The primary objective of this registry-based study was to compare patient-reported outcomes of cementless and cemented medial unicompartmental knee arthroplasty (UKA) during the first postoperative year. The secondary objective was to assess one- and three-year implant survival of both fixation techniques. We analyzed 10,862 cementless and 7,917 cemented UKA cases enrolled in the Dutch Arthroplasty Registry, operated between 2017 and 2021. Pre- to postoperative change in outcomes at six and 12 months’ follow-up were compared using mixed model analyses. Kaplan-Meier and Cox regression models were applied to quantify differences in implant survival. Adjustments were made for patient-specific variables and annual hospital volume.Aims
Methods
Abstract. 1.0 Objectives.
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 machine learning algorithms in order to predict five-year cancer-related mortality in these patients. Demographic, clinicopathological, and treatment variables of limb and trunk STS patients in the Surveillance, Epidemiology, and End Results Program (SEER) database from 2004 to 2017 were analyzed. Multivariable logistic regression was used to determine factors significantly associated with five-year cancer-related mortality. Various machine learning models were developed and compared using area under the curve (AUC), calibration, and decision curve analysis. The model that performed best on the SEER testing data was further assessed to determine the variables most important in its predictive capacity. This model was externally validated using our institutional dataset.Aims
Methods
While clinically important improvements in Oxford Shoulder Scores have been defined for patients with general shoulder problems or those undergoing subacromial decompression, no threshold has been reported for classifying improvement after shoulder replacement surgery. This study aimed to establish the minimal clinically important change (MCIC) for the Oxford Shoulder Score in patients undergoing primary total shoulder replacement (TSR). Patient-reported outcomes data were sourced from the Australian Orthopaedic Association National Joint Replacement Registry Patient-Reported Outcome Measures Program. These included pre- and 6-month post-operative Oxford Shoulder Scores and a rating of patient-perceived change after surgery (5-point scale ranging from ‘much worse’ to ‘much better’). Two anchor-based methods (using patient-perceived improvement as the anchor) were used to calculate the MCIC: 1) mean change method; and 2) predictive modelling, with and without adjustment for the proportion of improved patients. The analysis included 612 patients undergoing primary TSR who provided pre- and post-operative data (58% female; mean (SD) age 70 (8) years). Most patients (93%) reported improvement after surgery. The MCIC derived from the mean change method was 6.8 points (95%CI 4.7 to 8.9).
While clinically important improvements in Oxford Shoulder Scores have been defined for patients with general shoulder problems or those undergoing subacromial decompression, no threshold has been reported for classifying improvement after shoulder replacement surgery. This study aimed to establish the minimal clinically important change (MCIC) for the Oxford Shoulder Score in patients undergoing primary total shoulder replacement (TSR). Patient-reported outcomes data were sourced from the Australian Orthopaedic Association National Joint Replacement Registry Patient-Reported Outcome Measures Program. These included pre- and 6-month post-operative Oxford Shoulder Scores and a rating of patient-perceived change after surgery (5-point scale ranging from ‘much worse’ to ‘much better’). Two anchor-based methods (using patient-perceived improvement as the anchor) were used to calculate the MCIC: 1) mean change method; and 2) predictive modelling, with and without adjustment for the proportion of improved patients. The analysis included 612 patients undergoing primary TSR who provided pre- and post-operative data (58% female; mean (SD) age 70 (8) years). Most patients (93%) reported improvement after surgery. The MCIC derived from the mean change method was 6.8 points (95%CI 4.7 to 8.9).
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:
No predictive model has been published to forecast operating time for total knee arthroplasty (TKA). The aims of this study were to design and validate a predictive model to estimate operating time for robotic-assisted TKA based on demographic data, and evaluate the added predictive power of CT scan-based predictors and their impact on the accuracy of the predictive model. A retrospective study was conducted on 1,061 TKAs performed from January 2016 to December 2019 with an image-based robotic-assisted system. Demographic data included age, sex, height, and weight. The femoral and tibial mechanical axis and the osteophyte volume were calculated from CT scans. These inputs were used to develop a predictive model aimed to predict operating time based on demographic data only, and demographic and 3D patient anatomy data.Aims
Methods
The incidence of bone metastases is between 20% to 75% depending on the type of cancer. As treatment improves, the number of patients who need surgical intervention is increasing. Identifying patients with a shorter life expectancy would allow surgical intervention with more durable reconstructions to be targeted to those most likely to benefit. While previous scoring systems have focused on surgical and oncological factors, there is a need to consider comorbidities and the physiological state of the patient, as these will also affect outcome. The primary aim of this study was to create a scoring system to estimate survival time in patients with bony metastases and to determine which factors may adversely affect this. This was a retrospective study which included all patients who had presented for surgery with metastatic bone disease. The data collected included patient, surgical, and oncological variables. Univariable and multivariable analysis identified which factors were associated with a survival time of less than six months and less than one year. A model to predict survival based on these factors was developed using Cox regression.Aims
Methods
In recent years, machine learning (ML) and artificial neural networks (ANNs), a particular subset of ML, have been adopted by various areas of healthcare. A number of diagnostic and prognostic algorithms have been designed and implemented across a range of orthopaedic sub-specialties to date, with many positive results. However, the methodology of many of these studies is flawed, and few compare the use of ML with the current approach in clinical practice. Spinal surgery has advanced rapidly over the past three decades, particularly in the areas of implant technology, advanced surgical techniques, biologics, and enhanced recovery protocols. It is therefore regarded an innovative field. Inevitably, spinal surgeons will wish to incorporate ML into their practice should models prove effective in diagnostic or prognostic terms. The purpose of this article is to review published studies that describe the application of neural networks to spinal surgery and which actively compare ANN models to contemporary clinical standards allowing evaluation of their efficacy, accuracy, and relatability. It also explores some of the limitations of the technology, which act to constrain the widespread adoption of neural networks for diagnostic and prognostic use in spinal care. Finally, it describes the necessary considerations should institutions wish to incorporate ANNs into their practices. In doing so, the aim of this review is to provide a practical approach for spinal surgeons to understand the relevant aspects of neural networks. Cite this article:
Debate remains whether the patella should be resurfaced during total knee replacement (TKR). For non-resurfaced TKRs, we estimated what the revision rate would have been if the patella had been resurfaced, and examined the risk of re-revision following secondary patellar resurfacing. A retrospective observational study of the National Joint Registry (NJR) was performed. All primary TKRs for osteoarthritis alone performed between 1 April 2003 and 31 December 2016 were eligible (n = 842,072). Patellar resurfacing during TKR was performed in 36% (n = 305,844). The primary outcome was all-cause revision surgery. Secondary outcomes were the number of excess all-cause revisions associated with using TKRs without (versus with) patellar resurfacing, and the risk of re-revision after secondary patellar resurfacing.Aims
Methods
IM (Intra Medullary) nail fixation is the standard treatment for diaphyseal femur fractures and also for certain types of proximal and distal femur fractures. Despite the advances in the tribology for the same, cases of failed IM nail fixation continue to be encountered routinely in clinical practice. Common causes are poor alignment or reduction, insufficient fixation and eventual implant fatigue and failure. This study was devised to study such patients presenting to our practice and develop a predictive model for eventual failure. 57 patients who presented with failure of IM nail fixation (± infection) between Jan 2011 – Jun 2020 were included in the study and hospital records and imaging reviewed. Those fixed with any other kinds of metalwork were excluded. Classification for failure of IM nails – Type 1: Failure with loss of contact of lag screw threads in the head due to backing out and then rotational instability, Type 2A: Failure of the nail at the nail and lag screw junction, Type 2B: Failure of the screws at the nail lag screw junction, Type 3: Loosening at the distal locking sites with or without infection. X-rays reviewed and causes/site of failure noted.Introduction
Materials and Methods
We performed a retrospective cohort study in children from 1 month to 15 years old diagnosed with OAI, hospitalized between 2006 and 2018. Mann-Whitney test and Fisher's exact test were used for data analysis. The model predicting Aim
Method
The purpose of this study was to develop a personalized outcome prediction tool, to be used with knee arthroplasty patients, that predicts outcomes (lengths of stay (LOS), 90 day readmission, and one-year patient-reported outcome measures (PROMs) on an individual basis and allows for dynamic modifiable risk factors. Data were prospectively collected on all patients who underwent total or unicompartmental knee arthroplasty at a between July 2015 and June 2018. Cohort 1 (n = 5,958) was utilized to develop models for LOS and 90 day readmission. Cohort 2 (n = 2,391, surgery date 2015 to 2017) was utilized to develop models for one-year improvements in Knee Injury and Osteoarthritis Outcome Score (KOOS) pain score, KOOS function score, and KOOS quality of life (QOL) score. Model accuracies within the imputed data set were assessed through cross-validation with root mean square errors (RMSEs) and mean absolute errors (MAEs) for the LOS and PROMs models, and the index of prediction accuracy (IPA), and area under the curve (AUC) for the readmission models. Model accuracies in new patient data sets were assessed with AUC.Aims
Methods
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 machine learning to produce an accurate diagnostic biomarker model for end-stage knee OA (esOA). Synovial fluid (SF) from patients with esOA, non-OA knee injury, and inflammatory knee arthritis were analyzed for 35 potential markers using immunoassays. Partial least square discriminant analysis (PLS-DA) was used to derive a biomarker model for cohort classification. The ability of the biomarker model to diagnose esOA was validated by identical wide-spectrum SF analysis of a test cohort of ten patients with esOA.Aims
Methods
Technology within medicine has great potential to bring about more accessible, efficient, and a higher quality delivery of care. Paediatric supracondylar fractures are the most common elbow fracture in children and at our institution often have high rates of unnecessary long term clinical follow-up, leading to an inefficient use of healthcare and patient resources. This study aims to evaluate patient and clinical factors that significantly predict necessity for further clinical visits following closed reduction and percutaneous pinning. A total of 246 children who underwent closed reduction and percutaneous pinning following supracondylar humerus fractures were prospectively enrolled over a two year period. Patient demographics, perioperative course, goniometric measurements, functional outcome measures, clinical assessment and decision making for further follow up were assessed. Categorical and continuous variables were analyzed and screened for significance via bivariate regression. Significant covariates were used to develop a predictive model through multivariate logistical regression. A probability cut-off was determined on the Receiver Operator Characteristic (ROC) curve using the Youden index to maximize sensitivity and specificity. The regression model performance was then prospectively tested against 22 patients in a blind comparison to evaluate accuracy. 246 paediatrics patients were collected, with 29 cases requiring further follow up past the three month visit. Significant predictive factors for follow up were residual nerve palsy (p < 0 .001) and maximum active flexion angle of injured elbow (p < 0 .001). Insignificant factors included other goniometric measures, subjective evaluations, and functional outcomes scores. The probability of requiring further clinical follow up at the 3 month post-op point can be estimated with the equation: logit(follow-up) = 11.319 + 5.518(nerve palsy) − 0.108(maximum active flexion). Goodness of fit of the model was verified with Nagelkerke R2 = 0.574 and Hosmer & Lemeshow chi-square (p = 0.739). Area Under Curve of the ROC curve was C = 0.919 (SE = 0.035, 95% CI 0.850 – 0.988). Using Youden's Index, a cut-off for probability of follow up was set at 0.094 with the overall sensitivity and specificity maximized to 86.2% and 88% respectively. Using this model and cohort, 194 three month clinic visits would have been deemed medically unnecessary. Preliminary blind prospective testing against the 22 patient cohort demonstrates a model sensitivity and specificity at 100% and 75% respectively, correctly deeming 15 visits unnecessary. Virtual clinics and automated clinical decision making can improve healthcare inefficiencies, unclog clinic wait times, and ultimately enhance quality of care delivery. Our regression model is highly accurate in determining medical necessity for physician examination at the three month visit following supracondylar fracture closed reduction and percutaneous pinning. When applied correctly, there is potential for significant reductions in health care expenditures and in the economic burden on patient families by removing unnecessary visits. In light of positive patient and family receptiveness toward technology, our promising findings and predictive model may pave the way for remote health care delivery, virtual clinics, and automated clinical decision making.
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 machine learning (ML) models to select a surgical correction based on patient-specific intra-operative assessments. Based on a clinical series of 479 primary total knees and 1,305 associated surgical decisions, various ML models were developed. These models identified the indicated surgical decision based on available, intra-operative alignment, and tibiofemoral load data.Aims
Methods
Open fractures of the tibia are a heterogeneous group of injuries that can present a number of challenges to the treating surgeon. Consequently, few surgeons can reliably advise patients and relatives about the expected outcomes. The aim of this study was to determine whether these outcomes are predictable by using the Ganga Hospital Score (GHS). This has been shown to be a useful method of scoring open injuries to inform wound management and decide between limb salvage and amputation. We collected data on 182 consecutive patients with a type II, IIIA, or IIIB open fracture of the tibia who presented to our hospital between July and December 2016. For the purposes of the study, the patients were jointly treated by experienced consultant orthopaedic and plastic surgeons who determined the type of treatment. Separately, the study team (SP, HS, AD, JD) independently calculated the GHS and prospectively collected data on six outcomes for each patient. These included time to bony union, number of admissions, length of hospital stay, total length of treatment, final functional score, and number of operations. Spearman’s correlation was used to compare GHS with each outcome. Forward stepwise linear regression was used to generate predictive models based on components of the GHS. Five-fold cross-validation was used to prevent models from over-fitting.Aims
Methods
Considerable efforts have been invested into identifying risk factors for periprosthetic joint infection (PJI) after total joint arthroplasty (TJA). Preoperative identification of risk factors for developing PJI is imperative for medical optimization and targeted prophylaxis. The purpose of this study was to create a preoperative risk calculator for PJI by assessing a patient's individual risks for developing PJI with resistant organisms and S.aureus. A retrospective review of 27117 patients (43253 TJAs) from 1999 to 2014, including 1035 PJIs, was performed. A total of 41 risk factors including demographics, comorbidities (using the Elixhauser and Charlson Index), and the number of previous TJAs, were evaluated. Multivariate analysis was performed; coefficients of the models were scaled to produce useful integer scoring.
The April 2013 Spine Roundup360 looks at: smuggling spinal implants; local bone graft and PLIF; predicting disability with slipped discs; mortality and spinal surgery; spondyloarthropathy; brachytherapy; and fibrin mesh and BMP.
