable to curettage, and resection-grade chondrosarcomas (grade 2 and 3) which require en bloc resection. The aim of this study was to evaluate the use of a Radiological Aggressiveness Score (RAS) to predict the grade of primary chondrosarcomas in long bones and thus to guide management. A total of 113 patients with a primary chondrosarcoma of a long bone presenting between January 2001 and December 2021 were identified on retrospective review of a single oncology centre’s prospectively collected database. The nine-parameter RAS included variables from radiographs and MRI scans. The best cut-off of parameters to predict the final grade of chondrosarcoma after resection was determined using a receiver operating characteristic curve (ROC), and this was correlated with the biopsy grade.Aims
Methods
This systematic review aims to identify 3D predictors derived from biplanar reconstruction, and to describe current methods for improving curve prediction in patients with mild adolescent idiopathic scoliosis. A comprehensive search was conducted by three independent investigators on MEDLINE, PubMed, Web of Science, and Cochrane Library. Search terms included “adolescent idiopathic scoliosis”,“3D”, and “progression”. The inclusion and exclusion criteria were carefully defined to include clinical studies. Risk of bias was assessed with the Quality in Prognostic Studies tool (QUIPS) and Appraisal tool for Cross-Sectional Studies (AXIS), and level of evidence for each predictor was rated with the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach. In all, 915 publications were identified, with 377 articles subjected to full-text screening; overall, 31 articles were included.Aims
Methods
The June 2023 Spine Roundup360 looks at: Characteristics and comparative study of thoracolumbar spine injury and dislocation fracture due to tertiary trauma; Sublingual sufentanil for postoperative pain management after lumbar spinal fusion surgery; Minimally invasive bipolar technique for adult neuromuscular scoliosis; Predictive factors for degenerative lumbar spinal stenosis; Lumbosacral transitional vertebrae and lumbar fusion surgery at level L4/5; Does recall of preoperative scores contaminate trial outcomes? A randomized controlled trial; Vancomycin in fibrin glue for prevention of SSI; Perioperative nutritional supplementation decreases wound healing complications following elective lumbar spine surgery: a randomized controlled trial.
Cemented hemiarthroplasty is an effective form of treatment for most patients with an intracapsular fracture of the hip. However, it remains unclear whether there are subgroups of patients who may benefit from the alternative operation of a modern uncemented hemiarthroplasty – the aim of this study was to investigate this issue. Knowledge about the heterogeneity of treatment effects is important for surgeons in order to target operations towards specific subgroups who would benefit the most. We used causal forest analysis to compare subgroup- and individual-level treatment effects between cemented and modern uncemented hemiarthroplasty in patients aged > 60 years with an intracapsular fracture of the hip, using data from the World Hip Trauma Evaluation 5 (WHiTE 5) multicentre randomized clinical trial. EuroQol five-dimension index scores were used to measure health-related quality of life at one, four, and 12 months postoperatively.Aims
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We aim to explore the potential technologies for monitoring and assessment of patients undergoing arthroplasty by examining selected literature focusing on the technology currently available and reflecting on possible future development and application. The reviewed literature indicates a large variety of different hardware and software, widely available and used in a limited manner, to assess patients’ performance. There are extensive opportunities to enhance and integrate the systems which are already in existence to develop patient-specific pathways for rehabilitation. Cite this article:
The aim of this study was to estimate the 90-day periprosthetic joint infection (PJI) rates following total knee arthroplasty (TKA) and total hip arthroplasty (THA) for osteoarthritis (OA). This was a data linkage study using the New South Wales (NSW) Admitted Patient Data Collection (APDC) and the Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR), which collect data from all public and private hospitals in NSW, Australia. Patients who underwent a TKA or THA for OA between 1 January 2002 and 31 December 2017 were included. The main outcome measures were 90-day incidence rates of hospital readmission for: revision arthroplasty for PJI as recorded in the AOANJRR; conservative definition of PJI, defined by T84.5, the PJI diagnosis code in the APDC; and extended definition of PJI, defined by the presence of either T84.5, or combinations of diagnosis and procedure code groups derived from recursive binary partitioning in the APDC.Aims
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