Aims. Several previously identified patient-, injury-, and treatment-related factors are associated with the development of nonunion in distal femur fractures. However, the predictive value of these factors is not well defined. We aimed to assess the predictive ability of previously identified risk factors in the development of nonunion leading to secondary surgery in distal femur fractures. Methods. We conducted a retrospective cohort study of adult patients with traumatic distal femur fracture treated with lateral locking plate between 2009 and 2018. The patients who underwent secondary surgery due to fracture healing problem or plate failure were considered having nonunion. Background knowledge of risk factors of distal femur fracture nonunion based on previous literature was used to form an initial set of variables. A logistic regression model was used with previously identified patient- and injury-related variables (age, sex, BMI, diabetes, smoking, periprosthetic fracture, open fracture, trauma energy, fracture zone length, fracture comminution, medial side comminution) in the first analysis and with treatment-related variables (different surgeon-controlled factors, e.g. plate length, screw placement, and proximal fixation) in the second analysis to predict the nonunion leading to secondary surgery in distal femur fractures. Results. We were able to include 299 fractures in 291 patients. Altogether, 31/299 fractures (10%) developed nonunion. In the first analysis, pseudo-R. 2. was 0.27 and area under the receiver operating characteristic curve (AUC) was 0.81. BMI was the most important variable in the
Aims. Restoring the pre-morbid anatomy of the proximal humerus is a
goal of anatomical shoulder arthroplasty, but reliance is placed
on the surgeon’s experience and on anatomical estimations. The purpose
of this study was to present a novel method, ‘Statistical Shape
Modelling’, which accurately predicts the pre-morbid proximal humeral anatomy
and calculates the 3D geometric parameters needed to restore normal
anatomy in patients with severe degenerative osteoarthritis or a
fracture of the proximal humerus. Materials and Methods. From a database of 57 humeral CT scans 3D humeral reconstructions
were manually created. The reconstructions were used to construct
a statistical shape model (SSM), which was then tested on a second
set of 52 scans. For each humerus in the second set, 3D reconstructions
of four diaphyseal segments of varying lengths were created. These
reconstructions were chosen to mimic severe osteoarthritis, a fracture
of the surgical neck of the humerus and a proximal humeral fracture
with diaphyseal extension. The SSM was then applied to the diaphyseal
segments to see how well it predicted proximal morphology, using
the actual proximal humeral morphology for comparison. Results. With the metaphysis included, mimicking osteoarthritis, the errors
of
Objectives. Our objective was to predict the knee extension strength and post-operative function in quadriceps resection for soft-tissue sarcoma of the thigh. Methods. A total of 18 patients (14 men, four women) underwent total or partial quadriceps resection for soft-tissue sarcoma of the thigh between 2002 and 2014. The number of resected quadriceps was surveyed, knee extension strength was measured with the Biodex isokinetic dynamometer system (affected side/unaffected side) and relationships between these were examined. The Musculoskeletal Tumor Society (MSTS) score, Toronto Extremity Salvage Score (TESS), European Quality of Life-5 Dimensions (EQ-5D) score and the Short Form 8 were used to evaluate post-operative function and examine correlations with extension strength. The cutoff value for extension strength to expect good post-operative function was also calculated using a receiver operating characteristic (ROC) curve and Fisher’s exact test. Results. Extension strength decreased when the number of resected quadriceps increased (p < 0.001), and was associated with lower MSTS score, TESS and EQ-5D (p = 0.004, p = 0.005, p = 0.006, respectively). Based on the functional evaluation scales, the cutoff value of extension strength was 56.2%, the equivalent to muscle strength with resection of up to two muscles. Conclusion. Good post-operative results can be expected if at least two quadriceps muscles are preserved. Cite this article: A. Tanaka, Y. Yoshimura, K. Aoki, M. Kito, M. Okamoto, S. Suzuki, T. Momose, H. Kato. Knee extension strength and post-operative functional
Aims. Accurate skeletal age and final adult height
Aims. This study aimed to compare the performance of survival
Aims. This systematic review aims to identify 3D predictors derived from biplanar reconstruction, and to describe current methods for improving curve
Aims. To develop
Aims. The aim of this study was to evaluate the reliability and validity of a patient-specific algorithm which we developed for predicting changes in sagittal pelvic tilt after total hip arthroplasty (THA). Methods. This retrospective study included 143 patients who underwent 171 THAs between April 2019 and October 2020 and had full-body lateral radiographs preoperatively and at one year postoperatively. We measured the pelvic incidence (PI), the sagittal vertical axis (SVA), pelvic tilt, sacral slope (SS), lumbar lordosis (LL), and thoracic kyphosis to classify patients into types A, B1, B2, B3, and C. The change of pelvic tilt was predicted according to the normal range of SVA (0 mm to 50 mm) for types A, B1, B2, and B3, and based on the absolute value of one-third of the PI-LL mismatch for type C patients. The reliability of the classification of the patients and the
Aims. 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
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 machine learning (autoML) framework, “Autoprognosis 2.0”. Methods. All available 2-year follow-up periods of 600 patients from the FNIH OA Biomarker Consortium were analysed using “Autoprognosis 2.0” in two separate approaches, with distinct definitions of clinical outcomes: multi-class
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 machine learning (autoML) framework, “Autoprognosis 2.0”. All available 2-year follow-up periods of 600 patients from the FNIH OA Biomarker Consortium were analysed using “Autoprognosis 2.0” in two separate approaches, with distinct definitions of clinical outcomes: multi-class
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
Aims. The purpose of this study was to develop a personalized outcome
Aims. To develop and internally validate a preoperative clinical
Numerous
Aims. To develop and externally validate a parsimonious statistical
Background. Although there are predictive equations that estimate the total fat mass obtained from multiple-site ultrasound (US) measurements, the predictive equation of total fat mass has not been investigated solely from abdominal subcutaneous fat thickness. Therefore, the aims of this study were; (1) to develop regression-based
Over 300,000 total hip arthroplasties (THA) are performed annually in the USA. Surgical Site Infections (SSI) are one of the most common complications and are associated with increased morbidity, mortality and cost. Risk factors for SSI include obesity, diabetes and smoking, but few studies have reported on the predictive value of pre-operative blood markers for SSI. The purpose of this study was to create a clinical
Abstract. Introduction. Minimising postoperative complications and mortality in COVID-19 patients who were undergoing trauma and orthopaedic surgeries is an international priority. Aim was to develop a predictive nomogram for 30-day morbidity/mortality of COVID-19 infection in patients who underwent orthopaedic and trauma surgery during the coronavirus pandemic in the UK in 2020 compared to a similar period in 2019. Secondary objective was to compare between patients with positive PCR test and those with negative test. Methods. Retrospective multi-center study including 50 hospitals. Patients with suspicion of SARS-CoV-2 infection who had underwent orthopaedic or trauma surgery for any indication during the 2020 pandemic were enrolled in the study (2525 patients). We analysed cases performed on orthopaedic and trauma operative lists in 2019 for comparison (4417). Multivariable Logistic Regression analysis was performed to assess the possible predictors of a fatal outcome. A nomogram was developed with the possible predictors and total point were calculated. Results. Of the 2525 patients admitted for suspicion of COVID-19, 658 patients had negative preoperative test, 151 with positive test and 1716 with unknown preoperative COVID-19 status. Preoperative COVID-19 status, sex, ASA grade, urgency and indication of surgery, use of torniquet, grade of operating surgeon and some comorbidities were independent risk factors associated with 30-day complications/mortality. The 2020 nomogram model exhibited moderate
Total knee arthroplasty (TKA) is the most commonly performed elective orthopaedic procedure. With an increasingly aging population, the number of TKAs performed is expected to be ∼2,900 per 100,000 by 2050. Surgical Site Infections (SSI) after TKA can have significant morbidity and mortality. The purpose of this study was to construct a risk
Early and accurate
Introduction. Diaphyseal tibial fractures account for approximately 1.9% of adult fractures. Several studies demonstrate a high proportion of diaphyseal tibial fractures have ipsilateral occult posterior malleolus fractures, this ranges from 22–92.3%. Materials and Methods. A retrospective review of a prospectively collected database was performed at Liverpool University Hospitals NHS Foundation Trust between 1/1/2013 and 9/11/2020. The inclusion criteria were patients over 16, with a diaphyseal tibial fracture and who underwent a CT. The articular fracture extension was categorised into either posterior malleolar (PM) or other fracture. Results. 764 fractures were analysed, 300 had a CT. There were 127 intra-articular fractures. 83 (65.4%) cases were PM and 44 were other fractures. On univariate analysis for PM fractures, fibular spiral (p=.016) fractures, no fibular fracture(p=.003), lateral direction of the tibial fracture (p=.04), female gender (p=.002), AO 42B1 (p=.033) and an increasing angle of tibial fracture. On multivariate regression analysis a high angle of tibia fracture was significant. Other fracture extensions were associated with no fibular fracture (p=.002), medial direction of tibia fracture (p=.004), female gender (p=.000), and AO 42A1 (p=.004), 42A2 (p=.029), 42B3 (p=.035) and 42C2 (p=.032). On multivariate analysis, the lateral direction of tibia fracture, and AO classification 42A1 and 42A2 were significant. Conclusions. Articular extension happened in 42.3%. A number of factors were associated with the extension, however multivariate analysis did not create a suitable
Background. Dislocation is a common complication following total hip arthroplasty (THA), and accounts for a high percentage of subsequent revisions. The purpose of this study was to develop a convolutional neural network (CNN) model to identify patients at high risk for dislocation based on postoperative anteroposterior (AP) pelvis radiographs. Methods. We retrospectively evaluated radiographs for a cohort of 13,970 primary THAs with 374 dislocations over 5 years of follow-up. Overall, 1,490 radiographs from dislocated and 91,094 from non-dislocated THAs were included in the analysis. A CNN object detection model (YOLO-V3) was trained to crop the images by centering on the femoral head. A ResNet18 classifier was trained to predict subsequent hip dislocation from the cropped imaging. The ResNet18 classifier was initialized with ImageNet weights and trained using FastAI (V1.0) running on PyTorch. The training was run for 15 epochs using ten-fold cross validation, data oversampling and augmentation. Results. The hip dislocation
Aims. We report the use of the distal radius and ulna (DRU) classification
for the
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 machine learning algorithms for preoperative
Although 3D-printed porous dental implants may possess improved osseointegration potential, they must exhibit appropriate fatigue strength. Finite element analysis (FEA) has the potential to predict the fatigue life of implants and accelerate their development. This work aimed at developing and validating an FEA-based tool to predict the fatigue behavior of porous dental implants. Test samples mimicking dental implants were designed as 4.5 mm-diameter cylinders with a fully porous section around bone level. Three porosity levels (50%, 60% and 70%) and two unit cell types (Schwarz Primitive (SP) and Schwarz W (SW)) were combined to generate six designs that were split between calibration (60SP, 70SP, 60SW, 70SW) and validation (50SP, 50SW) sets. Twenty-eight samples per design were additively manufactured from titanium powder (Ti6Al4V). The samples were tested under bending compression loading (ISO 14801) monotonically (N=4/design) to determine ultimate load (F. ult. ) (Instron 5866) and cyclically at six load levels between 50% and 10% of F. ult. (N=4/design/load level) (DYNA5dent). Failure force results were fitted to F/F. ult. = a(N. f. ). b. (Eq1) with N. f. being the number of cycles to failure, to identify parameters a and b. The endurance limit (F. e. ) was evaluated at N. f. = 5M cycles. Finite element models were built to predict the yield load (F. yield. ) of each design. Combining a linear correlation between FEA-based F. yield. and experimental F. ult. with equation Eq1 enabled FEA-based
Background. There is a 20% dissatisfaction rate with knee replacements. Calls for tools that can pre-operatively identify patients at risk of being dissatisfied postoperatively have been widespread. However, it is unclear what sort of information patients would want from such a tool, how it would affect their decision making process, and at what part of the pathway such a tool should be used. Methods. Using focus groups involving 12 participants and in-depth interviews with 10 participants, we examined the effect outcome
Background. Stability of total knee arthroplasty (TKA) is dependent on correct and precise rotation of the femoral component. Multiple differing surgical techniques are currently utilized to perform total knee arthroplasty. Accurate implant position have been cited as the most important factors of successful TKA. There are two techniques of achieving soft gap balancing in TKA; a measured resection technique and a balanced gap technique. Debate still exists on the choice of surgical technique to achieve the optimal soft tissue balance with opinions divided between the measured resection technique and the gap balance technique. In the measured resection technique, the bone resection depends on size of the prosthesis and is referenced to fixed anatomical landmarks. This technique however may have accompanying problems in imbalanced patients.
Background. Total knee arthroplasty (TKA) is a proven and cost-effective treatment for osteoarthritis. Despite the good to excellent long-term results, some patients remain dissatisfied. Our study aimed at establishing a predictive model to aid patient selection and decision-making in TKA. Methods. Using data from our prospective arthroplasty outcome database, 113 patients were included. Pre- and postoperatively, the patients completed 107 questions in 5 questionnaires: KOOS, OKS, PCS, EQ-5D and KSS. First, outcome parameters were compared between the satisfied and dissatisfied group. Secondly, we developed a new
Introduction. Up to 60% of total hip arthroplasties (THA) in Asian populations arise from avascular necrosis (AVN), a bone disease that can lead to femoral head collapse. Current diagnostic methods to classify AVN have poor reproducibility and are not reliable in assessing the fracture risk. Femoral heads with an immediate fracture risk should be treated with a THA, conservative treatments are only successful in some cases and cause unnecessary patient suffering if used inappropriately. There is potential to improve the assessment of the fracture risk by using a combination of density-calibrated computed tomographic (QCT) imaging and engineering beam theory. The aim of this study was to validate the novel fracture
Introduction and Aims: We propose a new, simple, and universal method to predict adult height: the Height Multiplier Method. Our aim was to calculate height multipliers from various databases and validate their use for height
In conventional DXA (Dual-energy X-ray Absorptiometry) analysis, pixel bone mineral density (BMD) is often averaged at the femoral neck. Neck BMD constitutes the basis for osteoporosis diagnosis and fracture risk assessment. This data averaging, however, limits our understanding of localised spatial BMD patterns that could potentially enhance fracture
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
Fractures through the physis account for 18–30% of all paediatric fractures, leading to growth arrest in 5.5% of cases. We have limited knowledge to predict which physeal fractures result in growth arrest and subsequent deformity or limb length discrepancy. The purpose of this study is to identify factors associated with physeal growth arrest to improve patient outcomes. This prospective cohort study was designed to develop a clinical
Total hip replacements (THRs) provide pain relief and improved function to thousands of patients suffering from end-stage osteoarthritis, every year. Over 800 different THR constructs were implanted in the UK in 2017. To ensure reliable implants are used, a NICE revision benchmark of 5% after 10 years exists. Given the 10-year cumulative mortality of patients under 55 years of age receiving THRs is only 5% and that a recent study suggests 25-year THR survival of 58%, we aim to produce revision estimates out to 30 years that may guide future long-term benchmarks. The local database of the Princess Elizabeth Orthopaedic Centre (PEOC), Exeter, holds data on over 20,000 patients with nearly 30-years follow-up with contemporary prostheses. A previous study suggests that the results of this centre are generalisable if comparisons restricted to the same prostheses. Via flexible parametric survival analysis, we created an algorithm using this database, for revision of any part of the construct for any reason, controlling for age and gender. This algorithm was applied to 664,761 patients in the NJR who have undergone THR, producing a revision
Introduction. Integrating additively manufactured structures, such as porous lattices into implants has numerous potential benefits, such as custom mechanical properties, porosity for osseointegration/fluid flow as well as improved fixation features. Component anisotropic stiffness can be controlled through varying density and lattice orientation. This is useful due to the influence of load on bone remodelling. Matching implant and bone anisotropy/stiffness may help reduce problems such as stress shielding and prevent implant loosening. It is therefore beneficial to be able to design AM parts with a desired anisotropic stiffness. In this study we present a method that predicts the anisotropic stiffness of an additively manufactured lattice structure from its CAD data, and validate this model with experimental testing. The model predicts anisotropic stiffness in terms of density (ρ), fabric (M) and fabric eigen values (m) and is matched to stiffness data of the structure in 3 principal directions, based on an orthotropic assumption. This model was described in terms of 10 constants and had the form shown in Equation 1. Eq.1. S. =. ∑. i. ,. j. =. 1. . . . . i. ,. j. =. 3. λ. (. i. ,. j. ). ρ. k. m. (. i. ). 1. (. i. ). m. (. j. ). 1. (. i. ). |. M. i. M. j. '. |. 2. Methods. A stochastic line structure was formed in CAD by joining pseudo-random points generated using the Poisson-disk method Lines at an angle lower than 30° to the x-y plane removed to allow for AM manufacturing. Lines were converted to struts with 330 µm diameter. Second order fabric tensors were determined from CAD files of the AM specimens using the mean intercept length (MIL), the gold standard for determining a measure of the ‘average orientation’ of material within trabecular bone structures. 10 × 10 × 12 mm specimens of the CAD model were manufactured on a Renishaw AM250 powder bed fusion machine. The structure was built in 10 different orientations to enable stiffness measurement in 10 different directions (n=5 for each direction). Compression testing in a servohydraulic materials testing machine was performed according to ISO13314 with LVDTs used to measure displacement to remove compliance effects. Stress-strain curves were obtained and elastic moduli were estimated from a hysteresis loop in the load application, from 70% to 20% of the plateau stress. Specimen density and fabric data were fit to the observed stiffnesses using least squares linear regression. Experimental stiffnesses of the structure in 10 directions were compared to the model to evaluate the accuracy of model
To identify variables independently associated with same-day discharge (SDD) of patients following revision total knee arthroplasty (rTKA) and to develop machine learning algorithms to predict suitable candidates for outpatient rTKA. Data were obtained from the American College of Surgeons National Quality Improvement Programme (ACS-NSQIP) database from the years 2018 to 2020. Patients with elective, unilateral rTKA procedures and a total hospital length of stay between zero and four days were included. Demographic, preoperative, and intraoperative variables were analyzed. A multivariable logistic regression (MLR) model and various machine learning techniques were compared using area under the curve (AUC), calibration, and decision curve analysis. Important and significant variables were identified from the models.Aims
Methods
Previously, we showed that case-specific non-linear
finite element (FE) models are better at predicting the load to failure
of metastatic femora than experienced clinicians. In this study
we improved our FE modelling and increased the number of femora
and characteristics of the lesions. We retested the robustness of
the FE
Introduction:. Primary stability is crucial for long-term fixation of cementless tibial trays. Micromotion less than 50 μm is associated with stable bone ingrowth and greater than 150 μm causes the formation of fibrous tissue around the implant [1, 2]. Finite element (FE) analysis of complete activities of daily living (ADL's) have been used to assess primary stability, but these are computationally expensive. There is an increasing need to account for both patient and surgical variability when assessing the performance of total joint replacement. As a consequence, an implant should be evaluated over a spectrum of load cases. An alternative approach to running multiple FE models, is to perform a series of analyses and train a surrogate model which can then be used to predict micromotion in a fraction of the time. Surrogate models have been used to predict single metrics, such as peak micromotion. The aim of this work is to train a surrogate model capable of predicting micromotion over the entire bone-implant interface. Methods:. A FE model of an implanted proximal tibia was analysed [3] (Fig. 1). A statistical model of knee kinetics, incorporating subject-specific variability in all 6-DOF joint loads [4], was used to randomly generate loading profiles for 50 gait cycles. A Latin Hypercube (LH) sampling method was applied to sample 6-DOF loads of the new population throughout the gait cycle. Kinetic data was sampled at 10, 50 and 100 instances and FE
The prospective evaluation of two hundred and seven symptomatic total knee arthroplasties presenting for revision total knee arthroplasty is reported. On univariate analysis patients who had infection differed significantly (p<
.001) from those without infection with regards to: elevated ESR, CRP, positive aspiration, and history of; revision procedure less than two years since last surgery, early wound problems, ongoing pain since index procedure, and discharging wound. On multivariate analysis elevated ESR or CRP, positive aspiration, pain since index procedure and early wound complications were significant predictors of infection (p<
.05). These variables were then used to formulate an evidence-based multivariate predictive algorithm to assist the clinician in decision making prior to surgery. Differentiating septic from aseptic failure of total knee arthroplasty on the basis of clinical features and diagnostic tests can be troublesome for the clinician. The purpose of this paper is to describe significant differences between cases of septic and aseptic failure of total knee arthroplasty. The incorporation of these variables into a practical multivariate clinical
Introduction. From 2004 to 2015, elective lumbar fusions increased by 62% in the US. The largest increases were for among age 65 or older (139% in volume) and scoliosis (187%) [1]. Age is a well known factor of osteoporosis. The load-sharing may exceed the pedicular screws constructs in aging spine and lead to non-union and re-do. Surgical options may increase the screw purchase (e.g.: augmentation, extensions) at supplementary risks. Pedicular screw are known to cause vascular, nerve root or cord injuries. Facing these pitfalls, the surgeon's experience and rule of thumbs are the most deciding factors for the surgical planning. The aim of this study is to assess the accuracy of a patient specific tool, designed to plan a safe pedicular trajectory and to provide an intraoperative screw pullout strength estimate. Materials and Methods. Clinical QCT were taken for nine cadaveric spines (82 y. [61; 87], 6 females, 3 males). The experimental maximum axial pullout resistance (FMax) of twenty-seven pedicular screws inserted (nine T12, nine L4 and nine L5) was obtained as described in a previous study [2]. A custom 3D-WYSIWYG software simulated a medio-lateral surgical insertion technique in the QCTs coordinates reference, respecting the cortical walls. Repeatable density, morphometric and hardware parameters were recorded for each vertebrae. A statistical model was built to match predictive and experimental data. Preliminary results. Experimental FMax(N) were [104;953] (359 ±223). A further displacement of 1,81mm ±0,35 halved the experimental FMax. Predictive FMax(N) were [142;862] (359 ±220). A high positive correlation between experimental and predictive FMax was revealed (Pearson, ρ = 0.93, R2 = 0.87, p < .001, figure 1). Absolute differences ranged between 3N and 177N. Discussion. A high screw purchase in primary fixation is paramount to achieve spine surgical procedures (e.g.: kyphosis, scoliosis) and postoperative stability for vertebrae fusion. High losses of screw purchase by bone plastic deformation, begin with tiny pullouts. Theses unwanted intraoperative millimetric over-displacements are hard to avoid when monitoring at the same time tens of screws surrounded by bleedings. This advocates for including predictive FMax for each implantable pedicular screw in the surgical planning decision making process to prevent failures and assess risks. For the first time, this study presents an experimentally validated statistical model for FMax
Current levels of hip fracture morbidity contribute greatly to the overall burden on health and social care services. Given the anticipated ageing of the population over the coming decade, there is potential for this burden to increase further, although the exact scale of impact has not been identified in contemporary literature. We therefore set out to predict the future incidence of hip fracture and help inform appropriate service provision to maintain an adequate standard of care. Historical data from the Scottish Hip Fracture Audit (2017 to 2021) were used to identify monthly incidence rates. Established time series forecasting techniques (Exponential Smoothing and Autoregressive Integrated Moving Average) were then used to predict the annual number of hip fractures from 2022 to 2029, including adjustment for predicted changes in national population demographics. Predicted differences in service-level outcomes (length of stay and discharge destination) were analyzed, including the associated financial cost of any changes.Aims
Methods
Young patients receiving metallic bone implants after surgical resection of bone cancer require implants that last into adulthood, and ideally life-long. Porous implants with similar stiffness to bone can promote bone ingrowth and thus beneficial clinical outcomes. A mechanical remodelling stimulus, strain energy density (SED), is thought to be the primary control variable of the process of bone growth into porous implants. The sequential process of bone growth needs to be taken into account to develop an accurate and validated bone remodelling algorithm, which can be employed to improve porous implant design and achieve better clinical outcomes. A bone remodelling algorithm was developed, incorporating the concept of bone connectivity (sequential growth of bone from existing bone) to make the algorithm more physiologically relevant. The algorithm includes adaptive elastic modulus based on apparent bone density, using a node-based model to simulate local remodelling variations while alleviating numerical checkerboard problems. Strain energy density (SED) incorporating stress and strain effects in all directions was used as the primary stimulus for bone remodelling. The simulations were developed to run in MATLAB interfacing with the commercial FEA software ABAQUS and Python. The algorithm was applied to predict bone ingrowth into a porous implant for comparison against data from a sheep model.Abstract
Objectives
Methods
Introduction. Preoperative templating of femoral and tibial components can assist in choosing the appropriate implant size prior to TKA. While weight bearing long limb roentograms have been shown to provide benefit to the surgeon in assessing alignment, disease state, and previous pathology or trauma, their accuracy in size
The primary objectives of this study were to: 1) identify risk factors for subsequent surgery following initial treatment of proximal humerus fractures, stratified by initial treatment type; 2) generate risk
PURPOSE: To determine the capability of fellowship trained Orthopaedic Trauma surgeons to predict union or non-union of femoral and tibial shaft fractures. METHODS: A series of 50 patients with femur or tibia shaft fractures were evaluated. Patients were prospectively followed at 2,6,12, and 18 weeks after surgical intervention. At each interval surgeons evaluated factors related to fracture healing on AP and lateral radiographs and predicted the probability of union on a visual analog scale. Union was defined as radiographic evidence of healing three of four cortices, no tenderness with palpation of the fracture site, and full weight bearing without the use of assistive devices. RESULTS: Eight patients missed initial visits or were lost to follow-up, making for a total of 42 patients that were included in the results. Average patient age was 31 years. Eighty-one percent of the patients went onto union (N=34) and 19% went onto nonunion (N=8). Early clinical
A predictive model for final kyphosis was tested by evaluating the radiographs of forty-three patients with traumatic burst fractures. Since clinical outcomes are related to final kyphosis in the ambulatory patient rather than on the initial supine injury radiograph, the ability to predict final kyphosis is beneficial in determining treatment. This study demonstrated that in the appropriately selected patient for conservative care, the limit of final-kyphosis(Kf) can be predicted from the intial-kyphosis(KI) , such that Kf= <
KI+.5KI . Outliers from this equation were patients who had unrecognized posterior column fractures, superior and inferior end-plate fractures, and/or multiple level of injury. The purpose of this study was to define a
This is quite an innovative study that should lead to a multicentre validation trial. We have developed an FDG-PET/MRI texture-based model for the
Introduction. Despite generally excellent patient outcomes for Total Knee Arthroplasty (TKA), there remains a contingent of patients, up to 20%, who are not satisfied with the outcome of their procedure. (Beswick, 2012) There has been a large amount of research into identifying the factors driving these poor patient outcomes, with increasing recognition of the role of non-surgical factors in predicting achieved outcomes. However, most of this research has been based on single database or registry sources and so has inherited the limitations of its source data. The aim of this work is to develop a predictive model that uses expert knowledge modelling in conjunction with data sources to build a predictive model of TKR patient outcomes. Method. The preliminary Bayesian Belief Network (BBN) developed and presented here uses data from the Osteoarthritis Initiative, a National Institute of Health funded observational study targeting improved diagnosis and monitoring of osteoarthritis. From this data set, a pared down subset of patient outcome relevant preoperative questionnaire sets has been extracted. The BBN structure provides a flexible platform that handles missing data and varying data collection preferences between surgeons, in addition to temporally updating its